Data Analysis with Python – Full Course for Beginners


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Python is a powerful, modern programming language that has the capabilities required for experienced programmers, while being easy enough for beginners to learn.

The course covers everything you need to get started with Python. The course also provides regular quizzes and hands-on exercises to enable you not only to understand the concepts but to practice them thoroughly. “Talk is cheap, show me your code”, we want you to make mistakes, correct them and learn from experience.


Here is a brief description of what you will learn in each section.

Section 1. Python.

This section covers the basics of Python, from python introduction to installing the required tools.

  • Why learn Python?
  • What Python can do?
  • How to install Python tool kits?

Section 2. Fundamentals

In this section, we will lay foundations on programming basics, such as data types, operators, control flows, scope etc. These concepts can apply to other programming languages as well. You may have heard of If statement, for loops, while loop before. In this section, we will use real examples to demonstrate the usage.

Section 3: Python Data structures

Understanding data structures are vital to every programming. We will go through the three key data structures in Python and discuss how to use them efficiently.

  • List/Tuple/Dictionary
  • Methods in List/Tuple/Dictionary
  • List comprehension

Section 4: Pandas

Pandas is go-to library for data analysis in Python. In this section, we will go into the details of pandas library functions, and how to read, extract, process, manipulate data in Pandas. The techniques in this section are often used in data science and machine learning processes.

  • Slicing
  • Indexing
  • Grouping
  • Filtering
  • Updating
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Section 5: Numpy

Numpy is a python package for scientific computing. It provides a fast and flexible data processing data structure in Python. In this part, we will show how to use numpy to do data processing, such as slicing, indexing, grouping, filtering, updating, creating etc.

Section 6: Functional Programming

Python functional programming features can make data processing more efficient. In this section, we will cover a few functional programming, such as Lambda function, filter, map, reduce.

  • Lambda
  • Filter
  • Map
  • Reduce

Section 7: Exception handling

When writing codes, it takes time to debug. In this section, we will learn what are the usual type of errors in the code, how we can efficiently debug, and how to handle the exceptions.

  • try: except block
  • Raise error
  • Principles for using exceptions

Section 8: File Input/Output

In real life, data reside in files. In this part, we will introduce the python concepts necessary to use data from files in the programs, such as

  • locate files
  • open/read files
  • write files
  • close files

Section 9: Course project

In this section, you will get exposure to a real business case and process the data using Pandas and Numpy to solve a few business questions.

You will practice your python skills with real examples.


So what are we waiting for? Let’s begin our Python journey and start coding!

Data Analysis with Python – Full Course for Beginners, Free Tutorials Download

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