Programming Fundamentals: (40 Hrs.)

Python Programming

• Python basics (variables, data types, operators)

• Control structures (if/else, loops)

• Functions and lambda functions

• Object-Oriented Programming (OOP)

o Classes and objects

o Inheritance and polymorphism

o Encapsulation and abstraction

• Error handling and exceptions

• File I/O operations

• Regular expressions

• Modules and packages

• Virtual environments

• Python best practices

Essential Python Libraries

• NumPy (numerical computing)

• Pandas (data manipulation)

• Matplotlib (visualization)

• Seaborn (statistical visualization)

• SciPy (scientific computing)

Mathematics for Data Science: (16 Hrs.)

Linear Algebra

• Vectors and matrices

• Matrix operations

• Eigenvalues and eigenvectors

• SVD and matrix decomposition

• Linear transformations

• Applications in ML

Calculus

• Derivatives and partial derivatives

• Chain rule

• Gradient and Hessian

• Optimization (gradient descent)

• Integrals 

• Multivariable calculus

Statistics & Probability

• Descriptive statistics

• Probability theory

• Probability distributions

• Hypothesis testing

• Confidence intervals

• Correlation and regression

• Bayesian statistics

• A/B testing

Web Scraping

• BeautifulSoup

• Selenium (dynamic content)

Statistical Analysis

• Univariate analysis

• Bivariate analysis

• Multivariate analysis

• Distribution analysis

• Correlation analysis

• Statistical tests

Data Preprocessing: (16 Hrs.)

Data Wrangling

• Handling missing data

• Data type conversions

• Removing duplicates

• Outlier detection and treatment

• Data normalization and standardization

• Feature scaling

• Encoding categorical variables

o One-hot encoding

o Label encoding

o Target encoding

• Handling imbalanced data

• Text preprocessing

• Date/time manipulation

Feature Engineering

• Creating new features

• Feature extraction

• Feature selection methods

o Filter methods

o Wrapper methods

• Polynomial features

• Interaction features

• Binning and discretization

Data Visualization

• Matplotlib advanced

• Seaborn advanced

• Plotly (interactive visualizations)

• Bokeh

• Chart types and when to use them

• Dashboard creation

• Storytelling with data

Data Collection & Management: (16 Hrs.)

SQL & Databases

• SQL fundamentals

• Joins and subqueries

• Aggregations and window functions

• Database design

• Normalization

• Indexes and optimization

• Data pipelines

• Data warehousing concepts

Machine Learning: (20 Hrs.)

Supervised Learning

Regression

• Linear regression

• Polynomial regression

• Ridge regression (L2)

• Lasso regression (L1)

• Elastic Net

• Decision tree regression

• Random forest regression

• Gradient boosting regression

• XGBoost, LightGBM, CatBoost

• Support Vector Regression (SVR)

Classification

• Logistic regression

• K-Nearest Neighbors (KNN)

• Naive Bayes

• Decision trees

• Random forests

• Support Vector Machines (SVM)

• Gradient boosting classifiers

• XGBoost, LightGBM, CatBoost

• Neural networks for classification

• Ensemble methods

o Bagging

o Boosting

o Stacking

Unsupervised Learning

Clustering

• K-Means

• Hierarchical clustering

• DBSCAN

Dimensionality Reduction

• Principal Component Analysis (PCA)

• Linear Discriminant Analysis (LDA)

Model Evaluation & Selection

• Train-test split

• Cross-validation (k-fold, stratified)

• Evaluation metrics:

o Regression: MSE, RMSE, MAE, R², Adjusted R²

o Classification: Accuracy, Precision, Recall, F1-score, ROC-AUC, Confusion Matrix

• Bias-variance tradeoff

• Overfitting and underfitting

• Regularization techniques

• Hyperparameter tuning

o Grid search

o Random search

o Bayesian optimization

• Feature importance

• Model interpretation (SHAP, LIME)

Deep Learning: (20 Hrs.)

Neural Networks Fundamentals

• Perceptrons

• Activation functions

• Feedforward networks

• Backpropagation

• Loss functions

• Optimizers (SGD, Adam, RMSprop)

• Batch normalization

• Dropout and regularization

Deep Learning Frameworks

• TensorFlow

• Keras

• PyTorch

• Model building and training

• Callbacks and checkpoints

Convolutional Neural Networks (CNNs)

• Convolution layers

• Pooling layers

• CNN architectures

o LeNet

o AlexNet

o VGG

o ResNet

o Inception

• Transfer learning

• Image classification

• Object detection (YOLO, R-CNN basics)

• Image segmentation

Natural Language Processing (NLP)

• Text preprocessing

• Tokenization

• Word embeddings

o Word2Vec

o GloVe

o FastText

• TF-IDF

• Sentiment analysis

• Text classification

• Named Entity Recognition (NER)

• Part-of-Speech tagging

• Topic modeling (LDA)

• Transformers

o BERT

o GPT

o T5

• Hugging Face library

Advanced Deep Learning

• Generative Adversarial Networks (GANs)

• Autoencoders (VAE)

• Reinforcement Learning basics

• Graph Neural Networks (basics)

Time Series Analysis: (4 Hrs.)

Time Series Fundamentals

• Components (trend, seasonality, cyclic, irregular)

• Stationarity

• Autocorrelation (ACF)

• Partial autocorrelation (PACF)

Time Series Models

• Moving averages

• Exponential smoothing

• ARIMA models

• SARIMA (seasonal ARIMA)

• Prophet (Facebook)

• LSTM for time series

• Forecasting techniques

• Anomaly detection in time series

Model Deployment: (4 Hrs.)

• Streamlit

Specialized Topics: (4 Hrs.)

Computer Vision

• Image processing (OpenCV)

• Feature extraction

• Object detection

• Face recognition

• Image segmentation

• Video analysis

Business & Communication Skills: (10 Hrs.)

Data Storytelling

• Communicating insights

• Presentation skills

• Visualization best practices

• Executive dashboards

• Report writing

Domain Knowledge

• Business metrics and KPIs

• Industry-specific applications

• Problem framing

• Stakeholder management

Development Tools

• Jupyter Notebooks

• Google Colab

• VS Code

• PyCharm

• Git and GitHub

• Command line basics

Visualization Tools

• Excel

• Power BI