Python for Data Analysis: A 15-Lesson Beginner Bootcamp

Categories: Python Course
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About Course

Start your journey into data analysis with Python—the most in-demand language for data professionals.

This beginner-friendly course is designed specifically for aspiring data analysts who want to learn Python from scratch and build a strong foundation for working with data. In just 15 concise lessons, you’ll go from writing your first line of Python code to understanding how to manipulate, analyze, and prepare data for insights.

Whether you’re transitioning into tech or looking to boost your analytics skills, this course delivers practical, real-world Python skills that form the backbone of every successful data analyst’s toolkit.

Originally structured as a daily learning challenge, this course is now optimized for self-paced learning—allowing you to complete it all at once or space it out based on your schedule.

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What Will You Learn?

  • Setting up your Python environment for data work
  • Variables, data types, and how data is structured in Python
  • Conditional logic, loops, and how to automate repetitive tasks
  • Core data structures: lists, tuples, dictionaries, and sets
  • Writing reusable code with functions
  • Foundational thinking for data manipulation and preparation
  • A smooth on-ramp to libraries like Pandas and NumPy (covered in future lessons)

Course Content

Section 1: Python Fundamentals
Focus on setting up the environment, understanding Python syntax, variables, data types, and basic input/output.

  • Lesson 1: Introduction to Python
    00:00
  • Lesson 2: Variables & Data Types
    00:00
  • Lesson 3: Operators and Expressions
    00:00
  • Lesson 4: Data Structures – Lists
    00:00
  • Lesson 5: Data Structures – Tuples
    00:00

Section 2: Programming Logic & Data Structures
Dive into conditionals, loops, functions, and Python's key data structures like lists, dictionaries, and sets.

Section 3: Data Skills for Analysts
Apply your Python skills to basic data tasks: manipulating data, reading files, simple data analysis logic, and prepping for tools like Pandas and NumPy.

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