Honours in Data Science TCS iON - July 2024
Data Science
Enquire now: 9480223900
Why choose our Data Science
Learn Data processing, Data warehousing, Data mining, Text mining, Spatial data mining, Data sources, Reporting, Reports for data analysis, Data reporting tools, Data visualisation using R & Tableau
Virtual hands-on environment integrated within the course to help learners to try what they have learned practically, Project based industry assignments designed by industry experts to implement learning
Three Periodic Formative Assessments to assess course progress
Remote internship opportunity and Placement assistance for successful candidates
Dedicated online course communities to connect with academic and industry experts
Helpdesk support for quick resolution of queries
Accelerate your Career with a Honour Degree
Online | 30 Months | Expert Lectures
Criteria
B.E/B.Tech. Students in their 5th semester onwards who want to diversify their careers in the industry
Lectures from renowned Subject Matter Experts from leading Academia Industry, Access to self paced lea
Built in hands on environment for practical implemention of the technologies covered, Industry assignm
Enhance your career with Remote Internship Opportunity and Placement Assistance.
What will you learn in this Program?
Total Credits earned: 18 credits (across 3 courses and 270 hours of learning)
Key learnings: Data processing, Data warehousing, Data mining, Text mining, Spatial data mining, Data sources, Reporting, Reports for data analysis, Data reporting tools, Data visualisation u
Remote internships
Learn Data Mining and Warehousing, Data Analytics & Reporting, Data Modelling & Visualization
Project based industry assignments
Visibility to placement opportunities for successful candidates, subject to hiring criteria of corporates.
Curriculum
Course1. Data Mining and Warehousing:
The Data Mining and Warehousing course introduces students to the basic concepts, principles, methods, implementation techniques, and applications of data mining, with a focus on major data mining functions like pattern discovery, classification, text mining, spatial data mining, cluster analysis and more.
Course2. Data Analytics & Reporting:
The Data Analytics and Reporting course provides students with an introduction to data, the various steps involved in data preprocessing, and the tools used to analyse data. The course has two important objectives: (1) the use of tools and techniques to analyse data properties; (2) to extract relevant information from data and use different ways of reporting data. Data analysis is the cornerstone for any predictive analytics wherein the steps involved in getting clean model development data consume almost 90% of the entire project effort. This course helps students to derive insights from the data based on machine learning models and create reports.
Course3. Data Modelling & Visualization:
Data Modelling & Visualization course provides the basic framework for performing data analysis using some fundamental data modeling and data visualization techniques. Using R as a programming language, students will implement industry assignments to gain experience in framing a basic comprehensive solution to a data analysis problem in a structured framework leveraging an integrated virtual environment.
Data Mining and Warehousing:
Module 1: Data Preprocessing
- Submodule 1: Descriptive data summarisation
- Submodule 2: Data cleaning
- Submodule 3: Data integration and transformation
- Submodule 4: Data reduction
Module 2: Data Warehouse
- Submodule 1: A multidimensional data model
- Submodule 2: Data warehouse architecture
Module 3: Association Rule Mining
- Submodule 1: Frequent itemset mining
- Submodule 2: Mining various kinds of association rules
Module 4: Classification and Prediction
- Submodule 1: Various classification methods
- Submodule 2: Various prediction methods
Module 5: Cluster Analysis
- Submodule 1: Partitioning methods
- Submodule 2: Hierarchical methods
- Submodule 3: Density-based methods
Module 6: Outlier Analysis
- Submodule 1: Distance-based outlier detection
- Submodule 2: Density-based local outlier detection
Module 7: Mining Data Streams
- Submodule 1: Mining time-series data
- Submodule 2: Discussion on time-series (Case Study)
Module 8: Text Mining
- Submodule 1: Text retrieval methods
- Submodule 2: Text indexing techniques
- Submodule 3: Query processing techniques
Module 9: Spatial Data Mining
- Submodule 1: Spatial classification and spatial trend analysis
- Submodule 2: Spatial clustering methods
Data Analytics & Reporting:
Module 1: Introduction to Data Science and Analytics
- Submodule 1: Data, features
- Submodule 2: Preprocessing on data
- Submodule 3: Cleaning of data
- Submodule 4: Feature selection techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)
- Submodule 5: Components of Analytics - reporting and analysis
Module 2: Handling Data Sources
- Submodule 1: Different types of data sources: structured, unstructured and semi-structured data
- Submodule 2: Relational databases: normal forms, transactional data, Structured Query Language (SQL)
- Submodule 3: NoSQL databases and its types
- Submodule 4: Handling semi-structured data with JSON, CSV files, XML and more
Module 3: Exploratory Data Analysis (EDA), Models and Techniques
- Submodule 1: Working with trend detection, outlier detection, summarization, association rule mining, missing distribution and imputation technique, spurious relationship or spurious correlation, concept of performance window, missing trends or percentile distribution from time perspective and concept of winsorization or flooring
- Submodule 2: Regression models: linear and non-linear, logistic, variable transformation, spinning of variables, population stability index and characteristic analysis
- Submodule 3: Regularization, overfitting and underfitting, mean square error, root mean square error, mean absolute percentage error
- Submodule 4: Decision tree classification, support vector machine, k-means clustering, usage of clustering techniques in variable selection
Module 4: Reporting Fundamentals
- Submodule 1: Anatomy and types of reports
- Submodule 2: Top-down approach: drill down reports and dashboards
- Submodule 3: Bottom-up approach: analysis and prediction with ad-hoc queries
- Submodule 4: Strategies and techniques for effective reporting: best practices
Module 5: Reports for Data Analysis
- Submodule 1: Descriptive analysis and its reports: Key Performance Indicator (KPI) dashboards and periodic reports
- Submodule 2: Diagnostic analysis and detailed drill down reports
- Submodule 3: Predictive analysis and reports based on predictive models
- Submodule 4: Prescriptive analysis and reports based on AI/ML models
Module 6: Data Reporting Tools
- Submodule 1: Graphs and Charts: types and implementation
- Submodule 2: Tables: varieties and its usage in standard reports
- Submodule 3: Dashboards and drill down reports
- Submodule 4: Interactive reports
- Submodule 5: Report generation best practices based on case studies
3. Data Modelling & Visualization
Module 1: Data-Analytic Thinking
- Submodule 1: Knowing your data
- Submodule 2: Data preprocessing
- Submodule 3: Storytelling with data
Module 2: Data Visualization using R
- Submodule 1: Introduction to R programming
- Submodule 2: Visualization using R
- Submodule 3: Transformation using R
- Submodule 4: Exploratory data analysis
Module 3: Data Modeling
- Submodule 1: Linear regression
- Submodule 2: Logistic regression
- Submodule 3: K-nearest neighbors
- Submodule 4: K-means clustering
- Submodule 5: Performance measure
- Submodule 6: Implementation of some modeling algorithms using R
Module 4: Data Visualization using Tableau
- Submodule 1: Introduction to Tableau, data import and management, data type and operations
- Submodule 2: Different types of data visualizations, dashboards, storytelling
- Submodule 3: Understanding the concepts of dynamic/interactive data visualization and report generation
Module 5: Data Modeling from Different Data Sources for Visualization
- Submodule 1: Understanding structured, unstructured and semi-structured data sources
- Submodule 2:Data modeling and creating visualization charts/dashboards from structured data like databases (SQL and NoSQL)
- Submodule 3: Data modeling and creating visualization charts/dashboards from semi-structured data like CSV files, XML, JSON and others
- Submodule 4:Data modeling and creating visualization charts/dashboards from live streaming data.
Honours Degree In Data Science
|
||
Program Fee
Programme Fee: INR 40002 |
|
|
Application Process
Register to Honour/Minor degree by filling online application and by paying registration fee.
Get your online application approved.
Start enrolling to Honour/Minor degree program.
Admissons are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.
Deadline:10-Apr-2024
Frequently Asked Questions
1. What Is a Honours Degree?
1. The Honours degree programme is designed to let the graduate engineering students to earn experiences to enhance themselves as professional engineers in the competitive world. Through this
2. How will this programme help students in building their proficiency?
2. This programme is envisaged to help build a strong foundation across various streams through digital lectures, formative and summative assessments, mini projects, mentorship from renowned
3. How is this programme different from other industry programmes?
3. This programme is designed to continuously make academic content more relevant to students by integrating industry inputs, which is a compelling need of the industry.
4. Will there be any internship and job opportunity provided through this programme?
4. Remote Internship opportunities will be offered to students who have completed their industry assignments, subject to vacancies and the corporate hiring policy. Successful students who hav
Contact
Still have queries?
Contact Us
Please fill in the form and a Program Advisor will reach out to you. You can also reach out to us at onlineprograms@vtu.ac.in or 9480223900