Data analysis is a complex and intricate process. It is comprised of collecting and structuring data, forming and testing hypotheses, identifying patterns, and drawing conclusions. Data analysts are essential in business, administration, and science. They work with fundamental tools such as Python and its libraries, Jupyter Notebook, and SQL. Our mission is to teach you how to best use these tools. 


Python and Data Analysis Basics 

Free introductory course 

You discover what data analysis is and carry out your first bit of research as an analyst. You will then study the basics of Python, a key tool in this particular craft. After that you’ll know if you have the time and motivation to continue.

Chapter 1. Introduction and syntax basics

Chapter 2. Lists and loops

Chapter 3. Operations with tables

Chapter 4. Conditions and functions

Chapter 5. Pandas for data analysis

Chapter 6. Data pre-processing

Chapter 7. Data analysis and presenting results

Chapter 8. Selecting a profession

Final admission of the students 

Week 1

Introduction to data analysis

Start by meeting your tutors and mentors.

Learning about what it means to be an analyst. An overview of the areas where analysts can find work. Introducing different types of data analysis. The organisational aspect of the training process.


Chapter 1. Onboarding

Chapter 2. How We Teach

Chapter 3. Mindsets Influencing How You Learn 

Weeks 23

Data Preprocessing 

Clean and analysis-ready data as the first step towards solving an analytical problem. Taking a closer look at tools that are used to compensate for less-than-perfect data.


Chapter 1. Working with Missing Values

Chapter 2. Changing Data Types

Chapter 3. Looking for Duplicates

Chapter 4. Categorising Data

Chapter 5. Systems and Critical Thinking for Analysts

Final Project 

Weeks 45

Exploratory Data Analysis (EDA) 

Learning to perform an initial scan for patterns in data that offer the chance to put together your first hypotheses, while also avoiding weird mistakes. Learning how to use visualisation tools to work with data.


Chapter 1. First Graphs and Conclusions

Chapter 2. Studying Data Slices

Chapter 3. Working with Several Data Sources

Chapter 4. Joint Distribution

Chapter 5. Validating Results

Final Project 

Weeks 68

Statistical Data Analysis 

Probability theory, the most common distributions, and statistical methods in Python. Sampling and statistical significance. Identifying and handling anomalies.

Integrated Complex Project

20 hours

Preparing data for analysis. Initial study of the data set. Formulating and checking hypotheses. 

Weeks 9–11

Data Collection and Storage (SQL) 

Learning how databases are organised and how to pull data from them using SQL queries. Finding data online.


Chapter 1. Pulling Data from Web Resources

Chapter 2. SQL as a Tool for Working with Data

Chapter 3. Broader Opportunities for Analysts using SQL

Chapter 4. Table Relations

Chapter 5. Case Context and Elaboration

Final Project 

Weeks 1213

Analysis of Business Indicators  

Even closer to business, we take a detailed look at metrics and essential tools like cohort analysis, sales funnels, and unit economics.


Chapter 1. Metrics and Funnels

Chapter 2. Cohort Analysis

Chapter 3. Unit Economics

Chapter 4. User Metrics

Chapter 5. Investigating Anomalies

Chapter 6. Soft Skills

Final Project 

Weeks 1415

Making Data-Informed Business Decisions  

A/B testing: when to use it. Designing and identifying the sample size. Getting and validating results.


Chapter 1. Basics of Hypothesis Testing in Business

Chapter 2. Choosing a Method for the Experiment

Chapter 3. Hypotheses Prioritisation

Chapter 4. Preparing to Conduct A/B Testing

Chapter 5. Analysing the Results of A/B Testing

Chapter 6. Soft Skills

Final Project 

Weeks 1618

How to Tell a Story Using Data  

How to correctly present research results using graphics, key numbers, and solid interpretation.


Chapter 1. Preparing a Presentation

Chapter 2. Seaborn Library

Chapter 3. Plotly Library

Final Project 

Integrated Complex Project

20 hours

Pulling data from a database. Data set preprocessing and overview. Formulating hypotheses in light of business specifics. Checking hypotheses and preparing conclusions formatted as an analytical report. Event-based Analytics Project 

Weeks 19 –20


Automating data analysis processes. Scripting routine and regular tasks. Creating dashboards for different audiences and company needs.


Chapter 1. Basics of Executing Scripts

Chapter 2. Data Pipelines: What Are They Used For?

Chapter 3. Planning and Developing Dashboards in Dash

Final Project 

Weeks 21–22

Forecasts and Predictions

Basics of machine learning, project dealing with churn rate prediction.


Chapter 1. Business Tasks Involving Machine Learning

Chapter 2. Machine Learning Algorithms

Chapter 3. Solving Tasks Related to Machine Learning

Final Project 

Weeks 23–25

Final Project   

Independent solution of an analytical problem of your choice. All the stages of data analysis project flow.


Bootcamp Sprint Checking and Evaluation Additional Tasks Sending in your Project.

Week 26

Career Accelerator kickoff

Meet your career mentors.
Your career mentors will guide you throughout the 12-weeks Career Accelerator. 

Career accelerator activities will take place during the last months of the studies.

Weeks 27–28

Introduction to Machine Learning

Mastering the basics of machine learning.

How the scikit-learn library works and how to use it in order to complete your very first machine learning project.

Weeks 29–30

Supervised learning

Diving into the most highly demanded area of machine learning. Understanding how to tune machine learning models, improve metrics, and work with imbalanced data.

Weeks 31–32

Machine Learning for Business

Applying the acquired machine learning knowledge to business tasks. Discover business metrics, A/B testing, the Bootstrapping technique, and data labeling.

Weeks 3340

Career Accelerator

The Practicum100 team will help you to be 100% ready for the local job market by providing you with mock interviews, industry case studies, common paths of data analysts, and most importantly, connecting you to our industry partners.