Module I

Introduction: What is Data Science? Big Data and Data Science – Datafication – Current landscape of perspectives – Skill sets needed; Matrices – Matrices to represent relations between data, and necessary linear algebraic operations on matrices -Approximately representing matrices by decompositions (SVD and PCA); Statistics: Descriptive Statistics: distributions and probability – Statistical Inference: Populations and samples – Statistical modeling – probability distributions – fitting a model – Hypothesis Testing.

Module II

Data pre-processing: Data cleaning – data integration – Data Reduction Data Transformation and Data Discretization. Evaluation of classification methods – Confusion matrix, Students T-tests and ROC curvesExploratory Data Analysis – Basic tools (plots, graphs and summary statistics) of EDA, Philosophy of EDA – The Data Science Process.

Module III

Basic Machine Learning Algorithms: Association Rule mining – Linear Regression- Logistic Regression – Classifiers – k-Nearest Neighbors (k-NN), k-means -Decision tree – Naive Bayes- Ensemble Methods – Random Forest. Feature Generation and Feature Selection – Feature Selection algorithms – Filters; Wrappers; Decision Trees; Random Forests.

Data Visualization: Basic principles, ideas and tools for data visualization.

Course Objectives

• To gain useful conclusions from large and diverse data sets through exploration, prediction, and inference.

Course Outcomes

• CO1: Ability to understand the statistical foundations of data science.
• CO2: Ability to apply pre-processing techniques over raw data so as to enable further analysis.
• CO3: Ability to conduct exploratory data analysis and create insightful visualizations to identify patterns.
• CO4: Ability to identify machine learning algorithms for predictions and classification.
• CO5: Ability to analyze the degree of certainty of predictions using statistical test and models

CO – PO Mapping

Textbook(s)

• Cathy O’Neil and Rachel Schutt, “Doing Data Science, Straight Talk From The Frontline”, O’Reilly, 2014.
• Jiawei Han, MichelineKamber and Jian Pei, “Data Mining: Concepts and Techniques”, Third Edition. ISBN 0123814790, 2011.
• Mohammed J. Zaki and Wagner Miera Jr, “Data Mining and Analysis: Fundamental Concepts and Algorithms”, Cambridge University Press, 2014.
• Matt Harrison, “Learning the Pandas Library: Python Tools for Data Munging, Analysis, and Visualization, O’Reilly, 2016.
• Joel Grus, “Data Science from Scratch: First Principles with Python”, O’Reilly Media, 2015.
• Wes McKinney, “Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython”, O’Reilly Media, 2012.
• GalitShmueli, Peter C Bruce, InbalYahav, Nitin R Patel, Kenneth C Lichtendahl Jr. “Data Mining for Business Analytics: Concepts, Techniques, and Applications in R” ISBN: 978-1-118-87936-8, Wiley.

Evaluation Pattern 50:50 (Internal: External)