data analytics tutorial

Data Analytics Tutorial for Beginners: A Step-By-Step Guide

Vidhi Gupta
April 13th, 2024

In this Data Analytics tutorial, we will discuss what is Data Analytics, Cycle, and top tools to help you make better decisions for your businesses or organizations you are working with.

Data Analytics tutorial

Data is making extensive progress in how the world benefits from it. One of the most common lenses to look at it's effects by the interest-based ads you come across or getting suggestions for content that suits your taste. Implementing data analytics is a wise way to use data and no organization is willing to leave any stone unturned for the same.

This data analytics tutorial for beginners is your go-to guide to completely understand what it is and why companies use data analytics so widely.

Data Analytics Tutorial- An Introduction

The very first thing we tap into here is what is data analytics. Data analytics refers to the process of deriving meaningful information from raw data. This process is executed by data analysts, who employ various methods and tools, which aids in solving data problems and discovering hidden patterns. This data is accumulated from plenty of data sources.

Explore our Power BI training program to get complete knowledge of business analytics tool.

Data Analytics Lifecycle

The process of data analysis mainly involves five steps. Each of these play an imperative role and as a data analyst or scientist, you will be using these steps extensively.

  1. Understanding the Problem

Define the organizational goals, pin the business problem and plan a roadmap to reach the solution. This is a key step in the process and you should take your time understanding what areas need to be worked on.

  1. Collecting Data

Data is the core of almost all activities done today. That means you can collect data from many viable and trusted sources. This data could be historical data about existing customers or new ones from other means.

  1. Cleaning Data

This collected data is not all useful. It could be messy, have missing values or be disorderly. To perform data analysis, it needs to be cleaned and made fit for the process.

  1. Exploring & Analyzing Data

Executing exploratory data analysis is the next step wherein BI and data visualization tools, predictive modeling and data mining techniques are employed. This leads to analyzing and visualizing data to predict future trends and outcomes.

  1. Interpreting the Results

The result is interpreted to validate whether it is meeting the organization's expectations or not, or it's solving the problem first highlighted. In this step, the findings are showcased to the stakeholders and other associated teams/team members.

How Is Data Analytics Useful For Businesses?

In 2023, the big data market size was recorded at 220.2 billion USD. By 2028, registering an expected CAGR of 12.7%, it will almost double to $401.2 billion USD. None of this data goes unused as businesses employ all of it for their growth. But how exactly is data analytics useful for businesses?

Let's find out!

  1. Drives Business Growth

Data analytics works as a catalyst for business growth by offering valuable insights in market trends, operational efficiency and customer behavior. It gives knowledge about how to optimize strategies, expand market presence and tap into growth opportunities.

  1. Enables Innovation

Modern businesses cannot expand with innovation. With data analytics, important details around innovation and growth in services, processes and products are found.

  1. Stay Updated

The market is always changing and it's important to stay in tune with those advancements and evolutions. It is possible to monitor market trends with data analytics.

  1. Enhances Customer Satisfaction

Offering top-notch customer satisfaction is the key to growing any business. This means providing what they want and that can only be understood with data.

  1. Improved Decision Making

Numbers don't lie and implementing practices that those numbers are saying leads to improved decision making. These benefit organizations by giving them what they need to stay ahead in the game.

Top Data Analytics Tools

Data analytics, as a process, is only possible by incorporating the right tools and methodologies. These data analytics tools are some of the best ones out there. Having skills and knowledge to extract their maximum output will help organizations succeed in their mission to win.

Data Analytics Tools
  1. Python

Python is an extremely versatile programming language. Those who learn Python for data science can benefit a lot and succeed in their career. This one language benefits in streamlining complex and gigantic data collections. A Python data analysis course will teach you more about its features and libraries. Go through this Python training program to make your career in Python.

  1. SQL

SQL is used for operations such as updating, creating, deleting and modifying records in databases. It facilitates in creating user-friendly dashboards and reports.

  1. Splunk

With Splunk, businesses derive maximum output from server data. This is because of effective IT operations management, app administration, security monitoring and compliance.

  1. Hadoop

Hadoop ensures that meaningful patterns are derived from the big data collected. It does so with its features like speed, resilience, scalability and low cost.

  1. R Programming

R is an open-source programming language vastly used for graphics, statistical analysis, statistical computing and data mining. Software programs can also be developed.

  1. Tableau

This comprehensive data analytics tool aids in preparing, collaborating, sharing and analyzing big data insights. It is a pro at self-service visual analysis, enabling users to pose questions and then communicate those findings throughout the company.


The expected rise in both generation, and consumption of data is connected directly to the rise in demand for data analysts and data scientists. With this data analytics tutorial, we have given you a glimpse into what the field entails.

If you are interested in a career in this growing field, you can choose to learn Python for data analysis as a starting point. Every organization is either already implementing data best practices or is on the verge of doing so. The scope is great and the right means can get you there.

FAQs for Data Analytics Tutorial

Question 1. Is a Python data science course a good idea?

Yes, a Python data science course is a good idea for those interested in getting started in this field. Learning under experts will ensure quick and correct learning.

Question 2. How can I teach myself data analysis?

You can teach yourself data analysis by enrolling in a self-paced course. It will give recorded videos of actual sessions conducted by experts. This is a great way to begin learning by yourself.

Question 3. Can a beginner learn data analytics?

It is possible for anyone to learn data analytics. Yes, you should have interest in numbers and a little programming experience. These factors will help you learn faster and keep you hooked.

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