Data Scientists are responsible for sharing their findings with key stakeholders, so these roles require someone who is not only adept at handling the data, but who can also translate and Communicate findings across the organization
Math and Statistics
Statistics.
It is important to know key terms like mean, median, mode, maximum likelihood indicators, standard deviation, and distributions. Data scientists should understand sampling techniques and how to avoid bias in experiments. Descriptive statistics paint a picture of the data through charts and graphs, while inferential statistics help you make predictions using that data.
Probability.
Probability helps you perform statistical tests, so you can tell if you are truly uncovering meaningful trends in the data
Linear algebra.
Linear algebra is the backbone of important algorithms, and knowledge of matrices and vectors will definitely help, especially if you specialize more in machine learning.
Multivariate calculus.
Brush up on mean value theorems, gradient, derivatives, limits, the product, and chain rules, Taylor series, and beta and gamma functions. regression algorithms and may face calculus problems in interviews.
Programming and Database
To move from the theoretical into creating practical applications, a Data Scientist needs strong Programming skills.
Most businesses will expect you to know both Python and R, as well as other programming languages.
Object-oriented programming, basic syntax, and functions, flow control statements as well as libraries and documentation all fall under this umbrella.
You need to be proficient in SQL as a data scientist.
This is because SQL is specifically designed to help you access, communicate and work on data. It gives you insights when you use it to query a database.
Domain Knowledge
A Data Scientist should keep in mind, can be defined in context to the —
The source problem, the business is trying to resolve and/or capitalize on.
The set of specialized information or expertise held by the business.
The exact know-how, for domain specific data collection mechanisms.
Communication and Vizualisation
Data visualization is a key component of being a Data Scientist as you need to be able to effectively communicate key messaging and get buy in for proposed solutions.
Understanding how to break down complex data into smaller, digestible pieces as well as using a variety of visual aids (charts, graphs, and more) is one skill any Data Scientist will need to be proficient in order to advance career-wise.
Data doesn’t communicate without someone manipulating it to be able to do so, which means an effective Data Scientist needs to have strong communication skills.
Whether it’s disseminating to your team what steps you want to follow to get from A to B with the data, or giving a presentation to business leadership, communication can make all the difference in the outcome of a project.
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