📊 Module 1: Introduction to Data Science & Data Analytics (Week 1–2)

• What is Data Science? Role in Modern IT Consultancy

• Understanding the Data Science Workflow: From Collection to Insight

• Core Concepts: Data Mining, Predictive Analytics, Visualization

• Toolkits Overview: Python, R, SQL, Jupyter, Anaconda

• The Data Science Ecosystem Explained

🐍 Module 2: Python for Data Science (Week 3–6)

• Python Programming Fundamentals for Data Science

• Essential Libraries: Pandas, NumPy, Matplotlib, Seaborn

• Data Structures & Functions in Python

• Data Cleaning and Preprocessing with Pandas

• Basic Data Exploration & Visualizations

🧹 Module 3: Data Wrangling & Cleaning (Week 7–10)

• Why Data Cleaning Matters

• Techniques to Handle: Missing Data, Duplicates, Outliers

• Data Normalization & Standardization

• Feature Engineering & Feature Selection

• Dealing with Structured & Unstructured Data

📈 Module 4: Statistics for Data Science (Week 11–14)

• Descriptive vs. Inferential Statistics

• Probability & Theorems: Bayes’, Distributions, Hypothesis Testing

• Understanding Correlation vs. Causation

• Sampling Techniques & A/B Testing Methods

• Performing Statistical Analysis with Python (SciPy, Statsmodels)

🤖 Module 5: Machine Learning Algorithms (Week 15–18)

• Intro to ML: Supervised vs. Unsupervised Learning

• Core Algorithms: Linear/Logistic Regression, KNN

• Advanced Models: Decision Trees, Random Forests, Gradient Boosting

• Clustering Methods: K-Means, DBSCAN

• Model Evaluation: Cross-Validation, ROC Curve, Precision-Recall

🧠 Module 6: Deep Learning & Neural Networks (Week 19–22)

• Foundations of Deep Learning

• Understanding Neural Networks & Backpropagation

• Libraries Overview: TensorFlow, Keras, PyTorch

• CNNs for Image Recognition

• RNNs for Time-Series & Sequence Data

⚙️ Module 7: Big Data & Data Engineering (Week 23–24)

• Intro to Big Data: Hadoop, Spark, NoSQL

• Working with Data Lakes & Distributed File Systems

• Designing ETL Pipelines & Data Engineering Flows

• Real-Time Data Processing with Kafka

• Cloud Data Platforms: AWS, Azure, GCP

📉 Module 8: Data Visualization & Reporting (Week 25–26)

• Best Practices in Data Visualization

• Visualization Tools: Matplotlib, Seaborn, Plotly, Tableau

• Building Dashboards: Dash, Streamlit

• Data Storytelling: Turning Data into Business Insights

• Final Project: End-to-End Data Science Workflow (Collection → Modeling → Reporting)

🎓 Capstone Project & Evaluation (Week 26)

• Execute a Real-World Data Science Project

  • Data Acquisition, Cleaning, and Preprocessing

  • Feature Engineering & Model Development

  • Evaluation, Visualization, and Insight Communication

• Final Presentation to Industry Panel & Peer Review

• Written Test + Hands-On Practical

• Feedback & Certification Award Ceremony