A Peep into Data Scientist Knowledge and Skills
Though it intercepted years ago, Data Science is now majorly expanding due to the data flood caused by the Internet of Things. Data is being produced every second and owing to the advancement in technology, now it is possible to store this data and use it for various purposes.
As a result of this data surge, humans can benefit to an inexplicable level. It can be helpful in predicting a natural disaster, curbing crime and off course, add value to businesses. The business community is extraordinarily enthusiastic to leverage large sets of data, also called big data, to capture more profits.
Organizations are on a hunt for Data Scientists who can enable them in reaping the benefits out of the unimaginable amount of data around us that is being created by people through social media, Google searches and so on. Entrepreneurs have gotten the opportunity to get into the minds of the consumers and understand what they like and dislike. This way, it is becoming easy for them to offer better products and services to customers, make lucrative moves by judging market trends and attract more profits.
Hence, Data Science Knowledge is much in demand these days. Data Scientist is the most celebrated stratum of professional since it is declared to be the sexiest job of the 21st century by the Harvard Business Review. Their work revolves around extracting valuable information out of complex data by applying quantitative as well as programming methods to provide insights on how to use that information for a relevant purpose.
Now if you are wondering what are the skills that make someone a data scientist, here is all you need to know.
Data Science Knowledge and skills
- A data scientist must own skills as well as knowledge in the below-mentioned areas:
- Data, statistics, and other quantitative methods.
- Programming, engineering, or computer science.
- Domain under investigation.
Having an understanding of one area but not the other ones do not make someone a data scientist in a holistic manner. If you have statistic skills but do not have programming skills to run modern machine learning along with production models, then you can be called a well-rounded data scientist. Hence, attaining knowledge in all the areas makes a person successful in this field since an amalgamation of a multitude of skills goes in producing value out of puzzled, complicated data sets.
In a business setup, there is a procedure that every certified data science professional must have expertise in. It is called Cross-industry standard process for data mining which and it encompasses:
- Business understanding: the knowledge of a particular domain.
- Data understanding: descriptive statistics along with the analysis of the quality of data.
- Data preparation: data cleaning, building new variables together with integrating data sets.
- Modeling: Models can be seen as descriptions of assumed structures of the samples of data observations. So, basically, modeling involves the identification of techniques along with running them.
- Evaluation: the assessment of how appropriately the selected model meets the objectives of the business.
- Deployment: It is when the model is deployed for enabling users to utilize it in the future and develop schemes for maintenance.
- Certified Data Science Professionals have to own profound knowledge of data collection as well as data management techniques. They are also required to apply data visualizations to represent the discoveries made from the data. The visualizations incorporate pie charts, line graphs and bar charts.
