This module serves as the entry point into the world of data science by building a strong foundation in Python programming and data handling. Learners will become proficient in writing clean Python code, manipulating datasets using powerful libraries like Pandas and NumPy, and conducting basic exploratory data analysis. The module emphasizes practical, hands-on learning through real-world datasets and visualizations to prepare participants for deeper data science concepts in the coming months

• Understand Python syntax and data structures
• Perform data manipulation using Pandas and NumPy
• Conduct basic statistical analysis and data visualization
• Python basics: variables, loops, functions
• Pandas & NumPy for data manipulation
• EDA techniques
• Basic statistics: mean, median, std. dev
• Visualization: Matplotlib, Seaborn

This module focuses on preparing and transforming raw data into clean, structured formats suitable for analysis. Learners will master essential data wrangling techniques such as handling missing values, dealing with outliers, encoding categorical variables, and working with time-series data. In addition, the module emphasizes creating interactive and insightful visualizations using advanced tools like Plotly and Dash, enabling participants to communicate data-driven insights effectively

• Prepare datasets for analysis and visualization
• Build insightful dashboards for business understanding
• Data cleaning: null values, outliers, duplicates
• Encoding techniques: Label, One-Hot
• Working with time-series and categorical data
• Interactive visualizations: Plotly, Dash

This module dives deeper into advanced machine learning techniques and the art of feature engineering to enhance model accuracy and efficiency. Learners will explore powerful ensemble algorithms such as Random Forest, XGBoost, and LightGBM. The module also covers key preprocessing techniques like feature scaling, selection, and dimensionality reduction with PCA. Emphasis is placed on hyperparameter tuning using methods like GridSearchCV and RandomizedSearchCV to optimize model performance for real-world applications.

• Use ensemble techniques and improve model performance
• Understand unsupervised methods and dimensionality reduction
• Random Forest, Gradient Boosting (XGBoost, LightGBM)
• Feature scaling, feature selection
• Hyperparameter tuning (GridSearchCV, RandomSearchCV)
• PCA, Clustering (K-Means)

This module introduces learners to the fundamentals of deep learning and natural language processing (NLP), two cutting-edge domains in data science. Participants will explore the architecture and training of neural networks, including feedforward networks and convolutional neural networks (CNNs) for image data. On the NLP side, the module covers essential text preprocessing, tokenization, sentiment analysis, word embeddings, and an introduction to LSTM networks. Hands-on projects using TensorFlow, Keras, NLTK, and SpaCy provide practical experience in solving real-world problems with deep learning and language models.

• Understand deep learning and natural language processing basics
• Basics of neural networks (Perceptron, Feedforward, Backprop)
• CNNs for image classification
• Text preprocessing, tokenization
• NLP tasks: Sentiment Analysis, Word Embeddings, Intro to LSTM

In this final module, learners will apply everything they’ve learned to build and deploy a complete end-to-end data science solution. From identifying a real-world business problem and selecting relevant datasets to developing, optimizing, and evaluating predictive models, participants will gain hands-on experience delivering data-driven solutions.

• Deliver an end-to-end data science solution
• Deploy and present your model professionally
• Model development, evaluation, and optimization
• Business problem identification & dataset selection
• App deployment (Streamlit/Flask)
• Final presentation & report