Course Objective
The Foundational course in AI and Statistical Analysis, Python and Excel Analytics course aim to provide learners with a strong foundation in artificial intelligence, statistics, Python programming, and Excel-based data analysis. The course develops essential skills in data collection, cleaning, statistical analysis, data visualization, and problem-solving using industry-standard tools and techniques. Through practical exercises and real-world case studies, learners will gain hands-on experience in analyzing data, creating insightful reports and dashboards, and applying introductory AI and machine learning concepts to support data-driven decision-making. Upon completion, participants will be well prepared for advanced studies in AI, data analytics, and business intelligence, as well as entry-level roles in data analysis and AI-enabled applications.
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.
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. No prior programming or artificial intelligence experience is required; however, a willingness to learn Python programming, data analysis, and AI concepts is essential.
Course Content
Business Statistics
Descriptive Statistics Data Types, Measure of central tendency, Measures of Dispersion, Graphical Techniques, Skewness & Kurtosis, Box Plot, Probability, Random Variable, Probability Distribution, Normal Distribution, SND, Expected Value, Inferential Statistics, Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence interval, Hypothesis Testing (2 proportion test, 2 t sample t test) Anova and Chi- square.
Basics of Excel
Introduction to Excel, Navigating the Excel Interface, Basic Excel Functions and Formulas, Data Entry Techniques, Formatting Cells and Sheets, Data Sorting and Filtering, Introduction to Pivot Tables, Conditional Formatting for Data Insights, Excel Functions for Statistical Analysis, Date and Time Functions, Lookup Functions (VLOOKUP, HLOOKUP)
Fundamentals of AI
Introduction to Artificial Intelligence, History of AI, AI Applications and Case Studies, Ethics in AI, Basic Machine Learning Concepts, Supervised vs. Unsupervised Learning, Neural Networks, Deep Learning Fundamentals, Natural Language Processing (NLP), Computer Vision Basics, Robotics and AI, AI in Business and Industry, Future Trends in AI.
Analytics in Python
Introduction to Python, Python Setup and Environment Configuration, Basic Syntax and Variables, Data Types in Python, Control Structures (if-else, loops), Functions and Modules, Exception Handling, NumPy for Numerical Data, Pandas for Data Manipulation, Data Cleaning Techniques, Introduction to Statistics with Python, Basics of Data Gathering and Web Scraping
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/
