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Home / Syllabus / B.E Artificial Intelligence and Machine Learning / Third Year AI ML / Semester 6 / Data Analytics and Visualization

B.E Artificial Intelligence and Machine Learning Data Analytics and Visualization Syllabus - Mumbai University

This is the Third Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2022-23. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.

Data-Analytics-and-Visualization.pdf
Semester 6 · Third Year AI-ML · 3 credits · 100 marks

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Syllabus for Data Analytics and Visualization

Semester 6 · Third Year AI-ML · 3 credits · 100 marks

Module 1: Introduction to Data analytics and life cycle

  • Data Analytics Lifecycle overview:Key Roles for a Successful Analytics, Background and Overview of Data Analytics Lifecycle Project Phase 1 : Discovery: Learning the Business Domain, Resources Framing the Problem, Identifying Key Stakeholders. Interviewing the Analytics Sponsor, Developing Initial Hypotheses Identifying Potential Data Sources Phase 2: Data Preparation: Preparing the Analytic Sandbox, Performing ETLT, Learning About the Data, DataConditioning, Survey and visualize, Common Tools for the Data Preparation Phase Phase 3 : Model Planning: Data Exploration and Variable Selection, Model Selection ,Common Tools for the Model Planning Phase Phase 4: Model Building: Common Tools for the Model Building Phase Phase 5: Communicate Results Phase 6: Operationalize

Module 2: Regression Models

  • Introduction to simple Linear Regression: The Regression Equation, Fittedvalue and Residuals, Least Square Introduction to Multiple Linear Regression: Assessing the Model, Cross-Validation, Model Selection and Stepwise Regression, Prediction Using Regression Logistic Regression: Logistic Response function and logit, Logistic Regression and GLM, Generalized Linear model, Predicted values from Logistic Regression, Interpreting the coefficients and odds ratios, Linear and Logistic Regression: similarities and Differences, Assessing the models.

Module 3: Time Series

  • Overview of Time Series Analysis Box-Jenkins Methodology, ARIMA Model Autocorrelation Function (ACF) ,Autoregressive Models ,Moving Average Models ,ARMA and ARIMA Models , Building and Evaluating an ARIMA Model, Reasons to Choose and Cautions

Module 4: Text Analytics

  • History of text mining, Roots of text mining overview of seven practices of text analytic, Application and use cases for Text mining: extracting meaning from unstructured text, Summarizing Text. Text Analysis Steps, A Text Analysis Example , Collecting Raw Text ,Representing Text ,Term Frequency—Inverse Document Frequency (TFIDF),Categorizing Documents by Topics, Determining Sentiments , Gaining Insights .

Module 5: Data analytics and visualization with R

  • Introduction to R: Data Import and Export, Attribute and Data type, Descriptive statistics. Exploratory Data Analysis: Visualization before analysis, DirtyData, visualizing single variable, examining Multiple variable, Data Exploration versus presentation.

Module 6: Data analytics and Visualization with Python

  • Essential Data Libraries for data analytics:Pandas, NumPy, SciPy. Plotting and visualization with python: Introduction to Matplotlib, Basic Plotting with Matplotlib, Create Histogram, BarChart, Pie chart, Box Plot, violin plot using Matplotlib. Introduction to seaborn Library, MultiplePlots, Regressionplot, regplot. Total

Useful Links

  • 1 Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data,EMC Education services Wiley Publication
  • 2 Data Analytics using Python: Bharati Motwani, Wiley Publications.
  • 3 Practical Statistics for Data Scientists 50+ Essential Concepts Using R and Python, O‘Reilly Publications 2nd Edition st
  • 1 Data Mining, Concepts and Techniques: 3rd edition, Jiawei Han, Micheline Kamber and Jian Pei
  • 2 Data Analytics using R, Bharati Motwani, Wiley Publications
  • 3 Python for Data Analysis: 3rd Edition, Wes McKinney ,Publisher(s): O'Reilly Media, Inc.
  • 1 http://varianceexplained.org/RData/
  • 2 https://www.kaggle.com/code/iamleonie/time-series-interpreting-acf-and-pacf
  • 3 https://www.geeksforgeeks.org/data-visualization-using-matplotlib/

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) 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 7.8 (R-A) B.E. (Artificial Intelligence and Machine Learning) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.21 (N) B.E. (Artificial Intelligence and Machine Learning) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.42 (R) B.E. (Artificial Intelligence and Machine Learning) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF B.E. (Artificial Intelligence and Machine Learning) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
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