Honours in Data Analytics TCS iON - July 2024
Data Analytics
Enquire now: 9480223900
Why choose our Data Analytics
Learn Data Summarisation, Data Visualisation, Descriptive, predictive and perspective analytics with Excel, Data processing, Data Mining, Text analytics, Data analytics using programming tools, Data reporting and tools
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 Summarisation, Data Visualisation, Descriptive, predictive and perspective analytics with Excel, Data processing, Data Mining, Text analytics, Data analytics using program
Remote internships
Learn Data Analysis using Excel, Practical Approach to Data Mining and Analytics, Data Analytics & Reporting
Project based industry assignments
Visibility to placement opportunities for successful candidates, subject to hiring criteria of corporates.
Curriculum
Course 1. Data Analysis using Excel:
Data Analysis with Excel is a course that will help students gain knowledge and develop skills on Data Analytics and Microsoft Excel software to understand the importance of the software and apply the same in solving analytics problems and generating insights for business decisions. This course starts with basic topics such as Data Handling with Excel, followed by Advanced Data Handling, Data Visualisation and Dashboarding. The course also focusses on descriptive, diagnostic, predictive and prescriptive analytics using Microsoft Excel as a tool.
Course 2. Practical Approach to Data Mining and Analytics:
Practical Approach to Data Mining and Analytics lays the foundation of the basic and advanced concepts of data mining and data analytics supported with latest industry relevant business case studies. Students can learn data mining skills, tools and techniques in analytics, statistics and programming. This course demonstrates the usage of raw data in the data mining process, various visualisation techniques, and teaches students to perform analysis and interpretation of data.
Course 3. 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.
1. Data Analysis using Excel
Module 1: Refresher on Excel
- Introduction to Excel - Cell and formatting, report building
- Text operations with Excel
- Functions with Excel - vlookup, hlookup, match, sumproduct, ifelse and more
Module 2: Data Summarisation and Data Visualisation with Excel
- What-If analysis and goal programming with Excel, PivotTable
- Basic and advanced data visualisation with Excel
- Interactive data dashboards with Excel
Module 3: Descriptive and Diagnostic Analytics with Excel
- Introduction to data analysis
- Introduction to probability and statistics
- Distributions with Excel
- Hypothesis testing using Excel
Module 4: Predictive Analytics with Excel
- Introduction to regression analysis
- Linear regression analysis with Excel
- Advanced regression with Excel
Module 5: Prescriptive Analytics with Excel
- Introduction to linear programming
- Linear programming with Excel
Practical Approach to Data Mining and Analytics:
Module 1: Introduction to Data Mining
- Submodule 1 Basic concepts of Data Mining
- Submodule 2 Data Mining techniques
- Submodule 3 Related technologies
Module 2: Data Preprocessing
- Submodule 1 Data cleaning
- Submodule 2 Data transformation
- Submodule 3 Data reduction
- Submodule 4 Discretisation and generating concept hierarchies
Module 3: Data Mining Algorithms
- Submodule 1 Association rules
- Submodule 2 Classification
- Submodule 3 Prediction
- Submodule 4 Clustering
Module 4: Data Analytics Using Programming Tools
- Submodule 1: Programming and Visualization Tools with their Associated Packages
- Submodule 2: Data Structures for Data Analysis
Module 5: Data Analytics
- Submodule 1 Association and correlation analysis - regression models
- Submodule 2 Predictive analytics
- Submodule 3 Exploratory analysis
Module 7: Case Studies
- Submodule 1 Image analytics
- Submodule 2 Text analytics
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
Honours Degree In Data Analytics
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Program Fee
Programme Fee: INR 40002 |
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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
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