IMPACT OF DATA ANALYTICS IN EDUCATION SECTOR.pptx

it is all about data analytics

1. IMPACT OF DATA ANALYTICS IN EDUCATION SECTOR

2. TEAM MEMBERS Ugochukwu Ugonna Onuh Emmanuel Obiorah Divine Francis Tarilla

3. What is Data Analytics? Data analytics is the process of analyzing raw data to uncover meaningful insights, trends, and patterns. It involves various techniques and tools to extract valuable information from large datasets, helping businesses make informed decisions and optimize their operations. The workflow of data analytics typically includes data collection, data cleaning, data transformation, analysis, and interpretation.

4. What is Data Analytics in Educational Sector? It is the process of collecting, analyzing, and interpreting data to improve educational processes and outcomes. It can help schools and universities make better decisions about teaching, learning, and educational management.

5. TYPES OF DATA ANALYTICS Descriptive data analytics : This type of analytics is the foundation of reporting. Example it answers questions like “what, when, where, andhow many”. Diagnostic data analytics : It is used to identify the reasons for something happening by comparing historical data to other data. For example, a retailer can use it can be identified why they missed thier net profit target.

6. Predictive data analytics : It is used to forecast consumer actions, sales revenue, and stock levels. For example, like Jumia and Amazon use predicative analytics to recognizesales patternsand forecast consumer actions. Prescriptive data analytics : It is used to recommend the best course of action for patients and providers in healthcare, or to help financlial companies reduce product costs while keeping profits high.

7. IMPORTANCE OF DATA ANALYTICS IN EDUCATIONAL SECTOR It identify learning patterns : It helps in recognizing learning patterns, which can lead to personalized interventionsfor struggling students. Optimize resource allocation : It can help educational institutions optimise resouce allocation. Improve learner performance : With the aid of data analytcis, it is possible to monitor student actions, their learning patterns, and progress.

8. 4. Information security : Is the practice of protecting information by mitigating information risks. It is part of information risk management. In education is also a perfect way to improve information security.

9. ADVANTAGE OF DATA ANALYTICS IN EDUACTIONAL SECTOR It improves student learning outcomes : It can help teachers understand how students learn, what they know, and what they need. Better student support : it can help universitiesidentify students who are at risk of dropping out and offer them support. This can include tutoring, academic advising, and carrer counselling. Improved technology systems : Insights derived from big data can help in educational instituions improve thier technology systems.

10. Improves teching effectivenss : Data analysis can also help universities improvw teaching effectiveness by providing insights into instructional procatices and student learning outcomes. Improved work efficiency : businesses utilize data analytics frequently to find infective internal procedures and design new more effective workflows. Personalized learning : Data analytics enables educatore to gain valuable insights into students leaning patterns, preferences, and strength.

11. DISADVANTAGE OF DATA ANALYTICS IN EDUACTIONAL SECTOR Data Quality Issues : Poor data quality can lead to inaccurate insights and flawed decision-making, highlighting the importance of data cleansing and validation. Privacy Concerns : Collecting and analyzing personal data raises privacy concerns, requiring organizations to comply with data protection regulations and ethical standards. Complexity and skill gap : Implementing and managing data analytics tools and technologies can be complext requiring specialized skills and expertise that may not be readilty available.

12. 4. Security Risks : Storing and analyzing large volumes of data can pose security risks, such as data breaches and cyber threats, necessitating robust security measures. 5. Lack of Scalability : Scalability issues may arise as data volumes grow, requiring organizations to invest in scalable infrastrusture and solutions.

13. OVERCOMING CHALLENGES IN DATA ANALYTICS Investing in Data Quality : Prioritizing data quality through data cleansing, validation, and governance processes to ensure accuracy and reliability. Training and Upskilling : Providing training and upskilling programs to equip employees with the necessary skills and knowledge to leverage data analytics effectively. Collaboration and Integration : Encouraging collaboration across departments and integrating data from various sources to break down data silos and improve data consistency.