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1. DISCOVER . LEARN . EMPOWER UNIT-1 UNIVERSITY INSTITUTE OF COMPUTING MASTER OF COMPUTER APPLICATIONS DATA ANALYTICS 23CAH-725 1

2. 2 TYPES OF DATA ANALYTICS CO Number Title Level CO2 Apply the data distributions modes used to define and organize the data Understand   Course Outcome Will be covered in this lecture

3. Vision of the Department: To be a Centre of Excellence for nurturing computer professionals with strong application expertise through experiential learning and research for matching the requirements of industry and society instilling in them the spirit of innovation and entrepreneurship. Mission of the Department: M1 To provide innovative learning centric facilities and quality-oriented teaching learning process for solving computational problems. M2 To provide a frame work through Project Based Learning to support society and industry in promoting a multidisciplinary activity. M3To develop crystal clear evaluation system and experiential learning mechanism aligned with futuristic technologies and industry. M4 To provide doorway for promoting research, innovation and entrepreneurship skills in collaboration with industry and academia. M5 To undertake societal activities for upliftment of rural/deprived sections of society

4. Types of Data Analytics

5. Descriptive Analysis

6. Descriptive analysis is a sort of data research that aids in describing, demonstrating, or helpfully summarizing data points so those patterns may develop that satisfy all of the conditions of the data. It is the technique of identifying patterns and links by utilizing recent and historical data. Because it identifies patterns and associations without going any further, it is frequently referred to as the most basic  data analysis . When describing change over time, this analysis is beneficial. It utilizes patterns as a jumping-off point for further research to inform decision-making. When done systematically, they are not tricky or tiresome.

7. Diagnostic Analytics Diagnostic analytics, just like descriptive analytics, uses historical data to answer a question. But instead of focusing on “the what”, diagnostic analytics addresses the critical question of why an occurrence or anomaly occurred within your data. Diagnostic analytics also happens to be the most overlooked and skipped step within the analytics maturity model. Diagnostic analytics tends to be more accessible and fit a wider range of use cases than machine learning/predictive analytics. You might even find that it solves some business problems you earmarked for predictive analytics use cases. Diagnostic analytics is an important step in the maturity model that unfortunately tends to get skipped or obscured. If you cannot infer  why  your sales decreased 20% in 2020, then jumping to predictive analytics and trying to answer “what will happen to sales in 2021” is a stretch in advancing upward in the analytics maturity model.

8. Predictive Analytics

9. Prescriptive Analytics

10. Prescriptive Analytics

11. Prescriptive Analytics Examples Financial Services Healthcare Energy utilities Retail consumer Life Sciences Public sector Travel hospitality Manufacturing

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