This intensive career certification program delivers four months of applied Data Analyst training, followed by an additional one-month foundation in Machine Learning. Students progress from querying and preparing data to developing dashboards, communicating business insights and completing portfolio-ready analytical projects.
Designed for aspiring analysts, career changers and working professionals, the program combines expert-led live instruction with practical assignments, quizzes, business case studies and project-based learning. Each technical concept is connected to a real workplace application, helping students understand not only how to use analytical tools, but when and why to use them to support business decisions.
No previous programming experience is required. Students receive a structured learning pathway that builds technical confidence, analytical thinking and practical problem-solving skills without creating an unmanageable workload.
Your Week-by-Week Learning Journey
Build one practical skill at a time through focused learning, live instruction, assignments, quizzes and workplace-based projects. Each week is designed for an 8–10-hour total commitment.
SQL for Data Analysis
Learn how analysts retrieve, combine and summarize business data stored in relational databases.
SQL and Database Foundations
Understand databases, tables and relationships. Write basic queries using SELECT, FROM, WHERE, ORDER BY and LIMIT.
Filtering and Summarizing Data
Apply conditions, handle NULL values and use aggregate functions with GROUP BY and HAVING.
Combining Data with SQL Joins
Combine related tables, select the correct join and identify duplicate or missing records.
Applied SQL Business Case
Apply the complete SQL workflow to answer realistic business questions and communicate key findings.
Python and Pandas for Data Analysis
Use Python for practical data preparation, exploration and repeatable analytical workflows.
Python Foundations for Analysts
Learn essential Python concepts through practical analyst tasks rather than general programming theory.
Working with Data Using Pandas
Load datasets, inspect columns, select records, filter information and create calculated fields.
Data Cleaning and Validation
Handle missing values, duplicates, incorrect data types, inconsistent formats and data-quality issues.
Exploratory Data Analysis
Investigate distributions, trends, relationships and unusual values to identify meaningful insights.
Tableau and Data Storytelling
Transform analyzed data into interactive Tableau dashboards and clear business recommendations.
Tableau Foundations and Data Connections
Explore the Tableau workspace, connect to business data, review field types and prepare information for reliable analysis.
Visual Analysis and Calculated Fields
Build charts, organize dimensions and measures, create calculated fields and apply filters to investigate business performance.
Interactive Dashboard Design
Select appropriate visualizations, organize dashboard layouts and add filters and actions for business users.
Data Storytelling and Assessment Review
Build a Tableau story, communicate dashboard findings and prepare for the Foundation Belt Assessment.
Data Analyst Foundation Belt Assessment
Demonstrate practical competency in SQL, Python, data preparation, Tableau visualization and business interpretation. Students must achieve at least 70% and receive a maximum of two attempts.
AI-Assisted Analytics and Capstone
Complete an end-to-end analytical project while using AI responsibly to support your workflow.
Responsible AI for Data Analysts
Understand appropriate AI use, data privacy, limitations, verification and responsible practice.
AI-Assisted Analytical Workflows
Use AI to support query development, data cleaning, analysis and documentation while validating results.
Capstone Analysis and Development
Apply SQL, Python and Tableau to investigate a realistic business problem from beginning to end.
Capstone Presentation and Recommendations
Present analytical findings, explain limitations and provide evidence-based business recommendations.
Applied Machine Learning Foundations
Build awareness of predictive analytics and basic machine learning without advanced mathematics. This month is not required for the core Data Analyst Career Certification.
Machine Learning for Analysts
Understand predictive analytics, common business use cases and the basic machine learning workflow.
Regression Foundations
Explore how regression models support numerical predictions using a guided business example.
Classification and Model Evaluation
Build a basic classification model and interpret model performance using appropriate measures.
Applied Machine Learning Project
Apply the complete beginner workflow to a practical prediction problem and explain the results.
Important: Machine Learning Foundations is an optional extension. Students can complete the core Data Analyst certification without taking this module.
