├── .travis.yml ├── Big Data Fundamentals with PySpark ├── 1. Introduction to Big Data analysis with Spark.md ├── 2. Programming in PySpark RDD’s.md ├── 3. PySpark SQL & DataFrames.md └── 4. Machine Learning with PySpark MLlib.md ├── Cleaning Data with PySpark ├── 1. DataFrame details.md ├── 2. Manipulating DataFrames in the real world.md ├── 3. Improving Performance.md └── 4. Complex processing and data pipelines.md ├── Data Processing in Shell ├── I Downloading Data on the Command Line.py ├── II Data Cleaning and Munging on the Command Line.py ├── III Database Operations on the Command Line.py └── IV Data Pipeline on the Command Line.py ├── Database Design ├── 1. Processing, Storing, and Organizing Data │ ├── 1. OLTP and OLAP.txt │ ├── 2. Which is better?.py │ ├── 3. Name that data type.py │ ├── 4. Ordering ETL Tasks.py │ ├── 5. Recommend a storage solution.py │ ├── 6. Database design.txt │ ├── 7. Classifying data models.py │ ├── 8. Deciding fact and dimension tables.py │ └── 9. Querying the dimensional model.py ├── 2. Database Schemas and Normalization.md ├── 3. Database Views.md └── 4. Database Management.md ├── Introduction to AWS Boto in Python ├── 1. Putting Files in the Cloud! │ ├── I. Intro to AWS and Boto3.py │ ├── II. Your first boto3 client.py │ ├── III. Multiple clients Sam knows that she will often have to work with more than one service at once. She wants to practice creating two separate clients for two different services in boto3. When she is building her workflows, she will make multiple Amazon Web Services interact with each other, with a script executed on her computer. Her AWS key id and AWS secret have been stored in AWS_KEY_ID and AWS_SECRET respectively. You will help Sam initialize a boto3 client for S3, and another client for SNS. She will use the S3 client to list the buckets in S3. She will use the SNS client to list topics she can publish to (you will learn about SNS topics in Chapter 3). Instructions 100 XP Generate the boto3 clients for interacting with S3 and SNS. Specify 'us-east-1' for the region_name for both clients. Use AWS_KEY_ID and AWS_SECRET to set up the credentials. List and print the SNS topics..py │ ├── IV. Removing repetitive work.py │ ├── V. Diving into buckets.txt │ ├── VI. creating a bucket.py │ ├── VII. Listing buckets.py │ ├── VIII. Deleting a bucket.py │ ├── VIV. Deleting multiple buckets.py │ ├── X. Uploading and retrieving files.txt │ └── XI. Spring cleaning.py ├── 2. Sharing Files Securely │ ├── I. Keeping objects secure.txt │ ├── II. Making an object public.py │ ├── III. Uploading a public report.py │ ├── IV. Making multiple files public.py │ ├── V. Accessing private objects in S3.txt │ ├── VI. Generating a presigned URL.py │ ├── VII. Opening a private file.py │ ├── VIII. Sharing files through a website.txt │ ├── VIV. Generate HTML table from Pandas.py │ ├── X. Upload an HTML file to S3.py │ ├── XI. Combine daily requests for February.py │ ├── XII. Upload aggregated reports for February.py │ ├── XIII. Update index to include February.py │ └── XIV. Upload the new index.py ├── 3. Reporting and Notifying! │ ├── 1. SNS Topics.txt │ ├── 10. Sending a single SMS message.py │ ├── 11. Different protocols per topic level.py │ ├── 12. Creating multi-level topics.py │ ├── 13. Sending an alert.py │ ├── 14. Sending multi-level alerts.py │ ├── 2. Creating a Topic.py │ ├── 3. Creating Multiple Topics.py │ ├── 4. Deleting multiple topics.py │ ├── 5. SNS Subscriptions.txt │ ├── 6. Subscribing to topics.py │ ├── 7. Creating multiple subscriptions.py │ ├── 8. Deleting multiple subscriptions.py │ └── 9. Sending messages.txt └── 4. Pattern Rekognition │ ├── 1. Rekognizing patterns.txt │ ├── 10. Scooter Community Sentiment.py │ ├── 2. Cat detector.py │ ├── 3. Multiple cat detector.py │ ├── 4. Parking sign reader.py │ ├── 5. Comprehending text.txt │ ├── 6. Detecting language.py │ ├── 7. Translating Get It Done requests.py │ ├── 8. Getting request sentiment.py │ └── 9. Scooter dispatch.py ├── Introduction to Airflow in Python ├── I Intro to Airflow.py ├── II Implementing Airflow DAGs.py ├── III Maintaining and monitoring Airflow workflows.py └── IV Building production pipelines in Airflow.py ├── Introduction to Bash Scripting ├── I From Command-Line to Bash Script.py ├── II Variables in Bash Scripting.py ├── III Control Statements in Bash Scripting.py └── IV Functions and Automation.py ├── Introduction to Data Engineering ├── I Introduction to Data Engineering.py ├── II Data engineering toolbox.py ├── III Extract, Transform and Load (ETL).py ├── IV Case Study: DataCamp Ratings.py └── README.me ├── Introduction to MongoDB in Python ├── 1. Flexibly Structured Data.md ├── 2. Working with Distinct Values and Sets.md ├── 3. Get Only What You Need, and Fast.md └── 4. Aggregation Pipelines: Let the Server Do It For You.md ├── Introduction to PySpark ├── I Getting to know PySpark.py ├── II. Manipulating data.py ├── III. Getting started with machine learning pipelines.py └── IV. Model tuning and selection │ ├── I. Create the modeler.py │ ├── II. Create the Evaluator.py │ ├── III. Make a grid.py │ ├── IV. Make the validator.py │ ├── V. Fit the model(s).py │ ├── VI. Evaluating binary classifiers.py │ └── VII. Evaluate the model.py ├── Introduction to Relational Databases in SQL ├── 1. Your first database │ ├── 1. Attributes of relational databases.py │ ├── 2. Query information_schema with SELECT.py │ ├── 3. CREATE your first few TABLEs.py │ ├── 4. ADD a COLUMN with ALTER TABLE.py │ ├── 5. RENAME and DROP COLUMNs in affiliations.py │ ├── 6. Migrate data with INSERT INTO SELECT DISTINCT.py │ └── 7. Delete tables with DROP TABLE.py ├── 2. Enforce data consistency with attribute constraints │ ├── 0. query table data types INFORMATION_SCHEMA.txt │ ├── 1.Better data quality with constraints-changing datatypes CAST.txt │ ├── 10. .py │ ├── 10. What happens if you try to enter NULLs?.py │ ├── 2. Types of database constraints.py │ ├── 3. Conforming with data types.py │ ├── 4. Type CASTs.py │ ├── 5. Working with data types.txt │ ├── 6. Change types with ALTER COLUMN.py │ ├── 7. Convert types USING a function.py │ ├── 8. The not-null and unique constraints.txt │ └── 9. Disallow NULL values with SET NOT NULL.py ├── 3. Uniquely identify records with key constraints │ ├── 1. Get to know SELECT COUNT DISTINCT.py │ ├── 2. Identify keys with SELECT COUNT DISTINCT.py │ ├── 3. Identify the primary key.py │ ├── 4. ADD key CONSTRAINTs to the tables.py │ ├── 5. Add a SERIAL surrogate key.py │ ├── 6. CONCATenate columns to a surrogate key.py │ └── 7. Test your knowledge before advancing.py └── 4. Glue together tables with foreign keys │ ├── 1. REFERENCE a table with a FOREIGN KEY.py │ ├── 10. Change the referential integrity behavior of a key.py │ ├── 11. Count affiliations per university.py │ ├── 12. .py │ ├── 2. Explore foreign key constraints.py │ ├── 3. JOIN tables linked by a foreign key.py │ ├── 4. Model more complex relationships.txt │ ├── 5. Add foreign keys to the "affiliations" table.py │ ├── 6. Populate the "professor_id" column.py │ ├── 7. Drop "firstname" and "lastname".py │ ├── 8. Referential Integrity ON DELETE.txt │ └── 9. Referential integrity violations.py ├── Introduction to Scala ├── 1. A scalable language.md ├── 2. Workflows, Functions, Collections.md └── 3. Type Systems, Control Structures, Style.md ├── Introduction to Shell ├── I Manipulating files and directories.py ├── II Manipulating data.py ├── III Combining tools.py ├── IV Batch processing.py └── v Creating new tools.py ├── OOP-python-DataCamp ├── I OOP Fundamentals.py ├── II Inheritance and Polymorphism.py ├── III Integrating with Standard Python.py ├── IV Best Practices of Class Design.py └── README.md ├── Python-Data-Science-Toolbox-Part-2--case-study-main ├── README.md └── bank-data-code-case.py ├── README.md ├── Streamlined Data Ingestion with pandas ├── I Importing Data from Flat Files.py ├── II Importing Data From Excel Files.py ├── III Importing Data from Databases.py ├── IV Importing JSON Data and Working with APIs.py └── README.md ├── Unit Testing for Data Science in Python ├── .travis.yml ├── I Unit testing basics.py ├── II Intermediate unit testing.py ├── III Test Organization and Execution.py ├── IV Testing Models, Plots and Much More.py └── readme.md ├── Writing Efficient Python Code ├── I Foundations for efficiencies.py ├── II Timing and profiling code.py ├── III Gaining efficiencies.py └── IV Intro to pandas DataFrame iteration.py └── Writing Functions in Python ├── I Best Practices.py ├── II Context Managers.py ├── III Decorators.py └── IV More on Decorators.py /.travis.yml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ortizfram/datacamp-Data-Engineer-with-Python-course/HEAD/.travis.yml -------------------------------------------------------------------------------- /Big Data Fundamentals with PySpark/1. 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She will use the S3 client to list the buckets in S3. She will use the SNS client to list topics she can publish to (you will learn about SNS topics in Chapter 3). Instructions 100 XP Generate the boto3 clients for interacting with S3 and SNS. Specify 'us-east-1' for the region_name for both clients. Use AWS_KEY_ID and AWS_SECRET to set up the credentials. List and print the SNS topics..py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ortizfram/datacamp-Data-Engineer-with-Python-course/HEAD/Introduction to AWS Boto in Python/1. Putting Files in the Cloud!/III. Multiple clients Sam knows that she will often have to work with more than one service at once. She wants to practice creating two separate clients for two different services in boto3. When she is building her workflows, she will make multiple Amazon Web Services interact with each other, with a script executed on her computer. Her AWS key id and AWS secret have been stored in AWS_KEY_ID and AWS_SECRET respectively. You will help Sam initialize a boto3 client for S3, and another client for SNS. She will use the S3 client to list the buckets in S3. She will use the SNS client to list topics she can publish to (you will learn about SNS topics in Chapter 3). Instructions 100 XP Generate the boto3 clients for interacting with S3 and SNS. Specify 'us-east-1' for the region_name for both clients. Use AWS_KEY_ID and AWS_SECRET to set up the credentials. List and print the SNS topics..py -------------------------------------------------------------------------------- /Introduction to AWS Boto in Python/1. Putting Files in the Cloud!/IV. Removing repetitive work.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ortizfram/datacamp-Data-Engineer-with-Python-course/HEAD/Introduction to AWS Boto in Python/1. Putting Files in the Cloud!/IV. 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