Foundational course in Data Management, Visualisation and Analytical Modelling

Module of Advance Course in Embedded System Design with AI

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Course Objective

The Data Management, Visualization and Analytical Modelling course aims to develop learners' expertise in managing, processing, analyzing, and visualizing data to support informed decision-making. The course provides comprehensive knowledge of database management, data preparation, statistical analysis, predictive modelling, and interactive data visualization using industry-standard tools and technologies. Through hands-on projects and real-world case studies, learners will gain practical experience in designing data models, building analytical solutions, and creating meaningful dashboards and reports. Upon completion, participants will be equipped with the skills required to transform raw data into actionable insights and support business intelligence, data science, and AI-driven decision- making across diverse industries.

Course Structure

The course is designed as an 8-credit program, with a total duration of 180 hours. (Theory 60 and Practical 120) Classes will be conducted six days a week (Monday to Saturday). Each day will comprise 2 hours of theory lectures followed by 2 hours of practical laboratory sessions, providing learners with a balanced combination of conceptual knowledge and hands-on experience. Mode: Offline/Hybrid mode

Course Eligibility

Second year passed, B.Sc. / B. Com (Maths) /BCA/BCS, /B.E /B.Tech, any Diploma or Equivalent.

Prerequisite

The candidate must know basic statistical formulas and have an understanding of Excel. A basic understanding of machine learning concepts is not compulsory.

Course Content

Database Management Systems Introduction to Databases, Database Design Principles, SQL Syntax and Query Fundamentals, Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL), Transaction Management, Indexing and Optimisation Techniques, Normalisation, Entity-Relationship (ER) Model, SQL Joins and Subqueries, Stored Procedures and Functions, Database Security, Backup and Recovery Techniques Exploratory Data Analysis Data Quality Assessment, Handling Missing Values, Data Filtering, Outlier Detection, Data Transformation, Feature Scaling and Normalisation, Encoding Categorical Data, Data Integration, Date and Time Data Handling, Dealing with Duplicate Data, Using Regular Expressions for Data Cleaning, Data Validation Techniques Data Visualisation using Power BI Introduction to Power BI, Power BI Desktop Interface, Data Importing and Transformation, Data Modelling, Creating Dashboards, DAX Basics, Visualisation Techniques, Publishing and Sharing Reports, Power BI Service, Data Refresh and Scheduling, Integrating Power BI with Other Tools, Security Features in Power BI, Advanced DAX Functions Machine Learning Introduction to Machine Learning, Types of Machine Learning (Supervised, Unsupervised, Reinforcement), Data Preprocessing, Feature Engineering, Regression Analysis, Classification Techniques, Decision Trees, Random Forests, Support Vector Machines, Neural Networks, Deep Learning, Clustering Algorithms, Dimensionality Reduction, Ensemble Methods, Model Evaluation and Validation, Hyperparameter Tuning, Machine Learning Pipelines

Admission and Selection Process

The application form is available on the website. Candidates must submit the completed application form and appear for the Common Entrance Test (CET), which will be conducted offline at our centre. The CET syllabus and assessment will be based on the specific course module selected by the candidate. Candidates who successfully complete the written assessment will be required to appear for a Viva Voce (oral interview) as part of the admission process. Final selection will be based on the candidate’s performance in both the CET and the Viva Voce.

Fee Structure

Course Fees: Rs.25,000/