Data Science and Visualization 2021 scheme VTU University 6th SEM Computer science and Engineerings | 21CS644 notes

Scheme & Syllabus Copy of Data Science and Visualization 2021 scheme

Chapters of Data Science and Visualization 2021 scheme notes

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.