Data scientists are essentially the bridge between business and technology. Their job is to examine and process data in order to turn it into actionable information that can be used by the business to make informed decisions, create valuable products and services, and build profitable businesses and organizations.

If you have an interest in either computers or business, this might be the perfect career path for you! 

Data scientists are everywhere these days, and it seems like there are more jobs available for them than there are qualified people to fill them. What does it take to be a data scientist? How much does someone make? 

And what’s the difference between a data scientist and a data analyst? If you’ve ever wondered about any of these questions or thought about becoming one, you’ll find the answers in this guide on what data scientists do and how to become one.

What Does a Data Scientist Do?

A data scientist is a person who extracts knowledge or insights from large amounts of data. Data scientists are in high demand, but it's hard to know what they do because there isn't a clear job description. 

Data scientists don't work exclusively with one type of data; instead, they use many different types of data (e.g., spreadsheets, databases, surveys) in their work. With such an open-ended job description, you may be wondering what skills you need to become a data scientist. 

Well, you need experience with at least two programming languages  and expertise in statistics or machine learning. You also need an advanced degree that prepares you for the academic research involved in being a data scientist. 

And while some positions require previous experience as a data analyst, others are happy to take on fresh talent so long as you have the right skill set!

What Type of Companies Hire Data Scientists?

Companies that hire data scientists are looking for people with a background in statistics or machine learning, or a related field of study like mathematics or computer science. 

In general, the best candidates will have a degree in one of these fields, but this is not always the case. Data scientists often start out in more traditional roles like marketing research analyst or statistician before moving into data scientist roles. 

If you don't have an advanced degree yet but want to get your foot in the door as a data scientist, there are other ways you can prepare yourself for this type of work. Courses like statistics boot camps ( all provide valuable training in different areas. Graduates from these courses might also go on to enter master's programs or PhD programs in Statistics, which will better prepare them for careers as a data scientist.  

Boot camp programs typically run between two weeks and four months depending on how much time you need to make up ground on specific skills. For instance, a course on exploratory data analysis might take two weeks while one on statistical modeling would take longer.

What Skills Do I Need to Become a Data Scientist?

An important skill you need to become a data scientist is programming. You also need to know statistics, analysis, math, machine learning, and data visualization. If you want to be a data scientist at a tech company like Google or Facebook, you will also need experience with artificial intelligence. It can take anywhere from two to five years of education in the right fields to fully prepare yourself for this career. 

A data scientist needs an inquisitive mind because they are looking for patterns in the vast amount of information that they work with every day. They need to have good problem-solving skills as well as creativity in order to find solutions that make sense when there are no patterns or rules in place.

Which Tools & Technologies Do Data Scientists Use?

In order to be successful, data scientists need a variety of tools and technologies that they can use in their work. These include programming languages, databases, statistical software packages, machine-learning libraries, mathematical models or simulation packages. 

There is a vast array of options available depending on the type of project the data scientist is working on. For example, Python is often used for general analytics projects while R is typically used for statistical analysis; SQL is great for storing and querying relational data while MongoDB excels at unstructured datasets; SAS covers both statistics and analytics while MATLAB covers modeling; Hadoop provides scalable parallel processing while Spark offers iterative algorithms with fast computation times.

How Can I Get Started in Data Science?

So, you want to become a data scientist? That's great! Data science is a constantly evolving field with tremendous growth potential. Luckily, there are several ways that you can get started in data science. 

The first step is deciding what type of data scientist you would like to be. Typically, this decision will be based on your skill set or your interests. For example, some individuals decide that they want to apply their coding skills in order for them to work as a machine learning engineer. 

If you're more interested in the business side of things, then an individual may consider becoming a data analyst. Those who enjoy statistics might also find themselves drawn to the data scientist position.

If you're unsure about where to start, then consider taking some free online courses from Coursera or Udacity in order for you to learn more about the field. These types of courses generally provide learners with an understanding of how the internet works and how computers operate.

Difference between a data scientist and a data analyst

A data scientist is a person who primarily uses statistical analysis, machine learning and other data-driven techniques to provide business insight. Data analysts, on the other hand, are people who analyze information using standard reporting tools like Excel. 

While there is some overlap in skills and responsibilities between these two roles, the main difference between them is that data scientists use complex algorithms and advanced analytical techniques to extract insights from large sets of data. Data analysts use simpler statistical methods for smaller datasets.


In conclusion, data scientists are on the cutting edge of technology and are in high demand. 

If you have a knack for mathematics, coding, or just enjoy analyzing data, then this is the perfect job for you. But don't worry if it doesn't sound like your cup of tea! There are many other careers in tech that might be more suited to your personality.