Domain Syllabus

1. (CUDA2200) : STORY TELLING USING VISUALISATION AND DATA REQUIREMENTS  (0-2-0)

Module:  I  -  Introduction to Data analytics

Data analytics Impact and Importance, Type of Data analytics, Descriptive analytics, Diagnostic analytics,  Predictive analytics, Prescriptive analytics, Data analytics vs Decision making, Data analytics vs cost reduction, Dealing with different type of data, Qualitative and Quantitative data, Normal distribution of data, Statistical parameters

Module:  II -  Storyboarding with Data Visualization

Understanding data visualization, Visualization tools, Frequency distribution plot, Swarm plot, Story plotting, Building up storyline, Presentation of storyboarding with visualization, Data analytics in different sector - product and services, Analytics framework 

2. (CUDA2213): SQL (0-2-1)

Module:  I  - SQL Beginner 

CREATE, INSERT, Importing data from file, Select Statement, Select Distinct, WHERE, Logical Operator, UPDATE, DELETE, ALTER, Restore and Backup, Filter option - IN,  BETWEEN, LIKE

Module II - SQL Intermediate

ORDER BY, LIMIT, Sorting,  Alias,  Aggregate command - COUNT, SUM, MEAN, AVERAGE, MIN AND MAX,  Group by , HAVING,  Conditional statements, Joins - Inner, Left, Right, Full outer, Cross, Except, Unions, Subqueries , Views and Index, String function - Length, Upper, Lower, Trim, Concatenation, Substring, List aggregation

Module III - SQL Advance

Mathematical Function - CEIL and FLOOR, RANDOM, SETSEED, ROUND, POWER,  Date and Time function - CURRENT DATE AND TIME, AGE EXTRACT, Pattern (String) Matching - Basic and Advance, Date type conversion, User access control function

3. (CUDA2213) : Data Visualization with Tableau (0-2-1)

Module: I Introduction and Visualization

Download and installation, loading data from Excel and user interface, Core topic – Dimension vs measures, discrete vs Continuous, application of discrete and continuous fields, and aggregation in tableau.

Creating charts in Tableau – Bar Chart, stacked bar chart, line chart, scatter plot, Dual axis charts, and combined axis charts. Funnel chart and cross tabs, Highlight tables, Maps, measure name and Measure values

Module: II Analytics in Tableau

Working with metadata, Data types, rename, hide, unhide and sort, default properties of fields. Filters – Dimension, Data, Measure, Visual, Interactive, Data source, Context

Applying analytics – Sets, Parameters, Group, Calculated fields, Data Function, Text Function, Bins and Histogram, Sort, reference and trend line, Table calculation, Pareto Chart, Waterfall Chart

Module: III Dash boarding and data connection

Dashboard in tableau, working layout, Objects in dashboard, making interactive dashboard, action in dashboard, Dashboard for Mobile, Storyline – Case study

Modification to data connection, Edit data source, Union, Joins, Data blending, level of detail, Fixed LOD, Include LOD, Exclude LOD,  Publish to Tableau Public

4. (CUDA2210) : Data Analytics for Decision Making (0-2-1)

Module: I  Introduction to Data Analytics

Data type, Data Analytics process, exploratory data analysis (EDA), EDA – Graphical Technique, Data analytics conclusion and prediction

Module: II Statistics analysis and Business application

Introduction to Statistics, Statistical and non-statistical analysis, Major categories of statistics, Statistical analysis – population and sample, Statistical analysis process – Data distribution, dispersion, Histogram, Correlation and inferential statistics, Regression

Exercise – Based on case studies

Module: III Mathematical and scientific computation

Numpy basic – ndarray and basic arithmetic operation, Slicing, coy and view, Mathematical function of numpy,

Introduction to Scipy, Scipy sub package Integration and Optimization, Calculation of Eigenvalues, Eigenvector Scipy sub package – Statistics weave and IO

Practice project – Solving linear algebra using Scipy, perform CDF and PDF using Scipy

Note – Two projects to be submitted (including one practice project)

5. (CUDA2211): Natural Language Processing with Scikit Learn (0-1-1)

Module: 1 NLP Overview and Application

NLP Overview, NLP Application. NLP Libraries – SCIKIT, Extraction consideration, Scikit learn – model training and Grid search, Practice project – Analysing spam collection data or any other project, Demo assignment, Project for evaluation – Sentiment analysis using NLP

Project Covered - 

• Based on survey data collected from field on interested topic
• Project based on Aadhar data
• Project Based on Bank Data
• College based analysis
• Credit card related analysis project
• Diabetes patient analysis
• Facebook analysis
• Indigo operation
• IOT
• IPL
• Loan DB
• Marketing Campaign
• OYO
• Stroke data
• Telcome analysis
• You tube  analysis

link for Project dataset - 

https://drive.google.com/drive/folders/1qIba8ZsKRL8KvVfjIyAubQYZuo5DnycL?usp=sharing

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