Hands-on Training Workshop on Data Analytics

Professional Development Center (PDC) of Virtual University in collaboration with Department of Computer Science and Information Technology is organizing 4-days fully comprehensive hands-on training workshop on Data Analytics.

The four days’ workshop is designed with technical and business knowledge, enables you to navigate the world of Data Analytics. It will enable the participants to write software codes using Python Programming Language and using FORIS Analytics suite to perform their research on historical data available with the university or any external data sources. This workshop will support the participants to understand the basis of data, can plan for data collection and make structure. Participants would be able to perform Data Filtration, cleansing and managing the data for use as per analytics necessities.

Who Should Apply? Everyone who intends to have knowledge on Data Analytics.

Resource Person/Trainer: Industry professionals will train the participants.

1. Introduction to Data Analytics
2. Evolution of Data Analytics
3. Dealing with Different Types of Data
4. Data Science Methodology

Lunch Break

5. FORIS Introduction
6. FORIS Features
7. Experience with Data
8. Real Time Decision Making
9. Data Analytics
10. Learning Outcomes

1. FORIS Login/ Admin
2. Briefly Explain FORIS Modules
3. ETL Data Loading with Python
4. Data Mart Builder/Cube
5. Data Mart Builder/Cube for Practice

Lunch Break

6. Basic Plotting
7. Data Analysis Vs Data Science Vs Machine Learning Vs Deep Learning
8. Introduction to Python
9. Setting up environment and introducing Jupyter notebook
10. Basic mathematics function on Python
11. Develop a program based on control structures (IF, Else , ELIF) statement
12. Develop program using loop Structures
13. String Handling

Tea Break

14. Develop program using functions
15. Develop program using dictionaries
16. Develop program using list
17. Learning Outcomes
18. Descriptive statistic

1. Pandas data structures
2. Data preprocessing using pandas
3. Import and install Matplotlib
4. Preparing the data
5. Creating the plot

Lunch Break

6. Develop program using list
7. Mean, Median & Mode
8. Standard Deviation

Tea Break

9. Percentile
10. Correlation
11. KNN https://www.geeksforgeeks.org/
12. Learning Outcomes

1. Statistical Analysis
2. Graphical Representation

Lunch Break

3. Dashboard Development
4. Dashboard Customization
5. Case Study (Virtual University Students Data)
6. Learning Outcome & Closure

Closing Remarks by Rector and Shield Distribution
Vote of thanks by HOD
Tea and End of Workshop