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AKTU Introduction to Business Analytics & Data Science (BCBS070) Syllabus PDF – B.Tech 4th Year
Introduction to Business Analytics & Data Science BCBS070
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Official AKTU syllabus of Introduction to Business Analytics & Data Science (BCBS070) for B.Tech 4th Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Introduction to Business Analytics & Data Science
Download PDFBCBS070 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course, the student will be able to:
| CO 1 | Define the basics of business analysis and Data Science | K 1 |
| CO 2 | Explain data management and handling and Data Science Project Life Cycle | K 2 |
| CO 3 | Describe the data mining concept and its Techniques | K 2 |
| CO 4 | Apply the machine learning concept | K 3 |
| CO5 | Describe the application of business analysis in different domain | K 2 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Introduction: What is business analytics? Historical Overview of data analysis, Data Scientist vs. Data Engineer vs. Business Analyst, Career in Business Analytics, what is data science, Why Data Science, Applications for data science, Data Scientists Roles and Responsibility | 08 |
| II | Data: Data Collection, Data Management, Big Data Management, Organization/sources of data, Types of Data, Importance of data quality, Dealing with missing or incomplete data, Data Visualization, Data Classification Data Science Project Life Cycle: Business Requirement, Data Acquisition, Data Preparation, Hypothesis and Modeling, Evaluation and Interpretation, Deployment, Operations, Optimization. | 08 |
| III | Introduction to Data Mining, The origins of Data Mining, Data Mining Tasks, OLAP and Multidimensional data analysis, Basic concept of Association Analysis and Cluster Analysis. | 08 |
| IV | Introduction to Machine Learning: History and Evolution, AI Evolution, Statistics V/S Data Mining V/S Data Analytics V/S, Data Science, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Frameworks for building Machine Learning Systems. | 08 |
| V | Application of Business Analysis: Retail Analytics, Marketing Analytics, Financial Analytics, Healthcare Analytics, Supply Chain Analytics. | 08 |
Text Books
- B. Pochiraju and S. Seshadri, “Essentials of Business Analytics: An Introduction to the Methodology and Its Applications”, 1st ed. Springer International Publishing, 2019.
- A. C. Müller and S. Guido, “Introduction to Machine Learning with Python: A Guide for Data Scientists”, 1st ed. O’Reilly Media, 2016.
- L. Igual and S. Seguí, “Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications”, 2nd ed. Springer Cham, 2024.
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar, “Introduction to Data Mining”, Pearson Education India, 2021.
- G. Koole, “An Introduction to Business Analytics: An Introduction to the Methodology and Its Application”, 1st ed. Lulu Press, 2019.
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