The Best Skills to Become a Data Scientist

Data scientists are the most sought-after professionals in the technology industry. This is because there are huge demands for people who can help organizations make sense of their data. The value that these professionals bring to an organization cannot be understated!

The only thing standing between you and becoming a data scientist is acquiring the right set of skills. In this blog post, we will present eight essential skills that every aspiring data scientist needs to develop:

1. Essential Skills of a Data Scientist

Data science is a broad field, and there are many skills that you will need to become a data scientist. Here are the top essential skills of a data scientist:

  • Data manipulation and analysis: You may have heard that a lot of data scientists don’t write code or program computers. While it is true that they don’t necessarily write code, they definitely manipulate and analyze data! This means that you need to learn how to work with large datasets on your computer (such as Excel), extract meaningful pieces of information from them, and perform calculations on those pieces of information (like calculating averages) using various methods like statistical tests or machine learning algorithms, visualize this information in graphs, etc. In short – if it involves numbers crunching then it falls under this category!
  • Communication: Data science is not just about crunching numbers but also requires communicating with other stakeholders such as business people who want answers from their data. For example – if there is an increase in sales at stores near highways based on zip codes where most people own cars then what does this mean for our company? Understanding these questions requires communication between different departments within companies so we can decide whether expanding our business would be profitable for us!

2. Why is Python Important in the Data Science Industry?

Python is a general-purpose programming language that’s easy to learn. It’s also used by data scientists to build machine learning models, create data visualizations and build data products.

Python has become the go-to choice for building AI applications because it’s more powerful than R, but not as difficult to use as C++ or Java.

Python is great for beginners who want to get started with coding right away without having to learn many different concepts first (like C++).

3. Applied Knowledge of Statistics

Statistics is a powerful tool for data analysis. The field is broad and includes many sub-specialties, but it’s used in data science to find patterns in the data. Often, you’ll use statistics to measure the probability of an event or make predictions about the future based on your findings.

The most important thing you can do is learn different types of statistics and how they work together—but it’s important that you understand their individual roles as well as their relationships with each other so that you’re aware of when one type might be more appropriate than another type.

4. Expertise in Machine Learning Algorithms

Now that you have a good understanding of what data science is, let’s look at some of the skills that are required to become a data scientist.

Data scientists use machine learning algorithms to build predictive models and carry out statistical analysis on large datasets. Machine learning is simply a subset of artificial intelligence that gives computers the ability to learn without being explicitly programmed. It’s used in many fields where there is a need for computers or robots to make decisions based on past experiences, such as finance, medicine, and customer service chatbots.

Machine Learning Algorithms are an important skill for any Data Scientist because it allows them to build predictive models which can help companies make better decisions about their business operations or product development.

5. Advanced SQL Knowledge

Advanced SQL knowledge is the most important skill of all. Why?

Because it’s the language of data science.

Data scientists use SQL to query databases, which means they need to know how to write complex queries that can be formulated in a single line. These queries include:

  • Selecting specific columns from one or more tables and creating new columns based on data from those tables (i.e., joining them together so they can be analyzed together)
  • Filtering out certain rows or columns of data in order to focus on what you want to look at
  • Calculating average values for certain metrics like sales revenue over time

6. Familiarity with Big Data and Hadoop Ecosystems

You also need to know about Hadoop. Hadoop is an open-source framework for storing and processing big data. It’s a distributed file system, written in Java, and can be used to process large data sets stored on either a single computer or across clusters of computers using simple programming models. In other words, it’s a toolkit that makes it easy to handle immense amounts of information without having to worry about all the details of working with huge amounts of data at once.

7. Business Acumen for Enhancing Decision-Making Capabilities

Business acumen is the ability to understand the business context in which data science is applied. Data scientists need to be aware of the potential impact of their work on the business. They also need to understand how their findings will be used by others, so they can identify relevant use cases, and ensure that they’re able to communicate these effectively with non-technical colleagues (e.g., analysts).

For example, when I was working as a data scientist at Ocado Technology, we were tasked with developing machine learning models for customer segmentation — our goal was to help enrich customer experience and increase sales. We created several models based on different attributes like shopping behavior or customer type that could predict whether someone would go on to buy something from us or not (this sounds simple but requires complex statistical modeling). Once done with this exercise we wanted feedback from our marketing experts about which model worked best before deploying it into production because there were significant implications for them if one model outperformed another one: for example, some customers may stop buying from us altogether if we didn’t send them offers appropriate enough for them based upon what we knew about them through these models!

8. Hands-on Experience with Popular Analytics Tools

When you’re new to data science, it’s easy to get caught up in the excitement of learning the most popular tools. There are so many different ones out there—from R, Python, and SAS to C-based languages like C++ and Java—that it can be hard to decide which one(s) would be best for your specific needs.

In general, though, I’d advice against getting too carried away with this kind of tool trouble. To me, it seems that the “best” analytics tools are often defined by how well they fit into your workflow or how familiar you are with them. And since most people learn primarily through repetition and experience (rather than learning theory), having hands-on experience with as many different analytics tools as possible will help you figure out which ones work best for you personally.*

The best data scientists in the world have varied skill sets so it’s important to know what skills will make you stand out from the crowd.

The best data scientists in the world have varied skill sets so it’s important to know what skills will make you stand out from the crowd.

Data science is a broad field and there are many different ways to get involved. The skills listed are just a few examples, but if you want to be successful as a data scientist or data engineer, it’s important to develop yourself as broadly as possible.


We hope that this article has helped you understand how to become a data scientist. Whether you’re looking for an entry-level role or a management position, these are some of the key skills that will set apart an average candidate from the best data scientists in their field.

Leave a Reply

Your email address will not be published. Required fields are marked *