Data analytics guide for beginner

Want to make career in data analytics? if you have questions like- what to learn, which are the skill-set required, where to start to become a Data Analyst! In this post, I have summarized the overall data analytics career path and skill-set one must have, hope this will help beginners to become data analyst soon.

Data Analytics is one of hot topics in job market nowadays, almost everyone is interested to know, what it is! What would be the use! How to learn! What to learn and from where? and obviously the future of analytics!
And at the same time many organization have developed analytics product for different business vertical, trying to help their client simplifying data and make best out of that.

How to become a Data Analyst

So we thought of brining up some valuable information about analytics that may help new students to learn, practitioner to fine tune their best practices, organization to improve their analytics solution for better interpretability, cross platform integration, and to design small module analytics as service rather than whole enterprise.

Even we as new start-up, trying to analyse the trend of data analytics and its best-practices, finding new possibilities, learning and sharing at the same time.

Here are some basic questions about data analytics career scope that often we talk about!

• What is Analytics?

  Data Analytics is basically a mechanism of analysing huge amount of data, and in process of analysis we clean, organise, transform data and then create small subset of data for better usage, which can be easily search and exchanged for business operation, and should be able to help in decision making.
  Normally all those data are presented in two format, one for plain text like table/excel format and the other one in graphical representation using different type of chart

  One great example of Analytics is Google Analytics , also there are many different type of analytics products, we talk about them soon.

• Is there any difference between Data Analysts and Business Analysts profile?

  These two terms are often used in same context, sometimes it’s confusing, and in real life there is no difference actually, because all organization are making data-driven decision and approach to make their growth strategy, so data is the backbone of any business. In small organization both task are assigned to the same person, who helps in decision making with facts and figure made from data. so in that sense both are same profile.

  But, there are some subtle differences, In big organization "decision making based on data" is really complex job, so for more accuracy there are different profiles to handle the job.

  Though both roles are data centric, when Business Analyst analyse data from business domain point of view, Data Analyst look into more deeper side of data, like data relation, history etc, they compare with competitors positioning and forecast based on more statistical analysis.

  And because of huge volume of data, both jobs are challenging, and role of data analyst is to focus on data science and understand all advance technologies which helps analysing data with more accuracy.

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• Best analytics products and tools
  Now when we talk about best analytics products and/or tools, we need to understand the requirement and utilization aspect.
  There are some organizations who want "ready-to-use analytics product", which will allow them to customize and produce report for their day to day operation, where the type of customizations are pre-defined and obviously has a limit. This type of products involves less risk, quick to implement, and easy to replace in future if required.

  Like Tableau, Tableau is a BI software, very popular, because of its features like easy to use, smart dashboard, update automatically, and can fetch data from any data source like any database, spreadsheets, cloud services, Hadoop etc

  SAS is another big name in analytics world, this is a type of IoT (Internet of Things) solution, provide very advance features like Artificial Intelligence and Machine Learning, Risk Management, Fraud & Security Intelligence etc.

  On the other hand some organizations are keen about new possibilities, so they focus on tools which will allow them to create and enhance to any extent, this type of implementation involves high risk, huge efforts, huge maintenance task, but has limitless possibilities to invent something new.

  Now here are some open source technologies that allow us to develop very powerful customised analytics the way we want.

  R , R-programming has become very popular nowadays, especially in analytics software industry, R is robust. Now it can handle large data sets very efficiently. R-language is very versatile.

  Python Python – now python is most popular programming language, and highest number of projects are being developed using python, even Microsoft Visual Studio has integrated Python SDK in VS2017 and later version

  PIG and HIVE There are many big organization (like Uber, Baidu, Twitter) are using HIVE for developing their analytical tools.

• Can we develop our own Analytics?

  Absolutely Yes! Many organization develop their own analytics as per their requirement , though there are many big analytics company with huge range of products and services, but in many cases they are not fit for midsize and small company for various reasons.

  First reason that their analytic service doesn’t solve the typical purpose, which is very domain specific, And in some cases the cost is too high for any customization.

  Actually data analytics is nothing new, knowingly or unknowingly we have been practising for many years using different technologies like excel, Asp.Net MVC, SQL SSIS. Many small start-ups are building their analytics in-house for different business verticals like eCommerce, HR Technology etc. Here is a small example how you can analyse data and create dashboard, you can build for your organization and also can customise for reselling.

  But, if you are thinking of starting a data analytics company, or want to develop some data analytics product, here are some thoughts you may consider..

  • Create micro service rather than whole enterprise; it will be easy to position in market.
  • The time you spend on big market research and functionality development, instead think of developing cross product integration mechanism for your micro service, which will reduce huge risk and increase your product popularity.
  • Think of implementing industry specific customization framework.
  • No matter what technologies you use for development, if your service is not cross platform compatible, you will have tough time ahead.

• Data Analytics Career Opportunities

  Data Analytics skill has been a part of many (high paid) job profiles across multiple industries, Data Analytics is now a mandatory skill for new era.

  The demand is growing day by day, especially in business verticals like Health Care, Insurance, FMCG, HR Technology etc. And in next few years there will be tremendous growth.

  Here are some data analytics career options

  • Business Analyst
  • Data Scientist
  • Product Manager
  • Digital Marketing Manager
  • Data Architect / Information Architect

So if you are thinking of starting your data analytics career, this is the right time, start exploring data, understand some RDBMS, data relation, normalization, data mining, natural language processing, modelling, presentation etc, choose some tool that has wide acceptability.

Here are some you should join!

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