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1. 30 OCT, 2024 DATA ANALYTICS MADHU ARRURI 22011A0512

2. Introduction to Data Analytics Definition : The process of analysing, transforming, and modeling the data to find valuable insights and make decisions. This process is used across industries to optimize performance, improve decision-making, and gain a competitive edge.

3. Goals and Objectives of Data Analytics Purpose: To enable organizations to make data-driven decisions and optimize performance. Main Goals: Find important insights and trends in data. Understand why things happened. Predict what might happen in the future. Make recommendations for the best actions to take..

4. Types of Data Analytics Descriptive Analytics : Summarizes historical data to understand changes over time. Diagnostic Analytics : Identifies causes behind trends and events. Predictive Analytics : Uses statistical models and machine learning to forecast future outcomes. Prescriptive Analytics : Provides recommendations for possible actions based on predictive outcomes.

5. Descriptive Analytics Definition : Aims to answer “What happened?” by summarizing past data through statistical measures like averages, trends, and distributions. Purpose : Used to provide context and an understanding of historical patterns, setting a foundation for further analysis.

6. Diagnostic Analytics Definition : Aims to answer “Why did it happen?” by digging deeper into data to uncover patterns and root causes of outcomes. Approach: Involves techniques like correlation analysis, drill-down, and data mining to identify factors influencing results.

7. Predictive Analytics Definition : Aims to answer “What could happen?” by applying statistical and machine learning models to estimate the likelihood of future events. Methods Used : Includes regression analysis, classification models, and time-series forecasting to detect patterns and predict outcomes.

8. Prescriptive Analytics Definition : Addresses “What should we do?” by recommending specific courses of action based on data analysis and predictions. Application: Uses optimization, decision trees, and simulation techniques to evaluate potential actions and their outcomes.

9. DATA ANALYTICS LIFECYCLE DATA COLLECTION DATA CLEANING DATA PREPROCESSING DATA MODELING EVALUATION AND INTERPRETATION

10. Explanation: Involves gathering relevant data from various sources such as databases, IoT devices, and external APIs. Focus: Ensuring data relevance and accuracy, as the quality of data directly impacts the analysis outcome. DATA COLLECTION :

11. Explanation: The process of detecting and correcting inaccuracies, inconsistencies, and duplicates within the dataset. Importance : Maintains data quality by removing errors, which prevents misleading insights and ensures the reliability of analysis. DATA CLEANING :

12. Explanation: Preparing the cleaned data for analysis by transforming it into a suitable format through techniques like normalization, encoding, and feature selection. Goal: Ensures data consistency and prepares it for application of statistical and machine learning models. DATA PROCESSING :

13. Explanation: Applying mathematical and machine learning models to identify patterns, classify data, or make predictions . Common Models Used: Regression, clustering, classification, and neural networks, which provide different perspectives and insights based on data type and objectives. DATA MODELING:

14. Explanation: Assessing the accuracy and reliability of the model and interpreting the results to derive actionable insights. Objective: To validate findings and ensure the model’s effectiveness in solving the original problem or answering specific questions. EVALUATION AND INTERPRETATION:

15. KEY TOOLS FOR DATA ANALYTICS R: Specialized for statistical analysis and data visualization. Python: Used for data manipulation, machine learning, and visualization. Power B I : Business intelligence tool for creating dashboards and data reports. Apache Spark : Real-time data processing for large datasets.

16. Challenges in Data Analytics Data Quality : Poor data leads to unreliable insights. Data Security : Ensuring data privacy and protection. Handling Large Data : Managing and processing big datasets. Understanding Results : Making sure insights are clear and useful.

17. Data analytics plays a vital role in transforming raw data into insights that empower data-driven decisions and innovations.It allows businesses to understand trends, predict future outcomes, and optimize operations. As data continues to grow, the importance of data analytics in making informed choices will only increase.

18. THANK YOU 30 OCT, 2024