FREE
AKTU Introduction to Data Analytics (MNAM501) Syllabus PDF – B.Tech Minor Degree in Artificial Intelligence and Machine Learning
Introduction to Data Analytics MNAM501
All syllabus, notes, question bank & PYQ for this subject
Free
Download now
- English
- Semester 5
- 0 downloads
- 0 views
Official AKTU syllabus of Introduction to Data Analytics (MNAM501) for B.Tech Minor Degree in Artificial Intelligence and Machine Learning – 4 credits, L-T-P 3-1-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Introduction to Data Analytics
Download PDFMNAM501 · 4 credits · L-T-P 3-1-0
Course Outcomes
At the end of course , the student will be able to understand
| CO 1 | Provide you with the knowledge and expertise to become a proficient data scientist |
| CO 2 | Demonstrate an understanding of statistics and machine learning concepts that are vital for data science; |
| CO 3 | Produce Python code to statistically analyze a dataset; |
| CO 4 | Critically evaluate data visualizations based on their design and use for communicating stories from data; |
Course Content
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Introduction to Data Science, Different Sectors using Data science, Purpose and Components of Python in Data Science. | 07 |
| II | Data Analytics Process, Knowledge Check, Exploratory Data Analysis (EDA), EDA- Quantitative technique, EDA- Graphical Technique, Data Analytics Conclusion and Predictions. | 07 |
| III | 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. | 11 |
| IV | Data Visualization- Basic principles, ideas and tools for data visualization, Examples of inspiring (industry) projects- Exercise: create your own visualization of a complex dataset. | 10 |
| V | Applications of Data Science, Data Science and Ethical Issues- Discussions on privacy, security, ethics- A look back at Data Science- Next-generation data scientists. | 5 |
Text Books
- Joel Grus, Data Science from Scratch, Shroff Publisher Publisher /O’Reilly Publisher Media
- Annalyn Ng, Kenneth Soo, Numsense! Data Science for the Layman, Shroff Publisher Publisher
- Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk from The Frontline. O’Reilly Publisher Media.
- Jure Leskovek, Anand Rajaraman and Jeffrey Ullman. Mining of Massive Datasets. v2.1, Cambridge University Press.
- Jake VanderPlas, Python Data Science Handbook, Shroff Publisher Publisher /O’Reilly Publisher Media
- Philipp Janert, Data Analysis with Open Source Tools, Shroff Publisher Publisher /O’Reilly Publisher Media.
Ratings & Reviews
No reviews yet — share your experience with this item.