A Data Scientist incurs all the skills of a data analyst with the additional skills of data wrangling, complex machine learning, Big Data tools, and software engineering. It is observed that both Data analysts and Data scientists use the same tools and practices. However, the scope and nature of the problem addressed by a Data scientist differ from a data analyst. Data scientists mainly deal with large and complex data that can be of high dimension, and carry out appropriate Machine learning and visualization tools to convert the complex data into easily interpretable meaningful information.
Some of the fundamental prerequisites that a Data scientist should be thorough with are as follows:
Statistics: Statistics is the most prerequisite field in the area of data science. Data science is mostly about Statistics and to master Data science, good knowledge in statistics is mandatory. The two kinds of statistics mostly used in Data science are — Descriptive statistics and Inferential statistics.
Mathematics: To enhance one’s skills in Machine learning, a Data scientist should have a profound knowledge of Mathematics. The two most important topics in Mathematics in terms of use in data science are linear algebra and Calculus. While linear algebra is all about the study of vectors and linear functions, Calculus is the mathematical study of continuous change. Many of the concepts of Linear algebra, such as tensors and vectors are used in many areas of machine learning. Similarly, calculus is also required in various areas of Machine learning, such as optimization techniques.
Computer programming: A Data scientist should be a programming lover. Other than the basic computer application skills such as mastering in Microsoft Excel, a data scientist should have programming skills to be able to easily write code in Python or R for any given Data science project. MS Excel can be used as a basic tool for a beginner in the field of Data science as it can easily handle complex numerical calculations as well as allow plotting of data visualization graphs. Both Python and R are considered as excellent programming tools for handling Statistical analysis and Machine learning skills.
Database handling: A Data scientist also often has to deal with data that are stored in databases. In the case of Relational Database Management Systems (RDBMS), a data scientist should have the prerequisites of handling database queries using SQL commands. As data extraction is a primary task in data science, SQL is an important tool for accessing and manipulating data that is maintained in databases.
Data scientists can be engineers but are usually not involved in maintaining data architecture. The primary task of a data scientist is to use Machine learning and deep learning-based techniques to make an in-depth analysis of input data. This is where a data analyst lacks his/her skills as an analyst may not possess much Machine learning or Deep learning-based skills.
If you are interested in Data Science and would like to explore more out of interest or to apply it to certain real-life problems, then this book is for you — Data Science Fundamentals and Practical Approaches.
The content of the book describes the fundamentals of Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of the Python programming language.
Hope this was helpful.
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