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B.E. (Computer Engineering) Applied Data Science Syllabus - Mumbai University 2026

The University has moved this degree onto NEP 2020 one year at a time. The first and second years are NEP 2020 syllabi; the third and fourth years are still examined on the REV-2019 'C' Scheme, which is what the University sets for them this year.

Applied-Data-Science.pdf
Semester 8 · Fourth Year CE

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Syllabus for Applied Data Science

Semester 8 · Fourth Year CE

Module 1: Introduction to Data Science

  • Introduction to Data Science, Data Science Process 1.1 Motivation to use Data Science Techniques: Volume, Dimensions and
  • 1.2 Complexity, Data Science Tasks and Examples Overview of Data Preparation, Modeling, Difference between data
  • 1.3 science and data analytics

Module 2: Data Exploration

  • Types of data, Properties of data Descriptive Statistics: Univariate Exploration: Measure of Central Tendency, Measure of Spread, Symmetry, Skewness: Karl Pearson Coefficient of skewness, 2.1 Bowley’s Coefficient, Kurtosis Multivariate Exploration: Central Data Point, Correlation, Different forms of correlation, Karl Pearson Correlation Coefficient for bivariate distribution Inferential Statistics: Overview of Various forms of distributions: Normal, Poisson, Test 2.2 Hypothesis, Central limit theorem, Confidence Interval, Z-test, t-test, Type-I, Type-II Errors, ANOVA

Module 3: Methodology and Data Visualization

  • Methodology: Overview of model building, Cross Validation, K 3.1 fold cross validation, leave-1 out, Bootstrapping Data Visualization Univariate Visualization: Histogram, Quartile, Distribution Chart
  • 3.2 Multivariate Visualization: Scatter Plot, Scatter Matrix, Bubble chart, Density Chart Roadmap for Data Exploration Self-Learning Topics: Visualizing high dimensional data: Parallel
  • 3.3 chart, Deviation chart, Andrews Curves.

Module 4: Anomaly Detection

  • Outliers, Causes of Outliers, Anomaly detection techniques, Outlier
  • 4.1 Detection using Statistics Outlier Detection using Distance based method, Outlier detection using
  • 4.2 density-based methods, SMOTE

Module 5: Time Series Forecasting

  • Taxonomy of Time Series Forecasting methods, Time Series 5.1 Decomposition Smoothening Methods: Average method, Moving Average smoothing, Time series analysis using linear regression, ARIMA Model, 5.2 Performance Evaluation: Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, Mean Absolute Scaled Error Self-Learning Topics: Evaluation parameters for Classification, 5.3 regression and clustering.

Module 6: Applications of Data Science

  • Predictive Modeling: House price prediction, Fraud Detection Clustering: Customer Segmentation Time series forecasting: Weather Forecasting Recommendation engines: Product recommendation

Textbooks

  • 1 Vijay Kotu, Bala Deshpande. “Data Science Concepts and Practice”, Elsevier, M.K. Publishers.
  • 2 Steven Skiena, “Data Science Design Manual”, Springer International Publishing AG
  • 3 Samir Madhavan. “Mastering Python for Data Science”, PACKT Publishing
  • 4 Dr. P. N. Arora, Sumeet Arora, S. Arora, Ameet Arora, “Comprehensive Statistical Methods”, S.Chand Publications, New Delhi.
  • 1 Jake VanderPlas. “Python Data Science Handbook”, O’reilly Publications.
  • 2 Francesco Ricci, LiorRokach, BrachaShapira, Paul B. Kantor, “Recommender Systems Handbook”, Springer.
  • 3 S.C. Gupta, V. K. Kapoor “Fundamentals of Mathematical Statistics”, S. Chand and Sons, New Delhi.
  • 4 B. L. Agrawal. “Basic Statistics”, New Age Publications, Delhi. https://onlinecourses.nptel.ac.in/noc22_cs32/preview 1 https://onlinecourses.nptel.ac.in/noc21_cs69/preview 2

Reproduced from the University of Mumbai syllabus for B.E. (Computer Engineering) under REV-2019 'C' Scheme, in force from the academic year 2022-23. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 7.9 (R-A) B.E. (Computer Engineering) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.24 (N) B.E. (Computer Engineering) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.15 B.E. (Computer Engineering) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF 6.41 (R) B.E. (Computer Engineering) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
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