Chapter 1: An Introduction to Gautam Adani and the Adani Group
India’s most affluent personality, Gautam Adani, and his gargantuan conglomerate, the Adani Group, have recently been in the spotlight for all the wrong reasons. With a net wealth of roughly $118 billion as of April 2022, Adani has grown his fortune astronomically over the past three years, primarily due to the meteoric rise of his key listed companies’ share prices.
Adani, a self-starter who commenced his career as a commodity trader in the 1980s, eventually established the Adani Group in 1988. The conglomerate rapidly morphed into a behemoth private infrastructure empire that manages ports, airports, and coal mines across India and the globe. The group’s impressive portfolio also includes data and cable centers and defense goods manufacturing. It has ambitious plans to invest $70 billion in green energy businesses in the forthcoming year.
Despite Adani’s remarkable success, it has not been without controversy. The company’s rapid growth has often been attributed to generous government concessions, largely because of Adani’s close association with India’s ruling Bharatiya Janata Party.
Chapter 2: Unraveling Hindenburg’s Findings
Recently, activist investment firm Hindenburg Research released a damning report accusing the Adani Group of “brazen” stock manipulation and accounting fraud amounting to $218 billion. The report’s allegations are severe, claiming that the Adani Group engaged in stock price manipulation and accounting fraud over the course of decades.
Hindenburg’s findings suggest that even if one were to accept the financials of the Adani Group at face value, its seven main listed companies would face an 85% downside purely based on fundamentals, given their sky-high valuations. The report also warns that the group’s significant debt puts the conglomerate on shaky financial ground.
Chapter 3: The Adani Family and their Alleged Involvement
The Hindenburg report names several members of the Adani family, including Gautam Adani’s brothers, Rajesh and Vinod Adani, as well as associates of the Adani Group, allegedly involved in significant bribery and tax evasion cases.
Members of the Adani family have been the subjects of past corruption investigations carried out by the Securities and Exchange Board of India (SEBI) and the Directorate of Review Intelligence. The Hindenburg report claims that Adani family members allegedly cooperated in the creation of offshore shell entities worth $4.5 billion through forged documents, primarily in tax-haven jurisdictions like Mauritius, the UAE, and the Caribbean islands.
Chapter 4: The Offshore Shell Game
The report presents evidence of a complex web of offshore shell entities controlled by Vinod Adani or close associates. Thirty-eight such entities were identified in Mauritius alone, with additional entities found in Cyprus, the UAE, Singapore, and several Caribbean Islands. These entities, despite having no visible signs of operations, have collectively moved billions of dollars into Indian Adani publicly listed and private entities, often without the requisite disclosure of the related party nature of the deals.
Chapter 5: Stock Manipulation and Money Laundering Allegations
The shell entities associated with Vinod Adani are suspected of serving multiple functions, including stock manipulation and money laundering. The goal appears to be to funnel money from Adani’s private companies onto the listed companies’ balance sheets to maintain the appearance of financial health and solvency.
Chapter 6: Potential Breach of SEBI’s Rules
Publicly listed companies in India are subject to rules requiring disclosure of all promoter holdings. Rules also mandate that listed companies have at least 25% of the float held by non-promoters to mitigate manipulation and insider trading. Four of Adani’s listed companies are on the brink of the delisting threshold due to high promoter ownership.
Chapter 7: The Role of Offshore Funds
Hindenburg’s research indicates that offshore shells and funds tied to the Adani Group comprise many of the largest “public” holders of Adani stock. This issue could subject the Adani companies to delisting if the Indian securities regulator, SEBI’s rules, were enforced.
Chapter 8: The Adani Group’s Defense
In response to the allegations, Adani Group’s chief financial officer, Jugeshinder Singh, issued a statement calling the report a “malicious combination of selective misinformation and stale, baseless, and discredited allegations.” The conglomerate did not address specific allegations in its official statement but said it has always been in compliance with the law.
Chapter 9: Possible Legal Repercussions
Adani Group has hinted at pursuing legal action against Hindenburg. It is evaluating the relevant provisions under US and Indian laws for remedial and punitive action against Hindenburg Research.
Chapter 10: The Aftermath and Implications
The repercussions of the report have been significant, causing a dip in Adani’s personal wealth and a significant decline in Adani Group’s stock prices. The report also raises serious questions about corporate governance, regulatory oversight, and the use of offshore entities for financial manipulation.
As the saga continues to unfold, it serves as a stark reminder of the urgent need for increased transparency and accountability in corporate dealings, especially for companies of such monumental scale as the Adani Group.
In conclusion, the Adani Group’s narrative underscores the importance of robust corporate governance structures and regulatory oversight in safeguarding the interests of shareholders and maintaining the integrity of financial markets. As the world continues to scrutinize the unfolding drama, the final verdict remains to be seen.
Financial modeling is a crucial tool used by finance professionals and business executives to make informed decisions and project future financial performance. In this comprehensive guide, we will explore the various aspects of financial modeling, including its definition, types, uses, and the importance of accurate modeling in the world of finance.
What is Financial Modeling?
At its core, financial modeling is the process of creating an abstract representation of a real-world financial situation. It involves building mathematical models that estimate the performance of financial assets, businesses, projects, and investments. These models are typically created using spreadsheet software, with Microsoft Excel being the most popular choice due to its flexibility and customization options.
Financial models are designed to forecast future cash flows, determine financing requirements, evaluate the value of a company or assets, and guide investment decisions. They serve as a roadmap for business leaders, providing insights into the potential financial outcomes of various strategies and initiatives.
The Importance of Financial Modeling
Financial modeling plays a crucial role in the finance industry for several reasons. First and foremost, it enables finance professionals to make smarter financial decisions. By creating models that consider various factors and scenarios, they can assess the potential risks and rewards of different strategies and choose the optimal course of action.
Secondly, financial modeling helps identify the least bad decision in situations where positive outcomes may be limited. In such cases, accurate modeling allows decision-makers to evaluate the potential consequences of each option and choose the one that minimizes negative outcomes.
Lastly, financial models provide investors, partners, and lenders with confidence in the strength of a business. By presenting a well-constructed financial model, companies can demonstrate their viability and attract the necessary funding for growth and expansion.
Types of Financial Models
Financial modeling encompasses various types of models, each serving a unique purpose. Let’s explore some of the most commonly used financial models:
1. Three-Statement Model
The three-statement model is the simplest and most widely used financial model. It incorporates a company’s three most important financial statements: the income statement, balance sheet, and cash flow statement. By analyzing these statements, businesses can gain insights into their profitability, net worth, and cash flow.
The income statement, also known as the profit and loss (P&L) statement, provides a snapshot of a company’s revenue, expenses, and net income over a specific period. The balance sheet showcases a company’s assets, liabilities, and shareholders’ equity, providing a snapshot of its financial position at a given time. Lastly, the cash flow statement outlines the inflows and outflows of cash, helping businesses monitor their liquidity and cash management.
The three-statement model serves as the foundation for more advanced financial models, enabling businesses to project future financial performance based on historical data.
2. Discounted Cash Flow (DCF) Model
The discounted cash flow (DCF) model is a valuation model commonly used in equity research. It estimates a company’s unlevered free cash flow and calculates its net present value (NPV). By discounting future cash flows back to their present value, the DCF model determines the intrinsic value of a company or investment.
The DCF model takes into account the time value of money, assuming that cash received in the future is worth less than the same amount received today. This model helps analysts determine whether a company is undervalued or overvalued in the stock market, making it a valuable tool for investment decisions.
3. Mergers and Acquisitions (M&A) Model
The mergers and acquisitions (M&A) model is used to evaluate the financial viability of a pro forma merger between two companies. It estimates the potential synergies, cost savings, and financial impact of the merger, helping decision-makers assess the profitability and strategic value of the transaction.
The M&A model considers various factors such as projected revenue growth, cost reductions, and changes in market share to determine the potential financial outcomes of the merger. It enables companies to make informed decisions regarding potential acquisitions and identify opportunities for growth and expansion.
4. Budget Model
A budget model is a financial model used for financial planning and analysis (FP&A). It helps businesses create a comprehensive budget for a specific period, typically 3 to 5 years. The budget model considers various revenue streams, expenses, and financial goals to provide a roadmap for financial planning and resource allocation.
By analyzing the budget model, companies can make informed decisions about resource allocation, cost management, and revenue generation. It helps align financial goals with operational strategies and provides a framework for monitoring and controlling financial performance.
5. Forecasting Model
Similar to the budget model, a forecasting model is used in FP&A to project future financial performance based on historical data and assumptions. It helps companies anticipate potential outcomes, identify trends, and adjust their strategies accordingly.
A forecasting model considers various factors such as market conditions, industry trends, and internal capabilities to provide insights into the potential financial future of a business. It enables companies to make proactive decisions, adapt to changing market dynamics, and seize opportunities for growth.
The Process of Financial Modeling
Effective financial modeling involves a systematic process that encompasses several key steps. Let’s explore the typical process of creating a financial model:
1. Define the Objective
Before diving into the modeling process, it’s crucial to clearly define the objective of the financial model. Determine the specific questions you want to answer and the insights you hope to gain from the model. This will guide your modeling approach and ensure that the model aligns with your goals.
2. Gather Data
The next step is to gather the necessary data to populate the model. This includes historical financial statements, market data, industry trends, and any other relevant information. Ensure that the data is accurate, reliable, and up-to-date to ensure the accuracy of your model’s projections.
3. Structure the Model
Once you have the data, it’s time to structure the model. This involves organizing the data into appropriate sections, creating formulas and calculations, and setting up the necessary assumptions and inputs. Consider using Excel templates or financial modeling software to streamline the modeling process and ensure consistency.
4. Build Formulas and Calculations
The heart of a financial model lies in its formulas and calculations. This is where you translate the data and assumptions into projections and forecasts. Use Excel functions and formulas to perform calculations such as revenue growth rates, expense ratios, and cash flow projections. Double-check your formulas to avoid any errors that could impact the accuracy of your model.
5. Validate and Test the Model
Once the model is built, it’s crucial to validate and test its accuracy. Compare the model’s projections with historical financial data to ensure that it aligns with past performance. Conduct sensitivity analysis by changing key assumptions to assess the impact on the model’s outputs. This helps identify potential risks and uncertainties and ensures that the model provides reliable insights.
6. Interpret and Communicate Results
After validating the model, it’s time to interpret the results and communicate them effectively. Present the findings in a clear and concise manner, using visualizations such as charts and graphs to enhance understanding. Clearly explain the assumptions, limitations, and potential risks associated with the model to ensure that stakeholders have a comprehensive understanding of the results.
Conclusion
Financial modeling is a powerful tool that enables finance professionals and business executives to make informed decisions, project future financial performance, and evaluate investment opportunities. By utilizing various types of financial models, businesses can gain insights into their financial future, identify risks and opportunities, and optimize their strategies for success. Remember to approach financial modeling with care, ensuring the accuracy of data, assumptions, and calculations to maximize the reliability and usefulness of your models.
Python is most powerful tool for Financial Analysis and Data Analysis. You can use python for Machine Learning and Artificial Intelligence.
Syllabus Content
Subject Name: Python for Financial Analysis
Unit
Title
Details of Topic
Unit I
Getting Started with Python
This unit will prepare the learner to use basic python for data analytics- This foundational unit will equip the learner to use the basic syntax of Python
Unit II
Numpy, General Overview of Pandas and Matplotlib
Numpy for financial analysis, General overview of pandas and visualization with matplotlib and pandas. Pandas with timeseries data. Capstone stock market analysis project.
Unit III
Time series analysis
Time Series basic, Introduction to basic Stat Model, ETS theory, EWMA Theory, ARIMA theory, ACF and PACF theory, ARIMA with stat model.
Unit IV
Python finance fundamentals, Calculating and Comparing Rate of Returns, Measuring Investment Risk
Introduction to python finance fundamental, Shape ratio, Simple and moving average, Calculating rate of return of individual share and portfolio. Calculating rate of return of Index. Calculating risk of individual share and portfolio Calculating index risk.
Unit V
Using regression in python, Markowitz portfolio optimization, Capital Assets Pricing Model, Multivariate regression analysis, Monte Carlo Simulations.
Calculation of simple regression. Calculation of Alpha, Beta and R squire in python. Markowitz portfolio optimization. Calculation of Multivariate regression.
Lesson Plan
U. No
Title
Details of Topic
Duration
Unit 1
Getting Started with Python
Installing Python, Installing Jupyter Notebook, Introduction of Jupyter Notebook and Google colab
Python variables, Data types, Basic Python Syntax, Python Operators.
Conditional statements, Python Functions, Python sequence, Iteration in Python
Object oriented programming, Modules and Package, Standard Library, Importing modules.
Unit 2
Numpy, General Overview of Pandas and Matplotlib
Introduction to Numpy, Numpy Array, Numpy operation, Numpy indexing
Introduction to Pandas, Series, DataFrames, Missing Data, Groupby with Pandas, Mearging, Joining, Concatenating DataFrame, Pandas common operations, Data input and output
Introduction to Visualization in Python, Matplotlib
Introduction ot data source, Note on PandasDatareader, quandl, Introduction to time Series with Pandas. Datetime Index, Time resampline, Time Shift, Pandas Rolling and Expanding.
Introduction to time series, Time series basic, Introduction to stat models,
ARIMA theory, ACF and PACF, ARIMA with Statmodels, Discussion choosing PDQ.
Unit 4
Python Finance Fundamentals, Calculating and Computing Rate of Returns. Measuring Investment Risk
Welcome o the Finance Fundamentals, Sharp Ratio, Portfolio Allocation Code Along, Considering Both Risk and Retrun, Calculating Security Rate of Retrun (Simple retrun and logarithmic return),
What is a portfolio of securities and how to calculate its rate of return, Popular stock indices that can help us understand financial markets, Calculating the Indices’ Rate of Return
Calculating the Indices’ Rate of Return, Calculating a Security’s Risk in Python, The benefits of portfolio diversification, Calculating the covariance between securities, Measuring the correlation between stocks
Calculating Covariance and Correlation, Considering the risk of multiple securities in a portfolio, Calculating Portfolio Risk, Understanding Systematic vs. Idiosyncratic risk, Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio
Unit 5
Using regression in python, Markowitz portfolio optimization, Capital Assets Pricing Model, Multivariate regression analysis, Monte Carlo Simulations
The fundamentals of simple regression analysis, Computing Alpha, Beta, and R Squared in Python
Finance -Markowitz Portfolio Optimization, Obtaining the Efficient Frontier in Python
The intuition behind the Capital Asset Pricing Model, Understanding and calculating a security’s Beta, Calculating the Expected Return of a Stock.
Multivariate regression analysis – a valuable tool for finance practitioners, The essence of Monte Carlo simulations
Batch 1: The course started on the 15th and was completed on the 20th of June 2022 and on the 11th of August 2022.
In this post, we’ll look at the business analyst profession, which is one of the most common job options in the corporate sector. We’ll learn what a business analyst is, what they do, how much money they make, and what skills and education you’ll need to become one. Also the difference between Business Analyst and Data Analyst.
Alright! Now, let’s start with a brief overview of the business analyst profile.
Who are Business Analytics and how do they fit inside a company?
At first glance, the term “Business Analyst” may appear generic, which may cause concern among entry-level candidates. However, because the business analyst profession involves many various operations in a company, the fact that it is a versatile position may be promising. This might be an entry-level employment or a role for seasoned professionals, depending on the organisation. As a result, business analysts are responsible for a wide range of responsibilities, and job descriptions vary greatly from one company to another. Business analysts will be focused on the following types of activities in various businesses:
Process and systems improvement (in terms of efficiency and effectiveness)
Solving business problems
Looking for savings and efficiencies
Focusing on business development and searching for new opportunities
Performance analysis
Competitor analysis Indeed,
Data Analysits
One of the most dynamic roles in a firm is that of a “business analyst.” They may be reporting their results to the head of a certain division to which they have been assigned, or they may be discussing a specific instance with a product or project manager. In some cases, the business analyst serves as a mediator between the business development manager and the head of division or product owner. One thing is certain: you will never be bored at work.
That is the elevator pitch for this excellent career opportunity. However, in order to obtain a greater idea of what it means to be a business analyst, we must examine their typical day-to-day responsibilities. So,
What do business analysts do?
The answer is that it depends. Business analysts are expected to perform a variety of tasks in various organisations. However, these are some of the most prevalent responsibilities.
A business analyst examines the performance of a specific segment within a corporation. They frequently engage in the analysis of various processes, identifying goals, and creating hypotheses. Their goal is to improve the performance of the specific section of the firm to which they have been allocated. They not only gather data, but also use data-driven decision making, convey results, and oversee their implementation.
Furthermore, business analysts frequently train non-technical team members. As a result, we can confidently state that business analysts are the finest communicators among issue solvers, and they are always willing to contribute their expertise throughout the firm. Sales, supply chain, and administration are some of the divisions to which business analysts may be assigned. Within that structure, they do research, rely on data as much as possible, and are generally involved in the creation of dashboards and other BI tools to facilitate the sharing of their results. As you can see, being a business analyst is a difficult job…but it can also be incredibly rewarding!
Now that you know what it’s like to be a business analyst, let’s look at your options and how you can get started.
A job as a business analyst is an excellent alternative to consider, both on its own and as the first step on the career ladder to becoming a Product Lead, Head of Product, or Head of Division, and, why not, a Vice President. Full-time business analyst employment are available in the majority of midsize and big organisations across all industries, including consulting, finance, and technology.
Consultancy is also quite popular in this field, particularly in smaller firms. However, as compared to their counterparts hired by a corporation in a specialised industry, this alternative gives a business analyst with a limited picture of the business.
Data Analyst and Business Analyst
So, what are the essential abilities required to apply for a job as a business analyst?
According to a review of 1,395 job postings, business analyst applicants must have the following tools and skills. Here’s what the numbers show:
60% of job postings emphasized Excel skills
41% mentioned strong communication
6% requested Tableau
and 4% – Power BI
What about the educational background?
A Bachelor’s degree is required for 66 percent of job openings.
This is the standard for this field. In terms of experience, most businesses in our sample required an average of four years on the job, although 35% of job advertising were also appropriate for those with no prior working experience.
To summarise, if you want to increase your chances of securing a business analyst position, you must be fluent in Excel, have great communication skills, and potentially master a BI software (such as Tableau or PowerBI). However, python is also gaining great popularity.
Alright! You now understand the most significant components of the business analyst role, what to anticipate from it, and what talents you should work on to become one.
Difference between a business analyst and a data analyst
Financial Analyst
I will discuss the difference today under four main headings the first is responsibilities the next is qualifications skills and then salary
Work with client to understand the problem
So, to begin with, let’s look at the business analyst. The business analyst will spend a lot of time with the customers to understand what they need and what difficulties they’re experiencing, and then they’ll work with the managers to figure out how that’s going to work within their team.
Use current data to outline problem
The second step is to use current data to outline the problem, so they’ll have to go in and actually look at the data to understand what that client is looking for so that they can convey to the team what they’re trying to solve and what they’re looking for.
Outline and communicate the client problem to team.
Then they’ll describe and communicate to the team what the client genuinely wants, what the problem is, what they’re attempting to solve, and how we’re going to get there.
Business Analytics
Often, the business analyst will conduct all of the analysis and then pass it on to either the programming team or a data analyst to investigate further and resolve the issue.
Work with the Programming teams
work with the programming teams that collect and analyse the data The distinction between these two is that the business analyst will normally deal with the client side and management, whilst the data analyst will work with the programming side.
Use pre-existing data to solve a problem
Next, they’ll use pre-existing data to address the problem, so the business analyst will bring the problem, describe it, and present it to the programming team, and then the data analyst, along with the programmers or developers, will solve the problem and figure out a solution.
Create report and dashboard
Next, they’ll develop reports and dashboards, which is a requirement for any data analyst position, or they’ll create some form of visualisation
Present Analytical Finding to team
The last thing you’ll do is offer analytical findings to the team, so the business analyst will bring the problem to the team, and the data analyst will report back to the team with their findings and a possibility or potential remedy.
Next we’re going to look at qualifications
A bachelor’s degree or higher is necessary, and the criteria for the business are actually quite comparable unless you’re going to need a bachelor’s degree in normally it’s going to be anything in business and administration finance economics something business-related
Master requirement in sum position (MBA, M.Com or equivalent)
You may need a master’s degree, and business analysts, who frequently have MBAs, are the most common candidates.
Typically, data analysts do not have MBAs, but let’s look at that one next, thus for a data analyst
Bachelor’s degree or higher is preferred
A bachelor’s degree is also required, generally in computer science, statistics, mathematics, economics, or finance, and as you can see, there is a lot of overlap between the business analyst and the data analyst.
Masters required for some position
You may also require a master’s degree for some roles, and it isn’t always an MBA, such as a business analyst; sometimes they’re looking for statistics, mathematics, or an actual analytics master’s degree, and so those are some of the qualifications that differ between these two positions.
Skills
So, for a business analyst, you’ll need skills like knowing Microsoft Access and Excel, and those are some of the more technical skills you’ll need, but you’ll also need really solid soft skills like communication skills, presentation skills, and just general people skills. be able to successfully engage and converse with clients or members of the team
So for data analytics, you’ll need a little bit more technical skills, so you’ll be using things like sequel or in Python tableau or a data visualisation tool, you’ll be doing data modelling, so you’ll need to know SAS or SPSS Excel, and then some type of cloud platform like Azure or Amazon Web Services.
Salary
Business Analyst Salary in India
In this section, I will cover the average salaries earned by Business Analysts in India based on their position levels in a business and the Indian states in which they operate. I will also analyse the salaries offered by some of the largest firms in India to Business Analysts, based on job postings and salaries posted on Glassdoor.
Now discuss based on their experience and position levels.
Business Analyst Salary for Freshers
In India, the starting salary for a Business Analyst is between ₹350k and ₹500k per year. This is the compensation range for a professional with less than one year of experience. A Business Analyst’s remuneration rises in tandem with his or her level of expertise. Business Analysts with 1–4 years of experience may earn up to ₹523k per year, while those with more than 5 years of experience can earn up to ₹831k per year.
Senior Business Analyst Salary in India
Expert Business Analysts with 15+ years of industry expertise can earn up to ₹1,290k per year, albeit this varies based on position level and firm. Furthermore, you can see the wage packages of these professions in a variety of regions and towns across the country.
Business Analyst Salary in Bangalore
Bangalore, as we all know, is the nation’s IT capital. This Karnataka city boasts one of the most job postings for several IT professions, including Business Analysts. As a result, Business Analysts are among the city’s highest-paid IT workers. In Bangalore, Karnataka, business analysts make an average annual salary of ₹660k.
Business Analyst Salary in Delhi
Business Analysts in New Delhi make an average annual salary of ₹600k, which can rise to ₹1,251k with experience.
Business Analyst Salary in Chennai
Because there are several chances for individuals with a technical background in Chennai, the average pay of Business Analytics specialists in Chennai is substantially greater than in other cities. Business Analysts in Chennai make an average of ₹775k per year. Based on experience and skills, this income can be increased to ₹1,371k per year.
Business Analyst Salary in Hyderabad
Business Analysts in Hyderabad, Telangana, earn roughly ₹680k per year. This figure might rise to ₹1,345 per year depending on characteristics such as abilities, experience, and expertise. After learning about the incomes made by Business Analysts based on geography, we can now look at the salaries paid to Business Analysts by some of the world’s most well-known firms.
Average Salary of Business Analysts in Popular Indian Organizations
Now, I’ll go through the compensation Business Analysts earn at Accenture, Capgemini, Tata Consultancy Services (TCS), and other significant firms in India that give the top pay packages to these individuals.
The typical salary for these experts at Accenture is ₹705k per year, while Capgemini pays around ₹703k. Analysts at TCS earn an average of ₹697,000 per year.
Companies such as HCL and Wipro, on the other hand, provide a lower sum than the aforementioned companies. Wipro and HCL pay roughly 580k and 521k per year for Business Analysts, respectively.
Salary of a Data Analyst in India
The title of ‘Data Analyst’ is one of the most sought-after job alternatives and one of the highest-earning specialists in the field of information technology. Companies generate a tremendous volume of data every day, which is why these experts have a variety of career prospects.
According to PayScale, the average compensation for a Data Analyst in India is ₹432k per year. However, the income of Data Analysts varies depending on a variety of factors such as the city, the organisation, the job position, the job role, work experience, and so on. We shall go over them in depth in this blog post on ‘Data Analyst Salary in India.’
Data Analyst Salary in India by Experience
A Data Analyst with less than a year of experience can expect to earn around ₹342,716 per year as a starting salary in India. This is the starting salary for a Data Analyst.
Data Analysts with 1–4 years of industry experience make ₹414,330 per year on average.
Experienced Data Analysts with 5–9 years of professional experience may earn an average annual salary of ₹676,056.
Expert Data Analysts with 10–19 years of experience can expect to earn around ₹918,116 per year.
Senior Data Analyst salaries in India are ₹1,750,000 per year for experts with more than 20 years of industry expertise.
Data Analyst Salary in India by City
We will now learn about the pay of Data Analysts in various sections of the country.
Data Analysts in Bangalore, Karnataka, make approximately 17.7% more than the national average. The average annual income in this area is ₹508k.
These professionals make around 6.6 percent less than the national average in Mumbai, Maharashtra, with a salary of ₹403k per year.
In Hyderabad, Telangana, data analysts make around 4% less. The city’s average yearly salary for Data Analysts is ₹442k.
The average salary for Data Analysts in Kolkata, West Bengal is 6% lower than the national average. Data Analysts earn an average of ₹408k per year in this city.
Data Analysts earn around ₹404k per year in New Delhi, which is 6% less than the national average.
Data Analyst Salary in India by Company
Mentioned below are the average salaries earned by Data Analysts in various companies in India (in no particular order):
Tata Consultancy Services: ₹439k/year
Accenture: ₹494k/year
Ernst & Young (EY): ₹408k/year
Amazon: ₹450k/year
Genpact: ₹350k/year
HSBC: ₹712k/year
Capgemini: ₹316k/year
Cognizant: ₹525k/year
Deloitte: ₹540k/year
IBM: ₹548k/year
Data Analyst Salary in India by Job Profile
In India, the Data Analyst salary varies according to the job profiles as follows:
