How to think like a data scientist?
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The document discusses how to think like a data scientist through data literacy and analysis. It outlines the steps in data analysis which include defining the problem, collecting relevant data, visualizing the data, finding statistical solutions, gaining deeper insights, and further analysis if needed. It emphasizes that data is ubiquitous, there is a growing demand for data literacy, and critical thinking and problem solving skills are important for data analysis. Visualization helps recognize patterns in data, but qualitative insights are also needed to account for variations and fully solve problems. Mastering this thought process can make someone more data oriented, even if they are not a statistics expert.
How to think like a data scientist?
- 2. It is extensively used in every industry. DATA IS UBIQUITOUS
- 3. Those who are unwilling to onboard the data train, are at a disadvantage There is a growing demand for Data Literate Managers AND
- 4. One does not need to be a Statistics Guru to be data literate
- 5. One can become Data Literate, by just changing their Thought Process. Critical Thinking Skills Problem Solving Skills Data Literacy
- 6. The Steps in Data Analysi s: Find the problem stateme nt Identify required data Collect the Data Visualiz e the data Find the Statistic al Solution Gain deeper insights Obtain the qualitati ve solution Further Data Analysis
- 7. Asking the right questions takes as much skill as giving the right answers. - Robert Half 1
- 8. Before starting with the analysis, it is important to identify the required data This comprises of: Inclusion of all relevant definitions A precise protocol to collect the data 2
- 9. A vital factor in data collection, is ensuring that the data is TRUSTWORTHY 3
- 10. The purpose of visualization is insight, not pictures. - Ben Schneiderman Data in its raw form is very confusing! Visualization of data helps in recognizing the patterns present in the data easily. 4
- 11. In this step, we get the statistical answer to our problem. Without any context, this solution is MEANINGLESS for us. 5
- 12. Having statistical insight, is not the solution to our problems. To be able to give a more relevant answer to the problem, we need to understand VARIATIONS 6
- 13. A closer look in the data is necessary to account for the variations. 6
- 14. A qualitative solution can finally be found for the problem. With a deeper insight into the data 7
- 15. Sometimes, despite the deeper insights a solution is not possible. At that time, another analysis needs to be carried out with the focus on other parameters 8
- 16. To conclude, the above thought process might not turn one into a data wiz, but it will make them more Data Oriented
- 17. References • www.flickr.com/photos/renatoaugellicanoneos350d/6130839729/ • https://qph.ec.quoracdn.net/main-qimg- 24cfc5bdbf09e79039791d6f507e5185 • https://i.pinimg.com/originals/d0/61/66/d06166f03bc23d90a4a282a 833441480.jpg • https://memegenerator.net/img/instances/500x/74609276/all- aboard-the-data-governance-train.jpg • www.flickr.com/photos/xybo/4502547721/ • www.flickr.com/photos/anndouglas/422362185/ • www.flickr.com/photos/pulpolux/1035292057/ • www.flickr.com/photos/lergik/2894259671/ • www.flickr.com/photos/aftab/8388318279/ • www.flickr.com/photos/linhtinh/2299388958/
- 18. THANK YOU By Muyeena A Khanzada