NumPy & Pandas – Data manipulation, indexing, slicing, filtering, reshaping, aggregation, and real-world case studies.
SQL for Data Analysis – Query databases, joins, subqueries, filtering, window functions, and CTEs for handling structured data.
Advanced Excel – Pivot tables, Power Query, VLOOKUP, INDEX-MATCH, What-If analysis, and data visualization.
Case Studies & Real-World Projects – McKinsey dataset analysis, IMDb movie business insights, financial & marketing analytics, and AI-driven analytics.
Data Visualization – Master Matplotlib & Seaborn for bar charts, scatter plots, heatmaps, and advanced plots.
Power BI & Google Data Studio – Interactive dashboards, data modeling, advanced DAX, and real-time business insights.
Machine Learning Basics – Linear & logistic regression, regularization, feature engineering, bias-variance tradeoff, and real-world business applications.
Hands-on Practice & AI Integration – Implement Generative AI techniques in data analytics for automated insights and trend forecasting.
Master the fundamentals of Data Analytics with Generative AI, covering essential tools like NumPy, Pandas, Matplotlib, Seaborn, SQL, Power BI, Google Data Studio, Advanced Excel, and Machine Learning. This course is designed for aspiring data analysts, AI enthusiasts, and business professionals looking to extract insights, visualize data, and apply AI-driven analytics in real-world scenarios.
✅ NumPy & Pandas – Data manipulation, aggregation, and preprocessing.
✅ Data Visualization – Master Matplotlib, Seaborn, Power BI & Google Data Studio for impactful storytelling.
✅ SQL for Data Analysis – Query databases, filter data & perform complex joins.
✅ Advanced Excel – Pivot tables, Power Query & business dashboards.
✅ Machine Learning Basics – Regression, classification & model tuning.
✅ Real-World Case Studies – Analyze IMDb movies, financial reports & marketing data.
Gain hands-on expertise in AI-powered Data Analytics!
Numpy Arrays vs Lists, Importing of Numpy Array,Ndim and Shape
Numpy Arrays- 2D, 3D, Shape, Arange, Type Conversion, Dtypes
Aggregation in Mumpy Array for 1d and 2d Array
Sort and Argsort in Numpy Array
Case Study- Net Promoter Score on Numpy
Sorting and Multiplication in 2d and 1d Arrays
Case Study- Fitbit Health Data Analysis
Introduction to Pandas and Comparison with Numpy
Introduction to Pandas and Comparison with Numpy Part 2
Mckinsey Case Study- Data Statistics, Mean, Renaming, Value Counts, Unique and Nunique and Series
Indices and Inplace Usage in Pandas Dataframe
loc and iloc, Drop , Set Index, in Pandas Dataframe
Reset Index, Concat, Drop Duplicates in Pandas Dataframe
Merge, Type of Joins in Pandas Dataframe
Concat vs Merge and Netflix Dataset Case Study
Pivot, Melt, Netflix Case Study Part-2
Unpivot, None vs Nan, Date Time Functions, Save a csv file and Drugs Data Case Study
Introduction to SQL and RDBMS
Primary Key vs Foreign Key vs Unique Key, Relationship in SQL
First Step in Writing the SQL query, Datatypes in SQL
Type of SQL Commands, Relationships in SQL
Filtering using Where (and, or), Order By in SQL
LIKE, IN, Concat, Round, Derived Functions, Case Statements in SQL Part-1
Derived Functions, Case Statements in SQL Part-2
Is Null, Null vs Blank, Subqueries in SQL
Group by, Count, Sum,Avg , Order of Executions of Functions in SQL
Aggregation Functions- Min and Max, Having clause in SQL
Interview level Questions on Agggregation Functions
Joins-Need of Joins, Left, Right and Inner Joins
Joins- Duplicate, Null and Blank Records Concept and Interview Questions
SQL Questions on Joins, Self Join, Cross Join combined with Aggregation Statements
SQL Joins Interview Questions-Part-3
Window Functions-Part-1
Window Functions-Part-2
Pivot, Lead, Lag, Cummulative, Union vs Union all, Date and Time Functions
CTE Functions, Subqueries,Interview Level Questions on Joins
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