The story of how data scientists became mostly the story of the coupling of the mature discipline of statistics with a very young one–computer science. The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms. 1962 John W. Tukey writes in “The Future of Data Analysis”: “For a long time I thought I was a statistician, interested in inferences from the particular to the general. But as I have watched mathematical statistics evolve, I have had cause to wonder and doubt… I have come to feel that my central interest is in data analysis… Data analysis, and the parts of statistics which adhere to it, must…take on the characteristics of science rather than those of mathematics… data analysis is intrinsically an empirical science…
Data science is that an academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract the knowledge and insights from structured, and unstructured data.
A functional data scientist has a good understanding of software architecture and understands
multiple programming languages. The data scientist defines the problem, identifies the key sources of information, and designs the framework for collecting and screening the needed data. Software is specially responsible for collecting, processing, and modeling the data. They use the principles of Data Science, and all the related sub-fields and practices encompassed within Data Science, to gain deeper insight into the data assets under review.
While data science projects and tasks may vary depending on the enterprise, there are primary job functions that tend to be common among all data science positions such as:
1) Collecting massive amounts of data and converting it to an analysis-friendly format.
2) Problem-solving business-related challenges while using data-driven techniques and tools.
3) Using a variety of programming languages, as well as programs, for data collection and analysis.
4) Having a wealth of knowledge with analytical techniques and tools.
5) Communicating findings and offering advice through effective data visualizations and comprehensive reports.
6) Identifying patterns and trends in data; providing a plan to implement improvements.
7) Predictive analytics; anticipating future demands, events, performances, trends, etc.
8) Contributing to data mining architectures, modeling standards, reporting and data analysis methodologies.
9) Inventing new algorithms to solve problems and build analytical tools.
10) Recommending cost-effective changes to existing procedures and strategies.
Data Science has become an important part of business and academic research. Technically, this includes machine translation, robotics, speech recognition, the digital economy, and search engines. In terms of research areas, Data Science has expanded to include the biological sciences, health care, medical informatics, the humanities, and social sciences. Data Science now influences economics, governments, and business and finance.
What is the relation between Statistics and Data Science?
Statistics in data science, in its roots, find a structure and relations between various unflustered data. Structuring the data helps reveal different valuable insights behind your collected data.
For instance, in case of a medical emergency, knowing the percentage of people affected can help you devise methods to counter the issue. Similarly, structuring your buyers based on different age groups helps you devise ads and help you know your target audience better in data science.
But you can’t know these truths by collecting individual, irrelevant information. Statistics present the data in structured forms through tools like pie charts, bar graphs, among others.
Top 9 Job Roles in the World of Data Science for 2023
-Machine Learning Engineer
-Data and Analytics Manager
-NLP engineers (Natural Language Process)