top of page

Python

Digdata Python-logo.png

Foundations of Data Analysis in Python

Become a data-savvy professional by mastering the world's most popular programming language. This course is your launchpad into the exciting field of data analytics, starting from the absolute basics.

Who is this course for?

This course is designed for absolute beginners with no prior programming experience. It is perfect for aspiring data analysts, students, business professionals, or anyone curious about how to leverage Python for data-driven insights. If you can use a computer, you can learn to code with us!

What You Will Learn:

  • Write and execute your own Python scripts from scratch.

  • Understand and use core programming concepts like variables, data types, and control flow.

  • Manipulate and organize data using Python's fundamental data structures: lists, dictionaries, tuples, and sets.

  • Build reusable and modular code with functions.

  • Read data from and write data to various file types, including CSVs and text files.

  • Set up a professional data analysis environment with Jupyter Notebooks.

Detailed Course Curriculum

Module 1: Getting Started with Python

  • 1.1 Introduction to Python: Its history and role in data science.

  • 1.2 Environment Setup: Installing Anaconda and navigating Jupyter Notebooks.

  • 1.3 Your First Program: The "Hello, World!" of data.

  • 1.4 Understanding Variables and Basic Data Types (Integers, Floats, Strings).

Module 2: Core Data Structures

  • 2.1 Lists: Creating, indexing, slicing, and modifying ordered data.

  • 2.2 Tuples: Understanding immutable collections.

  • 2.3 Dictionaries: Working with key-value pairs for efficient lookups.

  • 2.4 Sets: Handling unique, unordered elements.

Module 3: Programming Fundamentals

  • 3.1 Operators: Arithmetic, Comparison, and Logical.

  • 3.2 Control Flow with if-elif-else statements.

  • 3.3 Looping with for and while loops for repetitive tasks.

  • 3.4 List Comprehensions for clean and efficient code.

Module 4: Functions and File Handling

  • 4.1 Defining and calling your own functions.

  • 4.2 Understanding function arguments and return values.

  • 4.3 Lambda Functions: Writing simple, anonymous functions.

  • 4.4 Reading from .txt and .csv files.

  • 4.5 Writing data to new files.

Hands-on Project:

  • Hands-on Project on real data sets

Prerequisites:

  • None! Just a computer with internet access and a desire to learn.

Certification

Earn a Certificate of Completion powered by DigData — trusted by professionals, valued by employers, and aligned with real-world industry needs.

bottom of page