Course Overview
| Module | Duration (Instruction al Hours) | Theory | Lab | Self-Study | Tool/Software |
|---|---|---|---|---|---|
| Data Analysis with MS Excel | 16 | 8 | 8 | 6 | Office 365 |
| Data Handling using T-SQL | 16 | 8 | 8 | 4 | Microsoft SQL Server 2019 |
| Managing Large DataSets with MongoDB | 32 | 16 | 16 | MongoDB 6.x | |
| Inferential Statistical Analysis | 16 | 16 | 4 | N/A | |
| Data Science using R Programming | 36 | 18 | 18 | 10 | R Studio 2.x or higher |
| Application Based Programming in Python | 36 | 18 | 18 | 10 | Python 3.6.x |
| Data Mining with WEKA | 24 | 12 | 12 | 8 | WEKA (Open- source AI and data mining tool) |
| Capstone Project for Data Mining | 24 | 2 | 22 | WEKA | |
| Total Hours | 200 | 98 | 102 | 42 |
EXIT PROFILE
Financial Data Analyst
MIS Manager
MIS Manager
CERTIFICATE NAME
Smart Professional-Foundation in Data Science
LEARNING OUTCOMES
- Work with spreadsheets and analyze data using MS Excel
- Learn fundamental concepts of Transact SQL(TSQL) commands
- Understand MongoDB concepts, features, architecture and data model, and learn to install, configure, and monitor open-source NoSQL databases
- Understand the basics of statistical analysis, descriptive statistics, predictive analytics, probability, and Bayes theorem
- Master data exploration, predictive s descriptive analytics techniques using R
- Develop applications using Python language
- Use WEKA to perform data mining tasks
- Develop a Capstone project using WEKA and implement data mining tasks