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    Top Data Analytics Skills to Gain in 2021

    by BML Munjal University March 2, 2021

    What is Data Analytics?

    To understand what skills you can gain with Data Analytics, it is important to understand what Data Analytics is. Data Analytics is a branch that comes under the larger umbrella of Data Science.

    Data Science involves procuring data, collecting them, data mining, analysis, data modeling, and implementing the models.

    At the same time, Data Analysis is just one part of the entire process. Data Analysis involves studying the data to look out for anomalies, cleaning the data, transforming the data to make it more insightful.


    Data Analysis is analyzing data to create models with which informed decisions can be taken and useful conclusions can be drawn.

    Currently, we are in the middle of a data deluge with the Internet of Things, social media, and other digital channels throwing up an incredible amount of data every second.

    Data Analysis is that branch of science that deals with these huge databases and complex ones and uses multiple approaches to dissect and pattern the data into models that help in decision-making.

    Data Analysis makes decision-making a more scientific process, with the support from past data, and helps businesses make important decisions related to the future.

    Given the voluminous amount of data in the digital revolution age, businesses are interested in deriving methods to analyze the data to find meaning in them, ultimately helping them build models that can predict future trends and increase the bottom line.

    This is what is achieved through Data Science. But for building Data models, structured, sorted, and cleansed data must be presented to the system or algorithm, which is devoid of randomness.

    This Data is then put through techniques and procedures in which they are broken up and inspected. Data Analysis is when Data is collected, cleaned, processed, explored, and modeled.

    Why is Data Analytics Required?

    Data Analysis is required to make meaning of the enormous data available in today’s digital age. It is a process of research and development.

    Data Analytics as a process started with the first set of data being stored in the computers. Today, Data Analytics has a huge set of applications, and therefore it is a reservoir of opportunities for the citizens of the coming years.

    Market Analytics, HR Analytics, Business Analytics are all different applications of Data Analytics. It has a huge market in the information and technology industry, in general businesses, society, and even governmental institutions.

    There is an imminent need to convert the widely available data into insightful information and knowledge that has its advantage. This is from where the process of knowledge discovery takes place and utilized:

    a) Scientific Data

    Due to the high volume of scientific activities in academic institutions, research houses, R&D departments of businesses, etc., a huge set of data gets amassed.

    There is a constant effort to utilize the data churned out to open further research opportunities. It also helps to avoid reinventing the wheel.

    b) Business Transactions

    An insane amount of data gets memorized with every transaction in a business – stocks, banking, purchases, exchange or returns, complaints raised, etc. These data provide valuable input for an organization.

    c) Surveillance video

    Surveillance cameras or CCTVs are found everywhere and are a massive store of important data that can be analyzed from criminal activities or investigation.

    d) Medical and healthcare data

    With the recent incidence of the pandemic, the world has seen an upsurge of data collection, collation, and analysis for identifying trends, symptoms for a new disease that the world was not aware of before.

    Sites like Worldometer showed fantastic data studies which are collected from various government healthcare sources across the world.

    Like the above, there are tons of different other sources from where enormous amounts of data are pouring in at all times, which are extremely beneficial when analyzed, and from where knowledge discovery takes place.

    These are – gaming apps, digital media, social media, emails and other communication channels, engineering data, etc.

    Data Analytics – Career Perspective

    Data Analysts job is a highly coveted job because it is greatly in demand and is a high paying one. Organizations hire Data Analysts to uncover patterns and information from sets of available random data.

    This revelation of insights helps a business grow and solve customer behavior, sales pitch, demand generation, etc.

    There was never a better time to study Data Analytics and build a career in this field. One major advantage of getting into this promising job landscape is that DA is not restricted to any specific field.

    Every industry in today’s world that is keen on enhancing the way they operate or optimize their profits invest in Data Analytics. So there is a huge demand. Some of the prominent roles that can be pursued after a DA Course are:

    1. Data Scientist
    2. BI Analyst
    3. Data Analyst
    4. Data Analytics Consultant
    5. Data engineer
    6. IT Systems Analyst
    7. Marketing Analyst

    Data Analytics at BMU

    At BMU, we attempt to build a future for our students to walk out of our campus with the confidence of someone who knows about working in the industry.

    We include the latest technological advancements in our courses, which we develop and design with industry experts’ help.

    Our partnership with the prestigious Imperial College of London has also helped us receive guidance on various areas such as research and development, student exchange, design of our courses, and faculty training.

    We not only have a Management course with a specialization in Business Analytics, but we also have a paper for our students in Bachelors of Electronics and Communications Engineering on Data Science (which includes Analytics).

    With the modern systems we have on-campus, which allows students to work real datasets, we hope to prepare them for the real industry interface.

    Data Analytics is a great career choice for engineers in the present times, and we firmly believe that our students should grab the opportunity to make the most of it.

    Skills gained with Data Analytics Course

    Anyone with formal exposure to Data, statistics, mathematics, and computer science can pursue a Data Analytics course.

    There are several courses available in the market for those who do not have a technical background.

    However, Engineers are already predisposed to some of the skill requirements in a Data Analytics course and later in a DA career. So they stand at a certain advantage, which goes without saying.

    Data Analytics is a vast subject that involves various skillsets ranging from knowledge to attributes to skills.

    While a formal structured course such as the one we have at BMU provides a very strong knowledge base about the subject, some skillsets are required over and above text-book concepts alone.

    We at BMU put in our best efforts to ensure that our students are exposed to all of these skills, and when they opt for a career, there is no gap in the skill requirements.

    Other than knowledge and skills, certain attributes are mandatory to excel in a Data Analytics career. Some of these attributes are inherent in a student or acquired over time with exposure and practice.

    Let us look at these skills that a student can gain out of a Data Analytics course, which will help them build a strong career.

    1) R and/or Python:

    Be it Python or R, and these are programming languages that a Data Analytics professional is expected to be an expert at.

    Python or R are strong tools that help in conducting complicated analyses and predictive modeling on data sets. Both of these languages have low entry barriers, are free and open-source.

    Both of these languages come with their libraries which help a novice pick up fast without bothering to build programs from scratch.

    Also, these are standard languages that are used everywhere across the industry for Data Analytics/Mining, machine learning, or Artificial Intelligence.

    A Data Analytics course will involve certain investments in developing programming skills in these languages.

    A Data Analyst will need to work with these languages in their career as an analyst trying to build models for businesses.

    There is no learning swimming without getting into the water. Likewise, to learn actual Data Analytics, one needs to gain a certain amount of efficiency using Python, which happens to be one of the most widely used languages.

    2) SQL (Structured Query Language):

    Although mentioned as a second point, SQL or Structured Query Language is the most important skill a Data Analyst will need to use and therefore learn.

    SQL is the most commonly used database language across the industry. SQL is a precursor to Python or R and may be viewed as a successor of Excel.

    Certainly, SQL is by far better than Excel in terms of its ability to deal with large data sets, which excel is incapable of.

    The advantage of learning SQL is not just to have a career in Data Analytics, but almost in any industry which requires work with large databases or handling multiple databases that can be related, combined.

    There are plenty of job opportunities with SQL knowledge as the main skill requirement on any job site. In the US, an advanced level SQL Engineer’s average salary is at least 75,000 US Dollars.

    SQL (pronounced as Sequel) provides plenty of opportunities to learn further. It is an important skill to learn because not only will it help you to navigate databases, recruiters on campus will talk a lot about your knowledge and experience with Structured Query Language.

    3) Data Visualization Skills:

    An extremely well-researched essay may be of no meaning to you if written in a language you cannot read. Likewise, a Data Analyst’s job is not just to analyze the Data but also to present it to necessary stakeholders to make decisions.

    For example, let’s say you are working on a project that requires you to analyze consumer behavior data in the past five years.

    You have used your mastery at analyzing the data. You have come up with some fantastic insights which you believe can revolutionize the way your company has been marketing its products.

    But how are you going to present these insights to someone who belongs to a department which requires no knowledge whatsoever of Data Analytics software or tools?

    Your useful, insightful revelation may be of no value to them unless you can “communicate” it to them through a presentation.

    Data Visualization is a way to present data in a visual story-telling manner that even a layman needs not to go into the nitty-gritty of analytics. It is an extremely important skill for a Data Analyst or a data scientist.

    4) Critical Thinking

    To understand this, let us take another example. Let us say you are a great cyclist; your cycling skills are world-class. You can navigate through any terrain, no matter how difficult, and still come out unhurt.

    The way you maneuver your cycle is something to take notice of, as well. However, there is just one problem – you do not know which route to select to reach your destination.

    Critical thinking is exactly that. Knowing how to operate the data analytics tools and mastery at processing the tasks does not ensure success in this career.

    A Data Analyst needs to have significant critical analysis ability to decide which route to take or which approach to follow to achieve the desired results in the shortest time, with minimal resources, or with minimum errors.

    To make that choice is not an easy task because that is probably the only thing where a successful data analyst is different from an unsuccessful one (if there be such a thing).

    Each Dataset is different, and each time, the results to be obtained are different. So the approach to analyze would differ too. A data analyst’s critical thinking can help him make the right decisions in this ambiguity.

    To highlight, Data Analytics is a buzzing field in the present day of data deluge, and if predictions are to be believed, it will stay this way for a long time.

    The skills gained with a Data Analytics course can be applied quite ubiquitously. For example, R or Python’s knowledge is required even by an AI engineer or ML scientist; SQL is practically required by every database job, and critical thinking is not unique to an Analyst’s career.

    These are some very important skills that a student can gain out of a DA course and can apply them in other fields.

    This makes a choice pretty simple: There is only to gain with a course that catapults your career towards a booming industry that requires good professionals with the right skill sets and attitude.

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