Data Science refers to extraction of knowledge from large volumes of data that are structured or unstructured, which is continuation of data mining and predictive analytics. It involves different categories of analytical approaches for modeling various types of business scenarios and arriving at solution and strategies for optimal decision-making in marketing, finance, operations, organizational behavior and other managerial aspects. This new field of study breaks down into a number of different areas, from constructing big data infrastructure and configuring the various server tools that sit on top of the hardware, to performing the analysis and developing the right transformations to generate useful results. Data Science is an interdisciplinary field that combines the magic of programming, mathematics and business. Combined with Machine Learning, it helps to identify a future trend which can be used to derive actionable insights for creating future impact. These skills will help for the role of a Data Scientist. As a Data Science aspirant, learner will be emphasizing of the knowledge to share from the quantitative analysis to programming concept and extended to business intelligence. Data science can add value to any business which can use the data well. Data Science consists of 3 parts namely: Machine Learning: Machine Learning involves algorithms and mathematical models, chiefly employed to make machines learn and prepare them to adapt to everyday advancements. Big Data: Everyday, we are producing so much of data in the form of clicks, orders, videos, images, comments, articles, RSS Feeds etc. These data is generally unstructured and is often called as Big Data. Big Data tools and techniques mainly help in converting this unstructured data into a structured form. Business Intelligence: Each business has and produces too much data every day. This data when analyzed carefully and then presented in visual reports involving graphs, can bring good decision making to life. This can help the management in taking the best decision after carefully delving into patterns and details the reports bring to life.
HSC or equivalent from any stream / 3 years Diploma from MSBTE or equivalent
Passing Marks – 40%
This course shall be a full time course. The duration of course will be six semesters spread over there years.
- PSO 1: Apply computing theory, languages, and algorithms, as well as mathematical and statistical models, and the principles of optimization to appropriately formulate and use data analyses.
- PSO 2: An ability to use current techniques, skills and tools for programming practically.
- PSO 3: Capability of the students to apply design and development principles in the construction of software systems.
- PSO 4: Enabling the student’s practical exposure in the software development field.
- Internal Evaluation (25 Marks)
- Test: 1 Class test of 20 marks. (Can be taken online)
Q | Attempt any four of the following | 20 |
a. | ||
b. | ||
c. | ||
d. | ||
e. | ||
f. |
- 5 marks: Active participation in the class, overall conduct, attendance.
- External Examination: (75 marks)
All questions are compulsory | ||
Q1 | (Based on Unit 1) Attempt any three of the following: | 15 |
a. | ||
b. | ||
c. | ||
d. | ||
e. | ||
f. | ||
Q2 | (Based on Unit 2) Attempt any three of the following: | 15 |
Q3 | (Based on Unit 3) Attempt any three of the following: | 15 |
Q4 | (Based on Unit 4) Attempt any three of the following: | 15 |
Q5 | (Based on Unit 5) Attempt any three of the following: | 15 |
- Practical / Tutorial Exam: (50 marks)
A Certified copy journal is essential to appear for the practical examination.
1. | Practical Question 1 | 20 |
2. | Practical Question 2 | 20 |
3. | Journal | 5 |
4. | Viva Voice | 5 |
OR
1. | Practical Question | 40 |
2. | Journal | 5 |
3. | Viva Voice | 5 |
For Tutorial Exam, a paper of 50 marks to be solved
SEMESTER I
Course Code | Course Type | Course Name | Credits | Marks |
USDS101 | DSC | Descriptive Statistics | 02 | 100 |
USDS1P1 | DSC | Descriptive Statistics Practical | 02 | 50 |
USDS102 | DSC | Introduction to Programming | 02 | 100 |
USDS1P2 | DSC | Introduction to Programming Practical | 02 | 50 |
USDS103 | DSC | Web Technology | 02 | 100 |
USDS1P3 | DSC | Web Technology Practical | 02 | 50 |
USDS104 | AECC | Business Communication and Information Ethic | 02 | 100 |
USDS1P4 | AECC | ICT Practical | 02 | 50 |
USDS105 | DSC | Precalculus | 02 | 100 |
USDS1P5 | DSC | Precalculus Tutorials | 02 | 50 |
20 | 750 |
SEMESTER II
Course Code | Course Type | Course Name | Credits | Marks |
USDS201 | DSC | Probability and Distributions | 02 | 100 |
USDS2P1 | DSC | Probability and Distributions Practical | 02 | 50 |
USDS202 | DSC | Database Management | 02 | 100 |
USDS2P2 | DSC | Database Management Practical | 02 | 50 |
USDS203 | DSC | R Programming | 02 | 100 |
USDS2P3 | DSC | R Programming Practical | 02 | 50 |
USDS204 | AECC | Environmental Science | 02 | 100 |
USDS2P4 | AECC | Project Presentation on Data Science in Environmental Science | 02 | 50 |
USDS205 | DSC | Calculus | 02 | 100 |
USDS2P5 | DSC | Calculus Tutorials | 02 | 50 |
20 | 750 |
SEMESTER III
Course Code | Course Type | Course Name | Credits | Marks |
USDS301 | DSC | Testing of Hypothesis | 02 | 100 |
USDS3P1 | DSC | SPSS Practical | 02 | 50 |
USDS302 | DSC | Data Structures | 02 | 100 |
USDS3P2 | DSC | Data Structures Practical | 02 | 50 |
USDS303 | SEC | Microeconomics / Principles of Management | 02 | 100 |
USDS3P3 | SEC | Case Studies on Microeconomics | 02 | 50 |
USDS304 | DSC | Data Warehousing | 02 | 100 |
USDS3P4 | DSC | Data Warehousing Practical | 02 | 50 |
USDS305 | DSC | Linear Algebra and Discrete Mathematics | 02 | 100 |
USDS3P5 | DSC | Tutorials on Linear Algebra and Discrete Mathematics | 02 | 50 |
20 | 750 |
SEMESTER IV
Course Code | Course Type | Course Name | Credits | Marks |
USDS401 | DSC | Optimization Techniques | 02 | 100 |
USDS4P1 | DSC | Optimization Techniques Practical | 02 | 50 |
USDS402 | DSC | Big Data | 02 | 100 |
USDS4P2 | DSC | Big Data Practical | 02 | 50 |
USDS403 | SEC | E-Commerce and Business Ethics / Fundamentals of Accounting | 02 | 100 |
USDS4P3 | SEC | MATLAB Practical | 02 | 50 |
USDS404 | DSC | Algorithms in Data Science | 02 | 100 |
USDS4P4 | DSC | Algorithms in Data Science Practical | 02 | 50 |
USDS405 | DSC | Numerical Methods | 02 | 100 |
USDS4P5 | DSC | Numerical Methods Practical | 02 | 50 |
20 | 750 |
SEMESTER V
Course Code | Course Type | Course Name | Credits | Marks |
USDS501 | DSC | Artificial Intelligence | 02 | 100 |
USDS5P1 | DSC | Artificial Intelligence Practical | 02 | 50 |
USDS502 | DSC | Business Research Methods | 02 | 100 |
USDS5P2 | DSC | Business Research Methods Practical | 02 | 50 |
USDS503 | SEC | Data Mining | 02 | 100 |
USDS5P3 | SEC | Data Mining Practical | 02 | 50 |
USDS504 | DSC | Campus to Corporate | 02 | 100 |
USDS5P4 | DSC | Project Dissertation | 02 | 50 |
Elective 1 (Select Any one of the following) | ||||
USDS505a | DSE | Reinforcement Learning | 02 | 100 |
USDS505b | DSE | Marketing and Retail Analytics | ||
USDS505c | DSE | Supply Chain and Logistics Analytics | ||
USDS505d | DSE | Robotic Process Automation | ||
Compulsory Practical | ||||
USDS5P5 | DSC | Data Visualisation with Power BI / Tableau | 02 | 50 |
20 | 750 |
SEMESTER VI
Course Code | Course Type | Course Name | Credits | Marks |
USDS601 | DSC | Machine Learning | 02 | 100 |
USDS6P1 | DSC | Machine Learning Practical | 02 | 50 |
USDS602 | DSC | Cloud Computing | 02 | 100 |
USDS6P2 | DSC | Cloud Computing Practical | 02 | 50 |
USDS603 | SEC | Internet of Things | 02 | 100 |
USDS6P3 | SEC | Internet of Things Practical | 02 | 50 |
USDS604 | DSC | Business Forecasting | 02 | 100 |
USDS6P4 | DSC | Business Forecasting Practical | 02 | 50 |
Elective 2 (Select Any one of the following) | ||||
USDS605a | DSE | Financial Analytics | 02 | 100 |
USDS605b | DSE | Social Media Analytics | ||
USDS605c | DSE | Knowledge Management | ||
USDS605d | DSE | Data Security and Compliance | ||
Compulsory (Project Implementation) | ||||
USDS6P5 | DSC | Project Implementation | 02 | 50 |
20 | 750 |