What you need to know: Data Scientist Vs Data Analyst

While data analysts and data scientists both work with data, the main difference lies in what they do with it. Understanding each position and the differences between these jobs is important for those looking to enter either one of these roles. This article will go through three main areas that shape these roles which are responsibilities qualifications and skills.

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Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modelling and production using prototypes, algorithms, predictive models, and custom analysis.

Responsibilities

Data scientist

As a data scientist you’re going to be using your data to discover opportunities. What that means is you’re going to be using your current data to find trends and patterns that are going to affect the future project/business that you were working on. You also will be developing analytical methods and machine learning models. Most of the time they have these models set up so you would just be working with the data to put it into these models. Then tweaking their hyper parameters to really narrow down accuracy and get better results. But generally they aren’t doing a tonne of work in these machine learning models and are not developing new models, they’re just trying to get the best results they can out of the tonnes of data they have.

Data cleaning

Data Scientists spend a lot of time just cleaning their data, making sure it’s going to be good and usable for their models. This is so that when they plug it into these models it gives them the most accurate results that are also formatted correctly for their machine learning algorithm. They will also be conducting AB testing. This is where you do two independent tests with two different results and see which one gives you better results.

Data analyst

As a data analyst you’re going to use your data to solve problems that you have right now. Instead of trying to find trends or opportunities for the future, you’re trying to answer questions that your company has now. Other responsibilities are also creating reports and creating dashboards. For creating reports a lot of times, you would use either SQL or some cloud platform or any number of other tools that are out there for creating reports. For dashboards you might be using something like Power BI or Tableau or maybe Python – it just depends on what your company is using. Creating reports and dashboards can be a large part of what a data analysts actually does. You would also often help with gathering incremental data from different sources. When you need data you may work with a client or an internal team to help gather that data or get that data into your systems.

Qualifications

Data Scientist 

As a data scientist you often need a Master’s degree or above which can be in anything from computer science, economics, mathematics, physics it really depends on what industry you are going into. It’s not to say you have to have a Master’s degree but oftentimes it is a prerequisite for most positions. However there are some positions where they really just look at experience and your skills to see if you’re a good fit and they might take you if you only have a bachelor’s degree. But again this is a prerequisite for a lot of positions that you find where you need a Master’s degree just to meet the basic requirements.

Data Analyst

For a Data Analyst you will need a bachelors or above for most positions and that will be for a lot of the same degree fields that data scientists have. These are similar to data scientists which are computer science, mathematics & economics. You don’t have to have a Bachelor degree or even a degree in those fields you can have no degree if you have really good proven skills and work your way up.

Skills

Data Scientist

For a data scientist some of the skills that you might need are

• SQL
• R/Python (Pandas, Numpy, Matplotlib)
• Tableau/Power BI
• NLP (structured/unstructured)
• Apache Spark
• Jupyter Notebooks/PyCharm
• SAS/SPSS

Data analyst

As a data analyst you may need you may need skills such as

• SQL
• R/Python (Pandas, Numpy, Matplotlib)
• Tableau/Power BI
• Data Modeling
• SAS/SPSS
• AWS/Azure
• Excel

Overall it really is up to you and which one you prefer – you should really look at yourself and see what kind of work you enjoy doing. Both of these careers are fantastic options and in the long term the popularity of these jobs are only going to increase over the next 10 years so getting started now and knowing what you want to do will support you.

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