Top 10 Data Science Skills Big Tech Looks For In Candidates


by Aratrika Dutta


February 19, 2022

There are various data science skills for candidates to apply for big tech companies, here is a list of 10

Data science as one of the biggest jobs in the contemporary tech market is more often seen as a highly technical role. Data science is the cutting-edge technology that powers a wide range of industries and businesses today. Data science skills are crucial in the age of AI, big dataand automating. Companies are looking for skilled professionals who can handle the ever-increasing amount of data generated by their operations. There are many practical data science skills for candidates to apply for large technology companies. This article lists the top 10 data science skills that big tech is looking for in candidates.

Multivariate calculus and linear algebra

Most machine learning models, invariably data science, are built with multiple predictors or unknown variables. Knowledge of multivariate calculus is important for building a machine learning model. Here are some of the mathematical topics you can know to work in Data Science: Derivatives and Gradients, Step Function, Sigmoid Function, Logit Function, ReLU Function (Rectified Linear Unit), etc.

Programming, packages and software

Data science is basically about programming. Although there is no specific rule on the selection of programming languages, Python and R are the most favored. Data Scientists choose a programming language that meets the need for a problem statement at hand. Python, however, seems to have become the closest thing to a lingua franca for data science.

Data conflict

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 of the data. Data wrangling is the process by which you prepare your data for further analysis; transform and map raw data from one form to another to prepare data for insights. This is the most important data science skill one should possess.

Data base management

With heaps and large chunks of data to work with, it is essential that a data scientist knows how to manage this data. Database management basically consists of a group of programs that can edit, index and manipulate the database. The DBMS accepts a request for data from an application and asks the operating system to provide the specific required data.

Critical mind

Using critical thinking, data scientists can objectively analyze questions, assumptions, and results and understand what resources are essential to solving a problem. They can also look at issues from different viewpoints and perspectives. Critical thinking is a valuable skill that transfers easily to any profession.

Effective communication

Effective communication is another skill that is in demand everywhere. Whether you’re in an entry-level position or a CEO, connecting with other people is a useful trait that helps you get things done quickly and easily. In business, data scientists must be skilled at analyzing data and then must clearly and fluently explain their findings to both technical and non-technical audiences.

Writing SQL and creating data pipelines

Learning how to write robust SQL queries and schedule them on a workflow management platform like Airflow is essential as a data scientist. This will help you build baseline data pipelines and improve the insights gathered, which will make things easier. This is one of the practical data science skills to learn to stay in demand in the market.

Regression and classification

Building regression and classification models, predictive models are not something you will always be working on, but it is something employers will expect you to know if you are a data scientist. To give some perspective, critical models have had a significant impact on the business. This is one of the practical data science skills to learn to stay in demand in the market.

Explainable AI

Many machine learning algorithms have long been considered “boxes of blocks” because it was unclear how these models derived their predictions based on their respective inputs. SHAP and LIME are two techniques that not only tell you the importance of each feature, but also the impact on the model output, similar to the coefficients of a linear regression equation.

Neural network architectures

Neural networks are part of the deep learning process and are inspired by the structure of the human brain. They are complex structures created from artificial neurons that can process multiple inputs and produce a single output. Understanding this architecture is essential for deep learning. It is one of the best data science skills one must have to enter the big world of technology.

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