In today’s World, Data is everywhere. Every time we use a mobile phone, browse the internet, shop online, watch videos, or use social media, we create data. This data is very valuable, but only when it is understood properly. This is where Data Analytics comes in. In a simple way, we can say that data analytics is the process of examining raw data to find patterns, draw Conclusions, and make Better Decisions. Basically, it looks like a lot of information has been carefully collected to understand what it is telling us.
Data analytics helps us to understand data, find useful and important information from it, and make your right decision to better Business, government, hospitals, and even sports teams, like in Cricket, show players last year's data to improve their performance and improve their work. Because of this growth need data analytics has become one of the most Popular and rewarding career Options today.
There are mainly 4 types of data analytics. They build on each other, starting from simple summaries to advanced recommendations:
We use statistics and visualizations, and reports to get answers to questions, like what happened, how much it cost, how often it happens, and when did it happen. Descriptive analytics is really important because, unlike more advanced analytics (diagnostic, predictive, or prescriptive), descriptive analytics does not explain why something happened or forecast the future—it simply describes patterns, trends, and key metrics from existing data.
Diagnostic analytics is the step after we look at what happened with the data. This is where we try to figure out why things turned out that way. We do this by looking closely at the data to find the main reasons, connections and patterns that are, behind the trends or things that do not seem right. Diagnostic analytics helps us understand these things.
Data analysis has a few steps and predictive analytics is the one.This is where we use data, statistics and machine learning to figure out what might happen next. Predictive analytics looks for patterns and trends in the data to make guesses about the future. We use analytics to forecast what will likely happen. Predictive analytics is really good, at identifying patterns and trends to make predictions that are probably going to be correct.
There are some techniques that companies use to make predictions. These techniques include regression models, time-series forecasting, decision trees and neural networks. For example a retailer might use these techniques to predict when customers will stop buying from them which is called customer churn. A bank could use these techniques to forecast when people will default on their loans. A manufacturer might use these techniques to anticipate when equipment failures will happen.
Prescriptive Analytics is a type of data analytics that helps us figure out what to do. It does not just tell us what already happened or what might happen later. Prescriptive Analytics also tells us what the best thing to do is, based on the data we have.
Prescriptive analytics uses past information, information, rules and advanced tools to give us clear ideas about what to do. Many companies use analytics to make better decisions and reduce problems. Prescriptive analytics is really helpful, for businesses because it helps them make choices and avoid mistakes.
For example:
An online shopping company uses analytics to figure out the best price for a product. This helps them to increase sales of the product. The online shopping company wants to get the price of the product Right so they can sell more of the product.
The online shopping company uses analytics for the product to make a good decision, about the price. The main goal of the shopping company is to increase sales of the product by using prescriptive analytics.
A delivery company uses this system to figure out the way to get things from one place to another. They want to find the cheapest delivery route for their packages. This means the delivery company can get the packages, to the customers quickly and it does not cost them much money. The delivery company uses this system to pick the cheapest delivery route.
A hospital uses this thing to figure out the way to treat people who are sick. They want to make sure patients get the treatment plan. The hospital is trying to help patients by using this to make a plan for them.
Prescriptive analytics is used a lot in business, healthcare, finance, marketing and supply chain management. This is because prescriptive analytics helps organizations do things faster it helps them save money. It also helps them work better. Prescriptive analytics is really good, at making organizations more efficient.This type of analytics is really cool because it uses things like intelligence and machine learning and optimization models.
Artificial intelligence and machine learning and optimization models are what make this type of analytics so powerful.It is more advanced, than types of analytics but that is what makes it very powerful.
My role is to look at data make reports and find patterns in the data. The data analysis and reports I create will help with making business decisions for the company. I will be working with the data to find patterns that can help the business. The business decisions will be based on the data analysis and reports that I create.
• Freshers: (₹3 L– ₹6L) per year
• Mid-level (3-5 yrs): (₹6L–₹12L)per year
• Senior (5+ yrs): (₹12L– ₹20 L+) per year
Higher salaries if you work in big tech, finance, or metro cities like Bangalore/Delhi. Some top data analysts at huge companies can earn ₹15 L – ₹21 L+.
Role: Study business needs and data to improve company performance.
Salary: ~₹6 L – ₹15 L per year (varies by experience and skill)
Role: Use data to build prediction models and advanced insights.
Salary: ~₹8 L – ₹20 L+ per year, and in top companies even higher.
Role: Build systems to collect and store data properly.
Salary: ~₹12 L – ₹25 L per year, depending on skills and experience.
Role: My job is to make machines learn from data. I work with machines. I teach them to learn from the information they get. This is what I do every day I make sure the machines can learn from the data they have. My work is, about machines and data and I make them work together.
Salary:
The salary is around ₹10 lakh to ₹20 lakh or more per year.
With better skills and experience, the salary can be higher.
6. Product / Marketing / Financial Data Analyst
Role: Use analytics in specific fields like sales, marketing, and finance.
Typical Salary:
• Product Analyst: ~₹10 L – ₹18 L
• Marketing/Insights Analyst: ~₹8 L – ₹14 L
• Financial Data Analyst: ~₹6 L – ₹12 L
Job Role Typical Salary (India)
Senior Analytics Manager ₹20 L – ₹40 L+
Lead/Principal Analyst ₹15 L – ₹25 L+
Directorate General of Employment
Data Architect / Analytics Lead ₹20 L – ₹30 L+
The world is a place, and salaries can vary greatly from one country to another. When we talk about Global Salary Context, we are looking at how money people make in different parts of the world.
* In some countries, people get paid a lot of money for the job.
* In countries, people do not get paid as much for the same job.
This is what we mean by Global Salary Context. It is like a picture that shows us how salaries are different everywhere. Global Salary Context is important because it helps us understand why people in some places get paid more or less than people in pother laces. Global Salary Context is something that people should know about Global Salary Context because it can affect how much money they make.
In countries like the United States of America and Europe, data professionals usually get paid a lot more than they do in India. For example:
• Senior analytics roles in the US can offer $150,000+ (~₹1.2 crore/year) and above.
Data analytics is really important these days. We are creating data all the time, every second. So it is becoming very necessary to understand data analytics and use it to our advantage. Data analytics helps companies take data and turn it into information that actually means something. This information from data analytics supports companies in making decisions planning for the future and growing as a business, with the help of data analytics.
Data analytics is used in a lot of areas, like businesses and healthcare, education, and the government. It really helps companies do better, save money, and get to know their customers. Data analytics also helps companies figure out what might happen in the future. There are kinds of data analytics like descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Data analytics is, about understanding what happened in the past and using that to decide what to do and it can even help companies decide what actions to take in the future with data analytics, companies can make good decisions.
As a career, data analytics offers high demand, good salary packages, job security, and continuous learning opportunities. Students and professionals from any background can enter this field by learning the required skills and tools.
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