The Data Science Mastery Program is designed to help learners build strong, industry-ready skills in data analysis, programming, and machine learning. This course blends theory with practical, hands-on projects using real-world datasets so you can confidently apply data-driven techniques in business, technology, finance, and other domains.
Whether you are a beginner or transitioning from a different field, this program guides you step-by-step from basic concepts to advanced data science techniques used by top companies worldwide.
Advanced formulas: VLOOKUP, XLOOKUP, INDEX-MATCH
Data cleaning & transformation techniques
PivotTables, PivotCharts & slicers
Conditional formatting for data visualization
Data validation & structured referencing
Advanced charting and dashboards
Excel functions for analytics (TEXT, DATE, LOGICAL, IFs, SUMIFS, COUNTIFS, etc.)
Working with Power Query
Automating tasks with Macros (basic)
Practical business case studies
Introduction to relational databases
Writing SQL queries from basic to advanced
Filtering, sorting, grouping & aggregations
Joins (Inner, Left, Right, Full)
Subqueries & CTEs
Views, stored procedures & functions
Data manipulation (INSERT, UPDATE, DELETE)
Database design & normalization
Real-world SQL problem-solving exercises
Getting started with Power BI interface
Importing, cleaning & transforming data
Data modeling using relationships
DAX functions (basic to intermediate)
Building interactive dashboards & reports
Introducing Power Query tools
Publishing reports to Power BI Service
Sharing dashboards & real-time collaboration
Real business reporting use cases
Understanding Tableau workspace
Connecting to data sources
Dimensions, measures & data types
Building charts: bar, line, pie, maps, scatter, heat maps, etc.
Dashboard & story creation
Filters, actions & parameters
Data blending vs data joins
Publishing dashboards to Tableau Public/Server
Industry-based dashboard projects
Python basics & programming concepts
Working with variables, loops, functions & libraries
NumPy for numerical computing
Pandas for data wrangling & analysis
Matplotlib & Seaborn for data visualization
Exploratory data analysis (EDA) techniques
Reading/writing CSV, Excel & JSON files
Handling missing, inconsistent & unstructured data
Mini projects & practice problems
Introduction to machine learning & types
Supervised learning: Regression & Classification
Unsupervised learning: Clustering & Dimensionality Reduction
Train-test split, cross-validation & model evaluation
Feature engineering & data preprocessing
Confusion matrix, accuracy, precision, recall, F1-score
Hands-on ML projects (real datasets)
Building & evaluating models using scikit-learn
Preparing ML model reports
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