Lecture 1.1.2 (2).pptxllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll

Lecture 1.1.2 (2).pptxllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll

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  DISCOVER . LEARN. EMPOWER UNIT-1 UNIVERSITY INSTITUTE OF COMPUTING MASTER OF COMPUTER APPLICATIONS DATAANALYTICS 23CAH-725 1
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  2 TYPES OF DATA ANALYTICS CO Number TitleLevel CO2 Apply the data distributions modes used to define and organize the data Understand Course Outcome Will be covered in this lecture
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  Vision of theDepartment: 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
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  Types of DataAnalytics Predictive analytics Descriptive analytics Prescriptive analytics Diagnostic analytics
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  Descriptive Analysis For atrue descriptive analytics program to be implemented, the concepts of repeatability and automation of tasks must be top of mind. Repeatability in that a data process is standardized and can be regularly applied with minimal effort (think a weekly sales report), and automation in that complex tasks (VLOOKUPS, merging of excel spreadsheets, etc.) are automated—requiring little to no manual intervention. The most effective means to achieve this is to adopt a modern analytics tool which can help standardize and automate those processes on the back end and allow for a consistent reporting framework on the front end for end users.
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  • Descriptive analysisis 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.
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  • Measurements ofFrequency • Measures of central tendency • Measures of dispersion • Measures of position Four Types of Descriptiv e Analysis
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  Diagnostic Analytics • Diagnosticanalytics, 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.
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  Purpose of DiagnosticAnalytics • Finding the root cause • Identifying and resolving issues • Improving processes • Evaluating performance • Validating hypothesis • Assessing data quality • Managing risk
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  Predictive Analytics Predictive analyticsis a form of advanced analytics that determines what is likely to happen based on historical data using machine learning. Historical data that comprises the bulk of descriptive and diagnostic analytics is used as the basis of building predictive analytics models. Predictive analytics helps companies address use cases such as: • Predicting maintenance issues and part breakdown in machines. • Determining credit risk and identifying potential fraud. • Predict and avoid customer churn by identifying signs of customer dissatisfaction.
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  How does predictiveanalytics work • Define the problem • Acquire and organize data • Pre-process data • Develop predictive models • Validate and deploy results
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  Prescriptive Analytics Prescriptive analyticsis the fourth, and final pillar of modern analytics. Prescriptive analytics pertains to true guided analytics where your analytics is prescribing or guiding you toward a specific action to take. It is effectively the merging of descriptive, diagnostic, and predictive analytics to drive decision-making. Prescriptive analytics requires strong competencies in descriptive, diagnostic, and predictive analytics which is why it tends to be found in highly specialized industries (oil and gas, clinical healthcare, finance, and insurance to name a few)
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  Prescriptive Analytics Prescriptive analyticshelp to address use cases such as: • Automatic adjustment of product pricing based on anticipated customer demand and external factors. • Flagging select employees for additional training based on incident reports in the field.
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  Prescriptive Analytics Examples •Financial Services • Healthcare • Energy utilities • Retail consumer • Life Sciences • Public sector • Travel hospitality • Manufacturing
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