INTRODUCTION TO DATA SCIENCE

Unit

Title and Contents

No. of Lecture Hours

1

Introduction

What and Why Learn Data Science? Types of Data -Structured, Semi-Structured, Unstructured Data

Applications of Data Science, The Data Science Lifecycle, Role of Data Scientists

Data Sources-Open Data, Social Media Data, Multimodal Data, Standard Datasets

06

2

Statistics for Data Science

Data Objects and Attributes, Attribute Types: Nominal, Binary, Ordinal Attributes, Numeric Attributes, Discrete Versus Continuous Attributes, Role of Statistics in Data Science

Descriptive Statistics - Measuring the Frequency, Measuring the Central Tendency: Mean, Median, and Mode, Measuring the Dispersion: Range, Standard Deviation, Variance, Inter Quartile Range

06

3

Data Science Models and Tasks

Predictive and Descriptive Models, Introduction to Data Science Tasks – Classification, Prediction, Association, Clustering, Performing Simple Data Science Tasks Using WEKA / R

06

4

Data Quality and Pre-processing

Data Quality: Why Preprocess the Data? Data Munging/Wrangling Operations

Data Cleaning - Missing Values, Noisy Data

Data Transformation – Rescaling, Normalizing, Data Reduction and Data Discretization

06

5

Data Visualization

Introduction to Exploratory Data Analysis (EDA), Data Visualization, Basic Data Visualization Tools – Box Plots, Histograms, Bar Charts/Graphs, Scatter Plots, Line Charts, Area Plots, Pie Charts

06