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Data Analytics

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Published in: Data Analysis Certification | Data Science | DBMS & RDBMS
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Data Analytics is the process of collecting, cleaning, analyzing, and interpreting data to discover useful insights, identify patterns, and support decision-making. It involves using statistical methods, programming skills, and visualization tools to turn raw data into meaningful information.

Ganesh M / Hyderabad

5 years of teaching experience

Qualification: Btech

Teaches: DBMS & RDBMS, PL/SQL, Tableau, Data Mining, Data Science, Matlab

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  3. BASIC DEFINITION : Data : Data is a set of values of Qualitative or Quantitative variables. It is information in raw or unorganized form. Its fact, figure, characters ,symbols etc. Information : meaningful or organized data is information, comes from analyzing data. .9. o
  4. WHAT IS DATA ANALYTICS? It is the process of analyzing raw data to get insights to find pattens above the information using varies techniques and processes. the raw data transform in to a format which could be a further visualized and made human readable . Ill Data Analysis Idea
  5. WHY IS DATA ANALYTICS SIGNIFICANCE? Businesses is using data analytics to optimize and grow by making better business decisions and finding more efficient way of doing business. The company can build a new and better products with the buying pattens and better understanding what the customers need
  6. HOW DOES DATA ANALYTICS WORK? The data analytics process some components that can variety of initiatives by combining those components. a successful data analytics initiative will provide a clear picture. Where you are? Where you have been? Where you should go?
  7. 4 types of Data Analytics Value Prescriptive What is the data telling you? Descriptive: What's happening in my business? Comprehensive, accurate and live data Effective visualisation [iagnostic: Why is it happening? Ability to drill down to the root-cause Ability to isolate all confounding i nformation Predictive: What's likely to happen? Business strategies have remained fairly consistent over time • Historical patterns being used to predict specific outcomes using algorithrns Decisions are automated using algorithms and technology Prescriptive: What do I need to do? Recommended actions and strategies based on champion / challenger testing strategy outcomes Applying advanced analytical techniques to make specific Complexity
  8. Data Anal sis Process O Business Problem Definition (6) O Inventory and Data Collection Result Commonication & O Data Cleaning Data Analysis Choose The Right Model Eventual Readjustment
  9. DATA ANALYTICS TOOLS on ac Ine earnmg Excel
  10. DATA ANALYTICS TECHNIQUES Machine learning Data mmmg Ne ural networks öö
  11. 7 Data Analytics Trends Facilitating Intelligent Decision Making Decision Intelligence (DI) It helps reduce the time and effort for deploying complex business models. Augmented Consumer Analytics Through this users can have augmented alerting and natural language queries when exploring their data. Edge Data and Analytics The power of edge analytics is enhanced by enabling the ML model to detect alarming situations and take necessa ry actions. Scalable Al & Analytics Scalable Al platforms built, must be able to operate with small and smart data like scoring machine learning (ML) for re- al-time information. Composable Data & Analytics Composable data and analytics are es- pecially useful in supply chain manage- ment in telematics, fleet operations, maintenance records, etc. Data Fabric It helps create new business propositions, activates new customer touchpoints, streamlines operations, and more. Small And Wide Data Analytics It focuses on small structured and unstructured data to enhance contextual understanding decisions.
  12. 1 VI PORTANCE OF DATA ANALYTICS the of operation predict future trends St r ity a Analyze, interpret and deliver data in meaningful ways. QPredict customer trends and behaviors. Olncrease business productivity. ODrive effective decision-making
  13. SKILLS REQUIRED FOR DATA ANALYTICS Prwamming python IRI MATLAB Data visualization Data warehousing Data mining, cleaning, and mumging Machinelearning 0 SQL and NOSQL Technical skills Non- Technical skills Problem-solving Teamwork
  14. BENEFITS OF BUSINESS DATA ANALYTICS Informed decision — making Higher competitive Improved efficiency Cost optimization Better customer understanding Ability to make faster, more informed business decisions, backed up by facts. Deeper understanding of customer requirements which, in turn, build better business relationships.
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