7 Essential Tools for a Competent Data Scientist

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 

• As data science deals with programming too, knowledge of Linux OS is essential for a data scientist. The tools and the environment that a Linux OS provides helps the data scientists to work more efficiently and that too at a faster rate. The usability of Linux is pretty good and you find it easy to work.

Technical Reasons to Possess Knowledge About Linux OS:

• Linux is quite fast and it takes minimal time from data scientists.
• Data scientists have a huge number of libraries that can get integrated with any hardware when Linux is used.
• As the deployment of the model in the cloud is based on Linux, it is essential to have an idea of working with Linux systems.
• Further, if you use an external virtual machine, know that they are built on Linux.
• Linux comes with Python installed; it is an added advantage.
• Technologies like Docker, Containers, etc., need a Linux platform.

Git

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:

• Git is centralized and keeps the project repository available for the team to work.
• It is distributed and facilitates the team to work locally either offline/online.
• Each team member can work on the same files simultaneously and merge changes once the work gets done.
• It helps data scientists to collaborate with their teams with more convenience.

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:

• It helps to respond to the latest data requests
• Enables the team to provide the latest model score for a client
• Helps to provide a model score when the arguments are represented as model coefficients to the API
• Deliver advanced analytics functionality in a web service

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:

• Use infrastructure efficiently
• Bring parity between production and development
• Manage various versions of tools
• Scale to meet business demand
• Migrate models to production easily
• Deploy continuously without downtime

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:

• · Helps to access the updated high-quality data
• · Makes the workflow transparent, efficient, and manageable
• · Orchestrate the whole ETL process proficiently

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: 

• It makes data analysis easier for small scale projects
• Easily connects with SQL
• Compute complex data analysis using ToolPak
• Comfortable to use for calculations and visualizations

Elasticsearch

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:

• Extremely fast to fetch results for simple and complex queries
• It is simple and distributed in nature and thus easy to work
• Provides multitenant capable full-text search engine

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.