Data Science is the buzzword for the current and perhaps the next few generations. If you want to read about Data science, this blog will certainly help you. While everyone is going gaga about data science and how to become a data scientist, it is important you know the difference between being a data scientist and a data analyst.
Appreciating the differences will help you choose the one that suits your skill set more.
Both data scientist and data analyst deal with a lot of data and need an analytical mindset. Currently, the designation of a Data Scientist is one of the highest paying jobs in the computer industry.
At the surface, both seem to be the same and that’s why we thought of digging into each in detail and found out that both positions are somewhat similar but a lot different in their scope!
To start with, a data scientist can be a data analyst too but the vice versa is not possible. Data Science is a vast field and encapsulates research, collection, processing, analysis, visualization and much more. Data analysis is a small part of data science where the analyst sits with the businesses and prepares reports and presentations.
What does a data analyst do?
A data analyst basically ‘analyses’ the data to answer questions related to business problems. They identify correlations and new metrics, find trends and patterns, apply statistical methods to analyze and mine business data, create data reports and communicate with other teams to get new data to solve the problem in hand. All this requires good knowledge of spreadsheets like Excel, writing queries to manipulate data, statistical tools to create charts and trends, and know-how of languages like R, Python, etc to write scripts that can automate certain repetitive tasks.
Data analysts can be architects, administrators, analytics engineers, and operations managers. You don’t need any extensive experience to become a data analyst. Here are some of the top data analytics courses to get you started.
What can a data scientist do?
Data Scientist can do all of the above plus a lot more things. The scope of the data analyst is limited whereas data science is vast and hence has a wide scope. It involves more thinking and analysis – not just of data, but also of potential business problems and their solutions.
As we can see, these are tasks that need in-depth knowledge of core subjects as well as domain. Apart from this, data scientist also needs to know about programming languages like R, Python, SQL amongst many. Believe me, it is all worth the type of job you will get – as I mentioned earlier, it is one of the highest-paid jobs today and it will be so for at least the next decade or two. While the salary of a data analyst depends on the domain – finance, operations, market research, etc…, data science can fetch you almost double of that in any domain you choose.
Data science is further broken down into several sub-jobs – data researcher, data developer, data creative and data business people. It is easy to know how to become a data scientist and you can right away start your journey with a few tutorials.
The differences in detail
Now that the overall picture is clear in your head, let us move on to look at more specific differences between the two fields and what you need to land a job for any of these titles.
Data Scientist is a cool title in today’s IT world and although data analysts play a major role in the entire data science lifecycle, their role is only limited in nature and gives you lesser opportunities to grow compared to a data scientist.
On the other hand, having done analytics training and being an expert in tools like Excel, R/Python, SQL, SAS can give you the necessary edge over others and you can analyze data with the least effort which is otherwise a time-consuming task. It can help you be closer to being a developer and master programming languages that you will use. It can also help you transition your career to a data scientist in the future with ease. You will be abreast with the latest technologies and tools used for data analysis.
The skills of a data scientist are different and ever-evolving and by now you must have understood that it takes more than just collecting, cleaning or analyzing data to be a data scientist.
Data Analyst vs Data Scientist: Head to head Comparison
Here is a head to head comparison for your easy reference and to further clarify the differences we have studied above –
|Data Scientist||Data Analyst|
|Manages the entire business process from defining the problem to making fast and accurate predictions and business decisions.||Part of the data science lifecycle where a chunk of data is given to the data analyst to give solution to a specific problem.|
|Explores data obtained from different unrelated sources and examine them.||Explores and analyses data only from a single source.|
|Performs predictions based on various chunks of data obtained over a period of time.||Does day to day analysis of data to arrive at trends and patterns.|
|Formulates questions problems relevant to the business and finds solutions using data.||Finds answers to the business problems posed by the business team using the problem statement as reference.|
|Works on SQL to get the data, clean it, perform munging (bringing data into correct format) and extract information from data.||Works on SQL, BI tools or statistical tools to analyze and prepare data.|
|It is important for the data scientist to know about machine learning, creation and training of the model, testing and improving the efficiency of the model.||Not required to know about statistical models or machine learning and advanced programming levels.|
|Needs strong data visualization abilities and knowledge to convert data into business stories or use cases.||The analyst doesn’t need to transform or create a roadmap with the data.|
|Needs to know SQL features but not as deep as a data analyst. Check out the SQL features needed by data scientist here.||Needs to have in-depth knowledge of SQL and analytics.|
|Requires deep knowledge of machine learning tools like Bayesian, clustering, etc…||Requires high skills for decision making based on statistics and analytical tools.|
|Has in-depth knowledge of predictive analysis, correlation, data mining, and statistics.||Has knowledge of ETL tools, tools, and components of data architecture.|
|Needs a basic understanding of Hadoop, SQL, Python, Java, etc..||Possesses detailed knowledge of Hadoop or Spark-based analytics such as MapReduce, Hive, etc… Similarly follow good coding practices for developing scripts in Python, Java, and SQL.|
In a nutshell, we can say that while a data analyst provides solutions to business problems and creates reports for the same, a data scientist creates business problems and also finds the most suitable solution to it based on the data analysis and other information at each stage. Getting insights from data is one thing, but interpreting those insights to arrive at a business solution is totally different. Being a data scientist is completely different from being a data analyst and comes with more responsibilities. Even if your goal is to be a data analyst, it is good to understand the whole picture of data science so that you can move up the career ladder anytime you want.
- Top 10 Python Data Science Libraries
- Top Data Science Interview Questions
- R for Data Science
- 10 Best Data Science Books
- How to become a data analyst without no Experience
- R vs Python: The notable difference you Might be Interested
- Best Data Analytics Courses
- Difference between Data Science vs Machine Learning
- Differences you Need to Know about Data Analyst vs Data Scientist