Python for Data Analysis: step-by-step with projects

person icon Lianne And Justin (Just Into Data)

Python for Data Analysis: step-by-step with projects

Learn Python for data analysis (pandas, data visualizations, statistics) with real world datasets and practice projects

updated on icon Updated on Sep, 2023

language icon Language - English

person icon Lianne And Justin (Just Into Data)

architecture icon Data Science,Data Analysis,Python,Data Visualization,Statistics


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Course Description

Welcome to your Python for data analysis course!

This course offers 11 hours of HD video lectures, detailed code notebooks, and 3 guided practice projects, based on multiple real-world datasets.

This course will guide you to learn from scratch how to analyze data efficiently in Python.

By following this course, you'll gain practical experience analyzing real-world datasets. So that by the end, you'll be able to conduct your own analysis with Python, and extract valuable insights that can transform your business!

What are the design principles of the course?

  • Instead of dumping all the available Python libraries or functions to you, we picked only the most useful ones based on our industry experience to cover in the course.  This allows you to focus and master the foundations.

  • The course is arranged in different sections based on the step-by-step process of REAL data analysis. Please check out the course overview lecture for details.

  • Besides Python programming, you'll also get exposed to the basic statistical knowledge necessary for data analysis.

  • Combined with the detailed video lectures, you'll be given a few projects to work on to reinforce your knowledge.

In the end, you'll have a solid foundation of data analysis, and be able to use Python for the whole process.

Why data analysis in Python?

Data analysis is a critical skill and is getting more popular.

Nowadays, almost every organization has some data. Data could be very useful, but not without appropriate analysis. Data analysis enables us to transform data into insights for businesses, and to make informative decisions.

You can find data analysis being used in almost every industry, be it health care, finance, or technology.

While Python is one of the employers' most in-demand skills for data science. It is not only easy to learn, but also very powerful.

Who is this course for?

This course is helpful for anyone interested in analyzing data effectively. Perhaps you want to become a data analyst or a data scientist, or maybe you just want the skills to work on your projects.

This course is beginner-friendly. However, we recommend you have some basic knowledge of Python or at least another programming language.

With that said, there is a Python crash course included, so you can pick up or review the skills needed.

What are the main Python libraries covered?

  • Pandas

  • Scikit-learn

  • Seaborn

All you need to start this course is the desire to learn, and a computer!

Looking forward to seeing you inside the course!


Lianne and Justin

Preview image designed by freepik


What will you learn in this course:

  • How to use Python for data analysis

  • Reach an intermediate level of Python

  • Experience analyzing real-world datasets in lectures and guided projects

  • Use Python data analysis libraries (Pandas, Scikit-learn, Seaborn)

  • Import, examine, export data in Python

  • Manipulate data

  • Clean data

  • Transform data

  • Calculate summary statistics

  • Create data visualizations in Python

  • Use JupyterLab/Jupyter Notebook


What are the prerequisites for this course?

  • Basic Python ONLY

  • If you have experience with other similar programming languages, take the Python Crash Course included

Python for Data Analysis: step-by-step with projects


Check out the detailed breakdown of what’s inside the course

Let's get started!
3 Lectures
  • play icon Course Preview 04:03 04:03
  • play icon Course overview 05:31 05:31
  • play icon Course Resources
Python crash course (optional)
10 Lectures
Importing data
6 Lectures
Exploring data (manipulation)
10 Lectures
Capstone practice project I
2 Lectures
Cleaning data
11 Lectures
Transforming columns/features
5 Lectures
Capstone practice project II
2 Lectures
Exploring data (Exploratory Data Analysis)
12 Lectures
Capstone practice project III
2 Lectures
Special topic: dealing with time series data
7 Lectures
Congrats and thank you!
2 Lectures

Instructor Details

Lianne and Justin (Just into Data)

Lianne and Justin (Just into Data)

Data Scientists

Justin: an experienced data scientist in many different fields, such as marketing, anti-money laundering, and big data technologies. He also has a bachelor’s degree in computer engineering and a master’s degree in statistics.

Lianne: an experienced statistician who has worked in the central bank as well as commercial banks, where she monitored major financial institutions and conducted fraud analysis. She has both a bachelor’s and a master’s degree in statistics.

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