Data Science has always been about combining the tools best suited to get the job done. It is about the extraction of knowledge from data to answer a particular question. For me, putting it simply, data science is a power that allows businesses and stakeholders to make informed decisions and solve problems with data.
Now, not every technologist is passionate about every other skill, but she would be excited about skills from her area of work. So are some of the skills for a Data Scientist. Let’s explore most in-demand skills for a data scientist in 2020!
Probability & Statistics
Data Science is about using capital processes, algorithms, or systems to extract knowledge, insights, and make informed decisions from data. In that case, making inferences, estimating, or predicting form an important part of Data Science. Probability with the help of statistical methods helps make estimates for further analysis. Statistics is mostly dependent on the theory of probability. Putting it simply, both are intertwined.
Multivariate Calculus & Linear Algebra
Most machine learning, invariably data science models, are built with several predictors or unknown variables. A knowledge of multivariate calculus is significant for building a machine learning model. Here are some of the topics of math you can be familiar with to work in Data Science: Derivatives and gradients, Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function, Cost function (most important), Plotting of functions, Minimum and Maximum values of a function, Scalar, vector, matrix and tensor functions.
Programming, Packages and Software
Data Science essentially is about programming. Programming Skills for Data Science brings together all the fundamental skills needed to transform raw data into actionable insights. While there is no specific rule about the selection of programming language, Python and R are the most favored ones. Data Scientists choose a programming language that serves the need of a problem statement in hand. Python, however, seems to have become the closest thing to a lingua franca for data science.
Often the data a business acquires or receives is not ready for modeling. It is, therefore, imperative to understand and know how to deal with the imperfections in data. Data Wrangling is the process where you prepare your data for further analysis; transforming and mapping raw data from one form to another to prep up the data for insights. For data wrangling, you basically acquire data, combine relevant fields, and then cleanse the data.
With heaps and large chunks of data to work on, it is quintessential that a data scientist knows how to manage that data. Database Management quintessentially consists of a group of programs that can edit, index, and manipulate the database. The DBMS accepts a request made for data from an application and instructs the OS to provide specific required data. In large systems, a DBMS helps users to store and retrieve data at any given point of time.