1. Big Data in Technical and Vocational Education (TVE)
Big Data in TVE By
NAMBOBI MUTWALIBI
Department of Technical and Vocational Education (TVE),
Islamic University of Technology, Gazipur-1704, Bangladesh

2. Big Data in Technical and Vocational Education (TVE)

3. What is big Data?
We define big data as the increase in volume, variety, velocity, veracity and value of data sets that require new forms of processing to enable greater decision making and insight discovery of interesting patterns. .

4. Outline and objectives
1. Concept of big data.
2. V’s of big data.
3. The tools and importance of big data in TVE.

5. V’s of big data cont.. 1.Volume – the amount of data is too big.
2. Variety- Data exists in different formats.
3. Veracity –trustworthiness and dependability of the information produced after analysis.

6. V’s of big data cont..
4. Velocity: Describes the rate at which incoming data requires to be processed. (its always high)
5.Value: Refers to the benefits accrued after using the analyzed data

7. How big is the data? Source: IDC’s Data Age 2025 study.

8. A big picture about the data?
The Library of congress collects text allover the world. And is the largest library in the world with digital books.

9. Big data! So what?
TVET Directorates in need of big data.

10. Table here TVET Directorates in need of big data. Research
Directorates/Bodies/Departments
Possibilities of big data
Research
Trending research, text research and archive retrieval
Student ,community development and Support
Sentiment Analysis, Student behavior (Mitigate issues and Student complaints before they go viral), reputation management.
Planning and Institutional Support
Analytics from financial audits, recommendation engines and forecasts
Examinations and Assessments
Performance Perditions
Curriculum Development and Support
Industrial requirement and trends through text analysis
Quality control
Be aware of damages, risky control and social impact of the social media. spoting anomalies
Legal
leverage s and documents
Procurement and logistics
Mileage analysis and changes in stock
Human resources
Performance tracking, recruitments, conflict resolution through text analytics of s
Table here

11. Where is the data? 1.Library and institutional clinics
2. , documents, spreadsheets
3.Data on mobile devices.
4.Web data and multimedia in files
5.File transfer and server logs.
6.Sensor data.
7.Social media, Messages.
8. ERP data, user engagement.
9. LMS, eLearning platforms, MOOCs

12. Big data tools

13. Big data tools
1.Spark- leverages grid computing for large parallel processing also using cloud services( Amazon)
2.R and Excel- statistical analysis
3.Hadoop/Map reduce -allows big problems to cut into smaller
4.Tableau-visualization and business intelligence tools
5.RapidMiner -predictive analytics & text analytics
6. Mapd,GraphX and Mlib- graphing analytics.

14. conclusion
In order for TVE stakeholders to cop up with the way society uses data, it has to shift; i) from business focused to personal targeting (skill development and innovation motives); ii) from structured to unstructured; iii) from reflecting yesterday’s data to here and now; All in all putting privacy measures at a peak.

15. A Big Thank you NAMBOBI MUTWALIBI www.nambobi.com
For more information, Reach me out or me at
NAMBOBI MUTWALIBI

16. References
Daniel, B. K. (2017). Big Data in Higher Education: The Big Picture. Switzerland : Springer International Publishing .
Goel, V. P. (2017). TECHNICAL AND VOCATIONAL EDUCATION AND TRAINING (TVET) SYSTEM\. Retrieved from unevoc:
IDC. (2017). Data Age 2025: The Evolution of Data to Life-Critical Don’t Focus on Big Data; Focus on the Data That’s Big.
Morabito, V. (2015). Big Data and Education: Massive Digital Education Systems Big Data and Analytics (pp ): Springer.