If big data is calling you and you are currently searching for a profession, you most probably hesitate between these two; data analyst vs. data scientist.
If you are not profoundly into data stuff, those two terms will sound very similar to you at first sight. However, your working routine as a data analyst will significantly differ from that of a data scientist, and in this blog post, we will tell you how.
What do Data Analysts do?
Data Analysts are responsible for collecting raw data from the company’s different communication channels and bringing that data to the condition that can help the company’s management make decisions.
Particularly, a data analyst is the person who gathers the data, cleans it, finds patterns, and visualizes those findings so that it’s easier to interpret what the data has to tell.
Data analysts should work with any data, as the organization’s data collection sources broadly vary. As a data analyst, you might deal with log files, customer reviews, sales reports, and many other data types, depending on the type of business you work with.
Why is data analytics a must for a business?
There are tons of insights that the company’s management can lose if there is no data analytics system. Imagine the company’s strategy, which is not able to analyze how their previous campaigns worked or how their customers behave in general.
For companies in their initial phase of development that might not be of critical importance, as it’s relatively easier to manually track the small business’s data.
However, as the business progresses, a need arises to collect and manage the data to understand how the company is performing and where it should move further.
Data Analyst’s day-to-day operations
It may vary from organization to organization
- Cooperate with all the departments of the company and access the information those departments collect
- Organize data collection and consolidation so that it becomes usable from organization members
- Examine data patterns and deliver reports to appropriate department and company’s management
- Perform statistical analyses and identify trends
- Visualize findings and communicate to main stakeholders
What do Data Scientists do?
Similar to data analysts, data scientists work with data; therefore, those two professions inevitably overlap in one way or another. Data scientists set the business’s questions and crawl different sources to find data that can answer those questions.
Data scientists are never limited to analyzing the data and visualizing the patterns. They dig deeper to find ways how those findings can apply to solving business decisions. That’s why data scientists need to have a strong business acumen along with analytical and statistical skills. What’s more, data scientists should have a strong awareness of machine learning techniques to build business models and make predictions.
It’s worth noting that data scientists often work with the unknown and extract actionable insights out of messy data. So, dealing with uncertainty should be your comfort zone if you are about to step into this profession.
Why is a Data Science must for business?
A combination of a sound business understanding and advanced mathematical skills is a competitive advantage that any business strives to have. Data scientists help companies to predict future trends, and therefore, be prepared with a proper strategy.
Companies that have a deep data analysis behind all their decisions tend to stand ahead of those that don’t. That’s why there is an unprecedented demand for data science specialists, and that’s why Business Harvard Review calls data science the sexiest profession of the 21st century.
Data Scientist’s day-to-day operations
It may vary from organization to organization
- Meetings and project reports to company’s management and appropriate decision-making departments
- Creation and periodic review and improvement of mathematical models
- Collection and cleaning of the large volumes of data
- Apply machine learning skills to create predictive models
- Apply the crafted insights from data to solve real business problems
- Communicate the work results to main stakeholders
Data Analyst vs. Data Scientist
As you can guess from what was stated above, the difference between data science and data analytics is mainly in the final work result.
Data analysts explore the available data and make its insights understandable for company members; data scientists go a step ahead and apply algorithms to make predictions on the existing database.
You can find a more in detail comparison of data analysts vs. data scientists below.
| Data Analyst | Data Scientist | |
| Education | Can be enough an undergraduate in maths, statistics, computer science, etc. | Usually requires higher degree; graduate in data science, information technologies, etc. |
| Skills | Foundational Math, basic fluency in Pthon/R, analytical thinking, data visualization | Advanced statistics, predictive analytics, machine learning, data modeling |
| Salary | Average yearly salary of $70,000 – $ 85,000 | Average yearly salary of around $ 100,000 |
| Programing languages | Basic Python, R, SQL | Advanced Python, R, SQL, MATLAB, Spark |
| Main goal | Clean and analyze the data, and visualize insights | Use data findings for solving business problems and create predictive models |
Which one should you choose?
The primary required skills and toolset of both professions are very close to each other. We can even state that data science is the advanced version of data analytics.
So, if you are new to the world of data and want to start from basic concepts, you can try data analytics first. Further, as you get more experienced and extract more profound results from data analysis, you can switch to data science.
Either way, you can start preparatory lessons today and acquire skills that you will need in both chases. In this regard, WildLearner’s FREE course of Python for data science might be what you are currently looking for.
