DATA ANALYSIS Presentation Computing Fundamentals.pptx
- 1. DATA ANALYSIS Prepared and Presented By: AMMAR ABBAS SHAH (2020-ME-73) HARIS RIAZ (2020-ME-61) AHSAN HASEEB (2020-ME-75) AWAIS AHMED (2020-ME-58)
- 2. DATA ANALYSIS • What is Data Analysis? • What are the types of Data Analysis? • How Data Analysis process is carried out? • Data Analysis Tools
- 3. What is Data Analysis? • Data Analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision making. Example: • Retailers use data analysis to understand their customer needs and buying habits to predict trends and boost their business. • Healthcare industries analyze patient data to provide lifesaving diagnoses and treatment options. They also deal with healthcare plans, insurance information to derive key insights.
- 5. Types of Data Analysis There are 4 main types of Data Analysis. Data Analysis Diagnostic Data Analysis Predictive Data Analysis Prescriptive Data Analysis Descriptive Data Analysis
- 6. Descriptive Data Analysis Descriptive data analysis looks at past data and tells what happened. This is often used when tracking Key Performance Indicators(KPIs), revenue, sales leads and more. Example: 1) CGPA of a student. 2) Batting Average of a Batsman. 3) Previous sales record of a specific product.
- 7. Diagnostic Data Analysis Diagnostic Analysis shows ”WHY did it happen?” by finding the cause from the insight found in Descriptive Analysis. This analysis is useful to identify behavior patterns of data. If a new problem arrives in your business process, you can look into this Analysis to find similar patterns of that problem. And it may have chances to use similar prescriptions for the new problems. Examples: A freight company investigating the cause of slow shipments in a certain region. A company drilling down to determine which marketing activities increased trials.
- 8. Predictive Data Analysis Predictive Analysis shows “What is likely to happen?” by using previous data. This analysis makes predictions about future outcomes based on current or past data. Forecasting is just an estimate. Its accuracy is based on how much detailed information you have and how much you dig in it. Example: Risk Assessment Sales Forecasting Customer Success Teams
- 9. Prescriptive Data Analysis • Prescriptive Data Analysis combines the insight from all the previous analysis to determine which action to take in a current problem or decision. Example: • Artificial Intelligence (AI) is a perfect example of prescriptive data analysis.
- 10. Data Analysis Process Following are the steps which are carried out to complete data analysis: 1. Data Requirement Gathering 2. Data Collection 3. Data Cleaning 4. Analyzing Data 5. Data Interpretation 6. Data Visualization
- 11. Data Analysis Process Following are the steps which are carried out to complete data analysis: 1. Data Requirement Gathering 2. Data Collection 3. Data Cleaning 4. Analyzing Data 5. Data Interpretation 6. Data Visualization
- 12. Data Requirement Gathering • In this process, you decide these things: Aim of analysis Type of data analysis you want What to analyze? How to measure?
- 13. Data Collection • In this process, you collect your data based on your requirements. As you collected data from various sources, you must have to keep a log with a collection date and source of the data. Data may be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation.
- 14. Data Cleaning • In this process, you clean your data by removing not be useful or irrelevant to your aim of analysis. The data which is collected may contain duplicate records, white spaces or errors. So it must be cleaned and made error-free before analyzing data.
- 15. Analyzing Data • In this process, you manipulate your data. During this phase, you can use data analysis tools and software which will help you to understand, interpret, and derive conclusions based on the requirements.
- 16. Data Interpretation • In this section, you interpret your results. You can choose the way to express or communicate your data analysis either you can use simply in words or maybe a table or chart. Then use the results of your data analysis process to decide your best course of action.
- 17. Data Visualization • In this process, you use a graphical method to communicate your conclusions, results and findings to make it easier for human brain to understand. It can be in the form of charts and graphs etc. By comparing datasets, you can find a way to find out meaningful information.
- 19. Data Analysis Tools Following are one of the best, widely use data analysis tools: • Microsoft Power BI • SKYTREE • ROOT • Julia • PAW • ELKI
Editor's Notes
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