Data Science

— Advanced Level Roadmap

Module 1: Unsupervised Learning & Dimensionality Reduction

  • Clustering Techniques: K-Means, Hierarchical Clustering, DBSCAN

  • Dimensionality Reduction: PCA, t-SNE, UMAP

  • Evaluating Clusters (Silhouette Score, Davies–Bouldin Index)

  • Feature Engineering for Clustering

Practice: Segment retail customers based on purchasing behavior.

Module 2: Deep Learning Basics

  • Understanding Neural Networks (Perceptrons, Layers, Activations)

  • TensorFlow and Keras Foundations

  • Building Feedforward Neural Networks

  • Overfitting & Regularization Techniques (Dropout, Batch Normalization)

Practice: Create a neural network to predict handwritten digits using MNIST dataset.

Module 3: Natural Language Processing (NLP)

  • Text Preprocessing (Tokenization, Stopword Removal, Stemming/Lemmatization)

  • Feature Extraction (Bag-of-Words, TF-IDF, Word Embeddings)

  • Sentiment Analysis

  • Introduction to Transformers & Pre-trained Models (BERT, GPT Basics)

Practice: Build a sentiment analysis tool for product reviews.

Module 4: Time Series Analysis & Forecasting

  • Time Series Fundamentals (Stationarity, Seasonality, Trends)

  • ARIMA & SARIMA Models

  • Introduction to Prophet for Forecasting

  • LSTM for Sequential Data

  • Evaluation Metrics (MAPE, RMSE)

Practice: Forecast monthly sales for a retail company.

Module 5: Big Data Tools & Pipelines

  • Introduction to Big Data Ecosystem

  • Hadoop Basics (HDFS, MapReduce Concepts)

  • Apache Spark with PySpark (RDD, DataFrames, MLlib)

  • Data Pipelines & Streaming with Spark

  • Integrating Spark with Cloud (AWS, GCP, Azure)

Practice: Process and analyze a large dataset (e.g., clickstream or IoT logs) using PySpark.

Module 6: Model Deployment & Productionization

  • Deploying Models with Flask and FastAPI

  • Interactive Dashboards with Streamlit

  • Introduction to Docker for ML

  • Model Hosting on Cloud (AWS Sagemaker, Google Vertex AI, Azure ML)

  • CI/CD Basics for Data Science Pipelines

Practice: Deploy a trained ML model on a public URL with a user-friendly dashboard.

Module 7: Capstone Project — End-to-End Data Science Solution

Project Example: AI-Powered Business Insights Platform

  • Data Collection: Scrape and integrate multiple real-world datasets.

  • Data Engineering: Clean, preprocess, and store data in a database.

  • Modeling: Use clustering, deep learning, and time series forecasting models.

  • Visualization: Build an interactive Streamlit/Power BI dashboard.

  • Deployment: Host the project on the cloud for real-time access.

Deliverable: A production-ready solution showcasing advanced modeling, big data handling, and deployment skills.

Key Outcomes

By the end of this level, students will be able to:
✔ Implement advanced ML and DL models for complex datasets
✔ Apply NLP and Time Series techniques for real-world applications
✔ Handle big data pipelines using Spark and cloud tools
✔ Deploy AI models with user-friendly interfaces
✔ Build end-to-end, scalable AI solutions

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