Course Page for DS250 (Data Analytics and Visualization) being taught at IIT Bhilai, India in the Winter Semester of 2022.
Course Instructor: Dr. Gagan Raj Gupta
The purpose of this course is to introduce students to various algorithms and techniques for data analysis and visualization. We will emphasize visualization and analysis of complex data sets such as large network data.
- Useful insights can be obtained from data that can help people: healthcare, industry, governments, science
- Getting data is becoming easier day by day, but is very complex and difficult for people to understand
- Data has errors of various types (missing, incorrect etc.), is incomplete and is hard to clean (e.g. user reviews/ratings, distorted images)
- Data usually has complex correlations and i.i.d. assumptions don't always work very well (e.g. graph data, time-series data)
- Data Visualization is critical to help us engage more diverse audience in the process of analytical thinking
In this course, we want to learn how that is being done and solve real-life problems that we are interested in.
- Motivate and demonstrate the benefits and uses of data science
- Impart the skills needed by a data scientist: acquire, clean, model, visualize data
- Teach fundamental algorithms for handling basic and complex datasets including recommendation, basket analysis, streaming algorithms
- Study techniques for creating effective visualizations based on principles from graphic design, perceptual psychology, and cognitive science
- Teach basic techniques of machine learning (unsupervised) which is important way to model relationships in data
- Provide hands-on experience to students in analyzing datasets in diverse fields (NLP, Image/Video, Graphs, Networks, Bio-informatics, Finance)
- Basic knowledge of Python (most assignments will be based on Python)
- Knowledge of basic computer science principles and skills
- Math
- Linear Algebra ( Matrix-factorization, Eigenvalues, Column and row spaces, Norms)
- Probability theory (Conditional, Bayes Rule, Concentration Inequalities, Distributions, Gaussian, Multi-variate)
- Basic Data Structures, Algorithms and Asymptotic Analysis (graphs, heaps, lists, dynamic programming)
- Calculus (Multi-variate)
- Web Technologies
- HTML
- JS
- CSS
If you don't meet one or more pre-requisites, be prepared to spend more time before or during the course in learning them.
- IVB: Interactive visualization for the Web, Scott Murray
- PRML: Pattern Recognition and Machine Learning, Christopher Bishop
- TDS: Think like a data scientist, Brian Godsey
- DSC: Data Science from Scratch, Joel Grus
- GA: Graph Algorithms: Practical Examples in Apache Spark and Neo4J, Mark Needham, Amy Hodler
- MML: Mathematics of Machine Learning
- CIML: A Course in Machine Learning
Google drive (for IIT Bhilai students): GDrive
Legend:
Theory
Labs
| # | Week | Topics planned in this week | Text Book Reference |
|---|---|---|---|
| 1 | Introduction to Data Science, Facets of Data, Probability Distributions | In D3 | |
| 1 | Lab: Web Development, Intro to D3: Data, Scales, Shapes | W3, Tutorials from UBC 2 | |
| 2 | Data Science Process, Data Models: Geometric view and Statistical view, Data Cleaning, EDA: Value of Visualization, Gaussian Distribution, Conjugate Prior | PRML Ch1, MML Ch 6 | |
| 2 | Lab: Intro to Numpy and Pandas; Intro to Matplotlib, Seaborn, Plotnine, | https://realpython.com/ggplot-python/ https://altair-viz.github.io/ | |
| 3 | Visualizing Data, Kernel Density, Describing Data, Correlation Metrics | ||
| 3 | Lab: Collect Data (Webscraping, API), Analyze and Make a chart in D3, Density Plots | IVB | |
| 3 | Case Studies: Predicting malicious URLs, Risk in Bank Loans, Sentiment Analysis, Healthcare Dataset | ||
| 4 | Distance Measures, Recommendation Rules, Association Rules, ML intro, Learning by Prototypes | ||
| 4 | Lab: D3 Data joins and basic interactivity | ||
| 5 | Learning to classify data: various algorithms, Generative and Discriminative Models, | PRML | |
| 5 | Lab: Connect Python to D3 using Flask | ||
| 6 | Mathematics of Neural Networks, Decision Trees | PRML | |
| 6 | Jan 31 | Project: Complete a Basic Data Classification Interactive Application | |
| 6 | Feb 3 | Tierce 1 Exam | |
| 7 | Networks: Basic Algorithms: Path Finding | GA Ch 4 | |
| 7 | Lab: Visualization of Networks and Trees in D3 | IVB Ch13 | |
| 8 | Networks Analysis: Random Walks, Counting Triangles | GA Ch 4 | |
| 8 | Lab: Intro to Neo4J | GA Ch3 | |
| 8 | Case Studies: Knowledge Graphs, Product Recommendation, Graphical Models | ||
| 9 | Centrality Algorithms, Community Detection Algorithms | GA Ch5 | |
| 9 | Lab: Data Analysis using Neo4J | ||
| 10 | Text and Graph Embedding Algorithms | GA Ch6,7 | |
| 10 | Lab: Word Cloud, Text Visualization, NLP libraries: NLTK | ||
| 11 | Graph Machine Learning | GA Ch6,7 | |
| 11 | Mar 7 | Project: Complete a graph data analysis Application | |
| 12 | Mar 12-20 | Mid Sem Break | |
| 12 | Mar 21 | Tierce 2 Exam | |
| 13 | Time Series Data Analysis | ||
| 13 | Lab: Interactive Dashboards | IVB Ch10 | |
| 14 | Streaming Data Algorithms | ||
| 14 | Lab: Time Series Prediction and Clustering | ||
| 14 | Case Studies: Weather, Financial Data, Imports/Exports, | ||
| 15 | Algorithms for Clustering Data: K-Means, K-Means++, DTW, DBSCAN | ||
| 15 | Lab: Geographical Maps | IVB Ch14 | |
| 15 | Dimensionality Reduction Algorithms | ||
| 16 | Lab: Visualization of High-Dimensional Data using T-SNE | ||
| 17 | Apr 25 | Major Project: Complete a major data analysis course project |
Both data-scientists and software developers are good at designing and building complex systems with many interconnected parts using different tools and frameworks. In general, software developers design systems consisting of many well-defined components, whereas data scientists work with systems wherein at least one of the components isn’t well defined prior to being built. That component is usually closely involved with data processing or analysis. Data scientists specialize in creating systems that rely on probabilistic statements about data and results. Well known examples of these are Google search engine (“These are probably the most relevant pages”), product recommendations on Amazon.com (“We think you’ll probably like these things”).
We will cover the skills necessary for cleaning, modeling and visualizing data by a data-scientist. We will also learn the skills needed for developers designing interactive dashboards and applications.
D3 (website)
D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.
To make life easy, we will be using the tutorials and notebook environment provided by Observable
Check these examples: Sankey, Cholorpleth, Hexbin,Fisheye
The project and lab component of this course will equip students with modern software toolkit to develop their own data analysis and interactive visualization applications (web/android) to better appreciate the data science process.