Data Analytics tutorial

Data Analytics as a profession has recently gained momentum in the market, but why? The rationale behind this is it helps to understand patterns, trends, and audiences, forecast the future and reduce costs. The importance of data analytics is explored by all small to large organizations. It is not restricted to any particular industry and thus can be leveraged by all. According to a study the Analytics market is estimated to grow to $100 Billion by the end of 2023. Companies like Netflix, Amazon, HUL, Accenture, and many more all are on a continuous hunt for experts in Data Analytics.

What is Data Analytics?

The process of studying disordered information ( raw) to find patterns and draw meaning is called Data Analytics. A Data analyst can support businesses in maximizing profits, improving performance, making sound strategic decisions, and others. It relies on various tools in the market for data manipulation which includes data visualization, reporting tools, applications for data mining, data visualization, or languages that are open-source.

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Types of Data Analytics

Predictive Analytics:

This form of analytics is used to predict things that will happen in the future in business. It will consider all the available data to know the future outcome. This will include market trends and the history of the company's performance. With the help of these sets of data analytics can predict the business in the next season.

Descriptive Analytics:

This is the most common data analytics used by businesses to understand events from the past and the customers. It helps to analyze the raw data and then find trends, to describe the past and present situation based on the data. Descriptive analytics is the best option if you are looking for insights into your sales data and why it differs monthly.

Prescriptive Analytics:

This analytics helps businesses take steps to improve their performance after thoroughly analyzing the available data. Based on the results from other analytics such as predictive and diagnostic analytics, prescriptive analytics will help businesses with an action plan to perform better. It is complicated but important for any business.
Diagnostic Analytics: Descriptive analytics helps in finding what happened, similarly Diagnostic analytics helps in finding the answer to why it happened. If you want to know the reason for all the negative and positive anomalies in your sales performance, Diagnostic analysis is the best option.

Why Learn Data Analytics

As we know data is the new asset of the business. It has become a core practice to understand customers in different ways. Without insights companies won't be able to stay competitive. The demand-supply gap is wide in the market. At present, more than 30 thousand jobs are available. Also, the salary paid to a data analyst is higher than any other profession in the IT industry. There is huge potential for a career. As per a study conducted by Glassdoor, the job of data analytics provides a work-life balance.

Therefore learning more about Data Analytics should be on your to-do list!

Importance of Data Analytics

Data analysis is analyzing large amounts of data from different sources to find insights and patterns. This helps businesses to make informed decisions. With the help of automation and technology like machine learning, even small businesses can easily analyze large amounts of data. Data analysis has become an essential part of running a business. This is because it helps businesses make better decisions and stay competitive.

1.Product Development: Data analytics helps businesses with deep insights such as trends in the market and development process. It also provides an accurate forecast about the products that will be popular in the future. This helps in the process of developing products with the required modification. Organizations can focus on products that will do well instead of wasting resources on products that will fail eventually. This will increase the revenue and also meet the demand of the customer.

2.Targeted content: In the business world, it's important to advertise to the right people. Data analytics can help you figure out who your target customers are and what they want from the market. This way, you can create a marketing campaign that appeals to your target customers. When your customers are interested in your products, they are more likely to buy them. This will give us an edge over other competitors.

3.The efficiency of operations: Using data analytics correctly can make a company work better. Analyzing data can help us understand what happened before, why it happened, what will happen soon, and what actions a business can take to improve. Data analytics can provide a plan of action to help businesses grow. This is why data analytics is important for improving the efficiency of a business.

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Data Analytics Tools


Python is a versatile language and provides users with various benefits to streamline large, complex collections of data. It is an excellent choice for analyzing data as it offers users a variety of features like

  • Flexibility
  • Built-in tools for analytics
  • Libraries
  • Easy and simple to learn and implement.

Hadoop is a tool used in big data analysis. All the data that is collected for instance data about people, technologies, processes, and items can add value only if patterns can be drawn that will result in better decisions and improved performance of the company. Hadoop helps users by analyzing large sets of data by offering features such as

  • Low cost
  • Speed
  • Resilience
  • Scalability
  • Data diversity

Structure Query Language ( SQL) helps to perform various operations in the database such as updating, creating, deleting, and modifying records. The fundamental use of SQL is to create simple and easy-to-understand dashboards and also reporting tools that are popularly known as SQL for data analytics. As it creates user-friendly dashboards it presents the data in many ways as it makes it easy for the users to send commands to databases that are complex and modify data in a jiff.


Tableau is a tool that helps you work with a lot of information, making it easier to understand and share with others. It's good at letting you explore the data and find answers to your questions, without needing help from someone else. This makes it easier for everyone to understand the findings and also communicate them throughout the company.


Splunk is a tool that helps businesses make sense of the data their servers produce. It can help with things like keeping applications running smoothly, managing IT operations, making sure the business follows rules and keeping everything safe. Splunk is really good at handling a lot of data - it can process huge amounts every day. It can also analyze the data as it comes in and figure out what's important, without needing someone to tell it how the data is structured. Splunk is easy to use - All you need to do is load the data and begin analyzing it.

R programming:

R is a free and open-source language that is commonly used for statistical computing and creating graphs. It is particularly useful for statistical analysis and data mining. By using R, analysts can identify patterns and create models that provide valuable insights. R can also be used to develop software programs that will assist businesses with data analysis.


PowerBI is a Microsoft product that is used for business analytics. It offers users interactive visualization that helps them to create dashboards and also reports without any support. Therefore it provides users with self-service capabilities for business intelligence.


QlikView is also a self-service tool that is used for data visualization, Data analytics, and business intelligence. It offers users features such as Data Literacy, Data Integration, and Data analytics that accelerates the value of the business with the help of data.


SAS stands for Statistical Analysis system is a tool used for predictive modeling, profiling customers, data mining, and reporting. The software is robust and easy for larger organizations to use. It keeps regularly adding modules on demand from the customers.

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Data Analytics Application

As the importance of data is increasing, data analytics has become more prominent. Let us understand how it can be implemented practically to improve the performance of the company. Data science is not restricted to any sector therefore here are a few examples of how it is implemented in other fields too.

  1. Retail
  2. Healthcare
  3. Manufacturing
  4. Banking Sector
  5. Logistics


Data Analytics in the retail industry helps companies to predict demand so that inventory can be managed. It also enhances the experience of the customers as it helps in offering personalized attention to every customer. It also supports retaining customers by predicting future trends.


Data Analytics can help to utilize and analyze a large amount of information to find treatment choices or have answers to numerous illnesses. Using recorded data, doctors can create personalized and precise treatment plans for individual patients, even for unique medical cases


The development of products in an organization can positively be affected by data analysis. Similarly, it helps manufacturers to predict and budget the goods. This leads to the manufacturing of the exact amount of goods thereby increasing the profit.

Banking sector:

Analytics can help banks and financial institutions identify customers who might be at risk of fraud. They can then keep a closer eye on those accounts to prevent fraud from happening. By using analytics, banks can figure out which accounts are most likely to be targeted by fraudsters and focus their efforts on those accounts


Data analysis helps in the operation management of the logistics company. Companies can use data analytics to find the most efficient delivery routes and estimate delivery times for packages. They can also track packages through different stages of the delivery process. Data science has helped to make online shopping more popular

Steps Involved in Data Analytics

The process of gathering, altering, cleaning, analyzing, and communicating is called Data Analysis. The results derived help in decision-making. Visualization of data helps in finding patterns. These patterns assist in understanding the insights easily. A Data Analytics process consists of the following steps:

  • Understanding The problem
  • Data Collection
  • Data Exploration and Analysis
  • Data Cleaning
  • Interpret the results
Step 1: Understanding The Problem

The first step of the data analysis process is to understand the problem. It means you have to create a hypothesis and find a way to test it. A job of a Data Analyst is to know the business and understand its goal to put forth the problem in the correct way. After defining the problem, figure out the sources of the data that will help to solve the problem.

Step 2: Data Collection

Data collection refers to the process of grouping all the information on the variables that are targeted and identified data requirements. It ensures that the collected data is accurate and valid. It provides a baseline for measuring and improving the information. Data is gathered from numerous sources such as organizational databases and information using web pages. Therefore the data collected is unstructured and needs further refinement.

Step 3: Data Exploration and Analysis

A Data Analyst needs to conduct an exploratory analysis. This helps in the identification of trends and also the modification of the hypothesis if required. The method for data analysis largely depends on the goal. Various techniques are available but depend on the kind of insights the analyst wants. All data analysis can be fit into one of the categories below:

  • Descriptive Analysis: What has happened in the past.
  • Diagnostic Analysis: Why something happened.
  • Predictive Analysis: To understand future trends based on historical data.
  • Prescriptive Analysis: To give suggestions for the future.
Step 4: Data Cleaning

Data that has been processed and organized may not be perfect. It might be missing some information, have extra copies of certain data, or contain mistakes. Data Cleaning is a method of identifying and fixing these issues. The specific type of Data Cleaning needed will vary depending on the type of data being analyzed.

Step 5: Interpret the results

After the analysis of the data, the final step is to understand what the results mean. This is important because it helps a business use the information to create value. When interpreting results, it's important to remember the reason for doing the analysis and to work together with others. It's also important to consider any challenges or issues that may have affected the data.

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How to become a Data Analyst

To become a data analyst you need to have academic qualifications as well as a few technical skills. Find below the details of all the requirements in brief.

Academic Qualification

A graduation degree in a data analysis program and a good CGPA are suggested. Even if the individual does not have a specialization in data analysis for an entry-level position as a data analyst, a degree in economics, statistics, or mathematics from a popular university also can help. Most entry-level jobs require a minimum of a bachelor’s degree. For a higher-level analyst job, a master’s degree is required. Apart from a degree, an individual looking to explore a career in data analysis can enroll in some online courses.


  • Programming Skills:

To work as a Data Analyst, it's important to know at least one programming language. Examples of programming languages used for manipulating data include C++, PHP, Java, R, MATLAB, and Python.

  • Data Management and manipulation:

A person working as a Data Analyst needs to know how to use computer languages like R, SQL, and Hive. They also need to know how to ask the right questions and get the information they need from a database. To make sure they give correct information, they should know how to use standard tools like SAS, PowerBI, Oracle Visual Analyzer, Tableau, and Cognos.

  • Excel:

The primary duty of a data analyst is to gather and organize data. Hence it is recommended that the data analyst should be familiar with the use of excel.
Good Mathematical Skills: A Data Analyst needs to be good at math and solve common business problems. They also need to be able to use visual tools like tables, graphs, and charts. To make data look attractive, they should be good at college-level algebra.

  • Good Mathematical Skills:

A Data Analyst needs to be good at math and solve common business problems. They also need to be able to use visual tools like tables, graphs, and charts. To make data look attractive, they should be good at college-level algebra.

What does a Data Analyst do?

The role of a Data Analyst is to collect, store, and interpret data that can be used to improve the performance and efficiency of the business. This will help to make better and more informed decisions in the organization. Thus a Data Analyst needs to analyze data and provide valuable insights. After finding the information, the Data Analyst needs to communicate it to different stakeholders, employees, clients, partners, and others. For this purpose, they use various analytics tools to make reports and presentations to make it easy for others to understand. These reports can also be used to make future predictions, improve processes and make decisions. Thus a job of a Data Analyst is to identify trends, patterns, and relationships with the data.

Career Scope
As a data analyst, you will expect good compensation, job security, and engaging work. This career opportunity is constantly evolving and requires attention to detail and focus on quality. As decisions in the organization now depend on the quality of insights from the data, the demand is always high. Experience and additional education help to differentiate between mid-level and executive-level positions. Data Analysts are in high demand and this demand is expected to grow in the next decade too. The job growth for different levels of Data Analysts varies from 5% to 25% for Financial analysts to Operations Research Analysts respectively.

Where Can Data Analysts work?

Data analysts can work in a variety of industries, including finance, healthcare, retail, marketing, and technology. They may work for corporations, government agencies, consulting firms, or as freelancers. Any organization that collects and analyzes data can benefit from the skills of a data analyst

Are Data Analytics in Demand?

Yes, Data is the new fuel for businesses as the quality of decisions is now dependent on the insights from data. Therefore organizations are constantly looking for experts in the field to add value and improve the performance of the business. This will indirectly lead to growth and help them stand out from the competition. Experience is one of the important factors that help in deciding the salary of the analyst. For instance A Data Analyst with 2 years of experience will be paid more than a fresher.

What is the average Data Analyst Salary?

As per the pay scale, the average salary of a Data Analyst is based on experience as mentioned below:

  • Entry level -> 3.50 Lakhs ( Less than 1 year of experience)
  • Early career -> 5.50 Lakhs ( up to 4 years of experience)
  • Mid Career -> 8.50 Lakhs ( up to 9 years of experience)
  • Experienced-> 9 to 14 lakhs ( 10 + year of experience)

The average salary differs based on skills, location, and industry. A few skills that influence the salary of a data analyst are Python, SQL, Data quality, Web Analytics, and others. The average salary ranges from 5 lakhs to 6.50 lakhs depending on the city. A few companies like Accenture, Flipkart, and Cognizant pay better than most of the companies.

Hope you have understood the concept of Data Analytics along with the importance of the profession. It guarantees a promising career as the demand will only keep increasing. Using Analytics is the only way an organization can thrive in this competitive market. If you want to explore career opportunities in the field of Analytics visit HKR training, a one-stop solution for your career growth and development.

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Research Analyst
As a content writer at HKR trainings, I deliver content on various technologies. I hold my graduation degree in Information technology. I am passionate about helping people understand technology-related content through my easily digestible content. My writings include Data Science, Machine Learning, Artificial Intelligence, Python, Salesforce, Servicenow and etc.