Data Science & Statistics

Covers the full data science workflow — statistical foundations, data wrangling, exploratory analysis, machine learning, and visualization using Python and R. Taught at PCU and Great Learning (2023–2025).

Instructor: Tushar R. Mahore

Term: AY 2024–25

Location: Pimpri Chinchwad University / Great Learning (Online)

Time: As per institute schedule

Course Overview

This course equips students with end-to-end data science skills — from raw data to actionable insights. Emphasis is on building intuition alongside practical implementation skills.

Students completing this course will be able to:

  • Apply statistical reasoning to real-world datasets
  • Clean, transform, and explore data using Python (Pandas, NumPy)
  • Build and evaluate machine learning models with Scikit-learn
  • Create compelling visualizations using Matplotlib, Seaborn, and Tableau
  • Present data-driven findings clearly and accurately

Tools & Environment

  • Languages: Python, R
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
  • Tools: Jupyter Notebooks, Anaconda, Power BI, Tableau, BigQuery

Prerequisites

  • Basic programming knowledge (Python preferred)
  • High school mathematics (algebra, basic statistics)

Taught At

  • Pimpri Chinchwad University (PCU), Pune — AY 2024–25
  • Great Learning (Online) — March 2023 – August 2025
  • Sipna College of Engineering & Technology (SCOET), Amravati — AY 2022–24

Contact

Email: mahoretushar@gmail.com · Office hours by appointment.

Schedule

Week Date Topic Materials
1 Module 1 Introduction to Data Science

Data science lifecycle, roles and tools, Python and R ecosystem, setting up the environment (Anaconda, Jupyter).
2 Module 2 Statistics for Data Science

Descriptive statistics, probability distributions, hypothesis testing, confidence intervals, correlation and regression fundamentals.
3 Module 3 Data Wrangling with Pandas

DataFrames, handling missing values, merging and reshaping datasets, groupby operations, time series basics.
4 Module 4 Exploratory Data Analysis (EDA)

Visual EDA with Matplotlib and Seaborn, outlier detection, feature distributions, correlation heatmaps, pair plots.
5 Module 5 Machine Learning Fundamentals

Supervised vs. unsupervised learning, train/test split, cross-validation, bias-variance tradeoff, model evaluation metrics.
6 Module 6 Supervised Learning Algorithms

Linear and logistic regression, decision trees, random forests, SVM, k-NN — implementation with Scikit-learn.
7 Module 7 Unsupervised Learning & Dimensionality Reduction

K-means clustering, hierarchical clustering, DBSCAN, PCA, t-SNE.
8 Module 8 Data Visualization & Storytelling

Principles of effective visualization, dashboards with Power BI / Tableau, communicating results to non-technical audiences.