17 Apr

What is Data Analytics? – A Beginner’s Guide

Team Apr 17, 2024 No Comments

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What is Data Analytics?

It was 1663 in London. John Graunt, a genius statistician, did something that became a pioneering example of data analytics.

He collected and analyzed mortality data. At that time, the terrible bubonic plague caused deaths in London. His analysis let him find patterns in how many people died, where, and when. 

This helped him figure out how the disease was spreading and what might be done to slow it down. Although his analysis methods were basic, Graunt showed us the power of analyzing data to understand a complex problem.

Over 360 years have passed, and data analytics have changed tremendously. Now, we have complex data coming from everywhere. The internet and digitization have created an explosion in the quantity and types of data generated. 

Luckily, we have advanced computers and powerful statistical tools that help us analyze massive sets of data and find valuable insights. Now, data analysis is a rapidly growing field that helps organizations make better decisions, optimize processes, reduce risks, and improve their overall performance. 

In this post, we will see what is data analytics, its application, its types, and how it’s done. Let’s start with the basics.

 

What is Data Analytics in Simple Words?

Data analytics involves studying raw data to find hidden patterns and useful insights. 

This data could be about customer behavior, sales figures, website clicks, sensor readings, etc. This raw data is often overwhelming and difficult to understand. Data analytics provides the tools and processes to understand it.

The first thing we do to analyze data is collect data from various sources. Then, we clean and organize this data to make it accurate and consistent. Next, we apply different analytical methods to find insights in the data. 

Insights helps organizations make informed decisions. For instance, businesses might use the findings to improve marketing campaigns, streamline operations, or develop new products. Similarly, scientists might use it to predict disease outbreaks or understand climate change.  

No wonder, data analytics is one of the highest-paying skills in 2024.

Data analytics involves studying raw data to find hidden patterns and useful insights.

What are the Applications of Data Analytics? 

Data analytics provides insights that replace guesswork and help make informed decisions. This optimizes processes, cuts costs, and improves outcomes for businesses. Here are some examples of how data analytics is used in real life:

  • Personalization: Companies like Netflix and Amazon use data analytics to understand user preferences. This helps them suggest movies you would like or products you would buy, creating a more personalized experience.

  • Effective marketing: Data analytics helps businesses identify their ideal customers and understand their behavior. This helps them run effective marketing campaigns that are targeted at the right people.

  • Improved healthcare: Hospitals and researchers use data analytics to suggest the most effective treatment plans, identify disease risks, and even develop new treatments. 

  • Fraud detection: Banks and financial institutions use data analytics to spot unusual transactions and patterns that might indicate fraudulent activity. This helps them protect customers and prevent financial losses.

  • Smarter cities: Cities use data analytics to handle traffic flow, improve public transportation, and manage resources more efficiently.

These are just a few examples. Data analytics is being used in countless ways to improve our lives. And as technology advances, the applications of data analytics will only continue to expand.

Now you understand what is data analytics and what are its applications. So, let’s move on to the next section:

Data analytics is used in countless ways to make this world a better place.

What are the Types of Data Analytics?

Data analytics can be divided into four main categories, each having a specific focus:

  • Descriptive Analytics: This is the most basic type of data analytics. It focuses on using past data to summarize what has already happened. For instance, it creates reports to represent things like sales trends, customer demographics, or website traffic patterns.

  • Diagnostic Analytics: This data analytics type aims to figure out the root cause behind what happened. Analysts might use techniques like drill-downs or data mining to uncover correlations and explanations for the trends found in descriptive analytics.

  • Predictive Analytics: Predictive analytics uses statistical techniques and machine learning to predict future outcomes. Businesses might use it to predict customer behavior, project sales, equipment failures, etc.

  • Prescriptive Analytics: This type of analysis helps determine the best course of action. It uses simulations and optimization strategies to provide recommendations for how to achieve a desired outcome. Prescriptive analytics is often used in fields like healthcare, finance, and supply chain management for complex decision-making.

Understanding these types is crucial. It helps data analysts choose the right approach to answer specific questions and make better decisions.

What are the Steps in Data Analytics?

So, Netflix is incredibly focused on data analytics. They track everything from what you watch to when you pause, scroll, or abandon a show. And they use this to improve recommendations. Let’s take the example of Netflix to understand the steps in data analytics: 

  1. Define the problem: First, define the problem you want to solve with data. This gives you direction and lets you better focus on the analysis. In the case of Netflix, the problem is: What shows should Netflix recommend to keep users engaged?

  2. Data collection: Now, you will collect data from relevant sources. This could be internal databases, surveys, public datasets, social media feeds, etc. Netflix may collect data related to viewing history, search patterns, genre of shows, time spent watching, etc.

  3. Data cleaning: Raw data is rarely perfect. That’s why this 3rd step involves cleansing the data. Here, you fix errors, remove duplicates, and handle missing values. This makes the data reliable and accurate for analysis.

  4. Data exploration: Now, you can study the cleaned data to understand its structure, identify patterns, and uncover any initial insights. Netflix may identify trends like popular genres or peak viewing times and understand user preferences related to actors or genres.

  5. Data analysis: Now, we come to the crucial part. Here, you will apply appropriate statistical methods or machine learning models to answer your initial question. This is where you extract meaningful insights from the data. Coming back to our example, Netflix may predict what a user might like based on the data collected and suggest personalized content.

  6. Interpretation and visualization: Represent the insights into visuals (charts, graphs, dashboards, etc.) for better understanding. And then, share your findings with stakeholders and show how those insights can help make better decisions.

Data analysis often requires several iterations. You may need to revisit earlier steps to collect additional data, refine your analysis methods, or answer new questions that arise during the process.

Want to become a certified data analyst? Join Ivy Pro School’s Data Analytics with Visualization Course.

Become a Certified Data Analyst

Now that you know what is data analytics, if you want to learn this in-demand skill from the basics, you can join Ivy Pro School’s Data Analytics with Visualization Certification Course.

This course will teach you all the industry-relevant tools like Adv. Excel, SQL, Tableau, Power BI, Python, etc. These skills make your resume impressive and help you land high-paying data analytics jobs.

Ivy Pro School has been a top-ranked Data Science and Analytics course provider since 2008. Fortune 500 companies like Tata Steel, Accenture, ITC, Cognizant, Capgemini, and more actively recruit Ivy’s graduates.  

Visit this page to learn more about Ivy’s Data Analytics with Visualization Certification course.

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