Data Scientist vs Data Analyst. Here’s the Difference. | by Matt Przybyla | Towards Data Science

IntroductionData ScientistData AnalystSimilaritiesDifferencesSummaryReferences
Introduction
Data Scientist
Data Analyst
Similarities
Differences
Summary
References
Whereas data science and machine learning fields share confusion between their job descriptions, employers, and the general public, the difference between data science and data analytics is more separable. However, there are still similarities along with the key differences between the two fields and job positions. Some would say to be a data scientist, a data analytics role is a prerequisite to becoming hired as a data scientist.
This article aims to shed light on what it means to be a data scientist and data analyst, from a professional in both fields.
While I was studying to become a data scientist, as a working data analyst, I realized that data science theory is vastly different from that of data analytics. That is not to say that data science does not share the same tools and programming languages as data analytics. One could also argue that data science is a form of data analytics because ultimately, you are working with data — transforming, visualizing, and coming to a conclusion for actionable change. So if they are so similar or one is under the other, why write an article on these two popular fields? The reason is that people who are coming into either field can learn from here — what they will be getting themselves into with either career — or if people are generally curious, and to further the discussion. Below, I will outline the main similarities, differences, and examples of what it means to be either a data scientist or data analyst.
Exclaimer — this DS section only has some information I have gathered from my previous article on data science versus machine learning along with new information as well [3]:
towardsdatascience.com
Data science can be described as a field of automated statistics in the form of models that aide in classifying and predicting outcomes. Here are the top skills that are required to be a data scientist:
Python or R
SQL
Jupyter Notebook
Algorithms/Modeling
Python — in my personal experience, I believe most companies are looking for Python more than R as the main programming language. Job descriptions may list both; however, most people you are working with like machine learning engineers, data engineers, and software engineers will not have familiarity with R. Therefore, I believe, to be a more holistic data scientist, Python will be more beneficial for you.
SQL, at first, can seem more like a data analyst skill — it is, but it should still be a skill you employ for data science. Most datasets are not given to you in the business setting (as opposed to academia), and you will have to make your own — via SQL. Now, there are plenty of subtypes of SQL; like PostgreSQL, MySQL, Microsoft SQL Server T-SQL, and Oracle SQL. They are similar forms of the same querying language, hosted by different platforms. Because these are so similar, having any of these is useful and can be translated easily to a slightly different form of SQL.
Jupyter Notebook, a data scientist’s playground for both coding and modeling. A research environment, if you will, allowing quick and easy Python coding that can incorporate commenting out of code, the code itself, and a platform to build and test models from useful libraries like sklearn, pandas, and numpy.
Algorithms — the main function of a data scientist is to utilize algorithms that quickly and accurately predict, classify, and suggest outputs from data. As you ingest data into the model, a new outcome is created. Examples of key algorithm types are usually bucketed in unsupervised learning (e.g., clustering) and supervised learning (e.g., classification/regression). Some specific key algorithms:
Random Forest (ensemble classification)
Logistic Regression (classification — not regression)
K-Means (clustering)
K-Nearest Neighbor (classification/regression)
Overall, a data scientist can be many things, but the main functions are to:
— meet with stakeholders to define the business problem— pull data (SQL)— Exploratory Data Analysis (EDA), feature engineering, model building, & prediction (Python, Jupyter Notebook, and Algorithms)— depending on the workplace, compile code to .py format and/or pickled model for production
To find out more information on what a data scientist is, how much they make, the outlook of the field, and more useful information, click this link here [4] from UC Berkeley.
A data analyst shares similar titles with business analyst, business intelligence analyst, and even a Tableau developer. The focus of data analytics is to describe and visualize the current landscape of the data — to report and explain it to nontechnical users. A data science crossover position is a data analyst who performs predictive analytics — sharing more similarities of a data scientist without the automated, algorithmic method of outputting those predictions.
Some of the main skills that are required to be a data analyst are:
SQL
Excel
Tableau (or other visualization tools — Google Data Studio, etc)
SQL — just like how a data scientist would use SQL as stated above, so does a data analyst. However, there is a strong focus on SQL in this field. Where some data scientists can get away with simply selecting columns from a table with a few joins, a data analyst can expect to perform much more involved querying (e.g., common table expressions, pivot tables, window functions, subqueries). Sometimes a data analyst can share more similarities between a data engineer over a data scientist depending on the company.
Excel — old school, yes, but still very powerful, even predictive analytics and trend analytics can be performed here. The main pitful oftentimes is a slower performance in Excel over other more robust tools that use Python.
Tableau — I would just say visualization tools, but most companies, in my experience, list this tool as a specific, top skill for data analysts. Dragging and dropping of data into a pre-created chart in Tableau is simple and powerful; there are more difficult and complex functions, too, like calculated fields and connecting to a live SQL database over basing your analysis via a static Excel sheet.
Overall, a data analyst can be many things as well, but the main functions are to:
— meet with stakeholders to define the business problem— pull data (SQL)— EDA, trend analysis, and visualizations (Excel and Tableau)— depending on the workplace, presenting findings and supplying actionable insights those same stakeholders
To find out more information on what a data scientist is, how much they make, the outlook of the field, and more useful information, click here [6], from Northwestern University.
Some of the similarities have already been outlined in the previous sections, but to summarize, data scientists share commonalities between both coding languages, platforms/tools, and problem-solving.
Similar tools include, but are not limited to SQL, Tableau, and that same concept of defining a probelm, analyizing the data, and outputting an analysis.
While there are similarities, there are still differences between the two fields and roles.
Some of the main differences revolve around automation of the analysis — data scientists focus on automating analysis and predictions with algorthims using programming languages like Python, whereas data analysts use stationary, or past data, and in some cases, will create predicted scenarios with tools like Tableau and SQL.
Data science and data analytics share more than just the name (data), but they also include some important differences. Whether you want to be a data scientist or data analyst, I hope you found this outline of key differences and similarities useful. If you are already one of these two roles, then I hope I taught you something new, and if you have any questions or comments, please do so below.
[1] Photo by Christina @ wocintechchat.com on Unsplash [1], (2019)
[2] M.Przybyla, Jupyter Notebook screenshot, (2020)
[3] M.Przybyla, Data Science vs Machine Learning. Here’s the Difference., (2020)
[4] UC Berkely, What is Data Science?, (2020)
[5] Photo by William Iven on Unsplash, (2015)
[6] Northeastern University, What Does a Data Analyst Do?, (2019)
[7] Photo by Filiberto Santillán on Unsplash, (2019)