13 In-demand skills you need to become a Data Scientist



In-demand skills you need to become a Data Scientist


While there are notable exceptions, data scientists are typically highly educated, with 88% having at least a Master’s degree and 46% having a PhD. While there are notable exceptions, a strong educational background is usually required to develop the depth of knowledge required to be a data scientist.

A bachelor’s degree in computer science, social sciences, physical sciences, or statistics is required to work as a data scientist. Mathematics and Statistics (32%) are the most popular disciplines of study, followed by Computer Science (19%) and Engineering (16%). Any of these degrees will equip you with the abilities necessary to process and evaluate large amounts of data.

You are not yet finished with your degree programme. The truth is that most data scientists have a Master’s or Ph.D. and also participate in online training to learn a specific expertise, such as Hadoop or Big Data querying. As a result, you can pursue a master’s degree programme in data science, mathematics, astronomy, or any other related discipline. You will be able to effortlessly transfer to data science using the abilities you obtained during your degree programme.

Apart from classroom learning, you can put what you’ve learned in class into practise by creating an app, writing a blog, or looking into data analysis to learn more.

R Programming

R Programming
R Programming

In-depth understanding of at least one of these analytical tools, with R being preferable for data science. R is a programming language that was created with data science in mind. You can use R to solve any data science problem you come across. In fact, R is used by 43% of data scientists to handle statistical challenges. R, on the other hand, has a steep learning curve.

Learning is challenging, especially if you already know a computer language. Nonetheless, there are plenty of online tools to help you get started with R, including Simplilearn‘s Data Science Training with R Programming Language. It’s an excellent tool for budding data scientists.

Technical Skills: Computer Science

Python Coding

Python Coding
Python Coding

Python, along with Java, Perl, and C/C++, is the most prevalent coding language I see in data science roles. For data scientists, Python is an excellent programming language. This is why, according to an O’Reilly survey, 40% of respondents use Python as their primary programming language.

Python can be used for practically all of the phases required in data science operations due to its versatility. It accepts a variety of data types and allows you to effortlessly import SQL tables into your code. You can build datasets using it, and you can find almost any form of dataset you need on Google.

Hadoop Platform

apache Hadoop Platform
apache Hadoop Platform

Although it isn’t always necessary, it is strongly recommended in many circumstances. It’s also a plus if you’ve worked with Hive or Pig before. Knowing how to use cloud solutions like Amazon S3 can also help. According to a CrowdFlower survey of 3490 LinkedIn data science positions, Apache Hadoop is the second most important expertise for a data scientist, with a rating of 49 percent.

As a data scientist, you may find yourself in a scenario where the amount of data you have surpasses your system’s memory or you need to send data to other servers; this is where Hadoop comes in. Hadoop may be used to swiftly send data to different parts of a system. That’s not all, though.

That’s not all, though. Data exploration, data filtration, data sampling, and data summarising are all possible with Hadoop.

SQL Database/Coding

SQL Database
SQL Database

Despite the fact that NoSQL and Hadoop have become important parts of data science, it is still anticipated that a candidate can develop and execute complicated SQL queries. SQL (structured query language) is a programming language that may be used to perform database operations such as adding, deleting, and extracting data. It can also aid in the execution of analytical operations and the transformation of database structures.

As a data scientist, you must be fluent in SQL. This is because SQL was created to assist you in accessing, communicating, and working with data. When you use it to query a database, it provides you with information.

It has short commands that can save you time and reduce the amount of code required to run complex searches. Learning SQL will improve your understanding of relational databases and help you advance your career as a data scientist.

Apache Spark

Apache Spark
Apache Spark

Apache Spark is quickly becoming the most widely used big data tool on the planet. It’s a Hadoop-like large data computing framework. The sole difference between Spark and Hadoop is that Spark is quicker. This is due to the fact that Hadoop reads and writes to disc, slowing it down, whereas Spark caches its computations in memory.

Apache Spark was created primarily for data science to speed up the execution of complex algorithms. When dealing with a large amount of data, it aids in dispersing data processing and so saves time. It also assists data scientists in dealing with large, unstructured data volumes. It can be used on a single machine or a group of machines.

Apache Spark allows data scientists to avoid data loss in data science. Apache Spark’s strength is its speed and platform, which makes data science projects simple to complete. You can use Apache Spark to do everything from data collection to computing distribution.

Machine Learning and AI


A vast number of data scientists lack expertise in machine learning techniques and topics. Neural networks, reinforcement learning, adversarial learning, and other techniques are examples of this. If you want to set yourself apart from other data scientists, you need be familiar with machine learning techniques including supervised machine learning, decision trees, and logistic regression, among others. These abilities will aid you in solving a variety of data science challenges based on important organizational outcomes projections.

ALSO READ: Artificial intelligence: A modern approach.

Data science necessitates the application of machine learning techniques in various fields. In one of Kaggle’s surveys, it was discovered that only a small percentage of data professionals are proficient in advanced machine learning skills such as supervised and unsupervised machine learning, time series, natural language processing, outlier detection, computer vision, recommendation engines, survival analysis, reinforcement learning, and adversarial learning.

Working with vast amounts of data sets is a requirement of data science. Machine learning is something you should be aware of.

Data Visualization


The corporate world generates a large volume of data on a regular basis. This information must be converted into a manner that is simple to interpret. Raw data is more difficult for people to comprehend than images in the form of charts and graphs. “A picture is worth a thousand words,” as the idiom goes.

As a data scientist, you’ll need to be able to visualise data using tools like ggplot, d3.js, and Matplotlib, as well as Tableau. These tools will assist you in converting complex project outcomes into a format that is easy to understand. The problem is that many people are unfamiliar with serial correlation or p values. You must graphically demonstrate what those terms in your results mean.

Organizations can work directly with data thanks to data visualisation. They can quickly absorb information that will enable them to capitalise on new business possibilities and stay ahead of the competition.

Unstructured data

A data scientist’s ability to work with unstructured data is crucial. Unstructured data is unstructured information that does not fit into database tables. Videos, blog articles, customer reviews, social network posts, video feeds, and audio are all examples. It’s a collection of lengthy texts. Because they are not streamlined, sorting this type of data is tough.

Because of its intricacy, most people referred to unstructured data as “black analytics.” Working with unstructured data allows you to discover insights that can help you make better decisions. You must be able to analyze and manipulate unstructured data from many platforms as a data scientist.

Non-Technical Skills

Intellectual curiosity

“I don’t have any exceptional abilities. I’m only intrigued because I’m passionate about it.” Albert Einstein once said, “There is no such thing as a good idea.”

You’ve probably heard this phrase a lot lately, especially in relation to data scientists. In a guest blog he wrote a few months ago, Frank Lo explains what it implies and discusses other important “soft talents.”

Curiosity is described as a desire to learn more about something. Because data scientists spend roughly 80% of their time acquiring and preparing data, you must be able to ask questions about it as a data scientist. This is due to the fact that the subject of data science is rapidly evolving, and you will need to learn more to keep up.

You should keep your expertise up to date by reading relevant books on data science trends and reviewing online content. Don’t be intimidated by the massive amount of info that is circulating on the internet; you must be able to make sense of it all. One of the abilities you’ll need to succeed as a data scientist is curiosity. For example, you might not see any insight in the data you’ve gathered at first. Curiosity will allow you to comb through the data in search of answers and new information.

Business acumen

To be a data scientist, you must have a thorough awareness of the industry in which you operate and be aware of the business problems that your organization is attempting to solve. In terms of data science, the ability to detect which problems are vital to solve for the organization, as well as identifying new ways the firm might leverage its data, is critical.

To do so, you must first comprehend how the problem you are solving may affect the organisation. This is why you must understand how businesses work in order to focus your efforts in the appropriate way.

Communication skills

Communication skills
Communication skills

Companies looking for a competent data scientist want someone who can communicate their technical findings to a non-technical team, such as the Marketing or Sales departments, in a clear and fluent manner. In order to manage the data effectively, a data scientist must enable the company to make decisions by providing them with quantitative insights, as well as knowing the demands of their non-technical colleagues. More information on communication abilities for quantitative experts can be found in our latest flash survey.

You must not only communicate in the same language as the organisation, but you must also use data storytelling.
As a data scientist, you must know how to weave a narrative around the data such that it is easy to comprehend. For example, displaying a table of statistics isn’t as successful as conveying the data’s insights in a narrative manner. Storytelling will assist you in effectively communicating your findings to your bosses.

Pay attention to the results and values embedded in the data you analysed when communicating. Most business owners aren’t interested in learning what you discovered; instead, they want to know how it will benefit their company. Learn to communicate in a way that focuses on offering value and establishing long-term relationships.


A data scientist can’t work by themselves. Working with firm executives to build strategies, product managers and designers to produce better products, marketers to launch better-converting campaigns, and client and server software developers to create data pipelines and optimize workflow are all things you’ll have to do. You’ll have to collaborate with everyone in the company, including your consumers.

Essentially, you’ll work with your teammates to create use cases so that you can understand the business goals and data that will be needed to address challenges. You’ll need to know how to approach the use cases correctly, what data you’ll need to solve the problem, and how to translate and present the results in a way that everyone can understand.


Advanced Degree – To meet the present need, more Data Science degrees are being developed, but there are also many Mathematics, Statistics, and Computer Science programmes available.

MOOCs – Coursera, Udacity, and Codeacademy are all excellent places to begin.

Certifications – KDnuggets has put out a comprehensive list.

Bootcamps – Check out this guest blog from Datascope Analytics’ data scientists for additional information on how this strategy compares to degree programmes or MOOCs.

Kaggle – Kaggle organises data science challenges where you can practise with messy, real-world data and solve real-world business problems. Kaggle rankings are taken seriously by employers since they are considered as relevant, hands-on project work.

LinkedIn Groups – To communicate with other members of the data science community, join relevant groups.

Data Science Central and KDnuggets – Data Science Central and KDnuggets are excellent resources for keeping up with data science industry trends.

The Burtch Works Study: Salaries of Data Scientists – If you’re interested in learning more about current data scientists’ wages and demographics, download our data scientist salary research.

I’m sure I missed something, so if you know of a key skill or resource that would be beneficial to any data science hopefuls, please post it in the comments below!


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