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
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
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
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
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
Preparing data for analysis. Initial study of the data set. Formulating and checking hypotheses.
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
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
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
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
Integrated Complex Project
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
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
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.
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.
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.
Diving into the most highly demanded area of machine learning. Understanding how to tune machine learning models, improve metrics, and work with imbalanced data.
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.
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.