• (CUDA2200): STORY TELLING USING VISUALISATION AND DATA
• REQUIREMENTS (0-2-0)
• (CUDA2213): SQL (0-2-1)
• (CUDA2214): Data Visualization with Tableau (0-2-1)
• (CUDA2210): Data Analytics for Decision Making (0-2-1)
• (CUDA2211): Natural Language Processing with Scikit Learn (0-1-1)
• (CUDA2212): Dashboarding (0-2-1)
• (CUDA2209): Applied Data Science Project (0+0+4)

• How to tell a story from data
• How to marshal the data for the story line
• The focus is on analysis of data using various tools for decision making
• Objective of this domain is to apply statistical analysis and technologies on data to find trends and solve problems.
• To build up effective story line from data for visualization, prediction and prescription
• Gather sufficient relevant data, conduct data analytics using scientific methods, and make appropriate and powerful connections between quantitative analysis and real-world problems.
• Demonstrate a sophisticated understanding of the concepts and methods; know the exact scopes and possible limitations of each method; and show capability of using data analytics skills to provide constructive guidance in decision making.
• Use advanced techniques to conduct thorough and insightful analysis, and interpret the results correctly with detailed and useful information.
• Show substantial understanding of the real problems; conduct deep data analytics using correct methods; and draw reasonable conclusions with sufficient explanation and elaboration.
• Write an insightful and well-organized report for a real-world case study, including thoughtful and convincing details.
• Make better business decisions by using advanced techniques in data analytics.

• To create impactful visualisation with good story line.
• Learners will be able to make inferences using different analytics tools.

• Data Architect
• Applications Architect
• Infrastructure Architect
• Enterprise Architect
• Data Scientist
• Data Analyst
• Data Engineer
• Statistician
• Business Analyst
• Systems Analyst
• Research Analyst
• Operations Analyst
• Marketing Analyst
• Researcher
• Statistician
• Data Scientist
• Data Engineer

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

Gate Process for Project

• Gate 0: Problem Identification
• Gate 1: Data Collection
• Gate 2: Model Development
• Gate 3: Testing and Validation
• Gate 4: Publication, Patent, Product

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