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Data Analyst — Master’s Course
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Course Features

Duration Self-paced
Level Beginner
Language English
Mode Online
Data Analytics

Data Analyst — Master’s Course

Master end-to-end Data Analysis: Python & R, SQL (MySQL/SQL Server/PostgreSQL), SPSS; NumPy & Pandas; visualization with Matplotlib & ggplot2; Big Data with PySpark; BI dashboards in Tableau & Power BI; Excel with PivotTables; cloud (GCP & Azure); databases & integration; data cleaning, EDA, and communication/storytelling—plus a real capstone. Includes 1:1 mentorship and mock interview prep.

Last updated December 2025
Next cohort starts Oct 1st
$1,200.00 $1,500.00
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Become a job-ready Data Analyst. Learn to inspect, clean, transform and model data to support decisions and uncover patterns across business functions.

  • Foundations: role of data analysis, key concepts, SDLC, Agile/Scrum, Waterfall
  • Programming: Python & R for analysis; SPSS for statistics
  • Data stack: NumPy, Pandas for wrangling; visualization with Matplotlib & ggplot2
  • Databases & SQL: MySQL, SQL Server, PostgreSQL — from fundamentals to advanced queries
  • Big Data & BI: PySpark for processing; Tableau & Power BI dashboards; Excel PivotTables
  • Cloud: Google Cloud Platform (GCP) and Microsoft Azure for data storage & analysis
  • Professional: data cleaning and transformation, critical thinking, communication & storytelling, and mock interview preparation

Finish with a capstone project from scope definition to data collection, analysis, and presenting findings to stakeholders.

1:1 Personalized Mentorship
Mock Interview Preparation
Python & R for Data Analysis
SQL (MySQL, SQL Server, PostgreSQL)
SPSS for Statistical Analysis
NumPy & Pandas wrangling
Matplotlib & ggplot2 visualization
Big Data with PySpark
Tableau & Power BI dashboards
Microsoft Excel with PivotTables
Google Cloud Platform (GCP)
Microsoft Azure for data workloads
Data cleaning, transformation & EDA
Critical thinking & communication
Capstone project
Overview of Data Analysis
Roles and Responsibilities
Importance in Business
Key Concepts & Terminologies
Software Development Life Cycle
Phases of SDLC
SDLC in Data Projects
Principles of Agile
Agile/Scrum in Data Projects
Waterfall Model & Phases
Comparing Waterfall vs Agile
Basic Syntax & Structures
Data Types & Operations
Intro to NumPy
Intro to Pandas
Data Visualization with Matplotlib
Data Manipulation with Pandas
R Basics & Environment Setup
SQL Fundamentals
Basic Queries & Retrieval
Joins, Subqueries & Indexing
Window Functions
CTEs & Recursive Queries
Performance Tuning
Advanced SQL Techniques
SPSS: Introduction
Data Entry & Management
Descriptive Statistics
Advanced Analysis Techniques
VS Code & PyCharm Setup
Writing & Running Code
Debugging & Version Control
NumPy Array Operations
Pandas DataFrames & Series
Creating Plots & Charts
Customizing Visualizations
Advanced Plotting Techniques
PySpark Overview
Setting Up Spark
Processing Data with PySpark
Data Visualization Tools
Interactive Visualizations
Dashboards & Stories
Introduction to Power BI
Connecting to Data Sources
Reports & Dashboards
Advanced Excel Techniques
PivotTables & Charts
Cloud for Data Analysis
Data Storage & Analysis on GCP
Setting Up Projects
Azure Data Services
Data Analysis with Azure
Setup & Management
Querying Data
Data Integration Techniques
Handling Missing Values
Transformation & Standardization
Wrangling Workflows
Developing Critical Thinking
Problem-Solving Strategies
Applying to Data Analysis
Effective Communication
Presenting Data Insights
Storytelling with Data
Defining Project Scope
Data Collection & Analysis
Presenting Findings
Project: Capstone: Data Analysis Project