Analytics with Python

Analytics with Python

3-day intensive course outline

This detailed 3-day course on Analytics with Python is designed to provide participants with a thorough grounding in Python programming, data analysis, visualization, and engineering practices. It covers basic to advanced Python, including Object-Oriented Programming (OOP), essential libraries (NumPy, pandas, Matplotlib), and moves towards complex data processing with PySpark and cloud-based analytics with Azure Databricks. Additionally, it includes web application development with Flask, file and OS operations, recursion, math functions, email handling, exception and error management, debugging, testing, and stream processing.

Learning Outcomes

Upon completing this course, participants will:

• Master Python for analytics, including advanced data manipulation techniques.
• Understand and apply OOP principles to organize and scale Python code.
• Utilize major Python libraries for data analysis, visualization, and processing.
• Develop proficiency in PySpark for handling big data analytics.
• Implement and deploy simple web applications using Flask.
• Apply debugging and testing methodologies to ensure code quality and reliability.
• Navigate and manipulate the OS and file systems for data projects.
• Conduct stream and batch data processing with Spark on Azure Databricks.
• Differentiate and leverage stream processing for real-time data analysis.

Prerequisites

• God programming knowledge.
• Familiarity with algebra and statistics.
• Google account (for colab usage).
• Azure account for Azure Databricks sections.
• Remote server for web modules.

Detailed Course Outline

NOTE: The training shall not necessarily follow in the sequence of the topics. Many of the topics overlap and shall be used in the training of other topics. There may be iterative coverage with increasing complexity and each iteration and combination.

1. Python Programming Fundamentals
   1. Introduction to Python
      1. Syntax, data types, variables
      2. Control structures: loops, conditionals
      3. Math operations and functions
   2. Functions in Python
      1. Defining and calling functions
      2. Scope and lifetime, arguments, and return values
   3. Basic I/O Operations
      1. Reading from and writing to files
      2. Handling different file formats (CSV, JSON)
   4. Error and Exception Handling
      1. Try, except, finally blocks
      2. Custom exceptions for robust error management
2. Advanced Python and Data Manipulation
   1. Object-Oriented Programming (OOP)
      1. Classes, objects, inheritance, polymorphism
      2. Encapsulation, abstraction, special methods
   2. Data Handling with NumPy
      1. Array operations, indexing, slicing
      2. Basic linear algebra and statistical operations
   3. Data Manipulation
      1. Recursion
      2. Data flattening
      3. Data Conversion
   4. Data Analysis with pandas
      1. DataFrame and Series, data wrangling
      2. Advanced operations: merging, joining, and concatenating
3. Web Basics and Data Visualization
   1. Data Visualization with Matplotlib
      1. Basic to advanced plotting techniques
      2. Customizing plots for publication
   2. Introduction to Flask for Web Applications
      1. Setting up a Flask environment
      2. Routing, templates, and form handling
   3. Working with the OS and File System
      1. File system navigation, path manipulation
      2. Reading and writing files, directory management
4. Data in Azure Databricks
   1. Data Processing with PySpark
      1. Setting up Azure Databricks, cluster management
      2. DataFrame operations, Spark SQL
   2. Debugging and Testing
      1. Running test scenarios
      2. Using Databricks magic commands
5. Databricks Data Processing
   1. Data Processing with Spark on Azure Databricks
      1. Transformations and clustering
      2. Advanced data analysis: aggregation, window functions
   2. Fundamentals of Stream Processing
      1. Stream vs. batch processing
      2. Basic concepts and applications of stream processing
   3. Advanced PySpark Techniques
   4. Advanced PySpark Techniques
      1. Buckets and Joins
      2. Spark Streaming Basics
      3. Working with Multiple Data Sources
6. Additional Topics
   1. Machine Learning with PySpark
      1. Introduction to MLlib
      2. Building and Evaluating Machine Learning Models
   2. ML Pipelines in Spark
   3. Spark with Kafka

This detailed 3-day course is designed to equip participants with the skills and knowledge needed to excel in the field of analytics using Python. Through a blend of lectures, hands-on exercises, and assessments, participants will gain practical experience and a deep understanding of how to apply Python and its libraries to solve complex data analysis challenges.