Data Analysis with Python: Full Course for Beginners

Learn Python Programming In Detail From Beginner to Expert Level What you'll learn:
Master Python basics, such as data structures, control flows
Learn to use frameworks like Pandas, NumPy, Matplotlib, etc.
Able to use Python for data analysis, data wrangling professionally
Learn to use both the Jupyter Notebook and create .py files
Able to use Python to process files, such as CSVs, Excels
Learn to Test, Debug and Handle Errors in your Python programs
Hands on Python examples and exercises

Requirements
No programming experience is required.
A computer with internet connection.
Passion and curiosity to learn Python.

Description
Get started

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.

Highlight

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

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.

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

Who this course is for
Beginners who want to learn Python
Anyone who wants to become a data analyst
Anyone who is interested in data analysis
Professionals who wants to automate daily tasks