Top 10 Data Science Skills Big Techs are Looking for in Applicants

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

Data science as one of the greatest jobs in the contemporary technology market is considered a highly technical role more often. Data science is the cutting-edge technologies that are ruling a wide range of sectors and companies today. Data science skills are crucial in this age of AI, big data, and automation. Businesses are looking for skilled professionals who can manage the ever-increasing amount of data generated by their operations. There are various practical data science skills for applicants to apply for big tech companies. This article lists the top 10 data science skills that big techs are looking for in applicants. 

Multivariate Calculus & Linear Algebra

Most machine learning, invariably data science models, are built with several predictors or unknown variables. 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, etc. 

Programming, Packages and Software

Data Science essentially is about programming. While there is no specific rule about the selection of programming languages, 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.

Data Wrangling

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 prepare the data for insights. This is the most important data science skill that one must-have. 

Database Management

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. 

Critical Thinking

With critical thinking, data scientists can objectively analyze questions, hypotheses, and results and understand what resources are critical to solving a problem. They can also look at problems from differing views and perspectives. Critical thinking is a valuable skill that easily transfers to any profession. 

Effective Communication

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

Writing SQL & Building Data Pipelines

Learning how to write robust SQL queries and scheduling them on a workflow management platform like Airflow is vital as a data scientist. It will help you in building core data pipelines and improving the insights that are gathered making 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 that you will always be working on, but it's something that employers will expect you to know if you're a data scientist. To give some perspective, mission-critical models 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 were considered 'block boxes' for a long time because it wasn't clear how these models derived their predictions based on their respective inputs. SHAP and LIME are two techniques that tell you not only the feature importance for each feature but also the impact on the model output, similar to the coefficients in a linear regression equation. 

Neural Network Architectures 

Neural networks make up 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 singular output. Understanding this architecture is essential for deep learning. It is one of the best data Science skills one must have to enter into the big tech world.