Skills Needed For Data Scientist and Data Analyst

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Data Analyst does a lot of descriptive analytics. On the other hand, Data Scientist also does descriptive analytics. But also data scientists do something called
predictive analytics.

So let's try to understand what Descriptive and Predictive analytics mean.
Descriptive Analytics is all about analyzing the historical data to answer this particular question which is "WHAT HAS HAPPENED TILL NOW??".
Predictive Analytics also involves analysis of historical data but, predictive analytics is mainly all about answering the question
which is.. "WHAT WILL HAPPEN IN THE FUTURE??"

Let's understand this with a simple example.
I have sales data of XYZ company in a table format.

As part of descriptive analytics, we can simply create a scatter chart so that we can quickly understand how the company has been performing
in terms of sales in the previous years.

Now let's look at predictive analytics. So now that we know how the company has been performing in the previous years, can we predict what's gonna
happen to the sales in the coming years?.. Will the sales increase, or decrease or does it remain the same??..
If we are able to answer these questions, then it is called as predictive analytics.

So coming back to the comparison of Data Analyst and Data Scientist roles,
Now that we have some idea about the differences between the two roles, lets now look at skills needed for each of these two roles.

Data Analysts should be good with Math and Statistics.
They should be good with handling the data.
-- This includes knowledge of ETL (or Extract Transform and Load) operations on data and experience working with popular
ETL tools such as Informatica – PowerCenter,IBM – Infosphere Information Server, alteryx,
Microsoft – SQL Server Integrated Services (SSIS), Talend Open Studio, SAS – Data Integration Studio
,SAP – BusinessObjects Data Integrator, QlikView Expressor or any other popular ETL tool.
-- They should be comfortable in handling data from different sources and
in different formats such as text, csv, tsv, excel, json, rdbms and others popular formats.
-- They should have excellent knowledge of SQL (or Structured Query Language).

Its a Bonus to have
-- The knowledge of Big data tools and technologies to handle large data sets.
-- NoSQL databases such as HBase, Cassandra and MongoDB.
They should be expert in Analysing and Visualizing the data.
-- They Should have experience working with popular data analysis and visualization packages in python and R
such as numpy, scipy, pandas, matplotlib, ggplot and others.
-- Experience with popular data analysis and visualization BI tools such as Tableau, Microsoft Power BI, SAP BI, SAS BI, Oracle BI, QlikView or any other
popular BI tool
They should have good communication and storytelling skills.
Lets now look at the skills needed for data scientist role.

Data scientist also does descriptive analytics just like data analysts. Apart from that, they also do predictive analytics.

So as part of Descriptive analytics:
Data Scientists should be excellent with Math and Statistics.
Data scientists should be good with handling data
-- So yes, they should have experience working with popular ETL frameworks.
-- They should have excellent knowledge of SQL.
-- Many companies expect data scientists to have mandatory knowledge of big data tools and technologies to
work with large datasets and also to work with structured, semi-structured and unstructured data.
-- Its good to have the knowledge of NoSQL databases such as HBase, Cassandra and MongoDB.
They should be expert in Analysing and Visualizing the data.
-- Experience working with popular data analysis and visualization packages in python and R.
-- Experience with popular data analysis and visualization BI tools such as Tableau, Microsoft Power BI, SAP BI, SAS BI, Oracle BI, QlikView or any other
popular BI tool.
They should also have excellent communication and storytelling skills.

And as part of predictive analytics,
They should be good in using the techniques in artificial intelligence, data mining, machine learning, and statistical modeling to make
future predictions using the historical data.
Exposure to popular predictive analytics tools such as SAP Predictive analytics, Minitab, SAS Predictive Analytics, Alteryx Analytics, IBM
predictive analytics or any other popular predictive analytics tool.
They should have very good exposure to popular machine learning and deep learning packages available for Python and R such as
scikit learn, tensorflow, theano,rpart, caret, randomForest, nnet, and other popular libraries.

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