Data Science + Gen AI
This course is designed to build a strong foundation in Data Science while seamlessly introducing the power of Generative AI. You will learn how data is collected, processed, analyzed, and transformed into intelligent solutions using modern AI techniques. Starting from core concepts like Python, statistics, and data analysis, the course gradually moves into machine learning, deep learning, and generative models. You will work with real-world datasets and industry-relevant tools to understand how data-driven decisions are made and how AI can generate text, images, insights, and predictions. By the end of the course, you will be able to analyze data, build predictive models, and apply Generative AI techniques to solve practical business and technical problems.
Curriculum
- Introduction And Basics
- Operators
- Conditional Statements
- While Loop
- Lists
- Strings
- For Loops
- Functions
- Dictonary
- Tuple
- Set
- 50+ Coding Questions Practise And Assignments
- Data Visualization using Matplotlib and Seaborn
- Uni-Variate Analysis
- Bi-Variate Analysis
- Multi-Variate Analysis
- Managing Data Frames with the Dplyr Package
- Control Structures
- Functions
- Lexical/Dynamic Scoping
- Loop Functions
- Debugging
- Data Visualization in R
- Storytelling with Data
- Spatial Analysis
- Other Visualization Concepts
- Operations On Table
- Data Modifications
- Constraints
- Indexes
- Views
- Data Retrieval
- Joins
- Functions
- Set Operator
- Sub Ǫueries
- Stored Procedure
- Advance Sǫl Topics
- Introduction to Power BI
- Power Query
- Power Pivot
- Power View
- Power BI Visualizations
- Power BI Service
- Statistics And Maths
- Measures And Statistical Difference
- Hypothesis Testing
- Methods Of Imputation
- Encoding
- Feature Scaling
- Handiling With Outliers
- Supervised Learning Regression
- Unsupervised Learning
- Ensemble Models
- Metrics
- Over Sampling And Under Sampling
- Cross Validation
- Hyper parameter Tuning
- Text Processing
- Feature Extraction And Vectorization
- Advanced Techniques And Models
- Libraries And Tools
- Foundational Neural Network Concepts
- Types of Neural Networks
- Neural Network Techniques and Components
- RAG
- Multimodal Rag
- Tool Augumentation
- AI Agents
- MCP
- Agentic AI
- Live Projects
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