├── .gitignore ├── Example.ipynb ├── README.md ├── data ├── all │ ├── proc-tags_Data scientist.json │ ├── proc-tags_Python.json │ ├── proc-tags_SQL.json │ └── proc-tags_machine learning.json ├── filtered_5 │ ├── filt-tags_data-scientist.json │ ├── filt-tags_machine-learning.json │ ├── filt-tags_python.json │ └── filt-tags_sql.json ├── for_visualization │ ├── Hello.json │ ├── filt-tags_data-scientist.json │ ├── filt-tags_machine-learning.json │ ├── filt-tags_python.json │ ├── filt-tags_sql.json │ └── index.json ├── processed_test.json ├── raw │ ├── raw-tags_Data scientist.json │ ├── raw-tags_Python.json │ ├── raw-tags_SQL.json │ └── raw-tags_machine learning.json └── scraped_test.json ├── flsite.py ├── graph_vis.py ├── graph_visualization.js ├── index.html ├── ipython notebooks ├── graph_samples.ipynb └── hh-ru_scraper.ipynb ├── landing_background.jpg ├── pic ├── Screenshot_2020-05-05 A visualisation of developer skills in demand.png ├── first_static_graph.png └── tags_graph.png ├── scrapers ├── hh_ru_scraper.py ├── preprocess.py ├── sql_database │ └── hh_vacancies.db └── sql_db_operation.py ├── static ├── style.css └── var_options.json ├── style.css └── templates └── index.html /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | .ipynb_checkpoints -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # skills-graph 2 | A graphical representation of relations between programming languages, technologies and skills in demand, based on thousands of job postings. 3 | 4 | Dynamic visualization: https://avk0.github.io/skills_graph/ 5 | 6 | Article on Habr.com (in Russian): https://habr.com/ru/post/500952/ 7 | 8 | ### Sample 9 | ~500 vacancies parced by keyword "Machine Learning" (headhunter.ru) 10 | 11 | ![Peek 2020-04-24 00-13](https://user-images.githubusercontent.com/47819971/80150148-846a5e80-85c0-11ea-82cc-cff6aef4900c.gif) 12 | 13 | We can see, that essential skills for machine learning jobs are Python, SQL, Linux and others 14 | 15 | ### Structure 16 | Backend: tags scraper and parser\ 17 | Frontend: dynamic graph visualization 18 | 19 | ### How to use 20 | Simple way: 21 | * run `/ipython notebook/hh-ru_scraper.ipynb`, 22 | * set SEARCH_WORD to desired, 23 | * run all cells. 24 | 25 | For full dynamic visualization 26 | 27 | * run `/scraper/hh-ru_scraper.py` folder to scrape more data, 28 | * run `/scraper/preprocess.py` to format data, 29 | * add resulting file to `/data/for_visualization/` folder, 30 | * add new file name in `/data/for_visualization/index.json` file, 31 | * load index.html, new button with dynamic graph should appear. 32 | 33 | Visualization is based on JavaScript and few [Observable notebooks.](https://observablehq.com/@avk0?tab=notebooks)\ 34 | Some additional Python visualization can be done using Ipython Notebook: `./scrapers/hh-ru_scraper.ipynb` 35 | 36 | Any ideas are highly appreciated! 37 | 38 | ### How to add data: 39 | You can add data in json for visualization to the `./data/for_visualization` folder and also insert the title and the name of the file in `./data/for_visualization/index.json`. The data should have the following structure (example): 40 | ```json 41 | { 42 | "items": { 43 | "nodes": [{"id": "data science", "popularity": 28}, ...], 44 | "links": [{"source": "data science", "target": "spark", "value": 8}, ...] 45 | } 46 | } 47 | ``` 48 | Popularity denotes the total number of occurences of a term (for nodes). Value denotes the number of co-occurences of source and target (for links). 49 | 50 | ### TODO 51 | 52 | ##### Data sources 53 | * Used: 54 | * hh.ru 55 | * To try: 56 | * Linkedin 57 | * Stackoverflow jobs 58 | * Who is hiring hackernews 59 | * Indeed 60 | * Glassdoor 61 | 62 | ### Contributors 63 | [Andrei Koval](https://github.com/avk0)\ 64 | [Serghei Mihailov](https://github.com/SergheiMihailov)\ 65 | [JleMyP](https://github.com/JleMyP) 66 | -------------------------------------------------------------------------------- /data/for_visualization/Hello.json: -------------------------------------------------------------------------------- 1 | { 2 | "items": { 3 | "nodes": [{"id": "Hello_World", "popularity": 1} 4 | ], 5 | "links": [{"source": "Hello_World", "target": "Hello_world", "value": 10} 6 | ] 7 | } 8 | } -------------------------------------------------------------------------------- /data/for_visualization/index.json: -------------------------------------------------------------------------------- 1 | { 2 | "Hello": "Hello.json", 3 | "python": "filt-tags_python.json", 4 | "data-scientist": "filt-tags_data-scientist.json", 5 | "machine-learning": "filt-tags_machine-learning.json", 6 | "sql": "filt-tags_sql.json" 7 | } 8 | -------------------------------------------------------------------------------- /data/processed_test.json: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/avk0/skills_graph/fbd8b2cf93038c31a7ff945007a9e6b754bd03e8/data/processed_test.json -------------------------------------------------------------------------------- /data/raw/raw-tags_Data scientist.json: -------------------------------------------------------------------------------- 1 | {"phrase": "Data scientist", "items_number": 231, "items": [["Python", "MS Visio", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", 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"\u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f", "SQL", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "\u0410\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445", "\u041c\u0430\u0448\u0438\u043d\u043d\u043e\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435"], ["Python", "NLP", "Deep Learning", "Machine Learning", "PyTorch", "TensorFlow"], ["Tensorflow", "Keras", "PyTorch", "Deep Learning", "NLP"], ["Python", "Data Mining", "SQL", "\u041c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u041f\u043e\u0440\u0442\u0444\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0438\u0441\u043a\u0438", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u0410\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430"], ["Python", "Big Data", "Data Analysis", "Data Mining", "\u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u0420\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u043e\u043d\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u041f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435"], ["NLP", "Python", "SQL", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f 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Oracle", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "\u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "SQL", "Oracle Pl/SQL", "MATLAB", "Python", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u0411\u0430\u0437\u044b \u0434\u0430\u043d\u043d\u044b\u0445", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435"], ["Python", "Data Mining", "Mathematical Analysis", "Mathematical Statistics", "Mathematical Modeling"], ["Data Mining", "SQL", "Python", "MATLAB", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435"], ["\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "sklearn", "pandas", "NumPy"], ["Python", "Game Dev", "Java", "SCALA", "NoSQL", "Apache Airflow", "Apache NiFi", "Apache Spark", "Apache Beam", "Linux", "Machine Learning"], ["Python", "Git", "\u0420\u0430\u0431\u043e\u0442\u0430 \u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u0435", "Linux", "MS Internet Explorer"], ["Python", "Data Mining", "SQL", "C++", "MS SQL", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "Big Data"], ["SQL", "MS SQL", "\u041a\u0440\u0435\u0430\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u044c", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "SQL Server"], ["Python", "SQL", "Presentation skills", "MS SQL Server", "Team management"], ["AutoML", "OCR", "NLP", "Python", "SQL", "Machine learning", "Artificial intelligence"], ["Python", "SQL", "MS Internet Explorer", "MS SQL", "Azure", "Power BI", "Data"], ["Python", "SQL", "\u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u0438 \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u0435", "\u0420\u0430\u0431\u043e\u0442\u0430 \u0441 \u0431\u0430\u0437\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445", "\u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437"], ["Python", "Data Analysis", "Big Data", "SQL"], ["Python", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0440\u0438\u0441\u043a\u0430\u043c\u0438", "MATLAB", "\u0420\u0438\u0441\u043a-\u043c\u0435\u043d\u0435\u0434\u0436\u043c\u0435\u043d\u0442", "SQL"], ["Python", "Data Mining", "SQL", "Spark", "Big Data", "Data Analysis"], ["Python", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u042d\u043a\u043e\u043d\u043e\u043c\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437"], ["Python", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "Machine Learning", "MATLAB"], ["Python", "MongoDB", "Flask", "RabbitMQ", "Redis", "Clickhouse"], ["Python", "SQL", "Data Mining"], ["Python", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "MATLAB", "Data Science"], ["Python", "SQL", "Data Mining", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f", "MS Access"], ["Python", "SQL", "CRM", "MS SQL", "R", "Sas", "Big Data"], ["Python", "C++", "SQL", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "\u0434\u0430\u0442\u0430\u0441\u0435\u0442\u044b", "\u041d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0435 \u0441\u0435\u0442\u0438", "Machine Learning"], ["Data Mining", "\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "Data Analysis", "Big Data"], ["SQL", "MS SQL", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f"], ["Python", "Data Mining", "SQL", "MS SQL", "Hadoop", "Spark"], ["Python", "SQL", "Numpy", "Pandas", "SkLearn", "PyTorch", "Spark", "Hive", "Impala", "Machine Learning"], ["Data Mining", "Python", "Data Analysis", "MATLAB", "SQL", "data science"], ["Python", "Spark", "Pandas"], ["Python", "Data Analysis", "Machine learning", "sklearn", "Linux", "Git"], ["Python", "NLP", "Artificial intelligence", "\u0418\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0439 \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442", "Machine Learning", "\u041c\u0430\u0448\u0438\u043d\u043d\u043e\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435", "\u041e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u044f\u0437\u044b\u043a\u0430", "Data science", "Language Processing", "Natural", "\u041e\u041e\u041f"], ["Data Mining", "Python", "\u041f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f", "SQL", "Computer vision", "Deep learning"], ["Python", "Data Mining", "E-Commerce", "IT"], ["MATLAB", "Data Mining", "SQL", "MS SQL", "\u041a\u043e\u043d\u0442\u0440\u043e\u043b\u044c \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430", "Data Analysis", "\u0411\u0430\u0437\u044b \u0434\u0430\u043d\u043d\u044b\u0445", "\u0410\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445", "Data Scientist", "Data Science"], ["Python", "Data Mining", "SQL", "MS SQL", "Big Data", "Data Analysis"], ["Python", "Data Mining"], ["SQL", "ML", "tensorflow", "skylearn", "keras"], ["Python", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437"], ["SQL", "\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "MATLAB", "MS Access", "\u0414\u0435\u043b\u043e\u0432\u0430\u044f \u043a\u043e\u043c\u043c\u0443\u043d\u0438\u043a\u0430\u0446\u0438\u044f"], ["Python", "Tensorflow", "PostgreSQL", "Keras", "sklearn", "NLU", "LSTM", "RNN"], ["Python", "Machine learning", "Data science"], ["Data Analysis"], ["Python", "SQL", "\u041e\u0446\u0435\u043d\u043a\u0430 \u0440\u0438\u0441\u043a\u043e\u0432", "MS SQL", "\u0420\u0430\u0431\u043e\u0442\u0430 \u0441 \u0431\u0430\u0437\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445"], ["Python", "SQL", "MS SQL", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "NLP", "\u0410\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445"], ["SQL", "\u0424\u0438\u043d\u0430\u043d\u0441\u043e\u0432\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "Business intelligence", "Python", "PostgreSQL", "R", "machine learning", "WEB \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430", "\u0412\u0435\u0431-\u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430", "\u041a\u043e\u043d\u043a\u0443\u0440\u0435\u043d\u0442\u043d\u0430\u044f \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430", "data science"], ["Python", "SQL", "Data Mining", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435"], ["SQL", 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["AngularJS", "Software Development", "JavaScript", "Java", "Design Patterns"], ["\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "CRM", "\u041a\u0440\u0435\u0430\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u044c"], ["Java", "Python", "Git", "Linux", "HTML"], ["SQL", "ORACLE", "ETL", "DWH", "UML", "BPMN"], ["AngularJS", "Software Development", "JavaScript", "Java", "Design Patterns"], ["VBA", "Python"], ["Python", "PostgreSQL", "SQL", "\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "MS SQL", "\u0410\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430", "\u0411\u0430\u0437\u044b \u0434\u0430\u043d\u043d\u044b\u0445", "\u0412\u0435\u0431-\u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430", "Digital Marketing", "data scientist"], ["\u0420\u0435\u043a\u0440\u0443\u0442\u043c\u0435\u043d\u0442"], ["Python", "Linux", "MATLAB", "SQL", 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"Strategic Marketing", "Social Media Marketing", "B2B Marketing", "SaaS", "Start-up project"], ["Python", "Machine learning", "Keras", "PyTorch", "Kaggle"], ["Python", "Machine learning", "Keras", "PyTorch", "Kaggle"], ["Python", "Linux", "C++", "Data Mining", "MATLAB", "Speech recognition", "Speech-to-text"], ["\u041f\u043e\u0434\u0431\u043e\u0440 \u043f\u0435\u0440\u0441\u043e\u043d\u0430\u043b\u0430", "\u041a\u0430\u0434\u0440\u043e\u0432\u0430\u044f \u043f\u043e\u043b\u0438\u0442\u0438\u043a\u0430", "\u0422\u0440\u0443\u0434\u043e\u0432\u043e\u0439 \u043a\u043e\u0434\u0435\u043a\u0441 \u0420\u0424", "\u0420\u0435\u043a\u0440\u0443\u0442\u043c\u0435\u043d\u0442", "\u041a\u0430\u0434\u0440\u043e\u0432\u043e\u0435 \u0434\u0435\u043b\u043e\u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0441\u0442\u0432\u043e", "\u041a\u043e\u0440\u043f\u043e\u0440\u0430\u0442\u0438\u0432\u043d\u0430\u044f \u043a\u0443\u043b\u044c\u0442\u0443\u0440\u0430", 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["Product Management", "Agile Project Management", "Project management", "Team management", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "\u041e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f \u0431\u0438\u0437\u043d\u0435\u0441-\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0432", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u043c"], ["Git", "SQL", "MS Internet Explorer", "\u041e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u0432\u0438\u0434\u0435\u043e", "\u0413\u0440\u0430\u043c\u043e\u0442\u043d\u0430\u044f \u0440\u0435\u0447\u044c"], ["MySQL", "SQL", "PHP5", "\u041e\u041e\u041f", "PHP"], ["\u041f\u043e\u0434\u0431\u043e\u0440 \u043f\u0435\u0440\u0441\u043e\u043d\u0430\u043b\u0430"], ["SQL", "ETL", "Python", "MySQL", "Oracle Pl/SQL", "PostgreSQL"]]} -------------------------------------------------------------------------------- /data/raw/raw-tags_machine learning.json: -------------------------------------------------------------------------------- 1 | {"phrase": "machine learning", "items_number": 468, "items": [["Python", "Sas", "Tableu", "Data Science", "Machine Learning", "R", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437"], ["Python", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "Teamplayer", "C++", "Machine Learning", "Reinforcement Learning", "Computer Vision", "PyTorch", "TensorFlow"], ["Data Mining", "Python", "Team management", "Atlassian Jira", "SQL", "Data Analysis"], ["Python", "MATLAB", "tensorflow 2.0", "C#", "Ansi C++", "R", 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"\u041d\u0435\u043c\u0435\u0446\u043a\u0438\u0439 \u044f\u0437\u044b\u043a"], ["Python", "PostgreSQL", "C/C++", "Teambuilding"], ["Python", "Flask", "Tornado", "Asyncio", "RabbitMQ", "Machine learning", "Computer vision"], ["Java", "Hadoop", "Spark", "SQL", "Spring Framework", "Spring Boot", "Highload systems", "Linux", "Cloud", "Docker"], ["\u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u0442\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0437\u0430\u0434\u0430\u043d\u0438\u0439", "\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "\u0413\u0440\u0430\u043c\u043e\u0442\u043d\u043e\u0441\u0442\u044c"], ["Android", "Kotlin", "Java", "Android SDK"], ["\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "Autodesk Maya", "3D Max"], ["MS SQL Server", "MS SQL", "SQL", "Transact-SQL", "\u0418\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0435 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"\u041f\u043b\u0430\u043d\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043f\u0440\u043e\u0434\u0430\u0436", "\u041f\u043b\u0430\u043d\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u0410\u043a\u0442\u0438\u0432\u043d\u044b\u0435 \u043f\u0440\u043e\u0434\u0430\u0436\u0438"], ["3D \u041c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "3D Max", "Autodesk Maya"], ["\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "Business English"], ["\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "Business English"], ["\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "\u0421\u0438\u0441\u0442\u0435\u043c\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "UML", "MS Visio", "MS Project", "\u0410\u043d\u0430\u043b\u0438\u0437 \u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u0438\u0439 \u0437\u0430\u043a\u0430\u0437\u0447\u0438\u043a\u0430", "\u0422\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044f"], ["QA", "\u0422\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435"], ["C++", "machine learning", "computer vision", "OpenCV"], ["\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 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"\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "C++", "Unix", "C/C++"], [".NET Framework", "C#", "LINQ", "SQL", "WPF"], ["Linux", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "C++", "Unix", "C/C++"], ["Linux", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "Unix", "C/C++", "Time management"], ["Java", "Kotlin", "JVM", "MongoDB", "Rabbit", "Redis", "Graphana", "ELK", "Docker"], ["BigData", "Big Data", "Spark", "Hadoop", "ML", "Python", "C/C++", "Java", "Hive", "Kafka", "Lucene", "Solr", "ElasticSearch", "TensorFlow", "Caffe", "CNTK", "Torch"], ["Golang", "machine learning", "Python", "PHP", "IT"], ["Java"], ["MongoDB", "Python", "Git", "PostgreSQL", "Linux"], ["Data Mining", "SQL", "Python", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "Sas", "Data Science", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "Qlik View", "Tableau"], ["\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442-\u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "1\u0421-\u0411\u0438\u0442\u0440\u0438\u043a\u0441"], ["DevOps", "Kubernetes", "OpenShift", "Docker", "\u041c\u0438\u043a\u0440\u043e\u0441\u0435\u0440\u0432\u0438\u0441\u043d\u0430\u044f \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430", "CI/CD", "Hadoop"], ["ETL", "ELT", "Machine learning", "Java", "Python", "RDBMS", "Spark"], ["SQL", "MS SQL", "Hadoop", "DataLike"], ["Java", "Hadoop", "Spark", "SQL", "Spring Framework", "Spring Boot", "Highload systems", "Linux", "Cloud", "Docker"], ["ITSM", "ITIL", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "Git", "Linux", "DevOps", "CI/CD", "Kubernetes", "K8s", "OpenShift", "AWS", "Azure", "DMVPN", "Jenkins", "VMware", "OpenStack"], ["ITSM", "ITIL", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "Git", "Linux", "DevOps", "CI/CD", "Kubernetes", "K8s", "OpenShift", "AWS", "Azure", "DMVPN", "Jenkins", "VMware", "OpenStack"], ["Java", "Spring Framework", "Hibernate ORM"], ["Python", "Django Framework", "Restful API", "SQL", "PostgreSQL", "Git"], ["Java", "Spring Framework", "PostgreSQL", "Hibernate ORM"], ["\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u044b\u0448\u043b\u0435\u043d\u0438\u0435", "SCALA", "Java", "Big Data", "Spark", "SQL"], ["\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043e\u0442\u043d\u043e\u0448\u0435\u043d\u0438\u044f\u043c\u0438 \u0441 \u043a\u043b\u0438\u0435\u043d\u0442\u0430\u043c\u0438", "\u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u043e \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u0439 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u043e\u0432", "\u0421\u043e\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0431\u0438\u0437\u043d\u0435\u0441-\u043f\u043b\u0430\u043d\u0430", "MS Office", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "\u0412\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0435\u0433\u043e\u0432\u043e\u0440\u043e\u0432", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0441\u043e\u043d\u0430\u043b\u043e\u043c"], ["Python", "Git", "Linux", "Unix", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a"], ["Hadoop", "Spark", "SCALA", "Java"], ["Hadoop", "Big Data", "Spark", "Java", "Python", "C#"], ["Java", "Linux", "SQL", "Spring Framework"], ["Java", "Spring Framework", "Hibernate ORM", "MySQL"], ["Java", "Python", "SQL", "Linux", "Git", "Spring Framework", "Kotlin", "\u041c\u0438\u043a\u0440\u043e\u0441\u0435\u0440\u0432\u0438\u0441\u043d\u0430\u044f \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430", "Kubernetes", "Docker", "S3 API", "Apache Kafka"], ["Java", "Python", "SQL", "Linux", "Git", "Spring Framework", "Kotlin", "\u041c\u0438\u043a\u0440\u043e\u0441\u0435\u0440\u0432\u0438\u0441\u043d\u0430\u044f \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430", "Kubernetes", "Docker", "S3 API", "Apache Kafka"], ["PHP", "MySQL", "JavaScript", "\u041e\u041e\u041f"], ["SQL", "ERP", "MS Visual Studio", "MS SQL Server", "MS SQL"], ["\u0421\u0438\u0441\u0442\u0435\u043c\u043d\u0430\u044f \u0438\u043d\u0442\u0435\u0433\u0440\u0430\u0446\u0438\u044f", "SAP BCS", "SAP BW", "SAP BPC", "SAP (FI, MM, SD, CO)", "\u0411\u0438\u0437\u043d\u0435\u0441-\u043f\u043b\u0430\u043d\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0447\u0435\u0441\u043a\u0430\u044f \u043e\u0442\u0447\u0435\u0442\u043d\u043e\u0441\u0442\u044c", "\u043a\u043e\u043d\u0441\u043e\u043b\u0438\u0434\u0430\u0446\u0438\u044f \u0443\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0447\u0435\u0441\u043a\u043e\u0439 \u043e\u0442\u0447\u0435\u0442\u043d\u043e\u0441\u0442\u0438"], ["MongoDB", "Python", "Git", "PostgreSQL", "Linux"], ["Linux", "SQL", "Java", "\u0411\u0430\u0437\u044b \u0434\u0430\u043d\u043d\u044b\u0445", "PostgreSQL", "\u0420\u0430\u0431\u043e\u0442\u0430 \u0441 \u0431\u0430\u0437\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445", "Zabbix"], ["Java", "SQL", "Oracle Pl/SQL", "Spring Framework", "Hibernate ORM", "Apache"], ["PostgreSQL", "Python", "Git", "Linux", "Java", "\u041e\u041e\u041f"], ["Python", "JavaScript", "Git", "Node.js", "Django Framework"], ["IT-\u0438\u043d\u0444\u0440\u0430\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430", "\u0426\u0438\u0444\u0440\u043e\u0432\u0430\u044f \u043a\u0443\u043b\u044c\u0442\u0443\u0440\u0430", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u043c\u0438", "\u0426\u0438\u0444\u0440\u043e\u0432\u0430\u044f \u0442\u0440\u0430\u043d\u0441\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044f"], ["Python", "MS SQL Server"], ["Python", "MongoDB", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "PostgreSQL", "Agile Project Management"], ["\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "\u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u0442\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0437\u0430\u0434\u0430\u043d\u0438\u0439", "\u0410\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445"], ["CSS", "JavaScript", "Typescript", "React", "MobX"], ["SQL", "ORACLE", "ETL", "DWH", "UML", "BPMN"], ["\u0425\u043e\u043b\u043e\u0434\u043d\u044b\u0435 \u043f\u0440\u043e\u0434\u0430\u0436\u0438", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "\u0412\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0435\u0433\u043e\u0432\u043e\u0440\u043e\u0432", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "\u041f\u0440\u044f\u043c\u044b\u0435 \u043f\u0440\u043e\u0434\u0430\u0436\u0438"], ["\u0412\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0435\u0433\u043e\u0432\u043e\u0440\u043e\u0432", "\u0422\u0435\u043b\u0435\u0444\u043e\u043d\u043d\u044b\u0435 \u043f\u0435\u0440\u0435\u0433\u043e\u0432\u043e\u0440\u044b", "\u0421\u043e\u0433\u043b\u0430\u0441\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u043e\u0433\u043e\u0432\u043e\u0440\u043e\u0432", "\u041a\u043e\u0440\u043f\u043e\u0440\u0430\u0442\u0438\u0432\u043d\u044b\u0435 \u043f\u0440\u043e\u0434\u0430\u0436\u0438", "\u0414\u0435\u043b\u043e\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043f\u0438\u0441\u043a\u0430", "B2B \u041f\u0440\u043e\u0434\u0430\u0436\u0438", "\u0412\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0435\u043f\u0438\u0441\u043a\u0438"], ["UI/UX", "Adobe After Effects", "Sketch"], ["Python", "SQL", "R"], ["\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u044b\u0448\u043b\u0435\u043d\u0438\u0435", "\u0422\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044f", "\u0423\u043c\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u043d\u0438\u043c\u0430\u0442\u044c \u0440\u0435\u0448\u0435\u043d\u0438\u044f", "\u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u0440\u0435\u0433\u043b\u0430\u043c\u0435\u043d\u0442\u043e\u0432"], ["\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "Project management", "Organization Skills", "\u0412\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0435\u0433\u043e\u0432\u043e\u0440\u043e\u0432"], ["Python", "ORACLE", "\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "\u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u043f\u0440\u0435\u0437\u0435\u043d\u0442\u0430\u0446\u0438\u0439", "SQL", "\u0411\u0430\u0437\u0430 \u0434\u0430\u043d\u043d\u044b\u0445: Oracle", "MS SQL", "\u0418\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0435 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0431\u0438\u0437\u043d\u0435\u0441 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0430\u043c\u0438", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f", "\u041f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u0421\u0438\u0441\u0442\u0435\u043c\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "C\u0438\u0441\u0442\u0435\u043c\u044b \u0443\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u044f \u0431\u0430\u0437\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430", "\u0410\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445", "SAP BI", "\u041c\u043d\u043e\u0433\u043e\u0437\u0430\u0434\u0430\u0447\u043d\u043e\u0441\u0442\u044c", "Oracle BI", "Business Intelligence Systems", "BI", "\u041e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f \u0431\u0438\u0437\u043d\u0435\u0441-\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0432", "Java", "MySQL", "C++", "JavaScript", "CSS", "\u041f\u0440\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0440\u0435\u0437\u0435\u043d\u0442\u0430\u0446\u0438\u0439", "PHP"], ["\u0421\u0442\u0430\u0436\u0435\u0440", "\u0421\u0442\u0430\u0436\u0438\u0440\u043e\u0432\u043a\u0430"], ["UX", "UI", "Adobe Photoshop", "Axure RP"], ["Python", "C++", "ML"], ["Agile Project Management", "Scrum", "Atlassian Jira", "UX", "UI"], ["VBA", "Python"], ["\u0418\u043d\u0442\u0435\u0440\u043d\u0435\u0442-\u0440\u0435\u043a\u043b\u0430\u043c\u0430", "\u0410\u043a\u0442\u0438\u0432\u043d\u044b\u0435 \u043f\u0440\u043e\u0434\u0430\u0436\u0438", "\u041a\u043e\u043d\u0442\u0435\u043a\u0441\u0442\u043d\u0430\u044f \u0440\u0435\u043a\u043b\u0430\u043c\u0430", "CRM", "\u041d\u0430\u0432\u044b\u043a\u0438 \u043f\u0440\u043e\u0434\u0430\u0436", "B2B \u041f\u0440\u043e\u0434\u0430\u0436\u0438", "\u0414\u0435\u043b\u043e\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043f\u0438\u0441\u043a\u0430", "Digital Marketing", "\u0423\u043c\u0435\u043d\u0438\u0435 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u0435", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0432\u0440\u0435\u043c\u0435\u043d\u0435\u043c", "\u0425\u043e\u043b\u043e\u0434\u043d\u044b\u0435 \u043f\u0440\u043e\u0434\u0430\u0436\u0438", "\u041e\u0440\u0438\u0435\u043d\u0442\u0430\u0446\u0438\u044f \u043d\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442"], ["SAP BW", "MS PowerPoint", "1C: \u0417\u0430\u0440\u043f\u043b\u0430\u0442\u0430 \u0438 \u043a\u0430\u0434\u0440\u044b", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f", "\u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u043f\u0440\u0435\u0437\u0435\u043d\u0442\u0430\u0446\u0438\u0439"], ["Python", "C++", "Data Analysis", "R", "\u0426\u0435\u043d\u043d\u044b\u0435 \u0431\u0443\u043c\u0430\u0433\u0438", "MATLAB", "Mathematical Statistics", "Data science", "Quantitative analysis", "Mathematical Modeling"], ["Python", "SQL", "\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "MS Visio", "\u0411\u0438\u0437\u043d\u0435\u0441-\u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u0421\u0438\u0441\u0442\u0435\u043c\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u0421\u0438\u0441\u0442\u0435\u043c\u043d\u043e\u0435 \u043c\u044b\u0448\u043b\u0435\u043d\u0438\u0435", "\u043f\u043e\u0440\u0442\u0444\u0435\u043b\u044c\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437"], ["Presentation skills", "Linux", "Team management", "Ruby"], ["Python", "PostgreSQL", "MongoDB", "C/C++", "Teambuilding"], ["Python", "PostgreSQL", "\u041f\u0435\u0440\u0432\u0438\u0447\u043d\u044b\u0435 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u044b", "C/C++", "Teambuilding"], ["Java", "SQL", "Selenium IDE", "API \u0442\u0435\u0441\u0442\u044b"], ["Big Data", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "Google Analytics", "\u042f\u043d\u0434\u0435\u043a\u0441.\u041c\u0435\u0442\u0440\u0438\u043a\u0430", "Google AdWords", "\u0412\u0435\u0431-\u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430", "HTML", "MySQL", "Data Mining", "WEB \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430", "ORACLE", "Machine Learning"], ["\u0420\u0430\u0431\u043e\u0442\u0430 \u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u0435", "\u0414\u0435\u043b\u043e\u0432\u0430\u044f \u043a\u043e\u043c\u043c\u0443\u043d\u0438\u043a\u0430\u0446\u0438\u044f", "\u041d\u0430\u0432\u044b\u043a\u0438 \u043c\u0435\u0436\u043b\u0438\u0447\u043d\u043e\u0441\u0442\u043d\u043e\u0433\u043e \u043e\u0431\u0449\u0435\u043d\u0438\u044f", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u044c\u044e"], ["Python", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "SCALA", "Java", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "Splunk", "PostgreSQL", "Redis", "Impala", "Pandas", "NumPy", "machine learning"], ["Product Management", "SQL", "Python", "Data Analysis", "Tableau"], ["Python", "MATLAB", "Data Mining", "SQL", "Java", "\u0420\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u043e\u043d\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "\u041e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f \u0431\u0438\u0437\u043d\u0435\u0441-\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0432", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f", "\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "\u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u0410\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445", "Data Science", "Data Analysis"], ["MySQL", "Git", "Yii", "PHP", "HTML5", "JavaScript", "SQL", "1\u0421-\u0411\u0438\u0442\u0440\u0438\u043a\u0441"], ["\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442-\u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "\u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u041f\u041e", "\u041c\u0435\u043d\u0435\u0434\u0436\u043c\u0435\u043d\u0442", "\u0420\u0430\u0431\u043e\u0442\u0430 \u0441 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u043c\u0438 \u043a\u043b\u0438\u0435\u043d\u0442\u0430\u043c\u0438"], ["Git", "JavaScript", "HTML", "C++", "Machine Learning", "Deep Learning", "Tensorflow"], ["Python", "Linux", "machine learning"], ["UI", "\u0414\u0438\u0437\u0430\u0439\u043d \u0438\u043d\u0442\u0435\u0440\u0444\u0435\u0439\u0441\u043e\u0432"], ["Microsoft Azure", "Azure Cloud"], ["JavaScript", "Node.js", "ASP.NET", "Git", "CSS"], ["SQL", "MS PowerPoint", "Data Mining", "1\u0421: \u0411\u0443\u0445\u0433\u0430\u043b\u0442\u0435\u0440\u0438\u044f \u0438 \u0441\u043a\u043b\u0430\u0434", "Data Analysis"], ["\u0423\u043c\u0435\u043d\u0438\u0435 \u0440\u0430\u0437\u043c\u044b\u0448\u043b\u044f\u0442\u044c \u0432 \u0443\u0441\u043b\u043e\u0432\u0438\u044f\u0445 \u043d\u0435\u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u043e\u0441\u0442\u0438.", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0441\u043a\u043b\u0430\u0434 \u0443\u043c\u0430, \u043b\u043e\u0433\u0438\u043a\u0430.", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435.", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "SQL", "Data science", "Big Data", "Data Analysis", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f", 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"Rasa Core", "NLU"], ["\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "\u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435", "AI", "Rasa Core", "NLU"], ["Java", "SQL", "Linux", "Spring Framework"], ["C++"], ["SCALA", "Git", "PostgreSQL", "MongoDB", "Linux", "Akka", "Java"], ["Java", "SCALA", "Hadoop", "Apache Spark", "MapReduce"], ["MATLAB", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "Linux", "SQL", "C/C++", "Java", "Cloud Storage", "Algorithm optimization"], ["Python", "Linux"], ["Java", "Test case", "JUnit", "Atlassian Jira", "SQL"], ["Agile Project Management", "\u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u043e \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u0439 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u043e\u0432", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 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"\u0412\u0435\u0434\u0435\u043d\u0438\u0435 \u0433\u0440\u0443\u043f\u043f \u0432 \u0441\u043e\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u0445 \u0441\u0435\u0442\u044f\u0445", "\u041a\u0440\u0435\u0430\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u044c", "\u041a\u043e\u043f\u0438\u0440\u0430\u0439\u0442\u0438\u043d\u0433", "Social Media Marketing", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "Internet Marketing", "\u041f\u0440\u043e\u0434\u0432\u0438\u0436\u0435\u043d\u0438\u0435 \u0431\u0440\u0435\u043d\u0434\u0430", "\u0421\u043e\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u0435 \u0441\u0435\u0442\u0438", "\u0418\u043d\u0442\u0435\u0440\u043d\u0435\u0442-\u0440\u0435\u043a\u043b\u0430\u043c\u0430"], ["Linux", "CI/CD", "Docker", "Kubernetes", "DevOps", "Git", "\u0410\u0434\u043c\u0438\u043d\u0438\u0441\u0442\u0440\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0435\u0440\u0432\u0435\u0440\u043e\u0432 Linux", "Virtualization"], ["\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "\u041f\u043b\u0430\u043d\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "PMBOK", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0440\u0438\u0441\u043a\u0430\u043c\u0438", "\u041e\u0440\u0433\u0430\u043d\u0438\u0437\u0430\u0442\u043e\u0440\u0441\u043a\u0438\u0435 \u043d\u0430\u0432\u044b\u043a\u0438"], ["Data Compression", "C/C++", "Python", "Shannon theorem", "Codec technology", "Algorithms", "Mathematics", "Image encoding", "Video encoding", "ML", "Machine Learning"], ["B2B \u041f\u0440\u043e\u0434\u0430\u0436\u0438", "ERP Systems"], ["B2B \u041f\u0440\u043e\u0434\u0430\u0436\u0438", "ERP Systems"], ["Python", "MATLAB", "Linux", "Mathcad", "Julia", "\u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435", "\u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0439 \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442"], ["Customer Support", "Linux", "SQL", "MS SQL"], ["Python", "Data Mining", "SQL", "MS SQL", "C++"], ["Java", "Kotlin", "JVM", "MongoDB", "Rabbit", "Redis", "Graphana", "ELK", "Kubernetes", "Docker"], ["SQL", "HTML", "CSS", "Data Mining", "MS SQL", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f", "elk", "ElasticSearch", "\u0421\u0438\u0441\u0442\u0435\u043c\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430", "Kibana"], ["Teamleading", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u0439", "\u041a\u043e\u043d\u0442\u0440\u043e\u043b\u044c \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430", "\u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u043e \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u0439 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u043e\u0432"], ["Business Development", "Project management", "Team management", "Product Management", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "Data Analysis", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430"], ["MATLAB", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430", "\u041c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435", "C/C++", "C++", "research scientist", "tensorflow", "pytorch", "mathematics", "Statistics", "network", "ai", "neural network", "machine learning"], ["B2B \u041f\u0440\u043e\u0434\u0430\u0436\u0438", "ERP Systems"], ["SQL", "Data Mining", "MS SQL Server", "\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "Data Lake", "ETL", "Machine learning", "BigData"], ["Python", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "C/C++"], ["\u041f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044c \u041f\u041a", "Business English", "MS Excel", "Research"], ["\u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u043d\u043e\u0432\u043e\u0433\u043e \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0430", "Product Promotion", "\u0421\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u043c\u0430\u0440\u043a\u0435\u0442\u0438\u043d\u0433", "\u041c\u0430\u0440\u043a\u0435\u0442\u0438\u043d\u0433\u043e\u0432\u044b\u0435 \u043a\u043e\u043c\u043c\u0443\u043d\u0438\u043a\u0430\u0446\u0438\u0438", "FinTech", "\u041f\u043b\u0430\u043d\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 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"\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u0439", "\u0420\u0430\u0431\u043e\u0442\u0430 \u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u0435"], ["\u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u041f\u041e", "SCALA", "Python", "Spark", "\u0412\u0435\u0431-\u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435"], ["Python", "C#", "Golang", "SQL", "Git", ".NET Framework", "NoSQL", "Net Core"], ["JavaScript", "AngularJS", "HTML5", "CSS", "Linux"], ["Customer Service", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "SaaS", "Customer Relationship Management", "Customer Needs Analysis"], ["Android", "AI model", "Machine learning", "Model Training", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a"], ["C", "Firmware", "Python"], ["Hadoop", "\u0410\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430"], ["MySQL", "Agile Project Management", "Team management", "Scrum", "PHP", "Teamplayer", "Node.js", "Product Management", "Leadership Skills", "Teambuilding", "Teamleading", "AngularJS", "API", "Git"], ["Product Management", "Agile Project Management", "Project management", "Team management", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "\u041e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044f \u0431\u0438\u0437\u043d\u0435\u0441-\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0432", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u043c"], ["Strategic Marketing", "Project management", "Budgeting", "HTML"], ["Atlassian Jira", "Spring Framework", "Java", "Git", "Java SE"], ["\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "\u0411\u0438\u0437\u043d\u0435\u0441-\u0430\u043d\u0430\u043b\u0438\u0437", "\u0410\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0430", "\u041d\u0430\u0432\u044b\u043a\u0438 \u043f\u0440\u0435\u0437\u0435\u043d\u0442\u0430\u0446\u0438\u0438", "\u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u041f\u041e", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u043e\u0439", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0440\u0438\u0441\u043a\u0430\u043c\u0438", "\u041f\u0440\u043e\u0435\u043a\u0442\u043d\u0430\u044f \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044f", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u044b\u0448\u043b\u0435\u043d\u0438\u0435"], ["\u041d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0430 \u0441\u0435\u0442\u0435\u0432\u044b\u0445 \u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0439", "Linux", "\u0410\u043d\u0442\u0438\u0432\u0438\u0440\u0443\u0441\u043d\u0430\u044f \u0437\u0430\u0449\u0438\u0442\u0430 \u0441\u0435\u0442\u0438", "\u0410\u0434\u043c\u0438\u043d\u0438\u0441\u0442\u0440\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0435\u0440\u0432\u0435\u0440\u043e\u0432 Windows", "\u041d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0430 \u041f\u041a"], ["Java", "SAP ERP", "Internet", "SQL", "MS SQL Server"], ["Java SE", "Java", "REST", "Java EE", "SOAP"], ["SQL", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430", "\u0410\u043d\u0430\u043b\u0438\u0437 \u0434\u0430\u043d\u043d\u044b\u0445", "\u0410\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043c\u044b\u0448\u043b\u0435\u043d\u0438\u0435"], [".NET Framework", ".Net Core", "Entity Framework", "MS SQL Server"], ["iOS", "Objective-C", "Swift"], ["Python"], ["ASR", "\u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u0440\u0435\u0447\u0438", "machine learning"], ["ASR", "\u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435 \u0440\u0435\u0447\u0438", "machine learning"], ["Android SDK", "\u041e\u041e\u041f", "Java", "SQL", "Kotlin"], ["iOS", "Objective-C", "Swift"], [".NET Framework", ".Net Core", "Entity Framework", "MS SQL Server"], [".NET Framework", ".Net Core", "Entity Framework", "MS SQL Server"], [".NET Framework", "C#", "Entity Framework", "MS SQL Server"], ["Android SDK", "\u041e\u041e\u041f", "Java", "SQL"], ["PHP", "HTML", "CSS", "MySQL"], ["Python", "C++", "SQL", "MATLAB", "C#", "\u0421\u0423\u0411\u0414"], ["Java", "MS Access", "SQL", "VBA", "C++", "\u0411\u0430\u0437\u0430 \u0434\u0430\u043d\u043d\u044b\u0445: Oracle"], ["\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043f\u0440\u043e\u0435\u043a\u0442\u0430\u043c\u0438", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043a\u043e\u043c\u0430\u043d\u0434\u043e\u0439", "Agile Project Management", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u043e\u0436\u0438\u0434\u0430\u043d\u0438\u044f\u043c\u0438", "\u0423\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0440\u0438\u0441\u043a\u0430\u043c\u0438"], ["B2B \u041f\u0440\u043e\u0434\u0430\u0436\u0438", "\u0412\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0435\u0433\u043e\u0432\u043e\u0440\u043e\u0432", "\u0420\u0430\u0437\u0432\u0438\u0442\u0438\u0435 \u043f\u0440\u043e\u0434\u0430\u0436", "\u041f\u043e\u0438\u0441\u043a \u0438 \u043f\u0440\u0438\u0432\u043b\u0435\u0447\u0435\u043d\u0438\u0435 \u043a\u043b\u0438\u0435\u043d\u0442\u043e\u0432", "\u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u043f\u0440\u0435\u0437\u0435\u043d\u0442\u0430\u0446\u0438\u0439 \u043d\u0430 \u0438\u043d\u043e\u0441\u0442\u0440\u0430\u043d\u043d\u043e\u043c \u044f\u0437\u044b\u043a\u0435", "\u041f\u0440\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0440\u0435\u0437\u0435\u043d\u0442\u0430\u0446\u0438\u0439"], ["Nginx", "Linux", "Zabbix", "Bash", "MongoDB"], ["B2B \u041f\u0440\u043e\u0434\u0430\u0436\u0438", "\u0412\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0435\u0440\u0435\u0433\u043e\u0432\u043e\u0440\u043e\u0432", "\u0425\u043e\u043b\u043e\u0434\u043d\u044b\u0435 \u043f\u0440\u043e\u0434\u0430\u0436\u0438", "\u0420\u0430\u0437\u0432\u0438\u0442\u0438\u0435 \u043f\u0440\u043e\u0434\u0430\u0436", "\u0410\u043a\u0442\u0438\u0432\u043d\u044b\u0435 \u043f\u0440\u043e\u0434\u0430\u0436\u0438", "\u041f\u0440\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u043f\u0440\u0435\u0437\u0435\u043d\u0442\u0430\u0446\u0438\u0439", "\u0420\u0430\u0437\u0432\u0438\u0442\u0438\u0435 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u043a\u043b\u0438\u0435\u043d\u0442\u043e\u0432", "\u041d\u0430\u0432\u044b\u043a\u0438 \u043f\u0440\u0435\u0437\u0435\u043d\u0442\u0430\u0446\u0438\u0438", "\u0420\u0430\u0431\u043e\u0442\u0430 \u0432 \u043a\u043e\u043c\u0430\u043d\u0434\u0435", "\u0414\u0435\u043b\u043e\u0432\u043e\u0435 \u043e\u0431\u0449\u0435\u043d\u0438\u0435", "\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a", "\u0414\u0435\u043b\u043e\u0432\u0430\u044f \u043a\u043e\u043c\u043c\u0443\u043d\u0438\u043a\u0430\u0446\u0438\u044f"], ["Python"], ["C#", "MongoDB", "Big Data", "Python", "Jupiter", "Tensorflow", "machine learning", "computer vision", "opencv", "C++", "TensorRT"], ["MongoDB", "Python", "Git", "PostgreSQL", "Linux"], ["PowerShell", "Hyper-V", "SQL", "Linux", "\u0422\u0435\u0445\u043d\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u043f\u0435\u0440\u0435\u0432\u043e\u0434", "Zabbix", "Docker", "Git"], ["MS SQL Server", "SQL", "C++", "MS SQL", "Analytical skills"], ["Budgeting", "Product Management", "Business English", "\u041f\u0435\u0440\u0432\u0438\u0447\u043d\u044b\u0435 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u044b", "IBM Lotus Notes"], ["\u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u0438\u0439 \u044f\u0437\u044b\u043a"], ["Python", "Django Framework", "Nginx", "PostgreSQL", "Machine Learning", "Docker"], ["C#", "Transact-SQL", "Git", "jQuery", "\u041e\u041e\u041f", "Ajax", "Python", "MS SQL", ".net core", "Azure"]]} -------------------------------------------------------------------------------- /data/scraped_test.json: -------------------------------------------------------------------------------- 1 | {"parse_date": "2021-10-11 01:56:22.758127", "phrase": "YOLO", "items_number": 15, "items": [["C/C++", "Linux", "Пользователь ПК", "Точность и внимательность к деталям"], ["Python", "Git", "Linux", "C/C++", "Bash"], ["Python", "PyTorch", "Английский язык", "MongoDB", "Docker", "Kubernetes"], ["Python", "OpenCV", "C++", "Tensorflow", "YoLo3+", "Машинное обучение", "Machine Learning", "Computer Vision"], ["Деловое общение", "Деловая переписка", "Английский язык", "Обучение и развитие", "Деловая коммуникация", "Zoom", "Преподавание"], ["Machine Learning", "Python", "Linux", "Git", "OpenCV"], ["Data Scientist", "Machine learning", "Computer Vision", "OpenVINO", "cv::dnn"], ["Git", "Phyton", "Agile", "Atlassian Jira"], ["Computer Vision", "Машинное обучение", "Python", "PyTorch", "TensorFlow", "Математическая статистика", "Анализ данных", "Deep Learning", "Neural Networks"], ["Python", "Agile Project Management", "machine learning", "data science", "Pandas", "PyTorch", "RecSys"], ["Python", "OpenCV", "Нейронные сети", "PyTorch", "Tensorflow", "Работа в команде"], ["Python", "PyTorch", "Английский язык", "MongoDB", "Docker", "Kubernetes"], ["Python", "PyTorch", "Tensorflow", "SQL", "Keras"], ["Python", "PyTorch", "Английский язык", "MongoDB", "Docker", "Kubernetes"], ["Python", "PyTorch", "Numpy", "Tensorflow", "Faiss", "MXNet", "PSPNet", "ResNet", "YOLO"]]} -------------------------------------------------------------------------------- /flsite.py: -------------------------------------------------------------------------------- 1 | from flask import Flask, render_template, url_for, request 2 | from graph_vis import gr_vis 3 | from os import remove 4 | import json 5 | 6 | app = Flask(__name__) 7 | 8 | 9 | def get_tag_list(): 10 | with open('data/for_visualization/index.json', 'r', encoding="utf-8") as f: 11 | return json.load(f).keys() 12 | 13 | 14 | @app.route('/', methods=['POST', 'GET']) 15 | def index(): 16 | style = url_for('static', filename='style.css') 17 | tags = get_tag_list() 18 | visualisation = url_for('user_graph', key_word='Hello', node_level=0, edge_level=0) 19 | return render_template('index.html', style=style, vis=visualisation, 20 | tags=tags, chosen_tag='Choose interested tag', node_level=0) 21 | 22 | 23 | @app.route('/send_', methods=['POST', 'GET']) 24 | def send(tag): 25 | style = url_for('static', filename='style.css') 26 | tags = get_tag_list() 27 | if request.method == 'POST': 28 | key_word = tag 29 | node_level = request.form['node_level'] 30 | visualisation = url_for('user_graph', key_word=key_word, 31 | node_level=int(node_level), edge_level=int(node_level)) 32 | return render_template('index.html', style=style, vis=visualisation, 33 | tags=tags, chosen_tag=key_word, node_level=node_level) 34 | 35 | 36 | @app.route('/user_graph___', methods=['POST', 'GET']) 37 | def user_graph(key_word, node_level, edge_level): 38 | path = gr_vis(key_word, int(node_level), int(edge_level)) 39 | with open(path, 'r', encoding="utf-8") as f: 40 | graph_html = f.read() 41 | remove(path) 42 | return graph_html 43 | 44 | 45 | if __name__ == "__main__": 46 | app.run(debug=True) 47 | -------------------------------------------------------------------------------- /graph_vis.py: -------------------------------------------------------------------------------- 1 | from pyvis.network import Network 2 | import json 3 | 4 | 5 | def gr_vis(key_word="Hello!", node_level=5, edge_level=5): 6 | 7 | with open('data/for_visualization/index.json', 'r', encoding="utf-8") as f: 8 | key_words_json = json.load(f) 9 | 10 | with open(f"data/for_visualization/{key_words_json[key_word]}", 'r', encoding="utf-8") as f: 11 | tags_json = json.load(f) 12 | 13 | node_max_popularity = max([node["popularity"] for node in tags_json["items"]["nodes"]]) 14 | link_max_value = max([link["value"] for link in tags_json["items"]["links"]]) 15 | node_nc = 100/node_max_popularity # node normalization coefficient 16 | link_nc = 100/link_max_value # link normalization coefficient 17 | 18 | nodes = [node["id"] for node in tags_json["items"]["nodes"] 19 | if int(node_nc * node["popularity"]) > node_level] 20 | nodes_size = [1 + int((node_nc * node["popularity"])**0.5) 21 | for node in tags_json["items"]["nodes"] 22 | if int(node_nc * node["popularity"]) > node_level] 23 | 24 | def color_from_popularity(popularity): # popularity 0-100 25 | if popularity > 50: 26 | return '#%02x%02x%02x' % (int((popularity-50)/50*255), int((100-popularity)/50*255), 0) 27 | else: 28 | return '#%02x%02x%02x' % (0, int(popularity/50*255), int((100-popularity)/100*255)) 29 | 30 | nodes_color = [color_from_popularity(node_nc*node["popularity"]) 31 | for node in tags_json["items"]["nodes"] 32 | if int(node_nc * node["popularity"]) > node_level] 33 | 34 | edges = [(link["source"], link["target"], int(link_nc * link["value"]/10)) 35 | for link in tags_json["items"]["links"] 36 | if link["source"] in nodes 37 | and link["target"] in nodes 38 | and int(link_nc * link["value"]) > edge_level] 39 | 40 | net = Network(800, 800) 41 | 42 | for node, node_size, color in zip(nodes, nodes_size, nodes_color): 43 | net.add_node(node, hidden=False, shape='dot', color=color, size=2*node_size, mass=12-node_size, 44 | borderWidth=0, borderWidthSelected=2) 45 | 46 | net.add_edges(edges) 47 | net.inherit_edge_colors(False) 48 | with open('static/var_options.json', 'r', encoding="utf-8") as f: 49 | var_options = f.read() 50 | net.set_options(f'{var_options}') 51 | # net.show_buttons() 52 | path = f'tmp/graph_visualisation_{key_word}_{node_level}_{edge_level}.html' 53 | net.save_graph(path) 54 | return path 55 | 56 | 57 | if __name__ == '__main__': 58 | gr_vis(key_word='python', node_level=5, edge_level=5) 59 | -------------------------------------------------------------------------------- /graph_visualization.js: -------------------------------------------------------------------------------- 1 | import { 2 | Runtime, 3 | Inspector, 4 | } from "https://cdn.jsdelivr.net/npm/@observablehq/runtime@4/dist/runtime.js"; 5 | 6 | function hideElement(id) { 7 | document.getElementById(id).style.display = "none"; 8 | } 9 | 10 | function showElement(id) { 11 | document.getElementById(id).style.display = "block"; 12 | } 13 | 14 | export async function createElementsFromDataUrls(data_urls) { 15 | async function doDisplay(nameToDisplay) { 16 | for (let name of Object.keys(await data_urls)) { 17 | hideElement(`${name}-graph`); 18 | } 19 | showElement(`${nameToDisplay}-graph`); 20 | } 21 | 22 | for (let [name, url] of Object.entries(await data_urls)) { 23 | url = `./data/for_visualization/${url}`; 24 | 25 | const button = document.createElement("button"); 26 | const graph = document.createElement("div"); 27 | const sizeSlider = document.createElement("div"); 28 | 29 | button.setAttribute("id", `${name}-button`); 30 | button.innerText = `${name}`; 31 | button.onclick = () => doDisplay(`${name}`); 32 | document.getElementById("button-container").appendChild(button); 33 | 34 | sizeSlider.classList.add("size-slider"); 35 | sizeSlider.innerHTML = ` Granularity (ignore nodes and links smaller than) `; 36 | sizeSlider.oninput = () => { 37 | graph.innerHTML = ""; 38 | graph.appendChild(sizeSlider); 39 | createGraphElement(url, name); 40 | }; 41 | 42 | graph.appendChild(sizeSlider); 43 | graph.setAttribute("id", `${name}-graph`); 44 | graph.setAttribute("class", `graph`); 45 | document.getElementById("graph-container").appendChild(graph); 46 | createGraphElement(url, name); 47 | } 48 | 49 | doDisplay(Object.keys(await data_urls)[0]); 50 | } 51 | 52 | function createGraphElement(data_url, name) { 53 | buildGraphFromNodesLinks(data_url); 54 | const inspect = Inspector.into(`#${name}-graph`); 55 | new Runtime().module( 56 | buildGraphFromNodesLinks(data_url, name), 57 | (name) => name === "chart" && inspect() 58 | ); 59 | } 60 | 61 | function buildGraphFromNodesLinks(data_url, name) { 62 | function define(runtime, observer) { 63 | const main = runtime.module(); 64 | main 65 | .variable(observer("chart")) 66 | .define( 67 | "chart", 68 | ["data", "d3", "width", "height", "color", "drag", "invalidation"], 69 | function (data, d3, width, height, color, drag, invalidation) { 70 | const min_node_popularity = document.getElementById( 71 | `${name}-node-size-slider` 72 | ).value; 73 | const min_link_value = document.getElementById( 74 | `${name}-node-size-slider` 75 | ).value; 76 | 77 | const nodes = data.items.nodes 78 | .filter((node) => node.popularity > min_node_popularity) 79 | .map((d) => Object.create(d)); 80 | const links = data.items.links 81 | .filter((link) => link.value > min_link_value) 82 | .map((d) => Object.create(d)); 83 | 84 | const simulation = d3 85 | .forceSimulation(nodes) 86 | .force( 87 | "link", 88 | d3.forceLink(links).id((d) => d.id) 89 | ) 90 | .force( 91 | "charge", 92 | d3.forceManyBody().strength((d) => -50 * Math.sqrt(d.popularity)) 93 | ) 94 | .force("center", d3.forceCenter(width / 2, height / 2)); 95 | 96 | const svg = d3.create("svg").attr("viewBox", [0, 0, width, height]); 97 | 98 | const link = svg 99 | .append("g") 100 | .attr("stroke", "#999") 101 | .selectAll("line") 102 | .data(links) 103 | .join("line") 104 | .attr("stroke-opacity", (d) => 0.2) 105 | .attr("stroke-width", (d) => d.value * 0.1); 106 | 107 | const node = svg 108 | .append("g") 109 | .attr("class", "nodes") 110 | .attr("stroke", "#fff") 111 | .attr("stroke-width", 0.1) 112 | .selectAll("circle") 113 | .data(nodes) 114 | .join("circle") 115 | .attr("r", (d) => Math.sqrt(d.popularity) * 2) 116 | .attr("fill", color) 117 | .call(drag(simulation)); 118 | 119 | node 120 | .append("title") 121 | .text((d) => d.id + ": " + d.popularity + " mentions"); 122 | 123 | const text = svg 124 | .selectAll("nodes") 125 | .data(nodes) 126 | .join("text") 127 | .attr("x", (d, i) => i * 15) 128 | .attr("y", 17) 129 | .attr("dy", "0.2em") 130 | .attr("font-family", "Verdana") 131 | .attr("font-size", (d) => Math.pow(d.popularity, 1 / 6) * 5) 132 | .text((d) => d.id); 133 | 134 | simulation.on("tick", () => { 135 | link 136 | .attr("x1", (d) => d.source.x) 137 | .attr("y1", (d) => d.source.y) 138 | .attr("x2", (d) => d.target.x) 139 | .attr("y2", (d) => d.target.y); 140 | 141 | node.attr("cx", (d) => d.x).attr("cy", (d) => d.y); 142 | 143 | text 144 | .attr("x", (d) => d.x) //position of the lower left point of the text 145 | .attr("y", (d) => d.y); //position of the lower left point of the text 146 | }); 147 | 148 | invalidation.then(() => simulation.stop()); 149 | 150 | return svg.node(); 151 | } 152 | ); 153 | main.variable(observer("data")).define("data", function () { 154 | return fetch(data_url).then((res) => res.json()); 155 | }); 156 | main.variable(observer("height")).define("height", function () { 157 | return 900; 158 | }); 159 | main.variable(observer("color")).define("color", ["d3"], function (d3) { 160 | const scale = d3.scaleOrdinal( 161 | [4, 3, 2, 1, 0], 162 | [`orange`, `lightgreen`, `cyan`, `khaki`, `lightgrey`] 163 | ); 164 | // d3.schemeSet2 165 | return (d) => scale(parseInt((Math.sqrt(d.popularity) / 25) * 5)); 166 | }); 167 | main.variable(observer("drag")).define("drag", ["d3"], function (d3) { 168 | return (simulation) => { 169 | function dragstarted(d) { 170 | if (!d3.event.active) simulation.alphaTarget(0.3).restart(); 171 | d.fx = d.x; 172 | d.fy = d.y; 173 | } 174 | 175 | function dragged(d) { 176 | d.fx = d3.event.x; 177 | d.fy = d3.event.y; 178 | } 179 | 180 | function dragended(d) { 181 | if (!d3.event.active) simulation.alphaTarget(0); 182 | d.fx = null; 183 | d.fy = null; 184 | } 185 | 186 | return d3 187 | .drag() 188 | .on("start", dragstarted) 189 | .on("drag", dragged) 190 | .on("end", dragended); 191 | }; 192 | }); 193 | main.variable(observer("d3")).define("d3", ["require"], function (require) { 194 | return require("d3@5"); 195 | }); 196 | return main; 197 | } 198 | 199 | return define; 200 | } 201 | -------------------------------------------------------------------------------- /index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | A visualisation of developer skills in demand 7 | 8 | 9 | 10 | 11 | 12 |
13 |
14 |

A visualisation of skills in demand

15 |

What skills are in demand? 16 | We scraped thousands of job postings, connecting skills into one graph. 17 | If you want to evolve as a professional, you can use this as a hint. 18 | 19 |

    20 |
  • The bigger the node, the more in demand the skill.
    • 21 |
  • The wider the link, the closer the nodes, the more related are the two skills (seen together).
    • 22 |
  • Choose the field of interest pushing the button.
    • 23 |
24 | Link to GitHub repo 25 |

26 |
27 | Star 30 | Issue 33 |
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35 | 36 |
37 |
38 | 39 | 52 |
53 | 54 | -------------------------------------------------------------------------------- /ipython notebooks/hh-ru_scraper.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Code for visualizing skills as a graph\n", 8 | "\n", 9 | "You may run all cells to test. It will take near one minute to scrape data and show the graph.\n", 10 | "\n", 11 | "Set SEARCH_WORD to scrape and visualize tags by your word search results. \n", 12 | "\n", 13 | "It may take up to 10 minutes to scrape all vacancies so please be patient." 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "import requests\n", 23 | "import itertools\n", 24 | "import time\n", 25 | "import pickle\n", 26 | "\n", 27 | "import matplotlib.pyplot as plt\n", 28 | "import networkx as nx" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": null, 34 | "metadata": {}, 35 | "outputs": [], 36 | "source": [ 37 | "# getting all pages with vacancies found by word from hh.ru\n", 38 | "\n", 39 | "SERCH_WORD = 'python hadoop' # word or phrase to search in vacancies\n", 40 | "\n", 41 | "\n", 42 | "ses = requests.Session()\n", 43 | "ses.headers = {'HH-User-Agent': \"Mozilla/5.0 (X11; Linux x86_64; rv:10.0) Gecko/20100101 Firefox/10.0\"}\n", 44 | "\n", 45 | "url = f'https://api.hh.ru/vacancies?text={SERCH_WORD}&per_page=100'\n", 46 | "res = ses.get(url)\n", 47 | "\n", 48 | "res_all = []\n", 49 | "for p in range(res.json()['pages']):\n", 50 | " print(f'scraping page {p}')\n", 51 | " p_url = url + f'&page={p}'\n", 52 | " res = ses.get(p_url)\n", 53 | " res_all.append(res.json())\n", 54 | " time.sleep(0.5)" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": null, 60 | "metadata": { 61 | "scrolled": true 62 | }, 63 | "outputs": [], 64 | "source": [ 65 | "# parcing vacancies ids, getting vacancy responce and scraping tags from each vacancy\n", 66 | "\n", 67 | "tags_list = []\n", 68 | "\n", 69 | "for page_res_json in res_all:\n", 70 | " for i in range(len(page_res_json['items'])):\n", 71 | " vac_id = page_res_json['items'][i]['id']\n", 72 | " vac_res = ses.get(f'https://api.hh.ru/vacancies/{vac_id}')\n", 73 | "\n", 74 | " if len(vac_res.json()[\"key_skills\"]) > 0: # at least one skill present\n", 75 | " print(vac_id)\n", 76 | " tags = [v for v_dict in vac_res.json()[\"key_skills\"] for _, v in v_dict.items()]\n", 77 | " print(' '.join(tags))\n", 78 | " tags_list.append(tags)\n", 79 | " print()\n", 80 | "\n", 81 | " time.sleep(0.1) # not to overload server " 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": null, 87 | "metadata": {}, 88 | "outputs": [], 89 | "source": [ 90 | "flattened_list = list(itertools.chain(*tags_list))\n", 91 | "\n", 92 | "# some filtering by occurences count\n", 93 | "# YOU CAN TURN IT OFF\n", 94 | "flattened_list = [x for x in flattened_list if flattened_list.count(x) > 10]\n", 95 | "\n", 96 | "# counting words occurences\n", 97 | "words_count = {i:flattened_list.count(i) for i in set(flattened_list)}\n", 98 | "print('Tags count:')\n", 99 | "print('\\n'.join(\n", 100 | " [f'- {k}: {v}' for k, v in sorted(words_count.items(), key=lambda x: x[1], reverse=True)]))\n", 101 | "\n", 102 | "\n", 103 | "# tags connection dict initialization\n", 104 | "formatted_tags = {}\n", 105 | "for tag1 in set(flattened_list):\n", 106 | " for tag2 in set(flattened_list):\n", 107 | " if tag1 == tag2:\n", 108 | " continue\n", 109 | "\n", 110 | " pair_tags = frozenset([tag1, tag2])\n", 111 | " formatted_tags[pair_tags] = 0\n", 112 | " \n", 113 | "# count tags connection\n", 114 | "for line in tags_list:\n", 115 | " for tag1, tag2 in itertools.product(line, repeat=2):\n", 116 | " current_key_pair = frozenset([tag1, tag2])\n", 117 | " if current_key_pair in formatted_tags:\n", 118 | " formatted_tags[current_key_pair] += 1 \n", 119 | " \n", 120 | " \n", 121 | "# filtering data from zero occurances\n", 122 | "for k, v in formatted_tags.copy().items():\n", 123 | " if v == 0:\n", 124 | " del formatted_tags[k]\n", 125 | " \n", 126 | "print('\\nTag to tag frequency:')\n", 127 | "for k,v in sorted(formatted_tags.items(), key=lambda x: x[1], reverse=True):\n", 128 | " print('-', list(k), v)" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": null, 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [ 137 | "# save results\n", 138 | "\n", 139 | "with open('formatted_tags.pkl', 'wb') as f:\n", 140 | " pickle.dump(formatted_tags, f)" 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": null, 146 | "metadata": {}, 147 | "outputs": [], 148 | "source": [ 149 | "# build and show graph\n", 150 | "\n", 151 | "G=nx.Graph()\n", 152 | "\n", 153 | "G.add_edges_from(list(formatted_tags.keys()))\n", 154 | "pos = nx.spring_layout(G, k=0.5, iterations=200)\n", 155 | "e_widths = [i/3 for i in formatted_tags.values()] # edge size\n", 156 | "n_widths = [words_count[i]*100 for i in list(G.nodes())] # node size\n", 157 | "\n", 158 | "f = plt.figure(figsize=(32,32))\n", 159 | "\n", 160 | "nx.draw_networkx_nodes(G, pos, node_color='#A0CBE2', node_size=n_widths, node_cmap=plt.cm.Blues)\n", 161 | "nx.draw_networkx_edges(G, pos, edge_color='#C0CBD2', edgelist=list(formatted_tags.keys()), width=e_widths, edge_cmap=plt.cm.Blues)\n", 162 | "nx.draw_networkx_labels(G, pos)\n", 163 | "\n", 164 | "plt.show()" 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": null, 170 | "metadata": {}, 171 | "outputs": [], 172 | "source": [ 173 | "# save graph as picture file\n", 174 | "\n", 175 | "f.savefig(\"tags_graph.png\", format=\"PNG\")" 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": null, 181 | "metadata": {}, 182 | "outputs": [], 183 | "source": [] 184 | } 185 | ], 186 | "metadata": { 187 | "hide_input": false, 188 | "kernelspec": { 189 | "display_name": "Python 3", 190 | "language": "python", 191 | "name": "python3" 192 | }, 193 | "language_info": { 194 | "codemirror_mode": { 195 | "name": "ipython", 196 | "version": 3 197 | }, 198 | "file_extension": ".py", 199 | "mimetype": "text/x-python", 200 | "name": "python", 201 | "nbconvert_exporter": "python", 202 | "pygments_lexer": "ipython3", 203 | "version": "3.7.5" 204 | } 205 | }, 206 | "nbformat": 4, 207 | "nbformat_minor": 4 208 | } 209 | -------------------------------------------------------------------------------- /landing_background.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/avk0/skills_graph/fbd8b2cf93038c31a7ff945007a9e6b754bd03e8/landing_background.jpg -------------------------------------------------------------------------------- /pic/Screenshot_2020-05-05 A visualisation of developer skills in demand.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/avk0/skills_graph/fbd8b2cf93038c31a7ff945007a9e6b754bd03e8/pic/Screenshot_2020-05-05 A visualisation of developer skills in demand.png -------------------------------------------------------------------------------- /pic/first_static_graph.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/avk0/skills_graph/fbd8b2cf93038c31a7ff945007a9e6b754bd03e8/pic/first_static_graph.png -------------------------------------------------------------------------------- /pic/tags_graph.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/avk0/skills_graph/fbd8b2cf93038c31a7ff945007a9e6b754bd03e8/pic/tags_graph.png -------------------------------------------------------------------------------- /scrapers/hh_ru_scraper.py: -------------------------------------------------------------------------------- 1 | """ 2 | Scraping vacancies from hh.ru. 3 | """ 4 | 5 | from datetime import datetime, timedelta 6 | import time 7 | import requests 8 | import sql_db_operation as db_op 9 | 10 | URL = 'https://api.hh.ru/vacancies' 11 | 12 | 13 | def create_session(url=URL): 14 | try: 15 | ses = requests.Session() 16 | ses.headers = {'HH-User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:10.0) Gecko/20100101 Firefox/10.0'} 17 | res = ses.get(url) 18 | return ses 19 | except Exception as ex: 20 | print('Error:', ex) 21 | return False 22 | 23 | 24 | def get_vacancies_id_list(session, specialization=1, phrase_to_search=False): 25 | 26 | date_to = datetime.now().replace(microsecond=0) 27 | date_from = date_to - timedelta(days=2) 28 | 29 | params = { 30 | 'page': 0, 31 | 'per_page': 100, 32 | 'order_by': 'publication_time', 33 | 'date_from': date_from.isoformat(), 34 | 'date_to': date_to.isoformat(), 35 | 'specialization': specialization, 36 | } 37 | if phrase_to_search: params['text'] = phrase_to_search 38 | 39 | res = {} 40 | try: 41 | res = session.get(URL, params=params).json() 42 | if 'errors' in res.keys(): 43 | print(res['errors']) 44 | return False 45 | except Exception as ex: 46 | print('Error:', ex) 47 | print('res=', res) 48 | return False 49 | 50 | print('vacancies_found : vacancies_added') 51 | while res['items']: 52 | time.sleep(0.1) 53 | try: 54 | res = session.get(URL, params=params).json() 55 | if 'errors' in res.keys(): 56 | print(res['errors']) 57 | return False 58 | vacancies_found = res['found'] 59 | vacancies = [(item['id'], item['published_at']) for item in res['items']] 60 | 61 | except Exception as ex: 62 | print('Error:', ex) 63 | print('res=', res) 64 | return False 65 | 66 | insert_vac_ids_to_db(vacancies) 67 | 68 | print(f' {vacancies_found:14d} :{len(res["items"]):16d}') 69 | params['page'] += 1 70 | if params['page'] == res['pages']-1: 71 | last_vacancy_date = res["items"][-1]["published_at"] 72 | params['date_to'] = last_vacancy_date 73 | params['page'] = 0 74 | 75 | return True 76 | 77 | 78 | def insert_vac_ids_to_db(vacancies, db_name=db_op.DATA_BASE): 79 | script = '' 80 | for vac_id, published_at in vacancies: 81 | published_at = f'"{published_at[0:10]}"' 82 | script = f'{script}' \ 83 | f'{db_op.insert_into_table("vacancy_ids", ["vac_id", "publish_date"], [vac_id, published_at])}\n' 84 | 85 | return db_op.execute_script(script) 86 | 87 | 88 | def update_skills_table_on_db(vac_ids, list_of_skills_id, db_name=db_op.DATA_BASE): 89 | script = '' 90 | for vac_id, skills_ids in zip(vac_ids, list_of_skills_id): 91 | if skills_ids: 92 | skills_ids = ['parsed'] + skills_ids 93 | script = f'{script}' \ 94 | f'{db_op.update_table("vacancy_ids", skills_ids,["1"]*(len(skills_ids)+1), f"vac_id = {vac_id}")}\n' 95 | else: 96 | script = f'{script}' \ 97 | f'{db_op.update_table("vacancy_ids", ["parsed"],["0"], f"vac_id = {vac_id}")}\n' 98 | # print(script) 99 | return db_op.execute_script(script) 100 | 101 | 102 | def get_unparsed_vac_ids_from_db(db_name=db_op.DATA_BASE): 103 | query = db_op.select_data('vacancy_ids', ['vac_id'], 'parsed IS NULL') 104 | query = f'{query} LIMIT 10;' 105 | vacancies = db_op.execute_query(query) 106 | return [vac[0] for vac in vacancies] 107 | 108 | 109 | def parse_vacancy_skills(session, vac_id, specialization='1', db_name=db_op.DATA_BASE): 110 | vac = {} 111 | try: 112 | vac = session.get(f'{URL}/{vac_id}').json() 113 | 114 | if 'errors' in vac.keys(): 115 | print(vac['errors']) 116 | return False 117 | 118 | s = vac['specializations'] 119 | if len(s) > 2*len([s['profarea_id'] for s in s if s['profarea_id'] == specialization]): 120 | return False 121 | 122 | skills = vac['key_skills'] 123 | 124 | except Exception as ex: 125 | print('Error:', ex) 126 | print(vac) 127 | return False 128 | 129 | if skills: # at least one skill present 130 | skills = [skill['name'] for skill in skills] 131 | else: return False 132 | 133 | return skills 134 | 135 | 136 | def get_all_skills(db_name=db_op.DATA_BASE): 137 | query = db_op.select_data('all_skills', ['skill_id', 'skill'], 'TRUE') 138 | return db_op.execute_query(query) 139 | 140 | 141 | def update_all_skills_on_db(new_skills, db_name=db_op.DATA_BASE): 142 | script = '' 143 | for skill in new_skills: 144 | skill = f'"{skill}"' 145 | script = f'{script}' \ 146 | f'{db_op.insert_into_table("all_skills", ["skill"], [skill])}\n' 147 | 148 | # print(script) 149 | return db_op.execute_script(script) 150 | 151 | 152 | def alter_table_on_db(new_skills, db_name=db_op.DATA_BASE): 153 | script = '' 154 | for skill in new_skills: 155 | script = f'{script}' \ 156 | f'{db_op.add_column("vacancy_ids", skill)}INTEGER ;\n' 157 | # print(script) 158 | return db_op.execute_script(script) 159 | 160 | 161 | def processing_vac_skills(session, db_name=db_op.DATA_BASE): 162 | vac_ids = get_unparsed_vac_ids_from_db() 163 | print('________________________') 164 | if not vac_ids: 165 | print('All vacancies in db have parsed') 166 | return 'DONE' 167 | list_of_skills = [] 168 | for vac_id in vac_ids: 169 | vac_skills = parse_vacancy_skills(session, vac_id) 170 | if vac_skills: 171 | list_of_skills.append([skill.upper() for skill in vac_skills]) 172 | else: 173 | list_of_skills.append(False) 174 | 175 | newly_parsed_skills = set() 176 | for skills in list_of_skills: 177 | if skills: 178 | newly_parsed_skills.update(set(skills)) 179 | 180 | all_skills = get_all_skills() 181 | new_unique_skills = newly_parsed_skills.difference({skill[1] for skill in all_skills}) 182 | 183 | if new_unique_skills: 184 | update_all_skills_on_db(new_unique_skills) 185 | 186 | all_skills = get_all_skills() 187 | all_skills_dic = {skill[1]: skill[0] for skill in all_skills} 188 | new_unique_skills_ids = [f'skill_id_{all_skills_dic[skill]}' for skill in new_unique_skills] 189 | alter_table_on_db(new_unique_skills_ids) 190 | list_of_skills_ids = [[f'skill_id_{all_skills_dic[skill]}' for skill in skills] if skills else False 191 | for skills in list_of_skills] 192 | update_skills_table_on_db(vac_ids, list_of_skills_ids) 193 | return True 194 | 195 | 196 | if __name__ == '__main__': 197 | ses = create_session() 198 | # get_vacancies_id_list(ses) 199 | p = processing_vac_skills(ses) 200 | while p != 'DONE': 201 | p = processing_vac_skills(ses) 202 | ses.close() 203 | -------------------------------------------------------------------------------- /scrapers/preprocess.py: -------------------------------------------------------------------------------- 1 | """ Preprocessing module 2 | 3 | Filters out small nodes and weak edges not to overload graph visualization 4 | 5 | Usage example: 6 | preprocess.py "./data/raw/raw-tags_machine learning.json" "./data/filtered_5/filt-tags_machine-learning.json" 7 | """ 8 | 9 | import sys 10 | import itertools 11 | import json 12 | from collections import Counter 13 | 14 | 15 | def preprocess(raw_tags_path, out_path, node_size_thresh=0, lower=True): 16 | 17 | # load tags 18 | with open(raw_tags_path, 'r', encoding="utf-8") as f: 19 | tags_json = json.load(f) 20 | 21 | print(tags_json['parse_date'], tags_json['phrase'], tags_json['items_number']) 22 | 23 | tags_list = tags_json['items'] 24 | if lower: 25 | tags_list = [[i.lower() for i in line] for line in tags_list] 26 | 27 | # counting words occurrences 28 | nodes_size = Counter([i for line in tags_list for i in line]) 29 | print('Number of unique nodes:', len(nodes_size)) 30 | 31 | # filtering by occurrences count 32 | nodes_size = {k: v for k, v in nodes_size.items() if v >= node_size_thresh} 33 | 34 | print(f'Len nodes dict >= {node_size_thresh}: {len(nodes_size)}') 35 | 36 | # count tags edges 37 | formatted_tags = dict() 38 | for line in tags_list: 39 | for tag1, tag2 in itertools.permutations([tag for tag in line if tag in nodes_size], 2): 40 | if (tag1, tag2) not in formatted_tags.keys(): 41 | formatted_tags[(tag1, tag2)] = 0 42 | formatted_tags[(tag1, tag2)] += 1 43 | 44 | #for k, v in formatted_tags.items(): 45 | # print(k, v) 46 | 47 | # prepairing for visualization 48 | count_color_step = (max(list(nodes_size.values())) - node_size_thresh) // min(7, len(nodes_size) - node_size_thresh) # 7 colors 49 | nodes = [{"id": node, "group": (count - node_size_thresh) // count_color_step, "popularity": count} \ 50 | for node, count in nodes_size.items()] 51 | 52 | links = [{"source": pair[0], "target": pair[1], "value": count} \ 53 | for pair, count in formatted_tags.items()] 54 | 55 | 56 | data_to_dump = {'parse_date': tags_json['parse_date'], 57 | 'phrase': tags_json['phrase'], 58 | 'items_number': tags_json['items_number'], 59 | 'items': {"nodes": nodes, "links": links}} 60 | 61 | print('phrase:', data_to_dump['phrase']) 62 | print('vacancies parced:', data_to_dump['items_number']) 63 | 64 | with open(out_path, 'w') as f: 65 | json.dump(data_to_dump, f, ensure_ascii=False) 66 | 67 | return formatted_tags 68 | 69 | 70 | if __name__ == '__main__': 71 | raw_tags_path = sys.argv[1] 72 | out_path = sys.argv[2] 73 | preprocess(raw_tags_path, out_path, node_size_thresh=5) 74 | -------------------------------------------------------------------------------- /scrapers/sql_database/hh_vacancies.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/avk0/skills_graph/fbd8b2cf93038c31a7ff945007a9e6b754bd03e8/scrapers/sql_database/hh_vacancies.db -------------------------------------------------------------------------------- /scrapers/sql_db_operation.py: -------------------------------------------------------------------------------- 1 | import sqlite3 as sq 2 | 3 | DATA_BASE = "scrapers/sql_database/hh_vacancies.db" 4 | 5 | 6 | def execute_script(script, db_name=DATA_BASE): 7 | try: 8 | with sq.connect(db_name) as con: 9 | cur = con.cursor() 10 | cur.executescript(script) 11 | return True 12 | except Exception as ex: 13 | print(ex) 14 | return False 15 | 16 | 17 | def execute_query(query, db_name=DATA_BASE): 18 | try: 19 | with sq.connect(db_name) as con: 20 | cur = con.cursor() 21 | cur.execute(query) 22 | return cur.fetchall() 23 | except Exception as ex: 24 | print(ex) 25 | return 26 | 27 | 28 | def query_generator(pattern: str, options: list): 29 | query = pattern 30 | for a in options: 31 | query = query.replace('|?|', a, 1) 32 | return query 33 | 34 | 35 | def create_table(table_name: str, columns: list): 36 | pattern = """ CREATE TABLE IF NOT EXISTS |?| ( |?| ) """ 37 | return query_generator(pattern, [table_name, ','.join(columns)]) 38 | 39 | 40 | def add_column(table_name: str, column: str): 41 | pattern = """ ALTER TABLE |?| ADD COLUMN |?| """ 42 | return query_generator(pattern, [table_name, column]) 43 | 44 | 45 | def insert_into_table(table_name: str, columns: list, values: list): 46 | pattern = """ INSERT INTO |?| ( |?| ) VALUES ( |?| ) ;""" 47 | return query_generator(pattern, [table_name, ','.join(columns), ','.join(values)]) 48 | 49 | 50 | def update_table(table_name: str, columns: list, values: list, conditions: str): 51 | pattern = """ UPDATE |?| SET |?| WHERE |?| ;""" 52 | return query_generator(pattern, 53 | [table_name, ','.join([f'{col}={val}' for col, val in zip(columns, values)]), conditions]) 54 | 55 | 56 | def select_data(table_name: str, columns: list, conditions: str): 57 | pattern = """ SELECT |?| FROM |?| WHERE |?| """ 58 | return query_generator(pattern, [','.join(columns), table_name, conditions]) 59 | 60 | 61 | if __name__ == '__main__': 62 | query = select_data("vacancy_ids", ["vac_id"], 'vac_id<40000000') 63 | print(query) 64 | print(execute_query(query)) 65 | -------------------------------------------------------------------------------- /static/style.css: -------------------------------------------------------------------------------- 1 | .page { 2 | display: flex; 3 | flex-direction: column; 4 | margin: -5px; 5 | align-items: center; 6 | height: 200vh; 7 | } 8 | 9 | .landing { 10 | display: flex; 11 | flex-direction: column; 12 | /* align-items: center; */ 13 | justify-content: center; 14 | width: auto; 15 | padding: 2% 25% 0% 25%; 16 | /* background-color: lightblue; */ 17 | } 18 | 19 | .landing h1 { 20 | font-weight: normal; 21 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, 22 | Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; 23 | } 24 | 25 | .chosen_tag { 26 | font-weight: normal; 27 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, 28 | Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; 29 | margin: 10px 0px 0px 0px; 30 | padding: 0 0 0 0; 31 | } 32 | 33 | .landing p,ul { 34 | font-weight: normal; 35 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, 36 | Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; 37 | font-size: 16px; 38 | padding: 0 0 0 0; 39 | } 40 | 41 | .button-container { 42 | display: flex; 43 | flex-direction: column; 44 | justify-content: center; 45 | padding: 0% 25% 0% 25%; 46 | margin: 10px 0px 10px 0px; 47 | align-items:center; 48 | } 49 | 50 | #graph { 51 | border: none; 52 | align: left; 53 | margin: 0px 0px 0px 0px; 54 | width: 820px; 55 | height: 830px; 56 | padding: 0% 0% 0% 0%; 57 | } 58 | .line { 59 | width: 80%; /* ширина линии */ 60 | height: 4px; /* высота / толщина линии */ 61 | background: #333; /* фон / цвет линии */ 62 | border: 0; /* рамка вокруг разделительной линии (уберем ее) */ 63 | margin: 10px 0 10px 0; /* отступ над и под линией 5 пикселей */ 64 | } 65 | .form_submit { 66 | align: center; 67 | margin: 3px 0 3px 0; 68 | padding: 0% 0% 0% 0% 69 | 70 | } 71 | .c-button { 72 | min-width: 100px; 73 | font-family: inherit; 74 | appearance: none; 75 | border: 0; 76 | border-radius: 5px; 77 | background: #4676d7; 78 | color: #fff; 79 | padding: 8px 16px; 80 | font-size: 1rem; 81 | cursor: pointer; 82 | margin: 5px 5px 5px 5px; 83 | } 84 | 85 | .c-button:hover { 86 | background: #1d49aa; 87 | } 88 | 89 | .c-button:focus { 90 | outline: none; 91 | box-shadow: 0 0 0 4px #cbd6ee; 92 | } 93 | .slider { 94 | -webkit-appearance: none; 95 | display: flex; 96 | width: 600px; 97 | height: 15px; 98 | border-radius: 5px; 99 | background: #d3d3d3; 100 | outline: none; 101 | opacity: 0.7; 102 | justify-content: center; 103 | } 104 | 105 | .slider::-webkit-slider-thumb { 106 | -webkit-appearance: none; 107 | appearance: none; 108 | width: 25px; 109 | height: 25px; 110 | border-radius: 50%; 111 | background: #4CAF50; 112 | cursor: pointer; 113 | } 114 | 115 | .slider::-moz-range-thumb { 116 | width: 10px; 117 | height: 25px; 118 | border-radius: 20%; 119 | background: #1d49aa; 120 | cursor: pointer; 121 | } 122 | 123 | .label { 124 | width: 600px; 125 | display: flex; 126 | flex-direction: row; 127 | justify-content: space-between; 128 | padding: 0% 0% 0% 0%; 129 | margin: 0px 0px 0px 0px; 130 | line-height: 30px; /* Выравниваем по высоте */ 131 | font-weight: normal; 132 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, 133 | Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; 134 | align-self: center; 135 | } 136 | 137 | #left_edge { 138 | width: auto; /* Ширина */ 139 | text-align: left; /* Выравниваем по правому краю */ 140 | line-height: 30px; /* Выравниваем по высоте */ 141 | font-weight: normal; 142 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, 143 | Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; 144 | margin: 0px 0px 0px 0px; 145 | } 146 | #right_edge { 147 | width: auto; /* Ширина */ 148 | text-align: center; /* Выравниваем по правому краю */ 149 | line-height: 30px; /* Выравниваем по высоте */ 150 | font-weight: normal; 151 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, 152 | Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; 153 | margin: 0px 0px 0px 0px; 154 | } 155 | #center { 156 | width: auto; /* Ширина */ 157 | text-align: center; /* Выравниваем по правому краю */ 158 | line-height: 30px; /* Выравниваем по высоте */ 159 | font-weight: normal; 160 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, 161 | Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; 162 | margin: 0px 0px 0px 0px; 163 | } 164 | .buttons-div { 165 | height: 30px; 166 | } -------------------------------------------------------------------------------- /static/var_options.json: -------------------------------------------------------------------------------- 1 | { 2 | "edges":{ 3 | "color": { 4 | "color":"rgba(66,66,68,0.6)", 5 | "highlight":"rgba(255,150,50,0.75)", 6 | "inherit": false, 7 | "opacity":1.0 8 | }, 9 | "smooth": { 10 | "enabled": true, 11 | "type": "cubicBezier", 12 | "roundness": 0.5 13 | } 14 | }, 15 | "nodes":{ 16 | "font":{ 17 | "strokeWidth": 3 18 | } 19 | }, 20 | "physics": { 21 | "barnesHut": { 22 | "gravitationalConstant": -800, 23 | "centralGravity": 5, 24 | "springLength": 50, 25 | "springConstant": 0.02, 26 | "damping": 0.5, 27 | "avoidOverlap": 1 28 | }, 29 | "minVelocity": 0.75 30 | }, 31 | "interaction":{ 32 | "navigationButtons": true 33 | } 34 | 35 | } 36 | 37 | 38 | 39 | -------------------------------------------------------------------------------- /style.css: -------------------------------------------------------------------------------- 1 | .page { 2 | display: flex; 3 | flex-direction: column; 4 | margin: -5px; 5 | align-items: center; 6 | height: 200vh; 7 | } 8 | 9 | .landing { 10 | display: flex; 11 | flex-direction: column; 12 | /* align-items: center; */ 13 | justify-content: center; 14 | width: auto; 15 | padding: 2% 25% 2% 25%; 16 | /* background-color: lightblue; */ 17 | } 18 | 19 | .landing h1 { 20 | font-weight: normal; 21 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, 22 | Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; 23 | } 24 | 25 | .landing p, 26 | ul { 27 | font-weight: normal; 28 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen, 29 | Ubuntu, Cantarell, "Open Sans", "Helvetica Neue", sans-serif; 30 | font-size: 16px; 31 | padding: 0 0 0 0; 32 | } 33 | 34 | .graph { 35 | width: 80vw; 36 | height: 100vh; 37 | } 38 | 39 | .button-container { 40 | display: flex; 41 | flex-direction: row; 42 | justify-content: center; 43 | } 44 | 45 | .size-slider { 46 | display: flex; 47 | justify-content: center; 48 | } 49 | #graph { 50 | border: none 51 | } -------------------------------------------------------------------------------- /templates/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | Visualisation of skills 7 | 8 | 9 | 10 | 11 |
12 |
13 |

A visualisation of skills in demand

14 |

What skills are in demand? 15 | We scraped thousands of job postings, connecting skills into one graph. 16 | If you want to evolve as a professional, you can use this as a hint. 17 | 18 |

    19 |
  • The bigger the node, the more in demand the skill.
    • 20 |
  • The wider the link, the closer the nodes, the more related are the two skills (seen together).
    • 21 |
  • Choose the field of interest pushing the button.
    • 22 |
23 | Link to GitHub repo 24 |

25 |
26 | Star 29 | Issue 32 |
33 |
34 |
35 |
36 |
37 | 38 |
39 | {% for t in tags%} 40 | 44 | {% endfor %} 45 |
46 |
0%
47 |
Cutoff tags with popularity less than %
48 |
10%
49 |
50 |
51 | 52 |
53 |
54 |
55 | 56 |

Current tag: {{chosen_tag}}. Cuttoff level: {{node_level}}%

57 |
58 | 59 |
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62 | 63 | --------------------------------------------------------------------------------