Data Analytics

Want to know how much a data analyst earns p.m.?  

In this article, we will tell you  how you can start, including from where you can choose your FREE introductory course. 

Besides everything else, a data analyst is an adventurer, a pioneer: a data analyst dives into the unknown— a Company’s data—and finds interesting threads to pursue, all with the aim of revealing insights that solve customer issues or help grow the business. By living in and examining large data sets, data analysts try to understand what is really happening to help the business to make better-informed decisions.

We can say a data analyst is kind of a  storyteller, transforming complex analysis and datasets into simple, absorbable insights than can be widely understood by the business and organization. Data analysts will often be responsible for working with the company’s management to create, track and remind upon KPIs (key performance indicators), against which the company defines and measures its success.

Let’s try to understand the example of  Spotify.  Spotify must be one of the most blatantly data-driven consumer products out there—everything about it, from the moment you search for your first song to the moment you select your Weekly Playlist from your personalized recommendations, is generated by insights from data analytics. The data analyst’s work forms the foundation of this; they build the dashboards that enable the business to understand what the biggest hits are, how many hours customers listen to their music, where and how often they listen, how they listen differently during work hours compared to after work, and which features get the most (and least) attention.

At this juncture,  you might be thinking, “But what about the data scientists?! What about the data engineers, and the data architects?!”

A data analyst’s job is different from a data scientist’s or a data engineer’s, although they’re often mentioned in the same breath. So let’s resolve the confusion: what’s the difference between a data analyst, a data scientist, and a data engineer?

  • Data engineers: A data engineer’s primary responsibility involves preparing data for analytical or operational purposes and building and maintaining software infrastructures. They’ll often work closely with the backend development team.
  • Data scientists: Data scientists use advanced analytics technologies (such as machine learning and predictive modeling) to provide insights beyond statistical analysis. Taking Spotify as an example once again; while the data analysts will recognize listening patterns and trends, the data scientists will use these analyses to write the algorithms behind its recommendation engine, thus automating and personalizing song recommendations for millions of people.

Data analytics is a field in its own right, but it can also be used as a stepping stone for getting into data science. But let’s not get ahead of ourselves. Data analytics is a pretty cool field as it is, right?

Is Data Analyst a most sought after a job?

Yes.  You have the following reasons to believe the same : 

  • According to a recent report from the World Economic Forum, data analysts will be in ever-higher demand. In fact, they constitute one of just two “stand-out jobs” which will experience rapid growth due to the fact that they will help companies “make sense and derive insights from the torrent of data generated by technological disruptions”
  • The LinkedIn Workforce Report maintains that, in the USA, demand for these professional figures has grown sixfold over the last five years, and data analysts will continue to be some of the most sought-after profiles over the next five years.
  • According to Glassdoor, the average salary for a data analyst in the USA lies north of $67,000, up to $7,000 from the beginning of 2017. Not a bad trend!

Please have a look at the below graph to understand the p.m. earning of a data analyst in India :

What skills do you need to become a data analyst?

Hard skills

  • Microsoft Office/Google Sheets, Google Data Studio: For the quick, small analyses.
  • SQL: To handle large datasets that Excel can’t process
  • Python (or R): Python and R are two powerful statistical programming languages that are used to perform advanced analyses on very large datasets
  • Other tools: Everything from Google Analytics and Tableau to Visual Website Optimizer, depending on the part of the business you’re working on

Soft skills

  • Business understanding: Strong data analysts will go beyond what and tackle why. To do this, one needs to understand what decisions were made from a product perspective (new features, competition, etc.) and a business perspective (change in pricing, customer segments, etc.).
  • Communication and creativity: You are a storyteller. You summarize complex topics into simple visualizations that everyone can understand.
  • Strong prioritization skills: There are never enough data analysts. All departments need data to understand what’s happening and what decisions to make. That means you will never be bored, and you will need to prioritize.

What are the main tasks of a data analyst?

A data analyst uses data to acquire information about specific topics.  This usually starts with the survey process, in which data analysts find survey participants and gather the needed information.  The data is then interpreted and presented in forms such as charts or reports. Data analysts may also put their survey data in online databases. 

Tasks will, of course, vary from company to company depending on the nature of the business and how large the data team is, but there are plenty of common threads. Typically, a data analyst will perform the following tasks to look at a data problem:

  1. Collect, analyze, and report data to meet customer needs.
  2. Identify new sources of data and methods to improve data collection, analysis, and reporting.
  3. Collect customer requirements, determine technical issues, and design reports to meet data analysis needs.
  4. Transforming and preparing data
  5. Analyzing and playing with data
  6. Finding interesting features that deserve follow-up
  7. Visualizing and presenting findings to different stakeholders by describing…
    • why they matter within the context of a particular business
    • how they can be leveraged
    • potential follow-up analyses 

How to start ?

Now  we would like to tell about the various online free introductory data analyst courses available.  After our extensive research, we have found that Edx course namely “Introduction to data analysis using Excel” is one of the best-suited courses for beginners. You can easily search and find the above course in Edx website.  Only if you need any certificate you will be required to pay else not. We suggest you to go for this course as a free option. If you want to go for a complete course with a placement option, please get in touch with our career counselor, through contact forms available in this site.

 For the purposes of data analysis, you must have minimum system requirements like a PC and MS Excel 2016. 

Below mentioned book on Data Analytics offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.