Data Science and Visualization 21CS644

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Data Science and Visualization 21CS644

Data Science and Visualization 21CS644

Course Code: 21CS644

Credits: 03

CIE Marks: 50

SEE Marks: 50

Total Marks: 100

Exam Hours: 03

Total Hours of Pedagogy: 40T

Teaching Hours/Weeks: [L:T:P:S] 3:0:0:0

MODULE-1

Introduction to Data Science

Introduction: What is Data Science? Big Data and Data Science hype – and getting past the hype, Why now? – Datafication, Current landscape of perspectives, Skill sets.

Needed Statistical Inference: Populations and samples, Statistical modelling, probability distributions, fitting a model.

MODULE-2

Data Analysis and the Data Science Process

Basic tools (plots, graphs and summary statistics) of EDA, Philosophy of EDA, The Data Science Process, Case Study: Real Direct(online realestate firm). ThreeBasic Machine LearningAlgorithms: Linear Regression, k-Nearest Neighbours (k- NN), k-means.

MODULE-3

Feature Generation and Feature Selection

Extracting Meaning from Data: Motivating application: user (customer) retention. Feature Generation (brainstorming, role of domain expertise, and place for imagination), Feature Selection algorithms. Filters; Wrappers; Decision Trees; Random Forests.

Recommendation Systems: Building a User-Facing Data Product, Algorithmic ingredients of a Recommendation Engine, Dimensionality Reduction, Singular Value Decomposition, Principal Component Analysis, Exercise: build your own recommendation system.

MODULE-4

Data Visualization and Data Exploration

Introduction: Data Visualization, Importance of Data Visualization, Data Wrangling, Tools and Libraries for Visualization.

Comparison Plots: Line Chart, Bar Chart and Radar Chart.

Relation Plots: Scatter Plot, Bubble Plot , Correlogram and Heatmap.

Composition Plots: Pie Chart, Stacked Bar Chart, Stacked Area Chart, Venn Diagram.

Distribution Plots: Histogram, Density Plot, Box Plot, Violin Plot; Geo Plots: Dot Map, Choropleth Map, Connection Map; What Makes a Good Visualization.

MODULE-5

A Deep Dive into Matplotlib

Introduction: Overview of Plots in Matplotlib, Pyplot Basics: Creating Figures, Closing Figures, Format Strings, Plotting, Plotting Using pandas DataFrames, Displaying Figures, Saving Figures.

Basic Text and Legend Functions: Labels, Titles, Text, Annotations, Legends; Basic Plots:Bar Chart, Pie Chart, Stacked Bar Chart, Stacked Area Chart, Histogram, Box Plot, Scatter Plot, Bubble Plot; Layouts: Subplots, Tight Layout, Radar Charts, GridSpec; Images: Basic Image Operations, Writing Mathematical Expressions.

Document

2021 SCHEME QUESTION PAPER

Model Paper

48 thoughts on “Data Science and Visualization 21CS644”

  14. Sir please upload the notes of all modules for data science as we need for all 3 IA exams as well as we need for external exam to read . Please upload as soon as possible

    Reply

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