Difference between Data Scientist vs Data Analyst

By | January 16, 2021
Data Scientist vs Data Analyst

Introduction

When I started learning data science, I always thought data science and Data Analysis were synonymous. When I started digging deep, I realized that although data analysis takes up a considerable portion of the data science process, data science is still very vast and contains multiple disciplines.

So today I thought, I should put my learnings somewhere so that others can benefit and make informed decisions about their career choices. Although both data analysts and data scientists have an equally satisfying career path with an excellent salary package, a data scientist has a broader scope of opportunities because of the various areas that it encompasses.

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Why the Confusion?

Most of us think that data science is all about analyzing data and finding insights to make some business decisions. Sure, data science does that, but not just that! All the math and statistics you need for data analysis are also used for the entire data science process from start to end at various stages like data collection, cleaning, visualization, etc. But since analysis goes deeper into finding more about users from data, we may think of it as the most crucial data science task.

Each step in the data science lifecycle is a big one and has its own set of tools and techniques. However, the most critical step is believed to be data analysis, which results in a conclusion for the data in hand and helps create new business opportunities.

That’s why the confusion!

Data science lifecycle

Data Scientist Skills, Roles, and Responsibilities

We have seen the entire data science lifecycle again. It is vast and takes time to learn. It is also not a one man’s job. Since data (mostly digital) has become critical now and is generated in huge volumes, the data scientist’s role has also been split into multiple disciplines—for example, data engineer, data analyst, machine learning engineer, business analyst, etc. For a complete list, check our data science career opportunities article.

You would notice a title called ‘Data Science generalist’. A generalist is a data scientist who can take up the job of a data analyst, data engineer, machine learning engineer, technical architect, etc., as and when required. All these jobs together form the essence of a data scientist. One day, you could be juggling with creating a good data set from a dirty one, the other day, you could be preparing presentations to present your data insights in the best possible manner.

You should know the end to end process – right from identifying and defining the business problem, getting all the possible relevant data, making it structured, cleaning it, analyzing it, and communicating the insights to other important people involved.

Well, that’s not it. You also need some business/domain knowledge, problem-solving and analytical skills, good (rather clever) communication skills, and a creative mindset.

Data scientist skills

Data Analyst skills, roles, and Responsibilities

Data analysis will always be fruitfully challenging and give you different situations each day. It will also allow you to work with different tools and techniques. A data analyst also earns equally well but has a specialized/focussed job. For example, you will be expected to have more technical knowledge than business acumen to perform well in your career. Some important technical skills Data analysts should possess are:

Data Analyst Skills

Data analyst skills are limited to technical skills and knowledge of different tools to make their job easier. You should also note that the job is focused – analyze the data!

What does this analysis involve?

Different types of analysis can be done on data:

Types of Data Analysis

From left to right, we move from Hindsight, insight, and then foresight. The predictive and prescriptive analysis is part of foresight, whereas diagnostic is insight.

I will give you a simple example:

  • You are informed that the sales of your product (say ice-cream) are decreasing. (This is the business problem that the data scientist has identified).
  • Now, you have to first understand why sales decreased. To find this, you will perform a diagnostic analysis. For example, ice-cream sales can drop because of the weather. There may be other competitors who are offering better prices, and so on.
  • If we see the previous ‘x’ months data, it looks like the sales will further go down (or maybe up) based on the reasons we found above. If the cause is the weather, then it will go up when summer comes. However, if the competitors are generating more sales, then the sales may further reduce. This is called predictive analysis – predicting the future based on past and current data.
  • Now, to increase the sales of our ice-cream, what should we do? Should we come up with a better campaign? Can we add some unique flavors that no one competitor brand has? Can we give better discounts and offers? All these come under prescriptive analysis – just like the doctor gives you a prescription for your illness (problem).

All these make the role of a data analyst exciting and full of variety. The roles and responsibilities, salary, skills, everything is different but related. We will see all the differences in the +pbelow head to head comparison chart.

Data Analyst vs Data Scientist in a Nutshell

Data Analyst Data Scientist
It is one of the essential phases of the data science lifecycle where many tools and techniques are applied to analyze data deeply It is the entire field that involves many tasks like defining a problem, collecting relevant data, transforming raw data into usable data, analyzing it, and presenting the insights for business solutions
Data analyst gets structured data for analysis. This data is already transformed into a usable one Data scientists obtain data from various sources, which is messy and raw.
Explores data only from a single source Data sources can be from anywhere and in any formats
Day to day analysis of data to determine trends and patterns Data scientist collects data over a period of time that is required for the business and then sends it for analysis
Involved in finding answers to the business problems already defined through trends and patterns Formulates various questions, does thorough research to find out the right business problem, and then collects data
Works with technical tools and is involved in technical tasks like database querying, writing algorithms, and programs, visualization tools to

analyze data. Some tools used are SQL, Excel, Python, etc.

Uses technical and business tools to view data from various perspectives, sort the data, clean it, do exploratory analysis, and then give it to data analysts to perform more in-depth analysis. Some standard tools are SQL tools, BI tools like PowerBI, statistical tools like SAS, MATLAB, big data tools like Spark, Visualization tools like Excel, Tableau, etc.
Doesn’t need to know about converting business problems into use cases or creating roadmaps A data scientist needs to create business user stories, roadmaps and generate visual and written reports based on the patterns given by analysts.
Needs in-depth knowledge of SQL and analytics tools Needs to know overall features of SQL and other technical tools but need not be expert on it
Need not be adept in decision-making tools, ETL tools, or data architecture In-depth knowledge of decision making, correlation, data mining, predictive modeling is a must
The overall roles and responsibilities are limited There are a lot more responsibilities when compared to any other sub-field of data science

Data Analyst Salary vs Data Scientist Salary

As the data scientist’s job comes with more responsibilities, the salary offered is also more. The starting salary itself is more for data scientists. The salary range offered to data scientists is around $83000-$115000, whereas, for data analysts, it is anywhere between $59000-$81000.

A data analyst is an excellent title to start with, as it will give you a lot of exposure to work with data in various ways. You can then slowly get into the overall data science process as you start learning more techniques of analysis, visualization, and big data analytics.

Further resources to learn

As cool as the words sound, data science, and data analysis are best learned when practiced well. If you know Python, you can readily get into analysis using the various data science libraries and do simple projects. Learn the basics of Python or R, whichever language you feel comfortable with. Start your data science journey here.

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