Python for Data Analysis
step-by-step with projects
A practical Online Course
Learn from scratch how to analyze data efficiently in Python
based on real-world datasets
Learn Python for data analysis (pandas, data visualizations, statistics) with real-world datasets and practice projects
▶ Course overview (Preview)
▶ Setting up Python environment
▶ Overview of data types, numeric, define variables
▶ Strings, common functions and methods
▶ Lists, tuples, sets, dictionaries, booleans
▶ if statements, loops
▶ Define functions, use packages
📄 Python basics cheat sheet
▶ Lambda functions, conditional expressions
📄 Introduction to NumPy
📄 What are Python errors and how to fix them
▶ Pandas data structures overview
▶ Loading data
▶ Previewing data (Preview)
▶ Pandas data types overview
▶ Exporting data
❓ Importing data
▶ Combining datasets
▶ Renaming columns
▶ Selecting columns
▶ Selecting rows and setting the index (1)
▶ Selecting rows and setting the index (2)
▶ Subsetting both rows and columns
▶ Modifying values
▶ Making a copy
▶ Sorting data
❓ Exploring data (manipulation)
▶ NBA games project overview (Preview)
📄 Practice Exercise: Importing data & Exploring data (manipulation)
▶ Data cleaning overview
▶ Removing unnecessary columns/rows
▶ Missing data overview
▶ Tackling missing data (dropping) (Preview)
▶ Tackling missing data (imputing with constant)
▶ Tackling missing data (imputing with statistics) and Missing Indicators
▶ Tackling missing data (imputing with model)
▶ Handling outliers (1)
▶ Handling outliers (2)
▶ Cleaning text
❓ Cleaning data
▶ Extracting date and time
▶ Mapping to new values
▶ Applying functions
❓ Transforming columns/features
▶ Czech bank project overview
📄 Practice Exercise: Cleaning data & Transforming columns/features
▶ EDA overview (Preview)
▶ Aggregating statistics
▶ Grouping by
▶ Pivoting tables
📄 FAQ: What is the difference between groupby and pivot_table?
▶ Distribution of one feature
▶ Seaborn library overview
▶ Relationship of two features (1)
▶ Relationship of two features (2)
▶ Relationship of multiple features
▶ Seaborn library recap
❓ Exploring data (Exploratory Data Analysis)
▶ Olympic games project overview
📄 Practice Exercise: Exploring data (Exploratory Data Analysis)
▶ Intro to time series (Preview)
▶ Review of date and time
▶ Manipulating datetime as index
▶ Resampling frequency: downsampling
▶ Resampling frequency: upsampling
▶ Rolling/Shifting time windows
📄 Please help us!
✏ Course evaluation survey
▶ Congrats and thank you!
📄 Reference to the datasets
Discover how to use Python for data analysis.
By the end of this course… You’ll be able to:
What you'll learn
Who is this course for?
- Anyone who wants to be a data analyst or data scientist
- Anyone with basic Python knowledge
- If you have experience with other similar programming languages, take the Python Crash Course included
30-Day Money-Back Guarantee
Try it risk-free
We are confident you’ll enjoy this course. But in the unlikely event, you decide it’s not for you, just contact us for a refund any time during the first 30 days and you’ll get your money back with no questions asked.
Frequently Asked Questions
In short answer: practical!
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 content for details.
Besides Python programming, you’ll also get exposed to 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 the knowledge.
In the end, you’ll have a solid foundation of data analysis, and be able to use Python for the whole process.
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, to make informative decisions.
Data analysis is 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.
The course starts the moment you enroll and never ends! It is a completely self-paced online course – you decide when you start and when you finish.
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact me in the first 30 days and I will give you a full refund.
Start using Python for data analysis today!
Lianne & Justin
Hi! Both Lianne and Justin are data scientists with 5+ years of experience.
We’ve been working in various data science industries such as banks, big data technologies, marketing. We also have solid educational backgrounds in both computer science and statistics, which are the foundations of data science.
We started Just into Data blog and have been posting articles on websites such as Towards Data Science and Hacker Noon (links to example articles).
We believe data science should be fun and accessible to everyone. So we are passionate about helping more people launch their data science careers.
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