Difference in between Data Analysts and Data Scientists

Difference in between Data Analysts and Data Scientists

There’s still a lot of confusion on what exactly is the difference in between Data Analysts and Data Scientists.

It’s understandable, because most companies don’t even know the difference in between Data Analysts and Data Scientists. As a matter of fact, they end up advertising jobs for data analysts when they actually need data scientists and vice-versa.

Also, data analysts and data scientists tend to use the same tools (Python, R, Spark, Hadoop, Tableau, Power BI), adding to the confusion.

In this article I will define the difference in between Data Analysts and Data Scientists. You will see that they differ quite a lot in goals, methods, skills, and education, even if there is some overlap.

Different Goals

Difference in between Data Analysts and Data Scientists - reporter

Think about a reporter giving the news, and you’ll have a good idea of the Data Analysts goals.

The first goal of Data Analysts is to understand the structure of data and explain the story to different stakeholders:

  • What’s the current situation?
  • What are the drivers?
  • What could we have done better or what did we do well?
  • How can we learn from this situation to improve the future?

The end decisions are left to management, and the Data Analyst is like a reporter, helping them by giving them the story using statistics and visualization.

The second goal of a Data Analysts is to extract and circulate information, promoting a data driven culture. In other words, Data Analysts will find problems that if solved, can improve the business. This is especially true for Senior Data Analysts.

The goals of Data Scientists are quite different because they typically are involved in later stages of the Data Cycle. Their work is often a continuation of trends and opportunities Data Analysts or Business stakeholders identified.

The main goals of Data Scientists is to predict future outcomes and categorize data within a range of accuracy and variance:

  • Which customers are more likely to be interested in this offer?
  • Which factor is the most important to increase sales?
  • What search algorithm offer the best conversion rate?
  • What images contain a house?

You can see that the difference in between Data Analysts and Data Scientists goals is important, and this also means that different methods and skills are at play.

Difference in methods

Data analysts will use data exploration, visualization techniques, and descriptive statistics to dissect data and understand its structure. They will monitor KPI and present related trends to relevant stakeholders when needed.

They will most often than not use business jargon such as trend, growth (YoY, MoM, QoQ), revenue seasonality, etc. They will also use some basic statistics such as hypothesis testing and descriptive statistics.

Data scientists on the other end will spend more time pulling out data, cleaning it, and applying algorithms and other mathematical/statistical methods on it. They will use data augmentation methods, machine learning algorithms (linear regression, random forest, k-means, neural networks), Markov processes, and other methods.

The difference in between Data Analysts and Data Scientists regarding the methods they use is pretty clear: Data Scientists are able to use the same methods as Data Analysts, but they can also use more advanced techniques.

Difference in Skills

In my article Data Analyst The Education and Skills You Need to Get a Job I present an exhaustive list of skills required for Data Analysts. The short version is that Data analysts are more involved in the day-to-day operations of the business, discussing with stakeholders, providing insights and finding new opportunities to leverage data. Data Analysts deliver reports, dashboards, and presentations.

The skills needed to be a Data Analyst are:

  • Applying simple Math/stats methods to data
  • Technical Skills (Programming, Data Manipulation, etc)
  • Presentation skills to explain complex problems and solutions in simple terms
  • Business acumen
  • Analytical thinking

Data scientists are less involved in the day-to-day operations of the business and will often work in the background. They typically work on longer, more complex problems, and they deliver models, models outputs, and presentations.

The difference in between Data Analysts and Data Scientists is that the skills required for Data Scientists are more abstract and include:

  • Applying complex Math/stats methods to data
  • Technical skills (All above plus Big Data & NoSQL)
  • Presentation skills to explain complex problems and solutions in simple terms
  • Business acumen
  • Analytical thinking

Difference in Education

Data scientists tend to have higher education than data analysts. Most data scientists have a masters degree or a PhD, like mentioned by Colleen Farrelly, Data Scientist, in an answer on Quora:

In the US, ~90% of Data Scientists have some sort of MS, MBA, or PhD, not necessarily in stat/math or CS. Most positions will require this or 5+ years as a data analyst with some sort of training program in machine learning/data science (usually paid for by the company to train someone internally).

This is especially true for R&D positions or businesses where the main product is related to Artificial intelligence.

For Data Analysts the story is different, because most of them have a bachelor degree in a quantitative field like computer science, mathematics, economics, or engineering. It’s very rare to see a job posting for a Data Analyst that requires more than a bachelor degree.

Data Cycle and Examples

By now you should understand that the main difference in between Data Analysts and Data Scientists is that they’re not used at the same step in the Data Cycle.

The difference in between Data Analysts and Data Scientists

Here are a couple of concrete examples on the different use of the same data by Data Analysts and Data Scientists:

  • Data Analysts will identify countries that are underperforming this month. Data Scientists will identify countries having similar purchasing behavior.
  • Data Analysts will look at how much a customer spent on average in the last 3 years. Data Scientists will estimate how much a customer acquired today will spend in the next 3 years.
  • Data Analysts will highlight that some customers are spending more than others. Data Scientists will create personas to identify each customer type and make them specific offers.
  • Data Analysts will identify correlation in between growth and factors. Data Scientists will identify the main driver(s) and show how we can use them to increase revenue.

Conclusion

The difference in between Data Analysts and Data Scientists can be resumed as follow:

  • The Goals of Data Analysts is to dissect data, tell the story to stakeholders, and discover new opportunities.
  • The Goals of Data Scientists is to predict future outcomes and categorize data within a range of accuracy and variance.
  • Data Scientists need higher education and more advanced technical and math/stats skills than Data Analysts.
  • Data Analysts are involved in day-to-day operations and Data Scientists often work in the background on longer, more complex projects.

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