Your Instructor
Course Highlights
Everything you need to go from zero to job-ready data scientist
Python to Advanced ML
From print() to production models
SQL & BigQuery
Window functions, CTEs & joins
Tableau & Excel
BI dashboards & visual analytics
Deep Learning & NLP
CNNs, RNNs, LSTMs, Transformers
Generative & Agentic AI
GANs, VAEs, LLMs, RAG, AI Agents
MLOps & Deployment
Docker, FastAPI, AWS/GCP CI/CD
8 Business Case Studies
Real problems across industries
365-Day Placement
Resume prep, mock interviews & drives
Complete Course Curriculum
Deep-detailed topics across all 12 modules — from absolute beginner Python to cutting-edge Agentic AI systems, authored by Y. Sri Ram PhD
Introduction to Programming — Python Foundations
Python setup · Data Types · Operators · Control Flow · Loops · Functions · Lists · Strings
Introduction to Python
History & features of Python
Installing Python & setting up environment
Writing your first Python program (print)
Comments in Python (#)
Basic syntax & indentation
Running Python scripts
Data Types
Primitive types: int, float, bool, str
Type casting: int(), float(), str()
type() function & NoneType
Variables & assignment
Input from user: input()
Operators
Arithmetic: +, -, *, /, //, %, **
Comparison: ==, !=, >, <, >=, <=
Logical: and, or, not
Assignment: =, +=, -=, etc.
Bitwise operators & precedence
Control Statements & Maths
if, else, elif — nested conditions
Boolean logic & indentation blocks
Basic decision-making programs
math module: sqrt(), pow(), ceil(), floor()
Modulo, absolute value, order of operations
Loops — While & For
while loop syntax, infinite loops
break and continue
Digit sum, factorials using loops
for loop with range()
Nested for loops, looping in reverse
for vs while comparisons
Functions 1 & 2
Defining functions with def
Parameters, arguments, return values
Scope: local vs global variables
Positional vs keyword arguments
Default params, *args, **kwargs
Recursion basics & modular programming
Lists — 1D, Slicing & 2D
Creating & accessing lists
append(), insert(), pop(), remove()
List slicing: list[start:stop:step]
Negative indexing & reversing
2D lists (nested lists), double indexing
Row/column ops, transpose
Strings 1 & 2
String declaration, indexing & slicing
lower(), upper(), strip(), find(), replace()
Immutability of strings
f-strings, format(), % formatting
split(), join(), string comparison
Palindrome, anagram, frequency count
Tableau, Excel & Data Analytics Python Libraries
NumPy · Pandas · Matplotlib · Seaborn · Tableau · Excel
NumPy (Numerical Python)
Introduction to NumPy & arrays
np.array(), arange(), linspace()
Array indexing & slicing
Element-wise addition & multiplication
reshape(), sum(), mean(), max(), min()
Broadcasting & basic statistical functions
Pandas (Data Analysis Library)
Series & DataFrames
pd.Series(), pd.DataFrame(), read_csv()
Indexing & slicing
head(), tail(), info(), describe()
Handling missing data: isnull(), dropna(), fillna()
groupby(), agg() — grouping & aggregation
Sorting & merging DataFrames
Matplotlib
Introduction to pyplot module
Plotting line charts: plot()
Titles, labels, legends
Customizing axes & ticks
Bar charts & pie charts
Saving figures: savefig(); Subplots; Scatter plots
Seaborn
Introduction to Seaborn
sns.lineplot(), barplot(), histplot(), boxplot()
Heatmaps
Pairplots & correlation plots
Styling & themes
Categorical plots: catplot(), countplot()
SQL — Database Analytics & BigQuery
8 SQL lessons · Extraction · Filtering · Subqueries · Joins · Window Functions · CTEs · Date & Time
Intro to Databases & BigQuery Setup
SQL-01: Extracting Data Using SQL
SQL-02: Functions, Filtering & Subqueries
SQL-03: Joins
SQL-04: Advanced Joins
SQL-05: Window Functions
SQL-06: Date & Time Functions & CTEs
SQL-07: Window Functions — Part 2
SQL-08: Date & Time and CTE — Advanced
Probability & Statistics for Data Science
Probability · Bayes · Descriptive Stats · Distributions · Hypothesis Testing · Feature Engineering · Business Case Study 1
Probability — Basic Definitions
Sample space, events
Types of events: mutually exclusive, exhaustive
Classical, empirical & axiomatic probability
Conditional Probability & Bayes' Theorem
Definition & formula: P(A|B)
Independent vs dependent events
Bayes' rule derivation
Prior, likelihood, posterior
Real-world apps: medical diagnosis, spam filtering
Descriptive Statistics & Distributions
Mean, median, mode
Range, variance, standard deviation
Outliers & skewness
Boxplots & histograms
Gaussian distribution & Z-scores
Empirical rule (68-95-99.7)
Central Limit Theorem (CLT)
Confidence intervals & margin of error
Combinatorics: permutations, combinations, nCr, nPr
Binomial & Geometric distributions
Hypothesis Testing
Null vs alternative hypothesis
Type I & II errors; p-values & significance
Z-test: one-sample & two-sample
T-test: one-sample, two-sample, paired
Chi-squared test: goodness-of-fit, independence
ANOVA: one-way, F-statistic
Correlation test: Pearson & Spearman
Feature Engineering (Basics)
Handling missing data (imputation techniques)
Encoding categorical variables: label, one-hot
Scaling & normalization
Outlier detection & treatment
Feature transformation: log, sqrt
Business Case Study-1: Probability & Stats
Maths for ML — Linear Algebra & Optimization
Vectors · Matrices · Gradient Descent · PCA · Linear/Logistic Regression · Classification Metrics
Linear Algebra — The ML Context
Vectors, matrices & operations
Matrix multiplication basics
Role of linear algebra in ML models
Dot products & geometric meaning
Hyperplanes in classification (SVMs)
Gradient Descent & PCA
Cost/loss functions
Gradient computation
Iterative optimization process
Learning rate intuition
PCA: dimensionality reduction concept
Variance & eigenvectors; visualization 2D/3D
Intro to ML & Regression
Supervised vs Unsupervised learning
Common ML applications & terminologies
Simple & multiple linear regression
Cost function (MSE), gradient descent
Underfitting & overfitting basics
Polynomial regression intuition
Bias vs Variance tradeoff
L1 (Lasso) & L2 (Ridge) regularization
Logistic Regression & Classification Metrics
Sigmoid function
Binary classification
Cost function for classification
Confusion matrix
Accuracy, precision, recall, F1-score
ROC curve & AUC (intro)
ML — Supervised Algorithms
k-NN · Decision Trees 1&2 · Random Forest · Boosting 1&2 (AdaBoost, GBM, XGBoost, LightGBM) · Business Case Study 2
ML: k Nearest Neighbours
Distance metrics: Euclidean, Manhattan, Minkowski
Weighted vs Unweighted k-NN
Choosing optimal k using cross-validation
Curse of Dimensionality
k-NN for classification vs regression
Lazy learning & prediction-time complexity
ML: Decision Trees 1 & 2
Structure of a Decision Tree
Splitting criteria: Gini Impurity, Entropy (Information Gain)
Binary vs Multiway splits
Handling numerical vs categorical data
Pre-pruning: max depth, min samples
Post-pruning (cost complexity pruning)
Tree visualization & interpretability
Feature importance from trees
ML: Bagging, Random Forest & Boosting
Bootstrap Aggregation (Bagging)
Random Forest: bagging + feature sampling
Out-of-Bag (OOB) error estimation
Feature importance: Mean Decrease in Impurity/Accuracy
Boosting: sequential weak learners
AdaBoost: weight updates, emphasis on hard examples
GBM: loss-based optimization
Regularization in boosting: shrinkage, subsampling
Early stopping & learning rate tuning
XGBoost / LightGBM advanced libraries
Business Case Study-2: Supervised Algorithms
Unsupervised Learning
KMeans · KMeans++ · Hierarchical Clustering · GMM · Anomaly Detection · PCA / T-SNE / UMAP · Business Case Study 3
KMeans & KMeans++
Supervised vs Unsupervised Learning
Clustering: What & Why
KMeans algorithm: assignment-update loop
Limitations of KMeans (initialization, # clusters)
Cluster evaluation: Inertia, Elbow Method, Silhouette Score
KMeans++: distance-based smart seeding
Effect on convergence & stability
Hierarchical Clustering & GMM
Agglomerative vs Divisive approaches
Linkage: Single, Complete, Average
Dendrogram construction & interpretation
Cutting the tree for cluster selection
Gaussian Mixture Models: soft clustering with probabilities
Multivariate Gaussian Distribution
Expectation-Maximization (EM) algorithm
GMM vs KMeans: flexibility & shape handling
Anomaly Detection & High-Dimensional Visualization
Types of Anomalies: Point, Contextual, Collective
Z-score, IQR & distance-based detection
Isolation Forest
One-Class SVM
Practical examples: fraud, system monitoring
PCA: linear dimensionality reduction, Scree Plot
T-SNE: non-linear, local structure, perplexity
UMAP: non-linear, faster & more scalable than T-SNE
Business Case Study-3: Unsupervised Algorithms
MLOps — Model Deployment & CI/CD
MLflow · Docker · FastAPI/Flask · AWS/GCP · GitHub Actions · Monitoring · Business Case Study 4
MLOps Overview
What is MLOps?
Lifecycle: Model Development → Deployment → Monitoring
Challenges in Scaling ML
Tools: MLflow, Kubeflow, Airflow
Model Deployment
Packaging models (Pickle)
REST APIs with FastAPI / Flask
Containerization (Docker Basics)
Deployment targets: Cloud (AWS/GCP)
CI/CD in ML
CI: Automating Tests & Code Quality
CD: Automated Model Deployment
GitHub Actions for ML
Monitoring & Retraining Pipelines
Business Case Study-4: TS & RecSys
Neural Networks (NN) — Deep Learning
Feedforward NN · Backpropagation · CNNs · RNNs · LSTMs · Business Case Study 5
Introduction to Neural Networks
What is a Neural Network?
Basic Components: Neurons, Layers, Weights
Feedforward NN: Input, Hidden, Output Layers
Forward Pass
How error is calculated (Backpropagation)
Gradient descent for weight update
CNN: purpose in image tasks
CNN: convolutional layers & pooling
RNN: use cases for sequential data
Simple RNN vs Long Short-Term Memory (LSTM)
Business Case Study-5: Neural Networks
Computer Vision (CV)
Image Processing · CNN Architecture · Object Detection · Image Classification · Segmentation · Feature Extraction · Business Case Study 6
Image Representation: Pixels, RGB
Filters & Edge Detection
CNN architecture for images
Convolution, Pooling, Fully Connected Layers
Detecting objects within an image
Bounding boxes & labels
Classifying images into categories
Basic CNN for classification
Dividing images into meaningful parts
Semantic vs Instance Segmentation
Feature Extraction: edges, corners
Business Case Study-6: Computer Vision
Natural Language Processing (NLP)
Text Preprocessing · TF-IDF · Word2Vec · Sequence Models · Transformer (BERT/GPT) · Sentiment Analysis · Business Case Study 7
Tokenization, Stopwords, Lemmatization
Text normalization
Bag of Words & TF-IDF
Representing text as vectors
Word frequency & importance
Word Embeddings (Word2Vec)
Representing words as vectors
Concept of word similarity
Sequence Models — RNNs: understanding sequences
LSTM: handling long-term dependencies
Transformer Models (BERT, GPT) — architecture basics
Pre-trained models for NLP tasks
Text Classification & Sentiment Analysis
Business Case Study-7: NLP
Generative AI & Agentic AI
GANs · VAEs · Text-to-Image · LLMs · RAG · Fine-Tuning · LoRA · QLoRA · Agentic AI · AI Agents Architecture · Business Case Study 8
Introduction to Generative AI
What is Generative AI?
Applications: Image, Text, Music
Generative Models Overview: GANs, VAEs, etc.
GANs, VAEs & Text-to-Image
GANs Overview
Generator & Discriminator
Adversarial training
VAEs: latent space, sampling & reconstruction
Text-to-Image mapping
Model examples: DALL-E, CLIP
Applications: image generation, text generation
⭐ Extra Advanced Topics (Bonus)
1. Introduction to Generative AI (Deep Dive)
2. Large Language Models (LLMs)
3. Embeddings & Vector Databases
4. Retrieval-Augmented Generation (RAG)
5. Fine-Tuning of LLMs
6. LoRA (Low-Rank Adaptation)
7. QLoRA (Quantized LoRA)
8. Agentic AI Fundamentals
9. AI Agents Architecture
Business Case Study-8: GenAI
Tools & Technologies You'll Master
Industry-standard stack used throughout the program
Python
SQL / BigQuery
NumPy
Pandas
Matplotlib
Seaborn
Tableau
Excel
Scikit-learn
TensorFlow
PyTorch
OpenAI / GPT
LangChain
FAISS / Pinecone
Docker
AWS / GCP
GitHub Actions
FastAPI / Flask
MLflow
Jupyter / Colab