Top Skills Recommended To Become A Data Scientist In 2021

Big data is used to generate insights that have driven data scientists' demand at the business level across all industries. Suppose it is to refine the product development process, enhance customer retention, or mine through data to get new business opportunities. In that case, businesses are increasingly depending on the data scientist skills to survive, flourish, and get one step ahead of their nemesis.

 

As the demand for data scientists rises, this domain seems to present an attractive career path for students & current experts. This includes those who aren't data scientists but are consumed with data and data science, which has left them asking what big data and data science skills are required to pursue data science careers. This article will discuss the 7 skills recommended to excel in a data scientist field in 2021.

 

You might observe that none of the 7 skills have anything to do with deep learning or machine learning, and this isn't a mistake to make it clear. At present, there is a much higher demand for skills that are applied in pre-and-post modeling phases. Hence, the 7 most suggested skills to learn overlap with the skills of a data engineer, a data analyst, and a software engineer.

 

Speaking of which, let's jump into the top 7 recommended data science skills to learn in 2021.

 

1. SQL

 

SQL is the ubiquitous language in the data world. If you're a data engineer, a data analyst, or a data scientist, you'll need to know SQL. This skill is used to pull data from a database, manipulate it, and create data pipelines; mainly, it's essential for almost all pre-modeling/analysis stages in the data lifecycle.

 

Building strong SQL skills will let you take your visualizations, modeling, and analyses to another level as you can extract & manipulate the data in advanced ways. Moreover, writing scalable and efficient queries is getting more & more vital for businesses that work with petabytes of data.

 

2. Data Visualizations and Storytelling

 

 

If you think making data storytelling and visualizations are specific to the data analyst's role, re-think.

 

Data visualizations refer to data that is presented visually, it can be in the graphical form, but it can even be presented in non-conventional ways.

 

Data storytelling takes data visualizations to another level. Data storytelling refers to 'how' you convey your insights. Think of it as a picture book. A good photo book has good visuals, but it even has an interacting and robust narrative that links the visuals.

 

Building your data visualization & storytelling skills are vital as you're always selling your ideas and models as a data scientist. And it's mainly crucial when interacting with others who are not as tech-savvy.

 

3. Python

 

Python seems to be the most dependable programming language to learn over R. That does not mean that you cannot be a data scientist if you use R; however, it simply means that you will be dealing in a language that is unique from what most of the people are using. Hence, it might seem a slight task to blend in.

 

Learning Python syntax is not challenging, but you must be able to write productive scripts and use the broad-range of packages and libraries that Python has to provide. Python programming is a building block for uses such as building machine learning models, manipulating data, writing DAG files, and more.

 

4. Pandas

 

The most important library in Python is Pandas, which is a package for data analysis and manipulation. As a data scientist, you will be utilizing this package every time, if you are cleaning data, manipulating the data, or exploring data.

 

This tool has become a widespread package, not just because of its functionality, but even due to DataFrames having become a usual data structure for machine learning models.

 

5. Git/Version Control

 

 

Git is the primary version control system used in the technology community. If that does not make sense, take this instance. If you ever had to script an essay in university or high school, you might have saved various versions of your paper as you went through it.

 

Git is a tool that caters to the same goal, except the fact that it's a distributed system. Meaning, that files are saved both in a local as well as a central server.

 

Git is super essential for many reasons, with a few being that:

 

- It lets you revert to previous versions of code

- It enables you to work simultaneously with several other data scientists & programmers

- It enables you to use the same codebase as others even if you're working on a completely different project

 

6. Docker

 

This is a containerization platform that lets you deploy & apps, like machine learning models.

 

It's getting increasingly crucial that data scientists know how to develop models and deploy them too, In fact, several job postings are now needing some experience in model deployment.

 

It's essential to learn to deploy models because a model offers no business value until it is actually synced with the product/process that it is related with.

 

7. Airflow

 

This tool is a workflow management tool that lets you automate workflows. Being more specific, this tool enables you to create automated workflows for machine learning pipelines and data pipelines.

 

This tool is robust since it lets you productionalize tables that you might want to use for further modeling and analysis, and it's even a tool that you can use to deploy machine learning models.

 

The Bottom Line

 

Hopefully, this guide helps in your learning process and provides you some guidance. This is a lot to learn hence we would recommend you to choose a couple of skills that sound most fascinating to you and take from there. HData Listed One of the Trusted Big Data Analytics Companies by Top Mobile App Development Companies.