How to Flourish as a Data Scientist and Data Analyst

As we all know the time is of DATA, there is no doubt in the mind when I say the boom in the data industry has driven and taken the demand for data science, data Analyst and Data engineers to the next level, across all industry verticals. There are job openings for data scientists, data engineers, and data analysts. And there seems to be a lot of confusion and lot many different opinions among people regarding the roles and skillsets driving this field. Here we will mostly discuss two major job titles, Data Scientist and Data Analyst.

Let us Understand the Main Difference Between Data Scientist and Data Analyst

I know many of you may not convince with this but yes, there are no defined skill-sets that can distinguish between the role of a ‘Data Scientist’ and ‘Data Analyst’. This is mainly because most of the companies have their own definition and role descriptions. In fact, different companies have different definitions for both these roles, and there is a lot of grey area in between the two job titles. Broadly analyzing, a Data Scientist is a professional who combines data handling and data visualization with sound business understanding to make smart business decisions.

A data scientist is expected to deliver business impact and take insights from the raw, chaotic data thereby uncovering answers to the problems we did not know existed. Data science as a job profile demands skills such as data structuring, data mining, data visualization, analytical skills, programming skills, machine learning skills, and customer insights

The role of a data analyst, on the other hand, is to summarize data and provide futuristic inputs by identifying consistent patterns from the past and the current data. The primary role of a data analyst is to collect, curate, process, and arrange data from different sources.  They are responsible for presenting data in the form of charts, graphs, and tables and use this structured data to build relational databases for companies. Less or no programming but yes, lot of report preparations.

Although there is a difference in the job responsibility of a data scientist and a data analyst, these two fields are exceptionally interconnected. They often work in close coordination to achieve the same goals i.e. of growth and development. For someone who aspires to become a data analyst, it is essential to understand the fields they should pick and the technology.

Some of the main stream data professional certifications are provided by IBM, SAS.

Following are the links to the best certification with all the details you may need and practice guidance.

IBM Certifications

SAS Certifications

EMC Certifications

QLIK Certifications

As we already have an overview, let us move to content as the title suggests, the Tips.

The Presentation

We believe all data and numbers have their own layer to share information. Actually, they share stories. Yes, it starts with the presenting and creating reports in an easy and understanding manner, but it is not limited to that, it has way beyond that to do and act.

Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” –Stephen Few

The efficiency of the report is always calculated on how and what level of information was consumed by a non-technical audience. If you created a visual report that cannot be interpreted by the consumer of the report, that is nothing but a picture or a tabular report of the above nature is just a bunch of a data with no impact.

Let us make simpler,

Always start with, who are the audience.

What business problems can be addressed and how, so focus on business problems

Understanding of the business and it's past

Choose the best report format, Visual/ Tabular or Mix.

Prepare a strong and impactful presentation and get yourself ready for Q&A session

Coding is the Key

As we discussed in the beginning, most of the data scientists are commonly comfortable with coding however the case is not true for many of the Data Analytics. And sometimes as we already discussed the two of the roles are so mixed up, many of the data scientist from the industry are shy in taking coding up. To be a really successful data expert, your programming skills should be a combination of computational and statistical abilities as well as coding. You should be able to handle a large volume of real-time data and apply statistical models like clustering, optimization, regression, etc. to it.

Many a time it is seen that, the data expert is having hands-on expertise on one language but he is hesitated to get his hands on other technology or to be specific on other programming languages. Currently, the preferred language among data scientist is Python with the use of other languages such as R, Scala, Clojure, Java, and Octave but there should not be any boundary, we feel, be open to learning anything. Try to do a dummy project that highlights your strengths. Code wildly and to the point, you lose your sleep. As a data scientist, this will help you grow, learn something new, and most importantly hone your coding skills. Remember, the more toy problems you solve, the better equipped you will be to handle the real ones.

Domain Knowledge is very important

Domain expertise is something that makes a Data Scientist an expert! Having domain knowledge is not enough. As a data scientist, it is crucial to stay in front of the curve and understand which technology to apply and when.  Unwavering focus on the domain helps us to understand the real problem which empowers us to create solutions that are useful on the ground, and not just “useless innovation”.

 A data scientist should always work closely with the business to measure and prove the effectiveness of the project on the ground.  In addition to having an in-depth understanding of the problem, being aware of the latency, bandwidth, interpretability and other system boundary conditions, will help you understand what technology to apply.

Be Creative in Solving Problem

A good data scientist is the one having traits of a good problem solver. Sometimes problem-solving needs assumption as you may not be able to test the solution on ‘real data’. To make such an assumption, you will need to bring critical thinking to the forefront and look at the problem from many perspectives.  These perspectives give the data science experts a view of what they are supposed to be doing before pulling all the tools so that they can work to completely solve the problem.

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