There is no doubt that data science is one of the fastest-growing fields in technology today. Organizations increasingly embrace big data in today's competitive business world, increasing the need for a data scientist skilled at transforming gigabytes of data into actionable insights. Did you know that according to research conducted by McKinsey. Customer analytics can help companies acquire customers 23 times more effectively than their competition?
A data scientist plays a very critical role when it comes to processing and analyzing data. There are different types of data scientists in an organization, and they are assigned various roles under various designations. But before we learn about the different types of data scientists, let us look at the benefits of data science.
Data scientists collect and analyze data to empower the management to make better decisions. These decisions, in turn, help businesses to define goals and develop an action plan. Data scientists also challenge their colleagues to learn and adopt best practices that generate the best results, leading to high ROI. Many HR professionals also seek the help of data science to identify resources for the organization and recruit the best talent. If you want to penetrate a new market or run a check on the profiles of your ideal customers, data scientists are your go-to professionals.
Having said that, let us now look into the different types of data scientists and their role in an organization.
Different types of Data Scientists
1. Data Scientist as Statistician
Statistics is data analysis in the traditional sense. There is a possibility that they have finance-related experience. Statisticians are adept at hypothesis testing, confidence intervals, ANOVA, and data visualization in addition to quantitative research. They are also known for their expertise in hypothesis testing and confidence intervals. They deal in both theoretical and applied statistics to facilitate business goals. As a result, they can acquire expertise in various fields of data science, including confidence intervals and data visualization. In addition to being highly literate in statistics, Statisticians will also have excellent communication skills.
2. Data Scientist as Actuarial Scientist
There has been an evolution in actuarial science over the last few decades. It is now heavily used by banks and financial institutions to forecast market conditions and determine future income, revenue, profit, and losses. Actuarial scientists are experts in mathematical and statistical knowledge from the BFSI industry and other associated professions. Knowing several interrelated mathematics subjects, such as probability, statistics, finance, economics, financial engineering, and computer programming, is also necessary.
Although being an actuarial scientist without that training is possible, the training and certification of a data scientist will improve your understanding of mathematical and statistical algorithms,
3. Data Scientist as Research Data Scientist
A Research Data Scientist enhances subject matter expertise, builds trust with partners, recognizes opportunities, develops a strategy, and leverages data science methodologies. They also create reports, make summaries, and conduct other analytical activities, which is crucial. They assist in achieving the goals in broad areas of operation by providing guidance and coordinating with other data scientists. Large think tanks, financial institutions, and research institutions especially benefit from the skills of research data scientists. Researchers who specialize in data science can handle large datasets well.
4. Data Scientists as Software Programming Analysts
Analysts in software programming are in charge of calculating data and automating routine tasks related to big data to speed up computing times. Besides handling the database, they also need to be proficient in using tools such as extract, transform and load (ETL) tools, which extract, transform and load data into visual dashboards, charts and histograms. Software Programming analysts have a knack for number crunching through programming. Logic is their forte; they learn new programming languages such as R programming, Python, Apache Hive, Pig, Hadoop, and data analytics and visualization.
5. Data Scientists as Mathematicians
Mathematicians are well-versed in broad hypothetical research. Owing to the development of big data and data science, they are now more involved in the functioning of the corporate world than ever before. They are also more acknowledged for their profound knowledge of operations research and applied mathematics. Mathematicians have become crucial to corporate teams due to their profound knowledge of applied mathematics and operational research. Businesses utilize their divine services to execute optimization and analytics in various areas, such as inventory management, supply chain, pricing algorithms, etc.
6. Data Scientist as Data Engineer
Data engineers thrive on programming skills and the ability to make data tangible to data scientists. They clean, aggregate, and perform ETLLinks to an external site, processes on large datasets as part of this software development role. They also build data pipelines to get the data to the analysts and scientists within an organization. A data scientist is tasked with analyzing and processing data in accordance with an organization's needs. Additionally, he or she designs, builds, and manages the information captured.
7. Data Scientists as Machine Learning Engineers
Machine Learning Scientists develop algorithms to suggest products, and pricing strategies, extract patterns from big data inputs, and demand forecasting. These strategies can be extrapolated for better inventory management, strengthening supply chain networks, etc.). Many ML engineers build neural networks programmed for adaptive learning, which can be trained to make some decisions when the same set of inputs is given to them.
Thus, to conclude
We hope this article helped you understand the different types of data scientists and their varied roles and responsibilities. You may now try to determine your personality if you are serving in one of these fields.
If you want to pursue a career as a Data Scientist, you can check out our Data Science BootCamp. In collaboration with IBM. We offer a self-paced learning format that will help you learn to make data-driven decisions. It also includes exclusive hackathons and Ask Me Anything sessions by IBM experts and Caltech instructors.
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