Difference between Data Analyst and Data Scientist

There is a lot of debate about data analysts vs. data scientists and whether they hold the same job or the same responsibilities. On the outside, it may seem like they share a similar job description and would complete the same work. However, there are some subtle differences that may help you decide which career choice between data science vs. data analytics is the right one for you.

To understand the differences, as well as some of the similarities that may make these two confusing, between a data scientist and a data analyst, we first need to dig into some of their job descriptions and see what each one does.

What is a Data Scientist?

A data scientist is going to seem more in-depth compared to a data analyst. They will spend their time really digging in and searching for the specific data necessary to help solve the problems for the company. On the outside, these two are going to seem very similar, but a data scientist may be more into research and exploring to see what new possibilities are out there, while a data analyst will just explore the data that is there.

As a data scientist, you will be seen as a new breed of an analytical data expert. You must have all of the technical skills necessary to solve the most complex problems, while also maintaining enough curiosity to explore any new problem that needs to be solved for you or for a business. These professionals will need to know a lot of things and wear many hats. Many times they need to be part business and part IT professional, while also knowing how to do a lot of math, how to handle computer science, and how to spot trends out of huge sets of data.

There are a lot of roles that a data scientist is likely to take on when they get to work in a professional field. Some of these include:

1. They must take the time to collect large amounts of data. This data is often very messy and unorganized. The data scientist then needs to transform it into a format that is more usable.
2. They will need to solve some problems for a business with many data-driven techniques.
3. They need to work with at least a few different types of coding languages. Python, R, and SAS are the most common and most efficient, though other coding languages are helpful.
4. They need to at least have a firm grasp on the idea of statistics. Knowing how to do statistical tests and distributions is a must, along with other options.
5. They need to always re-educate themselves to ensure they stay on top of different analytical techniques. Options like text analytics, deep learning, and machine learning are always changing, yet very important to the work of a data scientist.
6. They must take the time to communicate and work with both the business and the IT side of things.
7. They need to look for patterns and order in the data as possible. If they are able to spot some new trends that would affect the bottom line of the business, they need to focus on that as well.

What is a Data Analyst?

When we talk about a data analyst, we talk about a professional who spends their time collecting, processing, and performing statistical analysis of data. They are able to translate the numbers and data that they spend time exploring into plain English that they can then present to a company.

Data is king in our world today and many companies will spend a lot of time and money gathering up as much data as possible. However, many times after collecting the data, the company has no idea what to do with all of it. They don’t understand how the data works, what it says, or how to use it.

This is where the data analyst will come in. They know how to work with the data, and how to make better sense of all of it. They can then take all of the data, and after a bit of work and a few good algorithms, they can see the patterns and most valuable information inside of that large set of data.

When they are done with that, it is their responsibility to turn the insights into something that the business is better able to comprehend. This may be in the form of charts or graphs depending on how the company would like to see it and the type of data that is used. Many times companies will choose to look through this data and see the patterns and insights from the data analyst because they can use that to make the best business decisions.

Data analysts will need a number of skills to help them properly perform their jobs. They need to understand how to collect the data in the first place. Some businesses may already collect the data, and others do not. The analyst needs to sort through it all and find a safe place to store it until needed, with the goal of keeping all personal information from the customers and other sources safe.

When that is done, they will use a variety of algorithms and models to organize and sort the data. Often there need to be several algorithms completed to see what makes the most sense from all that data. This takes time because most data sets are very large and hard to sort through. Once the algorithms have done their job, the data analyst can present the results to the company and make decisions from there.

Data scientists and data analysts are important when it comes to helping a business sort through all of the data they have and try to make sense out of it all. When looking for a good profession that allows you to work with programming languages, machine learning and more while sorting through all types of data, then choosing one of these professions is probably one of the best options to help you out. Companies all around the world are always looking for the right data scientist or data analyst who can help them take all that data and use it to beat out the competition.