Introduction to Python for Data Analysis - Online Course
A 4-Day Livestream Seminar Taught by
Edwin DalmaijerTuesday, June 23 —
Friday, June 26, 2026
10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
This seminar is part of the Machine Learning Certification, a flexible 4-course pathway designed to build practical expertise in modern machine learning. Contact us to learn how you can complete the certification and access discounted pricing.
Python is one of the most popular languages in the world. It is a general-purpose language, but also highly user-friendly. This makes Python a very powerful tool, with a relatively easy learning curve. It is also open-source and supported by a large international community of users who support each other and continue to develop additional functionality.
In the field of data science, Python has become indispensable. It is used for quick prototyping of statistical models and machine learning pipelines, and you can even find highly mature Python applications in production environments! It has also become a go-to language in science, from astrophysics (e.g. black hole imaging) to zoology (e.g. evolution simulation).
This course is aimed at beginners, including those who are new to programming altogether. We will start with the basics of coding, including variables, logic, loops, functions, and object-oriented programming. In addition, we will discuss reading and writing data files, how to process large quantities of data quickly, and data visualization. Finally, the course will cover statistics, regression, models, and a bit of machine learning. No prior knowledge on any of these topics is assumed.
Starting June 23, this seminar will be presented as a 4-day synchronous, livestream workshop via Zoom. Each day will feature two lecture sessions with hands-on exercises, separated by a 1-hour break. Live attendance is recommended for the best experience. But if you can’t join in real time, recordings will be available within 24 hours and can be accessed for four weeks after the seminar.
Closed captioning is available for all live and recorded sessions. Captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
ECTS Equivalent Points: 1
More details about course content
More specifically, the course will cover how to write your own functions and classes, using variables, statements, and loops. These make up the majority of code-bases, and are thus a crucial skill to master. You will also be introduced to some of the most commonly used packages: NumPy and SciPy for fast computing, Matplotlib for publication-quality visualizations, and scikit-learn for machine learning.
The course will be very hands-on. It will run through interactive notebooks in your internet browser, so you won’t have to download anything. However, we will provide advice on how to install Python and additional packages, so that you can continue using it at home and at work.
At the end of this course, you should be able to find your own way in Python. You will be equipped to handle datasets and to write full analyses. You will also be well-equipped to start deepening your Python knowledge, as this course will have introduced some of the most commonly used tools.
More specifically, the course will cover how to write your own functions and classes, using variables, statements, and loops. These make up the majority of code-bases, and are thus a crucial skill to master. You will also be introduced to some of the most commonly used packages: NumPy and SciPy for fast computing, Matplotlib for publication-quality visualizations, and scikit-learn for machine learning.
The course will be very hands-on. It will run through interactive notebooks in your internet browser, so you won’t have to download anything. However, we will provide advice on how to install Python and additional packages, so that you can continue using it at home and at work.
At the end of this course, you should be able to find your own way in Python. You will be equipped to handle datasets and to write full analyses. You will also be well-equipped to start deepening your Python knowledge, as this course will have introduced some of the most commonly used tools.
Computing
To run hands-on exercises, we will be using carefully crafted interactive notebooks via Google Colaboratory. For this, you only need an internet browser (like Firefox) and a Google account.
Alternatively, you are welcome to install Python on your own computer. In addition to Python (version 3.7 or higher), you will need the packages NumPy, SciPy, Matplotlib, and scikit-learn. Python package installation can be a bit tricky for those who aren’t familiar with it. We will cover installing Python packages on the first day of the course, so you might want to wait to install anything until then.
To run hands-on exercises, we will be using carefully crafted interactive notebooks via Google Colaboratory. For this, you only need an internet browser (like Firefox) and a Google account.
Alternatively, you are welcome to install Python on your own computer. In addition to Python (version 3.7 or higher), you will need the packages NumPy, SciPy, Matplotlib, and scikit-learn. Python package installation can be a bit tricky for those who aren’t familiar with it. We will cover installing Python packages on the first day of the course, so you might want to wait to install anything until then.
Who should register?
This course is for everyone who would like to learn Python, or to dip their toes into programming. The content leans towards data science, so this course will be especially useful to those who would like to expand their expertise in data handling, visualization, statistics, and basic machine learning. No prior knowledge of coding or statistics is necessary: we’ll start with the basics, and work our way up from there.
This course is for everyone who would like to learn Python, or to dip their toes into programming. The content leans towards data science, so this course will be especially useful to those who would like to expand their expertise in data handling, visualization, statistics, and basic machine learning. No prior knowledge of coding or statistics is necessary: we’ll start with the basics, and work our way up from there.
Seminar outline
Day 1: Programming basics
-
- Variables
- Numerical values (int and float), operations, and functions
- Text values (str), operations, and functions
- Booleans and logical operation
- Collections (tuples, lists, dictionaries), operations, and functions
- If statements
- While loops
- For loops
- Functions
- What is a function?
- Input and output
Day 2: Data processing
-
- NumPy
- Arrays: fast, scalable, fantastic
- Useful array functions
- Random data generation
- Loading and writing data
- Paths and the os module
- Writing a CSV file
- Loading a CSV file
Day 3: Data visualization
-
- Selecting data to draw
- Pivot tables
- Boolean arrays
- Data visualization
- Matplotlib
- Scatter plots
- Lines plots
- Bar charts and error bars
- Drawing distributions
Day 4: Statistics
-
- Basic statistical tests
- Tests of relations
- Tests of differences
- Model fitting
- Scikit-learn
- Linear regression
- Multivariable regression
Day 1: Programming basics
-
- Variables
- Numerical values (int and float), operations, and functions
- Text values (str), operations, and functions
- Booleans and logical operation
- Collections (tuples, lists, dictionaries), operations, and functions
- If statements
- While loops
- For loops
- Functions
- What is a function?
- Input and output
- Variables
Day 2: Data processing
-
- NumPy
- Arrays: fast, scalable, fantastic
- Useful array functions
- Random data generation
- Loading and writing data
- Paths and the os module
- Writing a CSV file
- Loading a CSV file
- NumPy
Day 3: Data visualization
-
- Selecting data to draw
- Pivot tables
- Boolean arrays
- Data visualization
- Matplotlib
- Scatter plots
- Lines plots
- Bar charts and error bars
- Drawing distributions
- Selecting data to draw
Day 4: Statistics
-
- Basic statistical tests
- Tests of relations
- Tests of differences
- Model fitting
- Scikit-learn
- Linear regression
- Multivariable regression
- Basic statistical tests
Payment information
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 USD includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.