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Introduction to

Data Analytics

What Is Data Analytics?


Data analytics is the science of analyzing raw data in order to make conclusions about that information. Analytics can be used to help improve your organization's performance in countless ways. For example, it can help you determine which customers are most likely to suffer from credit card fraud or identify new opportunities for growth. 


There are three types of analytics: descriptive analytics, descriptive forecasting, and predictive analytics.


Descriptive analytics provides you with general information about a topic or situation; it gives you a quick understanding by telling you what the data shows rather than what it doesn't show.


Descriptive forecasting searches for patterns in the data that may provide insight into the future. But it is less useful than the predictive type of analytics because you don't necessarily know what will happen next.


Finally, predictive analytics can provide you with information about the future performance of a company or commodity. It is by far the most useful and also most difficult type of analytics.


Data Analytics is used by almost all organisations today. For example, financial institutions such as banks and brokerage firms use it to track trends, detect patterns, and make predictions. It's also a core part of many e-commerce businesses' strategies. A successful business starts with data analytics right from the start by identifying and overcoming market disruptors.


As a tech writer, I'm often asked about the best way to get started with data analytics – and often this is the point where people stop reading and go straight to talking about marketing or business strategy. But the better you understand the problems and opportunities of your target audience and your industry, the better your approach towards using data analytics.


Data Analytics and Big Data


Massive volumes of unstructured and raw data from multiple sources are referred to as Big Data. Big Data has a high degree of truthfulness and is large in volume, necessitating a lot of computational capacity to acquire and handle it. All of this information is gathered through a variety of channels, including social media, the internet, mobile phones, computers, and more.

Big data forms the raw material on which data analytics is applied. If you are a chef, big data is your raw materials (meat, vegetables, oil, spices, etc.) and data analytics tools and techniques is your cooking utensils. When together, they help you prepare consumable and delicious insights for the management / leadership.



Types of Industries Using Big Data and Data Analytics:

Finance, retail, travel, and healthcare industries are among the top industries that use big data and data analytics the most. Data analytics assists these businesses in developing new advances by examining historical data and identifying past trends and patterns.

Tools used:

For descriptive analytics Microsoft Power BI, Tableau, and Google Data Studio are the most popular tools. In Predictive Analytics, one can use applied mathematics modelling and predictive modelling techniques. Most of the scripting done are in R or Python languages.



Is a Masters Degree In Data Analytics Worth It?


Using data to solve problems and optimize marketing, sales, operations, investment space is a evolving space. The world is constantly moving towards a combination of analytics and insight. It's the most lucrative area in any organisation, right now. If you're looking to turn data into impactful insights, you might be wondering whether or not an analytics degree is worth the investment.


Let me help you out.


You can put your Master's Degree to work in more positions than you care to think about. And more significantly, if you are doing a Master's Degree in Information Technology, then you can get you hired for positions that would otherwise be unfilled. The world of Information Technology is growing rapidly - faster than we can say. Our skills are in demand and employers are looking for people with these advanced skills. The question is not whether you have a Master's Degree - it's how you apply that degree to your current position and career.


If the goal is to create decisions driven by data, then an M.S. degree in data analytics is the best investment you can make. The tools and techniques you learn are applicable across industries and businesses big and small. The M.S. in data analytics is a launching point for a career that will put you in the future-oriented workforce, whether a data analyst for a technology firm or a data scientist working for a government agency.


So my advice will be to do a Master in IT if you are not sure you want to stick to Data Analytics. But if you are decided you that you want to build a career in data analytics, a MS degree in Data Analytics is the best choice. But be sure to enrol into a premier institute for good job placements and alumni networks.



Hope this helps as you start your career in Data Analytics. Until next time.

AuthorPriyanka Dutta
Priyanka graduated with B.Tech in Electronics & Communication Engineering. She like organising data so much that building dashboards and models feel rewarding to her. She also likes to write about her work.

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