Course code: PEC-CSE-320G
Category: Professional Elective Course
Course title: Data Science
Scheme and Credits
| L | T | P | Credits | |
|---|---|---|---|---|
| Semester 6 | 3 | 0 | 0 | 3 |
| Marks | |
|---|---|
| Class work | 25 Marks |
| Exam | 75 Marks |
| Total | 100 Marks |
| Duration of Exam | 03 Hours |
Objectives of the course:
- The objective of this course is to impart necessary knowledge of the basic foundations needed for understanding data science domain and develop programming skills required to build data science applications.
- To introduce the conceptual knowledge of the area of data science domain, feature and scope of applications.
- To impart programming knowledge needed for data sciences.
- To understand the different issues involved in the design and implementation of a data science applications.
- To understand case studies of essential Data sciences applications.
Note: Examiner will set nine questions in total. Question one will be compulsory. Question one will have 6 parts of 2.5 marks each from all units and remaining eight questions of 15 marks each to be set by taking two questions from each unit. The students have to attempt five questions in total, first being compulsory and selecting one from each unit.
UNIT 1
Introduction to Data Science: Concept of Data Science, Traits of Big data, Web Scraping, Analysis vs Reporting, Collection, storing, processing, describing and modelling, statistical modelling and algorithm modelling, AI and data science, Myths of Data science
UNIT 2
Introduction to Programming Tools for Data Science: Toolkits using Python: Matplotlib, NumPy, Scikit-learn, NLTK, Visualizing Data: Bar Charts, Line Charts, Scatterplots, Working with data: Reading Files, Scraping the Web
UNIT 3
Data Science Methodology: Business Understanding, Analytic Approach, Data Requirements, Data Collection, Data Understanding, data Preparation, Modeling, Evaluation, Deployment, feedback
UNIT 4
Data Science Application: Prediction and elections, Recommendations and business analytics, clustering and text analytics
Suggested Text books:
- Joel Grus, "Data Science from Scratch: First Principles with Python", O'Reilly Media
- AurélienGéron, "Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems", 1st Edition, O'Reilly Media
- Jain V.K., "Data Sciences", Khanna Publishing House, Delhi.
- Jain V.K., "Big Data and Hadoop", Khanna Publishing House, Delhi.
Suggested Reference books:
- Data Science Workflow: Overview and Challenges by Philip Guo
- Python for Data Analysis, O'Reilly Media Rajiv, "Machine Learning", Khanna Publishing House, Delhi.
- Ian Goodfellow, YoshuaBengio and Aaron Courville, "Deep Learning", MIT Press
- http://www.deeplearningbook.org
- Jiawei Han and Jian Pei, "Data Mining Concepts and Techniques", Third Edition, Morgan Kaufmann Publishers
Course Outcomes:
- Understand the value of data science and the process behind using it.
- Use Python to gather, store, clean, analyse, and visualise data-sets.
- Apply toolkits to formulate and test data hypotheses and uncover relationships within data-sets
- Understand the data science methodology in the data science pipeline
- Understand real-world challenges with several case studies