With industrialization and digitalization taking the charge, the number of businesses and industries has risen significantly in the past few years or decades. This has only not only boosted the economy but has also had created a lot of employment opportunities.
With the rise in businesses, more and more insights are required by the companies to scale up their operations and become the leaders of their sectors. And for these reasons, industry requirements for data analysts have risen exponentially.
SOOOO? Let’s get staaarrtedd !!
Tune in with us, as here I’ll decode everything about data analytics for beginners, and after you finish reading this article, we’re certain to see you off with a clear blueprint about data analytics and clear cut steps on how you may get started with it, and weave a successful career in this industry.
Scope of data analytics in India and why it is a perfect career option today:
First, let’s start with a crazy fact!!
A lot of people talk about and question the scope of data analytics in India.
But did you know that more than 40% of the positions of data and business analysts are still vacant in the industries? Thanks to the exploding demand of data analysts and data engineers, due to which there is a large pool of open roles in analytics but not enough skilled candidates to fill them!
Shocking?! Yes, it is! And amazing as well at the same time, since it opens a new realm altogether for the beginners, that if they give their all in this field, there’ll be ample handsome opportunities waiting to welcome them and there are no limits to where they can reach!
Not only it has so many open positions, but this industry also has a significant increment, both in terms of pay scale and levels, as far as the data analyst career growth is concerned.
How is the analysis of data done:
SO, first things first! What is data analytics and what are the ideal prerequisites to get into data analytics for beginners?
The first and foremost thing to be kept in mind to define the goal behind the analysis, in terms of what we want to discover.
Essentially, data analytics involves sorting and filtering, and analyzing the raw available data, to get meaningful insights out of it. This involves certain algorithms depending upon which tool/platform we use and certain rounds of critical thinking. All these insights and information we try to extract from the raw data, have motives that can be categorized under one larger motive, i.e.- to drive better decision making.
For this pin-pointed guide for data analytics for beginners, we’ve broken down the process of analyzing data into the following simple steps:
Data Collection:
First, you need to understand what kind of data you precisely need to require, and then collect it from reliable sources. For instance, some reliable data sources can be Kaggle or LinkedIn. Accuracy while collecting data should be given utmost importance since if the data gathered is inaccurate, subsequent steps of analysis won’t make sense.
Data Processing:
It refers to the processing or simply arranging the data collected in a suitable form, for instance, you may choose to arrange the data in rows and columns in a spreadsheet.
Cleaning of data:
All the data collected as raw data may not be useful. Also, the raw data is very likely to have errors and invalid values, and even certain outliers which can significantly impact the analysis process at a later stage. Hence, the data has to be cleaned of all such anomalies, and the eros has to be fixed. This process helps to enhance the accuracy and the quality of insights that we’ll get through analysis.
Data analysis:
At this step, data is analyzed using softwares like Excel, Tableau, PowerBI, etc. Various data analysis techniques are deployed like data visualization, correlation, or linear regression to name a few. The errors that could not be removed in the data cleaning step are removed in this step.
Making the data communicable:
After analyzing the data, it has to be converted to a communicable form, such that it can be presented to a bunch of people effectively, and getting the message across.
How to start a career in data analytics?
Just to clear off the air in the beginning, you don’t need to be a specialist in statistical mathematics, computer science, or coding to step into this field. Knowing about them shall be a plus, but they are not essentially the deciding parameters.
Choosing the right formal education (even if not, don’t worry):
By choosing the right stream for graduation like in business management or related fields, may give an upper hand, but the right formal education is never the selection criteria for most companies. The only thing is that you need to be a graduate in any stream or course from a registered institution, to be eligible for a role concerned with data analytics in almost all companies.
Get relevant skills and experiences:
Get yourself acquainted with the relevant skills. Click here to read my another article for a detailed explanation of ideal and latest skills for a business/data analyst) and gain some relevant experience. This can either be done by doing an internship or working on some cool industry-relevant projects. Build a good portfolio, with good projects shine very bright on a beginner’s portfolio especially.
As a beginner, you may go ahead and start with some online course or instructor to learn the tools, technologies, and methodologies required by a data analyst, or simply you may start learning the basic skills, which shall take you miles ahead on your journey to a successful data analyst. Refer to this blog to know all the basic and advanced skills you need to possess to be a superstar as a data/business analyst:
Try to land up in a part-time/full-time job:
Experiences (as stated above) shall be pretty handy to land you an excellent job as a data analyst, you need not necessarily be a specialist in any particular field.
AND THE MOST IMPORTANT THING!!
Don’t ever assume data analytics to be a static career, its a pretty dynamic field, and its highly probable that the skill or tools which have learned right now may become obsolete after a year or two. So it very crucial to develop the mindset of “Unlearn and learn” constantly, and stay updated with the current market trends.
Python in Data analytics: The need of the hour!
Do data analysts code? This is one of the most sought questions, as far as data analytics is concerned.
If you are stepping into this field, it shall make a lot of sense to get versed with an open-source language like Python or R (preferably python since it has an extensive pool of libraries). As told above, you can get an internship or job as a data analyst without much programming language, but for sure you won’t be able to get much far without stepping into programming and the universe of algorithms.
Data analytics profiles/jobs for freshers:
There is a plethora of profiles you can work for if you acquire the data analytics skillset:
Data Analyst:
In the data analyst role, you shall be required to retrieve and collect large volumes of data, and extract meaningful insights out of them. It is one of the most paying jobs in India for a fresher candidate.
Business analyst:
To be honest the role of a business analyst pretty much coincides with that of a data analyst in most companies, still some companies hire business analysts separately from data analysts, and generally employ them to work on a day to day operations of the business, and how to fine-tune and enhance the efficiencies of the day to day processes. They are also paid par with the data analysts.
Product manager:
Product managers, in a sense, own the product and guide its path, right from ideation to its launch, from where it’s upon the marketing and sales team how well can advertise it.
At each stage of the product, data is leveraged to improve the features and make the product-market fit better and better.
Quantitative analyst:
Their primary job description includes leveraging data and data models in the financial industry, and hence, manage risks and make predictions of change in valuations of stocks or bonds.
There are certain similar profiles like market research analyst, business intelligence analyst, and many more.
But all of them have one thing in common, which if I quote in layman’s terms, shall be:
MAKE SENSE OUT OF THE AVAILABLE DATA
Conclusion:
Hence, we see this industry of data analytics is booming day by day, and there cant be a more secure career than that in data analytics, in today’s business-oriented times. If you are looking for some really kickass courses to get started with data analytics, click here and explore the endless world of data!