A data scientist extracts manipulate and generate insights from humongous data. To leverage the power of data science, data scientists apply statistics, programming languages, data visualization, databases, etc.

So, when we observe the required skills for a data scientist in any job description, we understand that data science is mainly associated with Python, SQL, and R. The common skills and knowledge expected from a data scientist in the data science industry includes – Probability, Statistics, Calculus, Algebra, Programming, data visualization, machine learning, deep learning, and cloud computing. Also, they expect non-technical skills like business acumen, communication, and intellectual curiosity.

However, when you ask experienced data scientists, they may share a different view altogether. Their experience says that data scientists’ knowledge must be beyond the mentioned skills in a typical job description. These tools and platforms make a data science professional more competent to demonstrate a holistic approach in their data science projects.

Let us understand a few of the tools and platforms other than Python, SQL, R, or the typically mentioned skills in a job description, that would help a data scientist shine better in their career.

Cool Data Science Tools for Modern Data Scientists 

It is undeniably agreed that the skills and knowledge mentioned in a job description are must-haves for a data scientist. Competent data scientists must have knowledge or experience in one or more of the tools/platforms mentioned here as applicable to the data science industry they are serving for. Take a look.

Linux OS 

Technical Reasons to Possess Knowledge About Linux OS:

Git is the best version control system for data systems. A version control system is a tool that saves different versions of files or track changes that you make on files. It is helpful for data scientists’ as they always work as a team.

Technical Reasons to Use the Version Control System-Git:

REST APIs

An understanding of APIs and their uses makes you a more competent data scientist. With APIs, data scientists can access data from remote services or build them to provide data science capabilities in their organization.

Technical Reasons to Learn APIs:

Docker and Kubernetes

As we all know, docker is a popular container environment whereas Kubernetes is a platform that orchestrates docker or any other containers. They both are important for the machine learning lifecycle mode concerning development and deployment aspects. It indeed makes the workflow very simple, scalable, and consistent.

Learning Docker and Kubernetes help data scientists to accelerate their data science initiatives like designing infrastructure, tooling, deployment, and scaling.

Technical Reasons to Know Docker and Kubernetes:

Apache Airflow 

Getting the data in a specified format, quantity, or quality is the most challenging part for any data scientists for that matter. Airflow, a python-based framework allows data scientists and data engineers to create, schedule, and monitor workflows programmatically. It can get automated too. Also, you have logs and error handling facilities to fix the failure.

Technical Reasons to Know Apache Airflow:

Microsoft Excel 

Though Excel cannot calculate humongous data, it remains as an ideal choice to create data visualizations and spreadsheets. Data scientists can connect SQL with Excel and use for data cleaning, data manipulation, and pre-process information easily.

Technical Reasons to learn MS-Excel: 

Many data scientists today use Elasticsearch than MongoDB or SQL for its astonishing capabilities. It is recommended to be familiar with this technology use as it can be used for an easy text search when incorporated into the analytics platform.

Technical Reasons to Use Elasticsearch:

To Conclude

Though these tools may not be required for all positions, they are equally important for the success of data science projects. Data science is a vast spectrum that requires data handling in a unique way. These data science tools cater to different stages of the data science life cycle and enable you to be more proficient.

Let us know in the comment section below about the data science tool you are working with or wish to learn in the near future.