Detailed Syllabus & Weekly Breakdown

Semester 1: Foundations of Python, SQL & Stats (Weeks 1–12)

• Module 1: Python Analytics & SQL (Weeks 1–6)
  • Week 1-2: Advanced Python for Data Science (NumPy, SciPy)
  • Week 3-4: Data Manipulation & Wrangling (Pandas)
  • Week 5-6: SQL for Data Science (Joins, Window Functions, Aggregations)
• Module 2: Statistics & Exploratory Data Analysis (Weeks 7–12)
  • Week 7-8: Descriptive & Inferential Statistics, Probability Theory
  • Week 9-10: Hypothesis Testing (A/B Testing, ANOVA, Chi-Square)
  • Week 11-12: Exploratory Data Analysis (EDA) Techniques & Feature Engineering

Semester 2: Machine Learning & Visualization (Weeks 13–24)

• Module 3: Machine Learning for Data Science (Weeks 13–18)
  • Week 13-14: Scikit-Learn: Linear & Logistic Regression, SVMs
  • Week 15-16: Decision Trees, Random Forests, Gradient Boosting
  • Week 17-18: Clustering (K-Means, Hierarchical) & Dimensionality Reduction (PCA)
• Module 4: Data Visualization & Storytelling (Weeks 19–24)
  • Week 19-20: Python Visualization Libraries (Matplotlib, Seaborn, Plotly)
  • Week 21-22: Building Dashboards using Tableau or Power BI
  • Week 23-24: Effective Data Storytelling & Business Presentations

Semester 3: Big Data, Cloud Analytics & Capstone (Weeks 25–36)

• Module 5: Big Data & Cloud Engineering (Weeks 25–30)
  • Week 25-26: Introduction to Big Data (Hadoop, MapReduce)
  • Week 27-28: Distributed Processing with Apache Spark
  • Week 29-30: Cloud Analytics platforms (AWS SageMaker / GCP BigQuery)
• Module 6: Enterprise Capstone (Weeks 31–36)
  • Week 31-32: System Design & Architecture Planning for Capstone
  • Week 33-34: Core Development & Integration
  • Week 35-36: Testing, Live Deployment & Final Viva

Learning Outcomes

• Extract, manipulate, and analyze massive datasets using Python and advanced SQL queries.
• Develop highly accurate predictive models leveraging supervised and unsupervised Machine Learning techniques.
• Construct compelling visual narratives and enterprise-grade dashboards using Tableau/Power BI.
• Design distributed data processing systems using Big Data tools like Apache Spark.

Assessment Weightage

Assessment Type	Weightage
Master Capstone Project & Live Deployment	30%
Hands-on Labs & Mini-Projects	30%
Module & Mid-Term Assessments	20%
Final Viva & Architecture Review	20%

The "WhiteCollar" Academic Rationale

This 9-month program takes you beyond basic statistics. By progressing through Python/SQL Foundations → Machine Learning → Big Data → Storytelling, WhiteCollar Academy ensures that graduates are not just parsing datasets, but uncovering actionable business intelligence ready for enterprise scale.