A business needs the help of a data scientist or an analyst to make sense of the data that the business generates through its offerings and operations. A data scientist can collect, manage, and then organize the data to find out hidden patterns and trends to solve business problems.
Businesses and industries need employees who have the relevant skills, which they could have learnt through their work, education, or data science course. They are needed to manage the data and guide the business in making informed decisions via data interpretation and visualization.
Imagine an ocean that is full of fish and other aquatic animals. Although you know that there are various living creatures residing in the ocean, you do not have any idea of their nature or functions. This is exactly what the situation is when businesses are working with massive datasets or big data when they can’t gather any meaningful insights that can inform key business decisions.
Why do businesses use Data Science and Analytics?
Businesses use various data science and analytics tools to navigate the world of “big data” properly. It helps them in monitoring and managing raw data which then in turn helps in collecting performance reports of a specific program or a service which leads to better decision making in the organization.
Data science and analytics have also been used by businesses to recognize various target audiences and recent trends, which is an extremely important element for any business.
Data scientists and data analysts help a company by providing smart systems of business intelligence, increased information security, complex data interpretation, and obviously improving techniques and strategies across business operations, marketing, sales, etc.
What is Data Analytics in the simplest terms?
Data analytics is used to solve business problems and find trends among a large set of data by applying several tools. This information which would otherwise be lost in the mass of information also called big data, can be used to then make better decisions and also improve their efficiency and performance.
Data analytics can be classified into the following broad categories:
1. Descriptive analytics: As the name suggests, it deals with describing a particular set of data that shows what has happened over a given period of time.
2. Diagnostic analytics: It involves thorough research into why something has happened rather than then when it had happened. This process includes diverse data inputs and also hypothesizing.
3. Predictive analytics: This deals with predicting the future of how the source of data will perform based on the trends exhibited by the current data set.
4. Prescriptive analytics: It includes a suggestion of taking a course of action based on the trends and metrics of the data.
What is data science?
Data science is simply the ability to take data and make it understandable in a business context through the process of capturing, maintaining, processing, analyzing, and communicating it.
Here is the lifecycle of data according to data science:
1. Capture: The very first stage requires the acquisition of data, where data scientists pull up the data to then categorize and label it later on.
2. Maintain: This step consists of data cleaning. The raw and unstructured data that has been recently gleaned then goes through a check which helps in data warehousing. It is put into a neat and structured pile to enable smoother processing.
3. Process: The step of data processing includes data mining. It means discovering patterns and trends through mathematical analysis from large sets of data. It then clusters those chunks of data into various categories. This step also includes data summarization.
4. Analyze: This part includes various types of analysis tools, i.e., predictive analysis and qualitative analysis, among others, to understand and analyze a particular set of data.
5. Communicate: This is the last process where visuals are made, i.e., flow charts, bars, graphs, infographics and pie charts, plots, or even animation to showcase the data. It helps in better decision-making and business intelligence.
What exactly are the roles and responsibilities of Data Scientists and Data Analysts?
People working as data scientists deal with some advanced techniques for handling data and making future predictions based on the target data, while a data analyst deals with a more structured set of data to solve regular business problems.
Data analysts use programming languages like SQL, R, or Python and other tools such as a data visualization software. They need to acquire data from various sources and identify the informational needs within an organization. Then, move on to analyze the data and recognize patterns and trends which then can be translated into actionable insights for the company’s decision-making process.
On the other hand, a data scientist’s responsibilities also include acquiring a large set of raw data and then running it through various algorithms to make various clusters. They can also write programs to automate these processes.
What is the demand for people with Data Science skills?
The World Economic Forum (WEF) in its Future of Jobs Report 2020 listed Data Science skills as the no. 1 sought-after skill by companies. Also, this field is the fastest-growing job market in the world.
According to Times of India, India has contributed up to 9.4% of the global job openings in this field. Also, the report of ‘The Humans of Data Science’ says that data science is going to create around 11.5 million jobs by 2026.
Why should you upskill in Data Science?
You should upskill in data science immediately because this field currently has the no.1 job opening both in India and the global job market. Data science is the future of artificial intelligence (AI).
Also, the future is going to be even more data-driven. With digital-first and digital presence being the norm nowadays, it is safe to say that your job will be here and relevant in the coming years.
How to Gain These Skills?
You can enrol for an online data science program to learn more deeply about these subjects. After clearing your basics through various learning modules and practical classes, all the necessary skills will come naturally to you.
Some other obvious options to learn data science and analytics skills quickly include reading more and more. Some popular books are ‘Practical Statistics for Data Scientists’, ‘Python for Data Analysis’, ‘Data Science and Big Data Analytics’, ‘Naked Statistics’, and ‘R for Data Science’, among others.
You can also improve your practical skills by participating in various open-source projects.
Things to keep in mind when picking an online course for data science
Choose an online course that suits your timing and learning pace. If you are currently employed full-time, then you should consider a data science course for working professionals that will let you learn while you work, and gain a lot of hands-on practical knowledge.
Why learning data skills just makes sense
Gaining data analytics skills is not only valuable, but also a necessity. Here are some real-life benefits of being skilled with working with data:
1. Problem-solving skills: At its core, data analytics is about solving a particular problem. Being able to look at unstructured and seemingly unfamiliar data and then draw a conclusion from it by seeing the big picture and connecting the dots, like in a puzzle is one of the skills that will help you throughout your life.
2. Huge demand: Because this is a relatively new career option, there is a steady gap in the demand for skilled data scientists and analysts, and their availability. A data science certification course will most definitely lead to high paying and rewarding jobs.
3. Diverse industries: Businesses across industries are dependent on data analytics now. Even an Instagram influencer has the option to use analytics on his/her channel to know more about the target audience and content interaction.
So, if you are still undecided about where to work after you finish the course, worry not! Data science and analytics professionals have numerous opportunities available to them.
Future scope of data science and data analytics skills
Data science and data analytics are seeing a rapid rise and the scope of data science in India is huge.
A fresher with a data science certification course under their belt can look to earn about Rs.5 lakh per annum, whereas an employee with up to 1-4 years of experience can get up to Rs.8 lakh per annum. With 5-9 years of experience, you can earn more than Rs.10 lakh per annum. And, with more than 10 years of experience, the amount also bumps up to almost Rs.20 lakh per annum.
In this huge ocean of immense and uncountable data, those who know how to manage it are the people on every company’s top hiring priorities. There is a huge skill gap in India currently because the concept of a data scientist or a data analyst is still very new to us here.
But nonetheless, the demand for these roles has been picking up lately and a lot of students have shown interest in learning these subjects as well. And, due to the supply-demand inequality in these fields in the current years, companies have also been paying more and more in order to attract and keep good employees on their payroll. So, if you think you want to learn and be a data scientist or analyst, then now is your time!
Notably, the skills required to master data science are analytics, data visualization, statistics, machine learning, computation and application, probability, and AI. You can apply for roles like Data Scientist, Data Engineer, Data analyst, Machine Learning Engineer, and more.
While deciding which program to choose to help you learn the subject, keep in mind that you need an all-rounder approach, which includes guidance from clearing the basics to hands-on practical knowledge. Check out Hero Vired’s Integrated Program in Data Science, Machine Learning, and Artificial Intelligence and the PG Certificate Program in Business Analytics and Data Science, which are among the best data science courses in India.
With these programs, you will be able to acquire all the essential skills you will need as a Business Analyst, Data Scientist, Data Analyst, or ML/AI developer.