Data Analytics FULL Course for Beginners to Pro in 29 HOURS - 2024 Edition

Introduction

This tutorial provides a comprehensive guide based on the "Data Analytics FULL Course for Beginners to Pro in 29 HOURS - 2024 Edition" from WsCube Tech. It covers essential concepts and tools in data analytics, including Python, MySQL, Excel, and Power BI, designed to take you from a beginner to a proficient level.

Step 1: Get Started with Python

• Install Python on your Windows system.
• Set up an IDE:
  • Download and install PyCharm.
• Write your first program:
  • Create a simple "Hello, World!" script in Python.
• Learn the basics:
  • Understand comments, variables, data types, and user inputs.
  • Explore type casting and subtypes.

Step 2: Master Python Control Structures

• Loops:
  • Learn about for loops and while loops.
  • Practice with nested loops.
• Conditional Statements:
  • Use if, elif, and else for decision-making.
• Break and Continue:
  • Implement break and continue statements in loops.
• Lists:
  • Understand list slicing, iteration, and functions.

Step 3: Explore Data Structures

• Tuples:
  • Learn about tuples, their slicing, and iteration.
• Dictionaries:
  • Explore dictionary methods, iteration, and nested dictionaries.
• Sets:
  • Understand set methods and their applications in problem-solving.

Step 4: Functions and Modules in Python

• Functions:
  • Define parameters and arguments, and use return statements.
  • Learn about recursion and lambda functions.
• Modules:
  • Discover built-in modules and how to create your own.
  • Use the random module for generating random values.

Step 5: Data Manipulation with Numpy

• Install Jupyter Notebook for an interactive coding environment.
• Numpy Arrays:
  • Create, index, and slice arrays.
  • Perform mathematical operations and manipulate arrays (combine, split, search, sort).
• Statistical Functions:
  • Utilize Numpy for aggregating and statistical computations.

Step 6: Analyze Data with Pandas

• DataFrames:
  • Create and explore DataFrames.
  • Handle duplicate values and missing data.
• Data Transformation:
  • Use GroupBy, Merge, and Join methods.
  • Learn about Pivoting and Melting DataFrames.

Step 7: Visualize Data using Matplotlib

• Introduction to Matplotlib:
  • Create various plots: bar, line, scatter, pie, and more.
• Advanced Plotting:
  • Use legends, subplot, and save charts.
• Seaborn for Visualization:
  • Explore data visualization with Seaborn.

Step 8: Database Management with MySQL

• Install MySQL and import data.
• SQL Queries:
  • Use SELECT queries, WHERE clause, and operators (AND, OR, NOT).
• Data Aggregation:
  • Master GROUP BY and HAVING clauses.
• Complex Queries:
  • Work with joins, subqueries, and stored procedures.

Step 9: Excel for Data Analytics

• Excel Basics:
  • Familiarize yourself with basic functions and data validation.
• Data Management:
  • Handle sorting, filtering, and dealing with null and duplicate values.
• Power Query:
  • Clean and transform data, and create dashboards.

Step 10: Power BI Essentials

• Install Power BI Desktop and learn to connect data.
• Data Transformations:
  • Use tools for pivoting and unpivoting data.
• DAX Functions:
  • Understand calculated columns, measures, and data visualization techniques.
• Report Creation:
  • Design reports for different devices and publish them to Power BI Services.

Conclusion

This tutorial provides a structured pathway to mastering data analytics, from foundational programming in Python to advanced data visualization in Power BI. Each step builds on the previous one, ensuring a comprehensive understanding of the tools and techniques used in the industry. Begin your journey by following these steps, and consider enrolling in live classes for hands-on experience and further learning.