Data Analyst vs Data Scientist!
Both data analyst and data scientist job profiles require a professional to analyze data for gaining actionable insights for organizations.
So what is the difference between data science and data analytics?
The difference between the two profiles lies in how data scientists and data analysts manipulate data in their own unique ways.
Data analysts work with structured data while data scientists work with both structured and unstructured data.
We have compiled various examples to ensure that your queries related to data analyst vs data scientist job profiles are cleared.
Read on to get a clear understanding of the following FAQs related to data analyst vs data scientist:
- What is the difference between a data scientist and a data analyst?
- Which is better data analyst or data scientist?
- What are the different data analyst vs data scientist tools?
- Who gets paid more data scientist or data analyst?
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Learn all about data science vs data analytics and stay a step ahead of every other professional during job applications.
Difference Between a Data Scientist and a Data Analyst
What is the difference between data analyst and data scientist?
One of the major differences between a data analyst and a data scientist is what they do with data.
The job of data analysts is to solve a problem whereas the data scientist is expected to identify the problems and then solve them.
Data Analysts work with data that are already structured to solve problems of an obvious issue. On the other hand, data scientists work with raw data to solve problems unknown to us.
The focus of data science is more on what questions to ask whereas data analytics focuses more on finding answers to questions that are raised.
We can consider data science and data analysis as two sides of the same coin, given the fact that both fields work with data and come up with solutions.
What Does a Data Analyst Do?
Business leaders make strategic decisions based on the trends identified by data analysts.
Hence, professionals in this discipline focus on answering questions and solving problems via statistical analysis. They collaborate with an interdisciplinary team to rule out the goal of an organization and mine, clean and analyze data.
Some of the common roles and responsibilities of a data analyst can be seen below:
- Design and maintain data systems & databases by fixing coding errors and data-related problems
- Mine data from primary & secondary sources and reorganize them in a format that can be easily read
- Create documents that allow stakeholders to understand the steps of the data analysis process
- Prepare reports to easily communicate trends and patterns using relevant data
So if you are looking for a data analyst job profile, ensure that your skills and years of professional experience are relevant to the specific job profile.
Also Read: How to Write the Best Data Analyst Resume
What Does a Data Scientist Do?
Data scientists design data modeling processes to create algorithms and predictive models to extract the data required by the business.
Unlike data analysts who work with existing data sets, data scientists prepare data for analysis.
Data science practitioners have a more advanced role as compared to data analysts as they emphasize possibilities and future predictions that can enhance the data analysis process.
Some common roles and responsibilities of a data science profile are listed below:
- Research and implement appropriate ML algorithms and tools
- Develop machine learning applications according to requirements
- Run machine learning tests and experiments
- Perform statistical analysis and fine-tuning using test results
- Build business algorithms based on observing trends within the data
The career path for a data scientist is vast and filled with opportunities. So if you are interested in this job profile you need to get yourself equipped with all the right set of skills and a killer data scientist resume!
Also Read: The Best Guide to Draft a Perfect Data Scientist Resume
Data Analyst vs Data Scientist
Even though both data analysts and data scientist profiles are interconnected, they pursue different approaches to produce different results.
Now the question ... Which is better data analyst or data scientist?
To be fair, both profiles are quite popular and in demand because of the increasing need for data from every domain.
Leaving aside which career option is better for you to your own judgment, let us look into some major differences around these two profiles.
Data Analyst vs Data Scientist Skills
If crunching numbers and working with big data is what you are interested in, then you need to make sure that you possess the right set of skills.
The two major paths you can choose from are data analyst and data science. And since potential employers are on the lookout for professionals with data-driven skills, you need to be prepared accordingly.
Look at the table to see the kind of skills you need to possess (data analyst vs data scientist skills):
| Data Analyst | Data Scientist |
|---|---|
| Data Visualization | Machine Learning |
| Analytical Thinking | Data Modeling |
| SAS | Hadoop |
| SQL | MySQL |
| Python | Spark |
Now when you compare it as data analyst vs data scientist skills, the latter profile requires an applicant to possess more complex expertise and knowledge.
So if you already are a data analyst and want to become a data scientist, then you need to upskill yourself with a higher level of programming & machine learning algorithms and develop your mathematical & analytical skills.
Data Scientist vs Data Analyst Tools
What is the difference between data science and data analytics tools?
Data analysts often use Excel-based formulas, whereas data scientists go for programming to manipulate and model data or to automate their predictions.
Let us get an insight into the different tools used in data science vs data analytics:
Data Analyst Tools:
- Programming: Excel VBA, R, or Python
- Databases: SQL and Relational Databases
- Visualization Tools: Tableau, Power BI, Qlik
- Spreadsheets: Excel or Google Sheets
- Modeling Tools: SAS, RapidMiner; AutoML tools like H2O, Amazon SageMaker, or DataRobot
Data Scientist Tools
- Programming: Python, Scala, Spark, R, C, C++, or Java
- Databases: NoSQL, SQL Databases
- Cloud Computing: AWS, Google Cloud, or Microsoft Azure
- Big Data: Hadoop, Elastic Map Reduce, Pig, Hive, Impala
- Visualization Tools: Matplotlib, Seaborn, ggplot, Tableau, Power BI, or Qlik
- Spreadsheets: Excel or Google Sheets
- Modeling Tools: SAS, RapidMiner, AWS SageMaker, H2O, DataRobot
Note: Every organization has different expectations from its employees so make sure to research the specific requirements while applying for a job as a data scientist or data analyst.
Data Scientist vs Data Analyst Salary
Who earns more salary?
A data analyst or data scientist?
The salary package for any job profile depends on various factors encompassing seniority level, industry trends, and geographical area.
Likewise, all these factors come into play when it comes to the difference in data analyst salary and data scientist salary around the globe.
But the fact remains that a professional with relevant work experience and notable certifications or training gets a higher chance of bagging a higher salary package.
Look at the given list to see the difference between the average salary of a data analyst and a data scientist in the US and the UK:
Data analyst vs data scientist salary US
Data analyst vs data scientist salary UK
To land a job as a data analyst or a data scientist, check out Hiration's interview preparation feature, which is filled with some of the best interview questions and sample answers that can help you prepare for an interview.
Also Read: The Trending Data Scientist Salary in 2022
Data Analyst vs Data Scientist Education and Background Requirements
To land a data analyst or data scientist job, one needs to possess a certain level of educational qualification.
For data analysts, a bachelor's degree in maths, physics, statistics, or any relevant field can be beneficial. Whereas for a data scientist a bachelor's degree in computer science, statistics, or relevant field is required.
As for background requirements, a data analyst can land a job with basic knowledge in any language that can help you create data visualizations. You simply need to have the desire to learn and keep picking up new skills that can help you with data analysis.
On the other hand, a data scientist needs to have a strong understanding of at least one statistical and database language such as Python and SQL. Since data scientists create models, predict future trends, and develop machine learning algorithms, you need to have mastery in coding and database languages.
Also Read: The Best Guide for Decoding Data Engineer vs Data Scientist
Data Analyst vs Data Scientist: Which Career to Choose
To choose a career between the two profiles, you need to identify where your interest lies.
A data analyst or data scientist profile revolves around data analysis in its own special way.
- If you are interested in processing data and modeling statistics, then you can opt for a data analyst job profile.
- If your interest lies in big data or machine learning, choosing a career path as a data scientist will make more sense.
Professionals in both profiles share a common need to possess skills such as data warehousing, data mining, data visualizations, and statistical analysis.
The bottom line is for you to consider various factors comprising of your educational background, personal interest, and salary package while choosing between data analyst and data scientist job profile.
Key Points
The following are some major points that you need to keep in mind to understand data analyst vs. data scientist:
- Both data analytics and data science profiles revolve around data, but their approach differs.
- Professionals in this field need to possess relevant skills to land the job.
- It is always a wise thing to prepare oneself before applying for a job.
- Getting professional assistance can raise your chances of landing the right job as a data analyst and data scientist.
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