Data Analyst Course from Scratch

Your introduction to the world of data analysis! Learn key concepts and Python’s basic syntax: loops, conditions, and functions. Explore the pandas library for data analysis. Begin with your first analytical case study, followed by your first project. Chapter 1: Introduction to your future profession Chapter 2: Variables, data types, and arithmetic operations Chapter 3: Strings Chapter 4: Lists

Continue your journey with Python dictionaries, commonly used in data fields. Make your code flexible and reusable by creating functions. Chapter 1: Loops Chapter 2: Conditional statements Chapter 3: Dictionaries Chapter 4: Functions

Use the Pandas library to process data. Handle imperfect data, manage missing values, and eliminate duplicates. Chapter 1: The Pandas library Chapter 2: Reading and visualizing data Chapter 3: Working with duplicate and missing values Chapter 4: Data filtering

Change data types, identify correlations, and create visualizations. Chapter 1: Data types Chapter 2: Feature engineering Chapter 3: Data transformation Chapter 4: Data visualization

Learn probability theory, common distributions, and statistical methods in Python. Sampling, statistical significance, and handling anomalies. Chapter 1: Descriptive statistics Chapter 2: Probability theory Chapter 3: Hypothesis testing

Identify patterns to determine whether a specific video game will succeed commercially.

This module is part of our broader data analytics training program, providing essential tools for analysts. Prepare your development tool ecosystem. Use the command line to access and manage files, learn Git and GitHub, and dive into advanced Python. Chapter 1: Introduction to the command line Chapter 2: Development environments Chapter 3: Git and GitHub Chapter 4: Intermediate Python Chapter 5: Individual development environment

Learn and integrate SQL techniques into the data analytics field with practical exercises. Understand database structures and extract data with SQL queries. Find data online. Chapter 1: Retrieving data from online resources Chapter 2: SQL as a tool for data analysis Chapter 3: Advanced SQL functions for analysts Chapter 4: Table relationships Chapter 5: Soft skills

Business analytics approach, business metrics, KPIs, user data analysis, marketing analysis tools, and sales funnels. Chapter 1: Metrics and funnels Chapter 2: Cohort analysis Chapter 3: Unit economics Chapter 4: User metrics Chapter 5: Self-assessment questions

This sprint focuses on practical data analytics training with real-world examples. Tools and methods for hypothesis testing, experimental design, seasonality, cohort analysis, and A/B testing. Chapter 1: Basics of hypothesis testing in business Chapter 2: Choosing an experimental method Chapter 3: Hypothesis prioritization Chapter 4: Preparing an A/B test Chapter 5: Analyzing A/B test results Chapter 6: Soft skills

Learn to test statistical hypotheses for A/B tests and prepare conclusions and recommendations in an analytical report format.

Automate data analysis processes and routine tasks. Learn data visualization methods and result presentation. Chapter 1: Data pipelines and their uses Chapter 2: Designing and developing dashboards with Dash Chapter 3: Tableau

Explore basic machine learning (ML) methods and applications, including classification, forecasting, clustering, regression, and decision trees. Chapter 1: Business tasks involving machine learning Chapter 2: Machine learning algorithms Chapter 3: Solving tasks related to machine learning

Apply everything you’ve learned in this two-week bootcamp that simulates the experience of a junior data analyst’s job.