1. 1. What is Data Science?

   2. Data Science Lifecycle

   3. Types of Data (Structured vs Unstructured)

   4. Where Data Science is used

   5. Real industry workflow

1. Python installation

2. Variables & Data Types

3. Operators

4. Conditional Statements

5. Loops

6. Functions

7. Lambda Functions

8. File Handling

9. Modules & Packages

1. Lists

2. Tuples

3. Sets

4. Dictionaries

5. List, Dict comprehensions

1. Reading CSV, Excel, JSON

2. Working with OS files

3. Working with Dates & Time

4. Using Regex

1. Arrays

2. Indexing & Slicing

3. Broadcasting

4. Mathematical Operations

1. DataFrames & Series

2. Data cleaning basics

3. Merging, Joining, GroupBy

4. Handling missing values

5. Duplicates & Outliers

1. Matplotlib

2. Seaborn

3. Plotly

4. Pairplots, Heatmaps

5. Interactive Dashboards

1. Descriptive Statistics

2. Probability Basics

3. Distributions (Normal, Binomial)

4. Sampling & Central Limit Theorem

5. Correlation & Covariance

1. Hypothesis Testing

2. t-test, z-test

3. ANOVA

4. Chi-square test

1. Handling missing data

2. Encoding categorical variables

3. Scaling & Normalization

4. Outlier detection techniques

5. Feature Engineering fundamentals

1. Understanding the dataset

2. Creating EDA Reports

3. Pandas Profiling

4. SweetViz

5. Business Insights Generation

1. Introduction to ML

2. Types of ML

3. Train-test split

4. Evaluation metrics

1. Linear Regression

2. Regularization (L1, L2)

3. Polynomial Regression

4. Decision Tree Regression

5. Random Forest Regression

6. Gradient Boost Regression

1. Logistic Regression

2. KNN

3. Decision Trees

4. Random Forest

5. XGBoost

6. SVM

7. Naive Bayes

1. K-Means Clustering

2. Hierarchical Clustering

3. PCA

4. Dimensionality Reduction

5. Anomaly Detection

1. Cross-Validation

2. Hyperparameter Tuning

3. GridSearchCV

4. RandomizedSearchCV

5. Feature Importance

6. SHAP & LIME

1. Text Cleaning

2. Tokenization

3. Lemmatization & Stemming

4. Stopwords removal

5. TF-IDF

6. Bag of Words

1. Word2Vec

2. GloVe

3. Text Classification

4. Sentiment Analysis

5. Topic Modeling

6. SpaCy basics

1. Neural Networks

2. Activations

3. Forward + Backprop

4. Loss functions

ANN Model building

1. Convolution Layers

2. Pooling Layers

3. Image Classification

4. Transfer Learning (VGG, ResNet)

1. Recurrent Neural Networks

2. LSTM Basics

3. Sequence Prediction

1. Prompt Engineering

2. Understanding LLMs

3. Using OpenAI/HuggingFace

4. RAG (Retrieval Augmented Generation)

5. Building Chatbots

6. AI Workflows

1. Flask model deployment

2. FastAPI

3. Streamlit App

4. Git & GitHub

5. Cloud Deployment (Render/Railway)

1. SQL Basics

2. GitHub

3. Google Colab

4. VSCode

5. Jupyter Notebooks

6. Bash commands

1. Resume Building

2. GitHub Portfolio Setup

3. LinkedIn Optimization

4. Interview Q&A

5. Mock Interviews

1. Requirement Gathering

2. Working on Real Dataset

3. Weekly Reporting

4. Final Capstone Project

5. Presentation & Review

Internship Certificate