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Complete Data Science and Analytics with Gen AI.
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Python

Module	Topic	Key Concepts Covered
1	Environment Setup	Anaconda Distribution, Jupyter Notebooks, Google Colab
2	Python Fundamentals	Variables, Dynamic Typing, Comments, Scalar Data Types
3	Operators & Logic	Arithmetic, Comparison, Logical Operators, Data Filtering
4	Control Flow	Conditional Statements (`if`, `elif`, `else`)
5	Iteration & Loops	`for` Loop, `while` Loop, `break`, `continue`
6	Strings & Formatting	String Manipulation, f-strings, Text Data Cleaning
7	Data Structures – Part 1	Lists (Indexing, Slicing, Mutability), Tuples (Immutability)
8	Data Structures – Part 2	Dictionaries (Key-Value Pairs, JSON Logic), Sets (Deduplication)
9	Functions & Modularity	Functions, Parameters, Return Values, Scope
10	Functional Programming	Lambda Functions, `map()`, `filter()`, `zip()`
11	Advanced Python	List & Dictionary Comprehensions, Efficient Data Cleaning
12	Error Handling	Exception Handling (`try/except`), Debugging
13	File Operations	Read/Write Files (Text, CSV), Context Managers (`with`)
14	Modules & Packages	Importing Libraries, Python Ecosystem, `pip`
15	OOP for Data Science	Classes, Objects, Attributes
16	OOP Implementation	Methods, Constructors (`__init__`), `self`
17	Applied OOP	Object-Oriented Design in Pandas & Scikit-learn

Power BI

Module	Topic	Key Concepts Covered
1	Introduction & Data Analyst Foundation	Role of Data Analyst, Data Lifecycle, Business Understanding
2	Data Preparation & Power Query Mastery	Data cleaning, transformation, ETL, Power Query Editor
3	Data Modeling & DAX Essentials	Relationships, Measures, Calculated Columns, DAX Functions
4	Data Visualization & Advanced Analytics	Interactive dashboards, KPIs, Advanced charts, Insights
5	Management, Security & Microsoft Fabric	Workspace management, Data security, Governance, Microsoft Fabric overview

Statistics

Module	Topic	Key Concepts Covered
1	Descriptive Statistics	Mean, Median, Mode, Standard Deviation, Variance
2	Data Visualization	Histograms, Scatter Plots, Box Plots, Outlier Detection
3	Statistical Distributions	Normal Distribution (Bell Curve), Skewness, Z-Scores
4	Correlation Analysis	Covariance, Pearson Correlation, Feature Selection
5	Probability for AI	Conditional Probability, Bayes’ Theorem, Naive Bayes Foundation
6	Hypothesis Testing	Null Hypothesis, P-Values, Statistical Significance

Pandas

Module	Topic	Key Concepts Covered
1	Pandas Series & DataFrame Structure	Series, DataFrames, Data Types, Shape
2	Reading & Writing Data	Read/Write CSV, Excel, JSON, Text Files
3	Data Inspection & Exploration	`head()`, `tail()`, `info()`, `describe()`
4	Indexing, Slicing & Filtering	`loc`, `iloc`, Boolean Indexing
5	Handling Missing Values & Duplicates	`isnull()`, `fillna()`, `dropna()`, `duplicated()`
6	Grouping & Aggregation	`groupby()`, Aggregation Functions (`sum`, `mean`, `count`)
7	Merging, Joining & Concatenation	`merge()`, `join()`, `concat()`

Numpy

Module	Topic	Key Concepts Covered
1	NumPy Array Basics	Array creation, Data types, Shape, Reshaping
2	NumPy Data Selection	Indexing, Slicing, Boolean Indexing
3	NumPy Operations	Vectorized operations, Fast mathematical computations without loops

Machine Learning

Module	Topic	Key Concepts Covered
1	Introduction to Machine Learning	ML overview, real-world applications
2	Types of Learning	Supervised, Unsupervised, Reinforcement Learning
3	Model Training Basics	Train & Test Split, Validation Split
4	Model Evaluation Fundamentals	Performance metrics, Overfitting & Underfitting
5	Linear Regression	Model concept, use cases
6	Linear Regression Assumptions	Linearity, Independence, Homoscedasticity, Normality
7	Regression Evaluation Metrics	R-square, Adjusted R-square
8	Scikit-learn Introduction	ML workflow, APIs, pipelines
9	Logistic Regression	Training methodology, classification logic
10	Classification Metrics	Precision, Recall, ROC Curve, F-score
11	Decision Tree	Tree structure, splitting criteria
12	Model Validation Concepts	Cross-validation, Bias vs Variance
13	Ensemble Learning	Ensemble approach overview
14	Bagging & Boosting	Bagging, Boosting techniques
15	Random Forest	Algorithm working, advantages
16	Feature Importance	Variable importance analysis
17	XGBoost	Gradient boosting, high-performance ML
18	K-Nearest Neighbor (KNN)	Distance-based learning
19	Lazy Learners	Concept of lazy learning
20	Curse of Dimensionality	High-dimensional data challenges
21	KNN Limitations	Performance issues, scalability
22	Text Analytics	NLP fundamentals
23	Text Preprocessing	Tokenization, Chunking
24	Feature Extraction (Text)	Document Term Matrix (DTM)
25	Sentiment Analysis	Hands-on sentiment analysis
26	Hierarchical Clustering	Agglomerative & divisive methods
27	K-Means Clustering	Algorithm steps, use cases
28	Clustering Evaluation	Performance measurement
29	Principal Component Analysis (PCA)	Feature compression
30	Dimensionality Reduction	High-dimensional data reduction
31	Factor Analysis	Latent variable modeling
32	Time Series Forecasting	Time-based data analysis
33	Moving Average	Smoothing techniques
34	ARIMA Model	Autoregressive Integrated Moving Average

Deep Learning

Module	Topic	Key Concepts Covered
1	Basics of Neural Networks	Neurons, layers, activation functions
2	Types of Neural Networks	ANN, CNN, RNN
3	Cost Function	Loss functions, optimization objective
4	Gradient Descent	Optimization techniques, learning rate
5	Linear Algebra Basics	Vectors, matrices, dot products
6	Neural Network from Scratch	Vanilla NN implementation in Python
7	TensorFlow Basics	Tensors, computational graphs, workflow
8	Simple Neural Network (Hands-on)	Building NN using TensorFlow
9	Word Embeddings	Word representation techniques
10	Word2Vec Models	CBOW, Skip-gram
11	Word Relationships	Semantic similarity, vector arithmetic
12	Convolutional Neural Networks (CNN)	Convolution layers, filters
13	Pooling & Padding	Max Pooling, window padding
14	CNN for Image Classification	End-to-end image classification
15	Recurrent Neural Networks (RNN)	Sequential data modeling
16	LSTM Architecture	Long Short-Term Memory units
17	Character-level RNN	Story writer implementation
18	Sentiment Analysis (Hands-on)	Text classification using DL
19	Sequence-to-Sequence Models	Seq2Seq architecture
20	Encoder–Decoder Framework	Attention-ready encoder-decoder models
21	Generative Adversarial Networks (GAN)	Generator vs Discriminator
22	Generative Models using GAN	Image/text generation
23	Semi-Supervised Learning with GAN	Learning with limited labels

Data Visualization

Module	Module Title	Topics Covered
Module 1	Foundations & Text Mastery	The Art of Prompt EngineeringAI for Content Writing & Copywriting
Module 2	Visuals & Multimedia Creation	Text-to-Image Generation & DesignAI-Powered Video Creation & EditingVoice Synthesis & Audio AIBuilding AI Avatars & Virtual Presenters
Module 3	Data, Code & Productivity	AI for Data Analysis & VisualizationCoding & Debugging AssistanceProductivity & Workflow Automation
Module 4	Professional Growth & Future Trends	AI in Digital Marketing & Social MediaResume Building & Interview Prep with AIFuture Trends & Ethics in AI

Data Visualization

Module	Topic	Key Concepts & Skills
Module 1: Foundations & Text Mastery	GenAI Introduction	Definition, core concepts, and key applications
	Model Architectures	Transformers and self-attention mechanisms
	Data Processing	Tokenization, embeddings, and vector representations
	LLM Behavior	Capabilities, hallucinations, and bias mitigation
Module 2: AI Workflows & Agents	LangChain Core	Chains, prompt templates, and output parsers
	LangGraph Logic	Graph-based orchestration and complex workflows
	AI Memory	Context window management and conversation history
	Agent Foundations	Agent definition, logic loops, and tool usage
	Agent Types	Reactive vs. proactive planning agents
Module 3: Retrieval & Knowledge Systems	Vector Databases	Storage and indexing of high-dimensional embeddings
	Similarity Search	Semantic search and context retrieval algorithms
	RAG Framework	Retrieval-Augmented Generation architecture
	RAG Implementation	Document Q&A and knowledge assistants
Module 4: Advanced Systems & Deployment	Multi-Agent Systems	Role delegation and collaborative task execution
	App Development	Full-stack GenAI apps using Streamlit and Python
	Model Integration	OpenAI, Gemini, and open-source model APIs

Python

Module	Topic	Key Concepts Covered
1	Environment Setup	Anaconda Distribution, Jupyter Notebooks, Google Colab
2	Python Fundamentals	Variables, Dynamic Typing, Comments, Scalar Data Types
3	Operators & Logic	Arithmetic, Comparison, Logical Operators, Data Filtering
4	Control Flow	Conditional Statements (`if`, `elif`, `else`)
5	Iteration & Loops	`for` Loop, `while` Loop, `break`, `continue`
6	Strings & Formatting	String Manipulation, f-strings, Text Data Cleaning
7	Data Structures – Part 1	Lists (Indexing, Slicing, Mutability), Tuples (Immutability)
8	Data Structures – Part 2	Dictionaries (Key-Value Pairs, JSON Logic), Sets (Deduplication)
9	Functions & Modularity	Functions, Parameters, Return Values, Scope
10	Functional Programming	Lambda Functions, `map()`, `filter()`, `zip()`
11	Advanced Python	List & Dictionary Comprehensions, Efficient Data Cleaning
12	Error Handling	Exception Handling (`try/except`), Debugging
13	File Operations	Read/Write Files (Text, CSV), Context Managers (`with`)
14	Modules & Packages	Importing Libraries, Python Ecosystem, `pip`
15	OOP for Data Science	Classes, Objects, Attributes
16	OOP Implementation	Methods, Constructors (`__init__`), `self`
17	Applied OOP	Object-Oriented Design in Pandas & Scikit-learn

Statistics

Module	Topic	Key Concepts Covered
1	Descriptive Statistics	Mean, Median, Mode, Standard Deviation, Variance
2	Data Visualization	Histograms, Scatter Plots, Box Plots, Outlier Detection
3	Statistical Distributions	Normal Distribution (Bell Curve), Skewness, Z-Scores
4	Correlation Analysis	Covariance, Pearson Correlation, Feature Selection
5	Probability for AI	Conditional Probability, Bayes’ Theorem, Naive Bayes Foundation
6	Hypothesis Testing	Null Hypothesis, P-Values, Statistical Significance

Numpy

Module	Topic	Key Concepts Covered
1	NumPy Array Basics	Array creation, Data types, Shape, Reshaping
2	NumPy Data Selection	Indexing, Slicing, Boolean Indexing
3	NumPy Operations	Vectorized operations, Fast mathematical computations without loops

Pandas

Module	Topic	Key Concepts Covered
1	Pandas Series & DataFrame Structure	Series, DataFrames, Data Types, Shape
2	Reading & Writing Data	Read/Write CSV, Excel, JSON, Text Files
3	Data Inspection & Exploration	`head()`, `tail()`, `info()`, `describe()`
4	Indexing, Slicing & Filtering	`loc`, `iloc`, Boolean Indexing
5	Handling Missing Values & Duplicates	`isnull()`, `fillna()`, `dropna()`, `duplicated()`
6	Grouping & Aggregation	`groupby()`, Aggregation Functions (`sum`, `mean`, `count`)
7	Merging, Joining & Concatenation	`merge()`, `join()`, `concat()`

Data Visualization

Module	Module Title	Topics Covered
Module 1	Foundations & Text Mastery	The Art of Prompt EngineeringAI for Content Writing & Copywriting
Module 2	Visuals & Multimedia Creation	Text-to-Image Generation & DesignAI-Powered Video Creation & EditingVoice Synthesis & Audio AIBuilding AI Avatars & Virtual Presenters
Module 3	Data, Code & Productivity	AI for Data Analysis & VisualizationCoding & Debugging AssistanceProductivity & Workflow Automation
Module 4	Professional Growth & Future Trends	AI in Digital Marketing & Social MediaResume Building & Interview Prep with AIFuture Trends & Ethics in AI

SQL

Category	Topic	Key Concepts Covered
Core Queries	SELECT & Data Filtering	SELECT statements, WHERE clause, and data sorting
Aggregations	Grouping & Summaries	GROUP BY, SUM, AVG, and other aggregate functions
Joins	Table Relationships	Inner Join, Left Join, and Right Join
Advanced Logic	Complex Queries	Subqueries, CTEs, and Window Functions

Power BI

Module	Topic	Key Concepts Covered
1	Introduction & Data Analyst Foundation	Role of Data Analyst, Data Lifecycle, Business Understanding
2	Data Preparation & Power Query Mastery	Data cleaning, transformation, ETL, Power Query Editor
3	Data Modeling & DAX Essentials	Relationships, Measures, Calculated Columns, DAX Functions
4	Data Visualization & Advanced Analytics	Interactive dashboards, KPIs, Advanced charts, Insights
5	Management, Security & Microsoft Fabric	Workspace management, Data security, Governance, Microsoft Fabric overview

Machine Learning

Module	Topic	Key Concepts Covered
1	Introduction to Machine Learning	ML overview, real-world applications
2	Types of Learning	Supervised, Unsupervised, Reinforcement Learning
3	Model Training Basics	Train & Test Split, Validation Split
4	Model Evaluation Fundamentals	Performance metrics, Overfitting & Underfitting
5	Linear Regression	Model concept, use cases
6	Linear Regression Assumptions	Linearity, Independence, Homoscedasticity, Normality
7	Regression Evaluation Metrics	R-square, Adjusted R-square
8	Scikit-learn Introduction	ML workflow, APIs, pipelines
9	Logistic Regression	Training methodology, classification logic
10	Classification Metrics	Precision, Recall, ROC Curve, F-score
11	Decision Tree	Tree structure, splitting criteria
12	Model Validation Concepts	Cross-validation, Bias vs Variance
13	Ensemble Learning	Ensemble approach overview
14	Bagging & Boosting	Bagging, Boosting techniques
15	Random Forest	Algorithm working, advantages
16	Feature Importance	Variable importance analysis
17	XGBoost	Gradient boosting, high-performance ML
18	K-Nearest Neighbor (KNN)	Distance-based learning
19	Lazy Learners	Concept of lazy learning
20	Curse of Dimensionality	High-dimensional data challenges
21	KNN Limitations	Performance issues, scalability
22	Text Analytics	NLP fundamentals
23	Text Preprocessing	Tokenization, Chunking
24	Feature Extraction (Text)	Document Term Matrix (DTM)
25	Sentiment Analysis	Hands-on sentiment analysis
26	Hierarchical Clustering	Agglomerative & divisive methods
27	K-Means Clustering	Algorithm steps, use cases
28	Clustering Evaluation	Performance measurement
29	Principal Component Analysis (PCA)	Feature compression
30	Dimensionality Reduction	High-dimensional data reduction
31	Factor Analysis	Latent variable modeling
32	Time Series Forecasting	Time-based data analysis
33	Moving Average	Smoothing techniques
34	ARIMA Model	Autoregressive Integrated Moving Average

Deep Learning

Module	Topic	Key Concepts Covered
1	Basics of Neural Networks	Neurons, layers, activation functions
2	Types of Neural Networks	ANN, CNN, RNN
3	Cost Function	Loss functions, optimization objective
4	Gradient Descent	Optimization techniques, learning rate
5	Linear Algebra Basics	Vectors, matrices, dot products
6	Neural Network from Scratch	Vanilla NN implementation in Python
7	TensorFlow Basics	Tensors, computational graphs, workflow
8	Simple Neural Network (Hands-on)	Building NN using TensorFlow
9	Word Embeddings	Word representation techniques
10	Word2Vec Models	CBOW, Skip-gram
11	Word Relationships	Semantic similarity, vector arithmetic
12	Convolutional Neural Networks (CNN)	Convolution layers, filters
13	Pooling & Padding	Max Pooling, window padding
14	CNN for Image Classification	End-to-end image classification
15	Recurrent Neural Networks (RNN)	Sequential data modeling
16	LSTM Architecture	Long Short-Term Memory units
17	Character-level RNN	Story writer implementation
18	Sentiment Analysis (Hands-on)	Text classification using DL
19	Sequence-to-Sequence Models	Seq2Seq architecture
20	Encoder–Decoder Framework	Attention-ready encoder-decoder models
21	Generative Adversarial Networks (GAN)	Generator vs Discriminator
22	Generative Models using GAN	Image/text generation
23	Semi-Supervised Learning with GAN	Learning with limited labels

Generative AI

Module	Topic	Key Concepts & Skills
Module 1: Foundations & Text Mastery	GenAI Introduction	Definition, core concepts, and key applications
	Model Architectures	Transformers and self-attention mechanisms
	Data Processing	Tokenization, embeddings, and vector representations
	LLM Behavior	Capabilities, hallucinations, and bias mitigation
Module 2: AI Workflows & Agents	LangChain Core	Chains, prompt templates, and output parsers
	LangGraph Logic	Graph-based orchestration and complex workflows
	AI Memory	Context window management and conversation history
	Agent Foundations	Agent definition, logic loops, and tool usage
	Agent Types	Reactive vs. proactive planning agents
Module 3: Retrieval & Knowledge Systems	Vector Databases	Storage and indexing of high-dimensional embeddings
	Similarity Search	Semantic search and context retrieval algorithms
	RAG Framework	Retrieval-Augmented Generation architecture
	RAG Implementation	Document Q&A and knowledge assistants
Module 4: Advanced Systems & Deployment	Multi-Agent Systems	Role delegation and collaborative task execution
	App Development	Full-stack GenAI apps using Streamlit and Python
	Model Integration	OpenAI, Gemini, and open-source model APIs