Data Analytics can be a great career choice for B.Com Graduates
10th June 2023

Graduates in commerce and business have a unique approach towards analytics because of their strong quantitative skills and understanding of business operations. Consequently, finance professionals with skills in data analysis are considered highly competitive in the industry. An additional certification in Data Analytics can therefore boost the career prospects of commerce graduates by improving their employability and decision making skills. 
Data sciences is one of the fastest growing sectors in the world, having multiple uses across a wide range of organisations and businesses. Data Analysis is amongst the most highly paid jobs in the world and the role of data analysts has become crucial to the sustenance of business. There is a huge demand for data experts in IT as well as non IT sectors such as finance, insurance, banking and e-commerce. Because of their strong quantitative skills and awareness of business operations, graduates in commerce and business have a distinct approach to analytics.
Everyone who interested in numbers and whose analysis shines out will be an excellent fit. A B.COM graduates has a strong statistical comprehension and mathematics skills, which puts them ahead of the competition when it comes to data analysis.
In this blog, we shall look at the future of Data Analytics in terms of the future scope of Data Analytics is the future of Data & Analytics. Given the rising significance of data driven decision making, we shall consider why Data Analytics is the future of everything.
The future scope of Data Analytics is all about Real –time Data Visulization.The future of Big Data Analytics will all be about accessing, analysing, exploring and visualizing live operational data. Moreover, different data sources can be linked in order to provide for continuous intelligence on real-time basis. The idea of continuous intelligence and real-time insights is based on the principle of data processing, information analysis against historical patterns and instantaneous action recommendation. 

What Is Data Analytics?

Data analytics is the process of analysing data to extract useful information. This information can be used to decide how to improve business operations. Data analysts use various tools to analyse data, including statistical software, spreadsheet software, and database management systems.
They also use their knowledge of mathematics and statistics to interpret data, aspiring data analysts should have a clear understanding of the skills and qualifications required to progress along the data analyst career path. The average data analyst salary can vary greatly depending on factors such as industry, location, and level of experience. 
The process of analysing data typically moves through five iterative phases:
⦁    Identify the data you want to analyse
⦁    Collect the data
⦁    Clean the data in preparation for analysis
⦁    Analyse the data
⦁    Interpret the results of the analysis

To analyse is to make sense of something and data analytics would simply mean to make sense data available to you. It is the field which deals with data management through data collection and data storage from disparate sources, as well as with the processes, tools & techniques which helps in analysing it. The Objective of data analytics is to extract correlations, derive insight as well as discern patterns by way of interrupting data. These actionable insights not only help in driving the decision making process of the organizing, but also in making predictions and improving efficiency.
Why Is Data Analytics Important?
Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business. A company can also use data analytics to make better business decisions and help analyse customer trends and satisfaction, which can lead to new—and better—products and services.
⦁    Gain greater insights into target markets
⦁    Enhance Decision making Capabilities 
⦁    Create targeted strategies and ⦁    marke⦁    ting Campaigns
⦁    Improve Operational inefficiencies and minimize risk   
⦁    Identify new product and Service Opportunities 
Who Is Using Data Analytics?
Data analytics has been adopted by several sectors, such as the travel and hospitality industry, where turnarounds can be quick. This industry can collect customer data and figure out where the problems, if any, lie and how to fix them. Healthcare is another sector that combines the use of high volumes of structured and unstructured data and data analytics can help in making quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers.

Types of Data Analytics: 

Data Analytics Techniques
There are several different analytical methods and techniques data analysts can use to process data and extract information. Some of the most popular methods are listed below.
⦁    Regression analysis entails analysing the relationship between dependent variables to determine how a change in one may affect the change in another.
⦁    Factor analysis entails taking a large data set and shrinking it to a smaller data set. The goal of this manoeuvre is to attempt to discover hidden trends that would otherwise have been more difficult to see.
⦁    Cohort analysis is the process of breaking a data set into groups of similar data, often broken into a customer demographic. This allows data analysts and other users of data analytics to further dive into the numbers relating to a specific subset of data.
⦁    Monte Carlo simulations model the probability of different outcomes happening. Often used for risk mitigation and loss prevention, these simulations incorporate multiple values and variables and often have greater forecasting capabilities than other data analytics approaches.
⦁    Time series analysis tracks data over time and solidifies the relationship between the value of a data point and the occurrence of the data point. This data analysis technique is usually used to spot cyclical trends or to project financial forecasts.
Data Analytics Tools
As you can imagine, every phase of the data analysis process requires the data analyst to have a variety of tools under their belt that assist in gaining valuable insights from data. We cover these tools in greater detail in this article, but, in summary, here’s our best-of-the-best list, with links to each product:
Top tools for data analysts

  • Microsoft Excel
  • Python
  • R
  • Jupyter Notebook       
  •  Apache Spark
  • SAS
  • Microsoft Power BI
  • Tableau

The data analysis process
In order to gain meaningful insights from data, data analysts will perform a rigorous step-by-step process. We go over this in detail in our step by step guide to the data analysis process—but, to briefly summarize, the data analysis process generally consists of the following phases:

Defining the question
The first step for any data analyst will be to define the objective of the analysis, sometimes called a ‘problem statement’. Essentially, you’re asking a question with regards to a business problem you’re trying to solve. Once you’ve defined this, you’ll then need to determine which data sources will help you answer this question.

Collecting the data
Now that you’ve defined your objective, the next step will be to set up a strategy for collecting and aggregating the appropriate data. Will you be using quantitative (numeric) or qualitative (descriptive) data? Do these data fit into first-party, second-party, or third-party data?

Cleaning the data
Unfortunately, your collected data isn’t automatically ready for analysis—you’ll have to clean it first. As a data analyst, this phase of the process will take up the most time. During the data cleaning process, you will likely be:
⦁    Removing major errors, duplicates, and outliers
⦁    Removing unwanted data points
⦁    Structuring the data—that is, fixing typos, layout issues, etc.
⦁    Filling in major gaps in data

Analysing the data
Now that we’ve finished cleaning the data, it’s time to analyse it! Many analysis methods have already been described in this article, and it’s up to you to decide which one will best suit the assigned objective. It may fall under one of the following categories:
⦁    Descriptive analysis, which identifies what has already happened
⦁    Diagnostic analysis, which focuses on understanding why something has happened
⦁    Predictive analysis, which identifies future trends based on historical data
⦁    Prescriptive analysis, which allows you to make recommendations for the future
Data analyst technical skills:

⦁    Database tools:  ⦁    Microsoft Excel and ⦁    SQL should be mainstays in any data analyst’s toolbox. While Excel is ubiquitous across industries, SQL can handle larger sets of data and is widely regarded as a necessity for data analysis. 
⦁    Programming languages: Learning a statistical programming language like ⦁    Python or ⦁    R will let you handle large sets of data and perform complex equations. Though Python and R are among the most common, it’s a good idea to look at several job descriptions of a position you’re interested in to determine which language will be most useful to your industry. 
⦁    Data visualization: Presenting your findings in a clear and compelling way is crucial to being a successful data analyst. Knowing how best to present information through charts and graphs will make sure colleagues, employers, and stakeholders will understand your work. ⦁    Tableau, Jupiter Notebook, and ⦁    Excel are among the many tools used to create visuals.
⦁    Statistics and math: Knowing the concepts behind what data tools are actually doing will help you tremendously in your work. Having a solid grasp of statistics and math will help you determine which tools are best to use to solve a particular problem, help you catch errors in your data, and have a better understanding of the results.
If that seems like a lot, don’t worry—there are plenty of courses that will walk you through the basics of the hard skills you need as a data analyst.

Data analyst workplace skills:

⦁    Problem solving: A data analyst needs to have a good understanding of the question being asked and the problem that needs to be solved. They also should be able to find patterns or trends that might reveal a story. Having the critical thinking skills will allow you to focus on the right types of data, recognize the most revealing methods of analysis, and catch gaps in your work.
Communication: Being able to get your ideas across to other people will be crucial to your work as a data analyst. Strong written and speaking skills to communicate with colleagues and other stakeholders are good assets in data analysts.
⦁    Industry knowledge: Knowing about the industry you work in—health care, business, finance, or otherwise—will give you an advantage in your work and in job applications. If you’re trying to break into a specific industry, take some time to pay attention to the news in your industry, or read a book on the subject. This can familiarize you with the industry’s main issues and trends.

The Future of Big Data Analytics 
⦁    Automation and dataops for improve data analytics
⦁    Rise of new Job Opportunities 
⦁    Infinite Possibilities in the field of machine learning ⦁    (ML) & Artificial Intelligence ⦁    (AI)  
⦁    The Internet of things ⦁    (IOT) shall witness tremendous growth
⦁    Data management will be a new challenge 
⦁    Importance of ⦁    cloud enterprise 
⦁    Continues intelligence and real time insights
⦁    Predictive, Prospective & Analysis   
Data analyst career advancement
Being a data analyst can also open doors to other careers. Many who start as data analysts go on to work as data scientists. Like analysts, data scientists use statistics, math, and computer science to analyse data. A scientist, however, might use advanced techniques to build models and other tools to provide insights about future trends.
A data analyst career path typically begins with a bachelor’s degree in a field such as mathematics, statistics, or computer science. From there, many data analysts gain experience through internships or entry-level positions in fields such as market research or business analysis.
Data analyst salary and job outlook
The average base salary for a data analyst in the US is $67, 00 and in India it is ₹8, 40,000 in May 2023, according to LinkedIn Salary This can vary depending on your seniority, where in the US you’re located, and other factors.
Data analysts are in high demand. The World Economic Forum listed it as number two in growing jobs in the US. The Bureau of Labour Statistics also reports related occupations as having extremely high growth rates.
From 2020 to 2030, operations research analyst positions are expected to grow by 25 percent, market research analysts by 22 percent, and mathematicians and statisticians by 33 percent. That’s a lot higher than the total employment growth rate of 7.7 percent.

Jobs in Data Analytics
There are three main subfields of jobs in data analytics — Data Analyst, Data Scientist, and Data Engineer — and they are all job titles in themselves, you can also think about them as the three main categories that most data jobs fall into. And there are many permutations of these positions, most of which constitute either an evolution of one of these roles (for example, the advancement from Data Engineer to Data Architect) or a specialization within them, often based on sector (such as the specialization from Data Analyst to Business Intelligence Analyst).
Let’s take a closer look at some common data jobs along the Data Analyst career path:
⦁    Data Analyst
⦁    Business Analyst
⦁    Systems Analyst
⦁    Research Analyst
⦁    Operations Analyst
⦁    Marketing Analyst
⦁    Researcher
⦁    Statistician
⦁    Data Scientist
⦁    Data Engineer

Top Companies hiring candidate for data analytics

⦁    Tata Consultancy Services
⦁    Navi Technologies
⦁    Swiggy
⦁    Citicorp Services India
⦁    American Express
⦁    Reliance Industries
⦁    Amazon
⦁    HCLTech
⦁    Zenefits
⦁    Razorpay
⦁    Accolite Digital
The demand for data analysts is on the rise, but so is the competition for jobs. By gaining specialized skills, building a strong network, gaining relevant industry experience, and customizing your application materials, you can increase your chances of landing a data analyst job in 2023. With perseverance and hard work, you can become a part of this exciting and growing field.
Remember that job seeking is a MARATHON, not a SPRINT.
From the career perspective, Data Analytics is definitely one of the budding domains of the technical word.IT has got immense relevance in today’s scenario and the future of Big Data Analytics promises attractive career opportunities. Baroda Institute of Technology provide you with the amazing opportunity of seizing this chance of being a Data Analyst export be a part of our top-notched Data Analytics training and see the differences. 
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