I am sure most of you would have heard the term data analysts from friends and family members quite often. When we hear a term, we want to know more about it. Are you interested in reading everything associated with data analyst salary in India? Data analysis is an in-demand profession across the world. As a result, data analyst salary in India is significantly higher than those of other IT professionals. So in the coming years future of data analyst professionals will have a bright career if they have the correct skills and related knowledge. This is correct, especially considering that data analyst salary in India is affected by their knowledge and skill sets.
All of us are surrounded by data everywhere. There arises a need for someone who can analyze different types of data and explain it in a simple language to the end users. As a result, the speculation surrounding the employment of a data analyst has grown significantly over the last several years. Of course, when you learn more about the responsibilities of a data analyst and their prospective salaries in Pune, Delhi, Chennai, or Hyderabad, you’ll realize that there is a great demand in the Indian market.
This is an excellent opportunity to obtain the most sought-after job positions as data analysts. Before going ahead, let’s understand the meaning of data analysis. In today’s corporate world, data analysis professionals help companies make more apt decisions and help in running a business more efficiently.
The process of reviewing, cleansing, converting, and modeling data to identify usable information, inform conclusions, and help decision-making is known as data analysis. It has different data analytics techniques and methodologies, spanning various titles, and is applied in fields such as business, science, and social science. To find answers to queries, hypotheses, or myths, data must be collected and processed. The process is described below.
Collection- For the requirements of end users or businesses, information is gathered from various sources via case studies, surveys, questionnaires, interviews, and direct observation. This collected data is arranged for analysis.
Cleaning- Not all the vast information collected is useful. The next step is cleaning. This is the method that involves eliminating blank spaces, duplicate records, and basic mistakes. Data cleansing is required before sending the information for analysis.
Analysis- This process requires using data analysis software and other data analytics tools to evaluate and understand the data and draw conclusions. The tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI.
Interpretation- After analysis, the step in-line is an interpretation of the analyzed data and deciding the next course of action based on the data analysis.
Visualization- The next step is visualization. The interpreted data is explained for easy understanding through charts, graphs, maps, bullet points, and a variety of additional ways. By observing and comparing data, visualization can help in making crucial decisions required for a company.
Difference Between Data Scientists and Data Analysts
Data scientists and Data analysts might sound the same as they both handle data, but the job role makes a difference. A data analyst collects data from different sources and cleans it. Data scientists perform data mining and gather large sets of organized and unorganized data from different sources.
Creating different reports with the help of charts and graphs using Excel is the role of data analysts and data scientists focus on building AI models using various algorithms and in-built libraries. Data analysts have to spot data analytics trends and patterns from different data while data scientists generate information with the help of AI tools.
Types of Data Analysis
Data can be studied in many ways to help make an informed decision for a firm. Each type of analysis has its purpose and technique. It can be divided into six categories:
- Descriptive Analysis- As the name goes, it includes summarizing and explaining important points of data. The data is arranged and presented in a simplified way using statistical tools such as mean, median, mode, and standard deviation. This presented data provides a summary and helps in the identification of patterns.
- Inferential Analysis- Inferential analysis makes conclusions or predictions. Important techniques such as hypothesis testing, confidence intervals, and regression analysis are used. It helps in getting conclusion from a sample data.
- Exploratory Data Analysis (EDA) – It studies and comprehends data. Visualizations, summary statistics, and data profiling techniques are used to reveal patterns and correlations. It helps in hypothesis generation for further investigation.
- Diagnostic Analysis- Understanding the cause-and-effect in the data is the primary objective of diagnostic analysis. Diagnostic analysis frequently uses methods like regression analysis, ANOVA (Analysis of Variance), or correlation analysis.
- Predictive Analysis- Utilizing past data to anticipate or forecast future results is called predictive analysis. To create prediction, statistical modeling methods, machine learning algorithms, and time series analysis are used. It is applied in risk estimation, consumer behavior prediction, and sales forecasting.
- Prescriptive Analysis- This type of analysis makes suggestions based on predictions. It uses a combination of historical data and business rules to improve results. It contributes to decision-making and resource allocation.
- Data Visualization- Data visualization, as the name implies, is the capacity to display data findings using graphics or other visuals. The goal is to allow even those who aren’t skilled in data analysis to gain a better understanding of data-driven insights. Data analysts can use data visualization to assist business decision-makers (who may lack extensive analytical knowledge) in identifying trends and understanding complicated ideas at a glance. This skill enables you — the data analyst — to obtain a deeper knowledge of a firm’s position, communicate relevant findings to team leaders, and even influence company decision-making.
- R– R is one of the most popular and widely used data analytics programming languages. R rated fifth in a poll held by the Institute of Electrical and Electronics Engineers’ (IEEE) professional journal, Spectrum, of the top 10 programming languages used in 2019. R’s vocabulary and structure were designed to enable analytical work; by default, it has several built-in, simple data-organizing instructions. It is quite popular because it can manage complex or enormous amounts of data.
- Linear Algebra and Calculus- When it comes to data analytics, significant mathematical abilities are a must. Some data analysts even specialize in mathematics or statistics as undergraduates to obtain a better knowledge of the theory that drives real-world analytical practice. In analytics, two specific branches there are two branches of mathematical research namely; linear algebra and calculus. Linear algebra is used in machine learning and deep learning, and it enables vector, matrix, and tensor operations. Calculus is also utilized to create objective/cost/loss functions that instruct algorithms on how to attain their goals.
- Microsoft Excel– The phrase “Excel skills” appears regularly in the credentials section of job postings on sites like Indeed or Monster. Excel is widely used in business. Furthermore, Excel is on the verge of automating some features and instructions for improved data analysis. VBA, Excel’s programming language, is used to build macros or pre-recorded commands. When used appropriately, VBA can save human analysts a significant amount of time on regularly performed, repetitive tasks like accounting, payroll, or project management. Microsoft’s Analysis ToolPak was likewise designed with statistical modeling and data analysis in mind.
- · Communication- Being a successful data analyst requires having good communication skills. One should be able to discuss highly technical issues with trained people and should be able to explain clearly and have clarity in making decisions.
- Critical Thinking- Data analysis in itself is not sufficient; you also need to understand its implications that go beyond numbers. As a critical thinker, you can analyze data by looking for patterns and drawing insights from the information available. Although it can be challenging, you can strengthen your critical thinking skills by challenging yourself. Don’t undervalue the value of developing your critical thinking abilities when training for a profession in data analytics.
- MATLAB– Another programming language MATLAB, among other things, helps in the development of algorithms, the manipulation of matrices, and data charting. Businesses interested in big data have started to turn to MATLAB because it enables analysts to drastically minimize the amount of time they traditionally spend pre-processing data and makes it easier to swiftly clean, organize, and visualize data. The most prominent characteristic of MATLAB is its capacity to run any machine learning models developed in its environment on a variety of platforms.
- Python- This high-level, general-purpose programming language took first place in IEEE’s Spectrum 2019 poll; it has an impressive array of specialized libraries, many of which are relevant to artificial intelligence (AI). Python must be capable of being utilized for AI development. Statista figures show that by the end of 2020, the market for AI software is estimated to have grown by 154 percent yearly and be worth $22.6 billion. If they wish to remain relevant in the increasingly AI-focused professional world, data analysts should be Python experts.
- Machine Learning- Statista study says that predictive analytics and artificial intelligence are currently receiving a lot of investment. Although not all analysts will work on machine learning projects, having a basic familiarity with associated tools and concepts may provide you with an advantage over rivals in the job market.
- SQL and NoSQL- If you want to make a career as a data analyst, it is important to have in-depth knowledge of database languages. SQL, or Structured Query Language, is the most significant of these. SQL was created in 1970, however, its value has not decreased. For processing and gaining access to data in relational databases in contemporary analytics, SQL remains the de facto way. Why should someone who wants to work in data spend time learning this ‘old’ language, asks data scientist Josh Devlin. Having a basic understanding of a more general-purpose language, such as Python or R, is essential, but at the same time, zero knowledge of SQL will make it much more difficult to find jobs.
Factors Influencing Data Analyst Salary in India
- Education– A Bachelor’s degree is required for each aspiring data analyst and that influences data analyst salary in India. The degree could be in Computer Science, Mathematics, Statistics, or any other related field. A master’s or doctorate is required for a senior-level mid-level or analytic position. A data science professional must be skilled in data mining and data modeling. Furthermore, the chosen degree must include specialties in the following areas:
Statistical modeler- A quantitative analyst who specializes in statistical modeling is known as a statistical modeler.
Evidence-Based Modeling: Shows students how to use advanced analytics to reduce decision-making uncertainty.
Informational Design: Extends and improves managerial knowledge through standardized data flow and data reporting.
- Job Roles- The difference between data scientist positions and data analyst positions is $50,000. Data Analytics and Data Science are popular roles for a successful job. Data modeling and data science are safe for candidates looking for long-term employment possibilities. As AI and Machine Learning have become an important part of our daily this will continue in the long run.
- Experience Level- The experience level has a great impact on the earnings of a data analyst. According to the Ambition box, the typical data analyst salary in India is 3.8 Lakhs with 2 years of experience and 5.2 Lakhs with 3-4 years of experience. For newly hired employees, the average yearly data analyst salary is approximately 1.9 Lakhs. Entry-level project managers earn Rs15,000- Rs 20,000 less than entry-level data analysts.
- Industry- Salary fluctuations also depend on the industry demand and the data analyst’s location. Insurance, finance, healthcare, information technology, and professional services are the well-paying professions.
- Location- When calculating data analyst salary in India, the place of work is crucial. Working in metro cities such as Bengaluru, Mumbai, and Delhi with reputed digital enterprises provides a significant advantage. Bengaluru, India’s Silicon Valley, is the ideal choice for an ambitious data science job because of reputed MNCs such as Flipkart, Amazon, Myntra, and other start-ups.
- Company– The compensation of a data analyst ranges with every pay grade. It indicates that each pay level has a minimum and maximum compensation range. Businesses consider both internal and external pay while framing compensation plans. A data analyst’s salary at Google may be between Rs 2.8 to Rs 60 lakhs.
Scope for Data Analyst Jobs
An industry’s main focus is on processing complex data for making informed management decisions. Companies in many sectors have a demand for data analysts. Enterprises of different sizes have learned the importance of data and analytics. This implies that there are a lot of opportunities for data analyst jobs. It is predicted that demand will outweigh supply.
Key Factors for Becoming a Data Analyst
Popularity- Data analysis is one of the most popular job roles in 2022. After the USA, India is the second largest source of employment for data scientists. Data analyst salary in India is very high due to a variety of factors, including high demand.
Diversification– Data analysts are in high demand, and there are numerous avenues for employment accessible.
Work Environment- The data analyst will shape future workspaces. Because of the advancement of artificial intelligence and robots, an increasing number of repetitive and manual processes are being mechanized.
Raising the bar- Organizations may now successfully personalize their offers and improve consumer experiences by applying machine learning.
Assisting world- Predictive analytics and machine learning have altered the healthcare industry.
Data Analyst Salary in India
The demand for a data analyst has developed because of emerging complex data. As a result, data analysts in India are in demand. Also, the compensation of a data analyst is dependent on factors, like experience, abilities, location, and company. The average salary of data scientists is $698,413.
A data scientist with less than a year of experience may earn around $500,000 yearly. The average yearly salary for entry-level data scientists with between one and four years of experience is 610,811. A mid-level data scientist in India with 5 to 9 years of experience can anticipate earning $1,004,082 per year. As the experience of senior-level data scientists in India increases, he or she can earn over Rs 1,700,000 per year!
Frequently Asked Questions: Data Analyst Salary in India
Here are the FAQs about Data Analyst Salary in India
-What is the average data analyst salary in India?
The average data analyst salary in India is Rs 4.3 lakh per annum. The average yearly data analysis salary is Rs 4.4 lakhs if you have less than three years of work experience. With 4 to 9 years of experience, the average salary will be Rs 6.9 lakhs per annum.
– For data analysts, what job roles can give a high salary?
The top data analyst positions are: Senior Analyst: $27,000 annually Lead Consultant: $24,000,000 annually Retail Data Analyst: $21,000 year.
– How can data analysts increase their pay scale?
A data analyst can increase his/her salary by switching careers to a company that pays more for skills. Simultaneously, they should focus on expanding their knowledge base too.
– How much time is required to enter the job market as a data analyst?
The time duration differs on background and education, learning pace, and specific skills. A bachelor’s degree in mathematics, computer science, or statistics is required. It is also important to obtain the required skills through independent study or through a course. These courses can be of few weeks and months, depending on length and level of intensity. Once you have the required training or abilities, you are ready to enter the job market.
-What are some examples of sectors where data analysts can work?
IT Industry: This sector holds a large share (43%) of the market for data analyst professionals. Some of the IT companies in India where data analysts have ample job opportunities are Wipro, Cognizant, Infosys, Tata Consultancy Services, Accenture, and Capgemini. India has also witnessed significant innovation in technology because of the IT sector.
E-Commerce: The e-commerce industry was estimated to be worth US$50 billion in 2020. NASSCOM claims that e-commerce drives 1.2 million transactions every day. By 2023, this market is predicted to become the second-largest retail market. With a 5.9% market share, the e-commerce industry has employed data analytics to predict trends, improve customer experience, and help in smooth warehouse operations. Since 2014, India has employed data analysts for the smooth functioning of the economy. The government is using data to improve the lives of its citizens. It has also made significant efforts to establish several data apps for processing enormous volumes of data.
Banking and Insurance sectors: With a 13.9% market share, this is the second largest sector. A number of multinationals are conducting analysis of data regularly. The fintech industry in India is the third biggest company in the world that employs the services of data analysts. AI and data analytics have been used by a number of large MNCs including PhonePe, Paytm, Policybazaar, and MobiKwik, to expand their operations.
Conclusion: Data Analyst Salary in India
Most businesses handle different types of data. Any company that you select will explain its reliability on data to continue its operation successfully. Companies employ different types of data analysts who can handle enormous amounts of information. The pay for data analysts in India is therefore increasing faster than it has ever been. The future of data analysts is very bright in cities like Mumbai, Bangalore, Hyderabad, Delhi, and Chennai. With the rapidly increasing demand for data analysts, one can expect a higher data analyst salary in India. Besides, a graduate can also become a data analyst if he or she acquires the required information and focuses on polishing skills.
Due to the Indian government’s investment in data science and analytics, job opportunities have developed for data analysts, providing a boost to this profession. It is anticipated to reach US$11.87 billion by 2026 if growth continues at the same rate. This sector will upend the market, bringing about a significant change and creating a number of work chances. Moreover, the figures mentioned in the article are estimate numbers. A candidate’s income is usually determined by how well a candidate uses their abilities, growth in the company, and new skills acquired. We hope that the information covered in the article about data analyst salary in India will help you in carving a career as a data analyst.