├── Course Certificate.pdf ├── Data Science Methodology ├── Week 1 │ ├── Quiz - From Proble to Approach.docx │ └── Quiz - From Requirements to Collection.docx ├── Week 2 │ ├── Quiz - From Modelling to Evaluation.docx │ └── Quiz - From Undestanding to Preparation.docx └── Week 3 │ ├── Final Assingment.md │ └── Quiz - From deployment to feedback.docx ├── Data Visualization with Python ├── Week 1 │ └── Quiz - Introduction to Data Visualization Tools.docx ├── Week 2 │ ├── Quiz - Basic Visualization Tools.docx │ └── Quiz - Specialized Visualization Tools.docx └── Week 3 │ ├── Final Assignment.docx │ ├── Quiz - Advance Visualization Tools.docx │ └── Quiz - Visualization Spatial Data.docx ├── Data analysis with Python ├── Week 1 │ └── Quiz - Importing Datasets.docx ├── Week 2 │ └── Quiz - Data wrangling.docx ├── Week 3 │ └── Quiz - Exploratory Data Analysis.docx ├── Week 4 │ └── Quiz - Model development.docx └── Week 5 │ └── Quiz - Model refinement.docx ├── Databases and SQL for Data Science ├── Week 1 │ ├── Quiz - Basic SQL.docx │ └── Quiz - Databases.docx ├── Week 2 │ ├── Quiz - Join operations.docx │ ├── Quiz - String patterns and ranges.docx │ └── dd ├── Week 3 │ └── Quiz - Database access from Python.docx └── Week 4 │ ├── Assignment Databases and SQL.docx │ └── dd ├── Open Source Tools for Data Science ├── Week 1 │ ├── Quiz 1 - Skill Network labs.docx │ └── Quiz 2 - Jupyter Notebooks.docx ├── Week 2 │ ├── Quiz 3 - Zepellin Notebooks.docx │ └── Quiz 4 - RStudio IDE.docx └── Week 3 │ ├── Assigment - Create and share your Jupyter Notebook.md │ └── Quiz - IBM Watson Studio.docx ├── Python for Data Science and AI ├── Week 1 │ ├── Quiz - Expressions and Variables.docx │ └── Quiz - types.docx ├── Week 2 │ ├── Quiz - Dictionaries.docx │ └── Quiz - Sets.docx ├── Week 3 │ ├── Quiz - Conditions and branching.docx │ ├── Quiz - Functions.docx │ ├── Quiz - Objects and Classes.docx │ └── dd ├── Week 4 │ ├── Quiz - Numpy.docx │ ├── Quiz - Pandas.docx │ ├── Quiz - Reading files with Python.docx │ └── Quiz - Write files.docx └── Week 5 │ └── Make a Fake Album Cover Game with Web Scraping.docx └── What is Data Science? ├── Week 1 ├── Quiz - Data Science The Sexiest Job in the 21st Century.docx └── quiz What Makes Someone a Data Scientist.docx ├── Week 2 ├── Quiz - Data Mining.docx └── Quiz - Regression.docx └── Week 3 ├── Final assignment - What is Data Science?.md ├── Quiz - The final delivery.docx └── Quiz - The report Structure.docx /Course Certificate.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Courses-Completed/IBM-Data-Science/6c74920847263c1160d30b3080bb65282fe44b38/Course Certificate.pdf -------------------------------------------------------------------------------- /Data Science Methodology/Week 1/Quiz - From Proble to Approach.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Courses-Completed/IBM-Data-Science/6c74920847263c1160d30b3080bb65282fe44b38/Data Science Methodology/Week 1/Quiz - From Proble to Approach.docx -------------------------------------------------------------------------------- /Data Science Methodology/Week 1/Quiz - From Requirements to Collection.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Courses-Completed/IBM-Data-Science/6c74920847263c1160d30b3080bb65282fe44b38/Data Science Methodology/Week 1/Quiz - From Requirements to Collection.docx -------------------------------------------------------------------------------- /Data Science Methodology/Week 2/Quiz - From Modelling to Evaluation.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Courses-Completed/IBM-Data-Science/6c74920847263c1160d30b3080bb65282fe44b38/Data Science Methodology/Week 2/Quiz - From Modelling to Evaluation.docx -------------------------------------------------------------------------------- /Data Science Methodology/Week 2/Quiz - From Undestanding to Preparation.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Courses-Completed/IBM-Data-Science/6c74920847263c1160d30b3080bb65282fe44b38/Data Science Methodology/Week 2/Quiz - From Undestanding to Preparation.docx -------------------------------------------------------------------------------- /Data Science Methodology/Week 3/Final Assingment.md: -------------------------------------------------------------------------------- 1 | # Data Science Methodology final assignment 2 | 3 | ### Which topic did you choose to apply the data science methodology to? (2 marks) 4 | 5 | I have chosen as topic for this task the application of data science in the field of credit cards. The reason behind this choice is that it is related with my finance education. 6 | 7 | ### Next, you will play the role of the client and the data scientist. Using the topic that you selected, complete the Business Understanding stage by coming up with a problem that you would like to solve and phrasing it in the form of a question that you will use data to answer. (3 marks) 8 | 9 | ### You are required to: 10 | 11 | ### 1) Describe the problem, related to the topic you selected. 12 | ### 2) Phrase the problem as a question to be answered using data 13 | 14 | For example, using the food recipes use case discussed in the labs, the question that we defined was, "Can we automatically determine the cuisine of a given dish based on its ingredients?". 15 | 16 | So the main problem for banks regarding credit cards is that they have to create a model to know to who they can provide them. Certain clients will not be feasible as they do not have the economic strenght to back up this service. 17 | 18 | So our question would be " Can we automatically determine if a client is suitable to obtain a credit card? 19 | 20 | ### Briefly explain how you would complete each of the following stages for the problem that you described in the Business Understanding stage, so that you are ultimately able to answer the question that you came up with. (5 marks): 21 | 22 | ### 1. Analytic Approach 23 | ### 2. Data Requirements 24 | ### 3. Data Collection 25 | ### 4. Data Understanding and Preparation 26 | ### 5. Modeling and Evaluation 27 | 28 | ### You can always refer to the labs as a reference with describing how you would complete each stage for your problem. 29 | 30 | 1. Analytic Approach: As the problem requires a yes/no answer we will use a classification model 31 | 32 | 2. Data Requirements: To create the classification model we will require information regarding the bank clients. This info should include personal data of the client and should include the ones that defaulted and the one that paid. 33 | 34 | 3: Data Collection: We would use techiques like descriptive statistics and data evalution should be implemented in this phase to make sure that we have useful data for our model. 35 | 36 | 4: Data Undestanding and Preparation: In this step we need to evaluate the different variables of our data in order to undestant it better. For example we would calculate univariate statistics, such as mean or median and the correlation between variables. So we need to evaluate the quality of the data. In the data preparation phase we have to prepare the data in an specific way depending on the model. 37 | 38 | 5: Modeling and Evaluation: Lastly we create a classification model, evaluate the outcome and perform the corresponding changes untill we have a suitable model. 39 | -------------------------------------------------------------------------------- /Data Science Methodology/Week 3/Quiz - From deployment to feedback.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Courses-Completed/IBM-Data-Science/6c74920847263c1160d30b3080bb65282fe44b38/Data Science Methodology/Week 3/Quiz - From deployment to feedback.docx -------------------------------------------------------------------------------- /Data Visualization with Python/Week 1/Quiz - Introduction to Data Visualization Tools.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Courses-Completed/IBM-Data-Science/6c74920847263c1160d30b3080bb65282fe44b38/Data Visualization with Python/Week 1/Quiz - 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To the best of your ability, does the title appear to be in H1 header styling? 10 | 11 | ### Does Cell 2 appear to include the author's name in bold, as well as an occupation in a separate line below in regular font? 12 | 13 | ### Does Cell 3 appear to include a sentence describing the author's interest in data science, in italic formatting? (Content or coherence the sentence should not be evaluated.) 14 | 15 | ### Does Cell 4 appear to have some text describing the code in Cell 5, and does the text in Cell 4 appear to be in H3 header formatting? 16 | 17 | ### Does Cell 5 contain code, and is there an output displayed from the code executed in Cell 5? (Please judge the presence of the output of the code, not its content. For example, error outputs should not be penalized.) 18 | 19 | ### Using Markdown or HTML, does the final cell(s) include at least 3 of the following: horizontal rule, bulleted list, numbered list, tables, hyperlinks, images, code/syntax highlighting, blocked quotes, strikethrough? 20 | -------------------------------------------------------------------------------- /Open Source Tools for Data Science/Week 3/Quiz - IBM Watson Studio.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Courses-Completed/IBM-Data-Science/6c74920847263c1160d30b3080bb65282fe44b38/Open Source Tools for Data Science/Week 3/Quiz - IBM Watson Studio.docx -------------------------------------------------------------------------------- /Python for Data Science and AI/Week 1/Quiz - Expressions and Variables.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Courses-Completed/IBM-Data-Science/6c74920847263c1160d30b3080bb65282fe44b38/Python for Data Science and AI/Week 1/Quiz - 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I have completed my studies in finance and accounting and a master's degree in international business. The past years I have been working in an audit so my knowledge in the field of data science is quite limited. 6 | I am doing this course since I consider that this field has a great future and i am very interested in learning about python and machine learning. 7 | 8 | ### Based on the videos and the reading material, how would you define a data scientist and data science? 9 | 10 | After reviewing the material I consider that data science is an interdisciplinary field that involves scientific methods, processes and systems to extract knowledge or a better understanding of data in its different forms, whether structured or unstructured, which is a continuation of some fields of analysis of data such as statistics, data mining, machine learning and predictive analytics. 11 | 12 | Additionally, I considert that a data scientist is a new profession that today is considered key in the world of technologies. It is a person trained in mathematical sciences and statistics that dominates programming and its different languages, computer science and analytics. 13 | The data science professional must also have the capacity and knowledge to communicate their findings as they have them, not only in the area of technology but also in the business sector. You must master the technology and databases to modify and improve the orientation of the business of the company for which you work. 14 | 15 | ### After reviewing the material I consider that data science is an interdisciplinary field that involves scientific methods, processes and systems to extract knowledge or a better understanding of data in its different forms, whether structured or unstructured, which is a continuation of some fields of analysis of data such as statistics, data mining, machine learning and predictive analytics. 16 | 17 | As discussed in the videos and the reading material, data science can be applied to problems across different industries. What industry are you passionate about and would like to pursue a data science career in? You must master the technology and databases to modify and improve the orientation of the business of the company for which you work. 18 | 19 | There are different applications for this field but i would be intereted in using it in the finance world. For example, you can use machine learning to predict the future value of a certain currency or you can program algorithms to automatically trade stocks. 20 | 21 | ### Based on the videos and the reading material, what are the ten main components of a report that would be delivered at the end of a data science project? 22 | 23 | 1. Cover page 24 | 2. Table of contents 25 | 3. Introduction 26 | 4. Methodology 27 | 5. Results 28 | 6. Conclusions 29 | 7. Reference 30 | 8. Discussion 31 | 9. Acknowledgement 32 | 10. 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