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AKTU Introduction to Data Science (BDS301) Syllabus PDF – B.Tech 2nd Year
Introduction to Data Science BDS301
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Official AKTU syllabus of Introduction to Data Science (BDS301) for B.Tech 2nd Year – 3 credits, L-T-P 2-1-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Introduction to Data Science
Download PDFBDS301 · 3 credits · L-T-P 2-1-0
Course Outcomes
At the end of course, the student will be able to understand
| CO1 | Explain the fundamental concepts of Data Science, its life cycle, data types, and applications in solving real-world problems. | K 2 |
| CO2 | Apply statistical methods and exploratory data analysis techniques to summarize, analyze, and interpret datasets for informed decision-making. | K 3 |
| CO3 | Develop Python programs using data science libraries to perform data analysis and implement the data science project development process. | K 6 |
| CO4 | Analyze and preprocess datasets using data wrangling techniques and create effective visualizations for data interpretation. | K 6 |
| CO5 | Evaluate the use of modern data science tools, ethical principles, AI agents, and their applications in real-world scenarios. | K 5 |
Course Content
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Introduction to Data Science: Introduction to Data Science Definition, scope, and evolution of Data Science, Data Science Life Cycle, Data Analytics and Business Intelligence. Real-world applications, Overview of Big Data and its challenges. Structured vs. Unstructured Data, Data types, Overview of datasets, Data Collection, Preprocessing, Analysis, Visualization, and Model Building. | 08 |
| II | Exploratory Data Analysis: Measures of Central Tendency, Measures of Dispersion. Scatter diagram, covariance, Correlation, Statistical Analysis, Hypothesis testing: t -Test, chi- square Test, F-Test, Z-Test, ANOVA | 08 |
| III | Programming in Data Science: Introduction to Python for Data Science Basic Syntax, Data Types, and Operators. Control Structure, Functions and Modules, Working with Data Science Libraries NumPy, Pandas, Scikit-Learn. Project Development Process, Problem Identification and Data Collection, Data Cleaning and Exploration, Model Selection and Implementation, Result Interpretation and Presentation. | 08 |
| IV | Data Wrangling, Prepossessing & Visualization (8) Data Cleaning & Preprocessing Handling missing values, removing duplicates, and outliers, Data transformation, Data Manipulation in Python, Working with NumPy and Pandas, Filtering, Sorting, Grouping, and Aggregation. Data Visualization Techniques Basics of Matplotlib and Seaborn, Types of Graphs: Bar Charts, Histograms, Box Plots, Scatter Plots, Line Graphs, Heatmaps, and Pair Plots. | 08 |
| V | Data Science Tools, Ethics and Applications (8) Tools for Data Science: Introduction, Basics of Tableu, Basics of Power BI, Basics of R-Programming, Applications Ethics in Data Science, AI Agents, types and applications. | 08 |
Suggested Readings:
- "Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython" by Wes McKinney (O'Reilly Media)
- Data Science from Scratch: First Principles with Python" by Joel Grus (O'Reilly Media).
- Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk from The Frontline. O’Reilly. 2014.
- W. H. Inmon, "Building the Data Warehouse", 3rd edition.
- Anahory and Murray. Data warehousing in the real world, Pearson Education/Addison Wesley.
- Margaret Dunham, Data Mining: Introductory and Advanced Topics, Published by Prentice Hall.
- Jiawei Han, Micheline Kamber, "Data Mining: Concepts and Techniques",
- Morgan Kaufmann Publishers, 2002. (www.cs.sfu.ca/~han/DMbook.html). Alex Berson, Stephen J. Smith, "Data Warehousing, Data Mining, & OLAP", Tata Mcgraw- Hill, 2004.
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