Data Science Course in Bangalore with Placement Assistance

Introduction to Python Programming

• What is Python programming language?
• Why Python programming language required for Data Science?
• What is Anaconda? Installation of Anaconda
• Understanding Jupyter notebook & Basic commands
• Understanding Python Syntax
• Data types in python broadly discussed

Literals, Keywords and Data Types

• What is an Identifier? Rules for Identifier Naming
• Inbuilt Keywords, Variables and Data Types
• print() and input(), Python Operators
• Arithmetic, Assignment, Comparison, Logical, Identity operators

Conditional Statement

• if, if-else, if-elif-else, nested if, elif ladder

While Loops & Control Flow

• while loop syntax, Control Flow, Pattern Problems

Lists, Tuple & Set

• Lists: define, access, indexing, slicing, built-in methods
• Tuple: create, access, differences with list
• Set: create, built-in methods, set operations

Dictionary & Strings

• Dictionary: create, add, modify, retrieve values
• Strings: create, indexing, slicing, built-in methods
• Mutable vs Immutable Data Types

For Loop & Functions

• For loop, range(), enumerate(), For-else
• List & Dictionary Comprehension
• Functions: define, call, positional/keyword args
• Lambda, Map, Filter & Reduce

Modules, OOPS & File Handling

• Modules & Packages: Datetime, Random, math, os
• Classes, Objects, Constructor, Access modifiers
• Inheritance, Abstraction, Polymorphism, Encapsulation
• File Handling & Exception Handling

VS Code & Streamlit Deployment

• What is Streamlit, Components, Creating application
• VS Code, Setting Environment
• Backend with FastAPI, Client Server Architecture
• Integration of FastAPI with Streamlit

Introduction to EDA

• How is EDA different from python programming?
• EDA vs Python with case study
• Types of data (Numerical, Categorical)
• Types of Analysis (Nonvisual and visual, Univariate and Bivariate, Descriptive, Inferential & Probabilistic)

Descriptive Statistics

• What and why Statistics? Data and its Measures
• Measures of central tendency (Univariate Analysis)
• Measures of dispersion (Bi Variate)

Core Numpy Operations

• Generating Random Numbers, Indexing and Slicing
• Boolean Arrays, Updating, Insert, Append, Delete
• reshape, ravel, flatten, transpose
• Mathematical & Statistical Functions
• Vectorization and Advanced Numpy Functions

Introduction to Pandas

• Pandas Series & DataFrame
• Data Exploration and Understanding
• Loading data from csv and excel files
• Groupby, Pivot, Joins (Merge Operation)
• Handling Missing Values, String and Datetime Manipulations
• Advanced Data Transformations: apply(), map(), Window functions

Data Visualization

• Univariate analysis with Matplotlib/Seaborn
• Bivariate and Multivariate analysis
• Scatter plot, heat map, pairplot, boxplot, violin plot

Probability & Distributions

• Introduction to Probability, Conditional Probability
• Discrete: Bernoulli, Binomial, Poisson
• Continuous: Uniform, Normal, Exponential, Log Normal
• 68-95-99.7% Rule, QQ Plot

Inferential Statistics

• Population vs Sample, Sampling Techniques
• Central Limit Theorem, Confidence Interval
• Hypothesis Testing, p-value, Type I & II Errors
• chi-square test, ANOVA

Web Scraping & EDA Project

• Regular Expressions, Pattern Matching
• Requests Library, BeautifulSoup, HTML Parsing
• Data Cleaning & Preprocessing
• Project: Collect, clean, analyze real-time data

Introduction to SQL

• Data, Database, DBMS, RDBMS
• SQL vs MySQL, SQL vs NoSQL
• CRUD operations, Pandas vs SQL

Data Exploration and Filtering

• Client Server Architecture, Workbench
• SELECT, Data Exploration, Filtering
• LIKE, Regexp, Between operators

Clauses

• GROUP BY, HAVING, ORDER BY, CASE
• Order of execution

Multiple Tables & Joins

• Primary key, Foreign key, ER diagram
• Inner, Outer, Left, Right, Cross, Self Join
• UNION, UNION ALL, Subquery
• Temporary Tables, CTE, Window Functions

SQL Fundamentals & Advanced

• DDL (CREATE, ALTER, DROP, TRUNCATE)
• DML (Insert, Update, Delete)
• Views, Stored Procedure, Functions
• Transaction Control, ACID, Triggers

Project on MySQL

• Analyze normalized relational databases
• Write advanced SQL with CTEs, Views, Stored Procedures
• Solve domain-specific business problems

Introduction To Power BI

• What is Business Intelligence?
• Power BI Introduction, Quadrant report
• Comparison with other BI tools
• Power BI Desktop overview & workflow

Data Import And Visualizations

• Data import options, Import from Web
• Categorical data visualization, Trend Data viz

Power Queries

• Power Query Introduction, Data Transformation
• M Language briefing, Power BI Datatypes
• Filtering, Column & Row Transformations
• Combine Queries, Merge Queries

Power Pivot And DAX

• Intro to Data Modeling, Relationship and Cardinality
• Calculated Columns vs Measures
• DAX: logical, text, math, statistical, filter, time intelligence functions
• Creating a Date Dimension table

Login, Publish & RLS

• Power BI services, Dashboard creation
• Sharing your dashboard, RLS introduction
• Conditional Formatting, Drill Through, Drilldown

Project on Power BI

• Build data models and establish relationships
• Design interactive dashboards and reports
• Generate insights across multiple industry domains

ML vs DL vs AI

• AI vs ML vs DL
• Supervised vs Unsupervised Learning
• Classification Task, Regression Task

Data Preprocessing Pipelines

• Intro to Numerical & Categorical Data Preprocessing
• Nominal Encoding, Ordinal Encoding
• Introducing sklearn module

Text & Image Preprocessing

• Text Data: Tokenisation, Stop Words, Lemmatization, Stemming
• Bag of Words, TF-IDF, Spam-Ham Detection
• Image Data: RGB channels, Images as 3D numpy arrays

KNN, Naive Bayes, Decision Tree

• kNN for Classification & Regression
• Classification & Regression Evaluation Metrics
• Naive Bayes Derivation & Code Implementation
• Decision Tree: ID3, C4.5, Entropy, Gini Impurity

Linear & Logistic Regression

• Simple & Multiple Linear Regression
• Gradient Descent, Regularization
• Logistic Regression: Sigmoid, Decision Boundary

SVM & Ensemble Methods

• Support Vector Machines, Kernel Trick
• Bagging: Random Forest
• Boosting: ADABoost, GBDT, XGBoost

Unsupervised Learning & PCA

• K-Means, K-Means++, Hierarchical Clustering
• Customer Segmentation
• PCA, Dimensionality Reduction

Project on Machine Learning

• End-to-end ML pipelines
• MLflow for experiment tracking
• Deploy ML applications using Streamlit

Introduction to Deep Learning and ANN

• Biological Neuron vs Artificial Neuron
• Single Layer Perceptron Model
• Weights, Bias and Activation
• MLP / FCNN / ANN Model

Training Neural Network

• Forward pass with formulation
• Backward Pass & Backpropagation
• Classification & Regression Model Building using TensorFlow/Keras

Activation Functions & Optimizers

• Linear, Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax
• Gradient Descent, Mini Batch, Momentum
• Adam, RMS Prop, Adaptive Gradient

Overfitting & Regularization

• L1 and L2 Regularization
• Dropout Regularizer, Early Stopping
• Batch Normalization

Hyperparameter Tuning with Optuna

• Introduction to Optuna, Creating a Study
• Finding & Importance of Hyperparameters

Project on ANN

• Build supervised ANN models
• Optimize using hyperparameter tuning
• Deploy using Streamlit

Introduction to NLP

• Text Preprocessing: BOW, TF-IDF
• Word Embeddings: Word2Vec, CBOW, Skip-gram
• Text Classification Case Study

Sequence Modelling

• Intro to RNN, Training of RNN, Types of RNN
• LSTM, GRU, Limitations of RNN
• POS Tagging

Self Attention & Transformers

• Seq2Seq Architecture, Attention Mechanism
• Transformer Architecture, Positional Encoding
• Self and Multihead Attention
• GPT (Autoregressive) & BERT (Auto-encoding)

HuggingFace API

• HuggingFace Embeddings
• Sentiment Analysis, NER, POS, Q&A
• Text Generation, Summarization, Translation
• Image & Audio Classification

Evaluation Metrics

• BLEU, ROUGE, METEOR, Perplexity

Project on NLP

• Sentiment Analysis, Text Classification, Spam Detection
• Build scalable NLP solutions
• Deploy NLP applications using Streamlit

Introduction to Generative AI & LLMs

• What is GenAI? Large Language Models
• OpenAI / Gemini API / Groq API authentication
• Prompt Engineering: Temperature, top-p, System & User prompts

Introduction to LangChain

• Import Chat Models, LCEL Chain
• Prompt Template, Chat Prompt Template
• Output Parser: StrOutputParsers, Pydantic Parser
• Runnables: Passthrough, Parallel

Conversation Memory & Monitoring

• Adding memory to LangChain applications
• Context Window Optimization
• Observability Tools, Tracing Implementation

RAG Fundamentals

• Document loaders (PDF, web, CSV)
• Text splitters, chunking, Vector Database
• Keyword, Semantic and Hybrid Search
• Re-ranking, RAGAS evaluation, LLM-as-a-judge

Tool Calling & Agents

• ReAct loop, Build ReAct Agent
• Custom tools (search, wikipedia)
• MCP Client Server Architecture

LangGraph

• StateGraph: nodes, edges, Sequential, Parallel, Conditional Workflows
• Conversational Chatbot with Memory and Tool
• Streaming, Human In The Loop, LangGraph Deployment

Fine Tuning & Projects

• LoRA, QLoRA, PEFT, Bits and bytes
• Project: AI Chatbots, Document Q&A, RAG pipelines
• Project: Agentic AI for Automation, Research Assistance

Intro to Images & OpenCV

• How Images are formed and stored in machines
• Introduction To OpenCV: Read, Write and Save images
• Converting Color Spaces (RGB, BGR, HLS, HSV)
• Bitwise Operators, Drawing on images
• Edge detection, Blurring, Histograms
• Read videos, Capturing images with web camera

Convolutional Neural Networks

• Introduction to CNN, Why CNN over MLP
• Convolution on Color & Grayscale images
• Padding, Stride, Maxpooling Operations
• Image Classification: HandWritten Dataset, Face Mask Detection

CNN Architectures & Transfer Learning

• AlexNet, VGG16, Inception, ResNet, Skip Connections
• Plant Diseases Prediction using Transfer Learning
• CIFAR using Transfer Learning

Object Detection

• R-CNN, Fast R-CNN, Faster R-CNN
• YOLO Algorithm: How it works, Introduction to Roboflow
• Case Study: Helmet Detection using YOLO

Image Segmentation

• Introduction to Image Segmentation
• Case Study on Image Segmentation

Project on Computer Vision

• Object Detection, Face Recognition, Image Classification
• Integrate CV pipelines with live webcam/video streams
• Deploy using Streamlit