Course Overview

This course provides a comprehensive introduction to Data Science using Python and Power BI, equipping learners with the essential skills to analyze, visualize, and interpret data effectively.

Students will start by learning Python programming fundamentals, data manipulation with Pandas and NumPy, and data visualization using Matplotlib and Seaborn. The course then covers exploratory data analysis (EDA), statistical methods, and an introduction to machine learning techniques such as regression and classification.

In the second part, learners will explore Power BI for creating interactive dashboards and business intelligence reports. They’ll learn how to connect data sources, perform transformations with Power Query, and design insightful visualizations to support data-driven decisions.

By the end of the course, participants will be able to:

  • Clean, analyze, and visualize data using Python.
  • Build predictive models using machine learning algorithms.
  • Create professional dashboards and reports in Power BI.
  • Combine Python analytics with Power BI insights for end-to-end data storytelling.

Ideal for: Students, analysts, and professionals seeking to enhance their data analysis and visualization skills for real-world applications.

Curriculum

  • Data Science with Python & Power BI

    • Total Duration: 12 Weeks (≈ 288 Hours, 6 days/week)

      Goal: From Basics → Advanced (Python, ML, Power BI)

      Outcome: Job-ready Data Analyst + Data Scientist skills with capstone projects.

      Phase 1: Python Foundations (Week 1–2 | Day 1–12)

      • Intro to Data Science & Jupyter
      • Python basics: variables, datatypes, operators
      • Strings, functions, control structures
      • Data structures (Lists, Tuples, Sets, Dicts)
      • File handling (CSV, JSON, Excel), Modules
      • Error handling, OOP basics
      • Mini Project – Student Marks Analyzer

      Phase 2: Data Handling & Visualization (Week 3–4 | Day 13–24)

      • NumPy arrays & operations
      • Pandas (Series, DataFrames, cleaning, aggregation)
      • Data transformation & wrangling
      • Visualization: Matplotlib, Seaborn, Plotly
      • Exploratory Data Analysis workflow
      • Mini Project – EDA on Finance Dataset

      Phase 3: Statistics & Machine Learning (Week 5–6 | Day 25–36)

      • Statistics basics: mean, variance, probability
      • Hypothesis testing, correlation
      • ML intro: regression, classification, clustering
      • Linear/Logistic Regression, KNN, Decision Trees, Random Forests
      • Model evaluation: accuracy, precision, recall, F1
      • Mini Project – Customer Churn Prediction

      Phase 4: Advanced ML & AI Basics (Week 7–8 | Day 37–48)

      • Feature engineering, scaling, cross-validation
      • Ensemble methods: Bagging, Boosting, XGBoost
      • Dimensionality reduction: PCA
      • Intro to Neural Networks, TensorFlow/Keras basics
      • Simple Neural Net in Keras, regularization
      • NLP basics & Sentiment Analysis
      • Mini Project – Sentiment Analysis

      Phase 5: Power BI for Business Analytics (Week 9–10 | Day 49–60)

      • Intro to BI & Power BI Desktop
      • Data import (Excel, SQL, APIs), Power Query Editor
      • Data modeling & relationships
      • DAX basics & advanced functions
      • Building visuals & interactive dashboards
      • Publishing & sharing reports
      • Mini Project – Finance Dashboard

      Phase 6: Capstone Projects & Industry Prep (Week 11–12 | Day 61–72)

      • End-to-end project workflow
      • Capstone Project – dataset selection & planning
      • Data cleaning, EDA, feature engineering
      • Model building & evaluation
      • Integrating Python results with Power BI
      • Dashboard finalization & storytelling
      • Resume prep, mock interview, final presentations