Click Virtual University

Category: Data Analyst

  • Understanding the Power of Business Intelligence

    Understanding the Power of Business Intelligence

    Business intelligence, often abbreviated as BI, is a technology-driven process that uses data analysis and interpretation to facilitate informed business decisions. It’s a collection of strategies and tech tools that converts raw, seemingly daunting data into valuable, actionable insights. In the world where information is power, BI can be seen as the golden key that unlocks a business’s potential.

    1. The Core Concept of Business Intelligence

    BI is a broad term encapsulating a gamut of technologies and methodologies aimed at improving business operations through the strategic use of data. It encompasses components such as:

    • Data Mining: The process of extracting useful patterns and trends from large data sets.
    • Reporting: The act of communicating data analysis to stakeholders to facilitate decision making.
    • Performance Metrics and Benchmarking: A process that compares current performance data to historical data to evaluate performance against set goals.
    • Descriptive Analytics: Utilizing preliminary data analysis to understand what has happened.
    • Querying: Asking specific questions from the data and extracting answers from the data sets.
    • Statistical Analysis: Taking results from descriptive analytics and further exploring the data using statistics.
    • Data Visualization: Converting data analysis into visual representations such as charts, graphs, and histograms for easier data interpretation.
    • Visual Analysis: Exploring data through visual storytelling to communicate insights effectively and efficiently.
    • Data Preparation: Collating multiple data sources, identifying dimensions and measurements, and readying it for data analysis.

    In a nutshell, BI is all about making the best use of data, providing companies with the tools to turn data into insights and facilitating better, data-driven decisions.

    2. Why is Business Intelligence Essential?

    In today’s highly competitive business environment, BI plays a crucial role in improving an organization’s business operations. Companies that effectively employ BI tools can translate their data into beneficial insights about their business processes and strategies. These insights can then be utilized to make better business decisions, enhancing productivity, increasing revenue, and leading to accelerated business growth.

    Without BI, organizations might struggle to capitalize on data-driven decision-making, and instead, they might base significant business decisions on accumulated knowledge, previous experiences, intuition, and gut feelings. While these methods might sometimes result in sound decisions, they’re also fraught with the potential for errors and missteps due to the lack of data underpinning them.

    3. Benefits of Employing Business Intelligence

    Here are some of the benefits that organizations can reap by implementing BI:

    • Data-driven Decision Making: BI provides businesses with accurate, timely data, empowering them to make informed decisions.
    • Efficient Analysis and User-friendly Dashboards: BI improves efficiency by transforming data into easy-to-analyze, intuitive dashboards, saving time and making it easier to glean insights from data.
    • Boosted Organizational Efficiency: BI can provide holistic views of business operations, allowing leaders to benchmark results against broader organizational goals and identify areas of opportunity.
    • Enhanced Customer Experience: Ready access to data can help customer-facing employees deliver better experiences.
    • Improved Employee Satisfaction: Providing business users access to data without having to contact analysts or IT can reduce friction, increase productivity, and facilitate faster results.
    • Trusted and Governed Data: Modern BI platforms can combine internal databases with external data sources into a single data warehouse, allowing departments across an organization to access the same data at one time.
    • Increased Competitive Advantage: A robust BI strategy can help businesses monitor their changing market and anticipate customer needs.

    4. The Business Intelligence Workflow

    Business intelligence works through a systematic process that involves several steps. The key stages are:

    4.1 Data Collection and Transformation

    BI tools typically use the Extract, Transform, and Load (ETL) method to aggregate structured and unstructured data from multiple sources. This data is then reshaped and stored in a central location, allowing for easy analysis and querying as a cohesive data set.

    4.2 Trend Discovery and Anomaly Detection

    Data mining or data discovery is a process that uses automation to quickly analyze data and uncover patterns or outliers. These insights provide an accurate picture of the current state of business. BI tools often feature various data modeling and analytics that help explore data, predict trends, and make recommendations.

    4.3 Data Visualization

    Business intelligence reporting employs data visualizations to make findings easier to understand and share. Reporting methods include interactive data dashboards, charts, graphs, and maps that help users understand the current state of their business.

    4.4 Actionable Insights

    Viewing current and historical data in the context of business activities equips businesses with the ability to swiftly move from insights to action. Business intelligence enables real-time adjustments and strategic changes that eliminate inefficiencies, adapt to market shifts, fix supply issues, and address customer problems.

    5. Types of Business Intelligence Tools

    There’s a broad range of BI tools available, each offering a unique set of capabilities. Let’s explore some of them:

    • Spreadsheets: Tools like Microsoft Excel and Google Sheets are some of the most widely used BI tools.
    • Reporting Software: This software is used to organize, filter, display, and report data.
    • Data Visualization Software: This software translates datasets into easy-to-read, visually appealing graphical representations.
    • Data Mining Tools: These tools use databases, statistics, and machine learning to uncover trends in large datasets.
    • Online Analytical Processing (OLAP): OLAP tools allow users to analyze datasets from diverse angles based on different business perspectives.

    6. The Role of Artificial Intelligence in Business Intelligence

    Artificial Intelligence (AI) plays a crucial role in Business Intelligence (BI) by helping organizations analyze large amounts of data, identify patterns, and make strategic decisions. Here are some key ways AI supports BI:

    1. Data analysis: AI-powered algorithms can process and analyze large volumes of structured and unstructured data much faster than humans. This ability enables organizations to gain insights from their data more effectively and make data-driven decisions.

    2. Predictive analytics: AI can use historical data and patterns to analyze and predict future trends, customer behavior, and market dynamics. This helps businesses plan and strategize for the future by identifying potential risks and opportunities.

    3. Personalization: AI enables businesses to create personalized experiences and recommendations for their customers by analyzing their preferences, behavior, and historical data. This can enhance customer satisfaction, increase sales, and boost customer loyalty.

    4. Automation: AI-powered tools help automate routine tasks and mundane data processing, freeing up valuable time for employees to focus on more strategic and creative tasks. This improves operational efficiency and productivity.

    5. Natural Language Processing (NLP): AI can understand and process human language through techniques like NLP, enabling businesses to extract valuable insights from unstructured data such as customer reviews, social media posts, and emails. NLP also enables chatbots and virtual assistants to provide efficient and personalized customer support.

    6. Fraud detection: AI can analyze transactional data and identify patterns of fraudulent activities or suspicious behavior. It helps businesses detect and prevent fraud in real-time, minimizing financial losses and reputational damage.

    7. Market intelligence: AI algorithms can monitor and analyze vast amounts of online and offline data sources, including social media, news articles, and competitor information. This provides organizations with valuable insights about market trends, customer sentiment, and competitor strategies, enabling them to stay ahead of the competition.

    Overall, AI augments and enhances the capabilities of Business Intelligence by providing advanced data analysis, prediction, automation, personalization, and real-time insights. This helps businesses make informed decisions, improve operational efficiency, and gain a competitive edge in their respective industries.

    You may be interested in AI Writer: ChatSonic vs ChatGPT – Revolutionizing Content Creation – Click Virtual University (clickuniv.com)

  • Optimization of K-means clustering using Artificial Bee Colony Algorithm on Big Data

    Afroj Alam1* (alamafroj@gmail.com)

    Department of Computer Application Integral University, Lucknow(U.P) Inida, Sambhram University Jizzax Uzbekistan

    Mohd Muqeem2

    Department of Computer Application Integral University, Lucknow(U.P) Inida

    Introduction:

    Bee Colony Algorithm
    Bee Colony Algorithm

    From past few decades, there rapid development of the advanced technology and IoT based sensor devices which resulted with an explosive growth in data generation and storage. The amount of data which is generated is constantly growing even exponential growing and thus cannot be predicted or even cannot find the hidden information traditional way. Indeed, many new applications producing this huge amount of data, especially those where users can write, upload, post and share a lot of data, information and videos, such as social media sites like Facebook, twitter, telegram, instagram where every second every minutes huge amount of image, video and data are post and shares . Accordingly, as mentioned in [1], it is approximately up to 45 Zeta bytes digital data we have up to 2020. In the Current information technology world, this huge amount and the massive volume of data with more attributes is called “High dimensional Big Data”. A lot of important frequent-pattern, meaningful information and valuable hidden pattern can be extracted from this huge amount of data, which help the organization for improving the business intelligence, decision-making, fraud detection etc. K-means clustering is a most important and powerful un-supervised partitioning machine learning techniques for division of this big data into homogenous group i.e. cluster [2][7][8].

    There are lot of limitation of K-means in big and high dimensional data: it converges to the local optimal solution, no of cluster is to be defines in advance, initialization of clusters centroid, lack of quality of clusters [3]. We have proposed a hybridized K-means with nature inspired Artificial Bee Colony global optimization algorithm that resolve the limitation of K-means clustering.

    Nature inspired optimization:

    There are lot of Population-based meta-heuristic Evolutionary Algorithms (EAs) global optimization algorithms which are inspired by the natural behaviour of the population evolution such as Genetic Algorithm, Artificial Bee Colony (ABC), Artificial Ant Colony and particle swarm based intelligence algorithm.

    Artificial Bee Colony

    ABC is a global optimization met-heuristic algorithm which is inspired by the intelligent behaviour of honey bees. This algorithm is popular due to its flexible computational time. In our proposed method we use the ABC algorithm for the initialization and selection of cluster centroids [6].

    This algorithm is executed in 4 steps as given below:

    • Initialization
    • Employed Bee
    • On-looker Bee
    • Scout bees

    The objective function of Artificial Bee Colony (ABC) algorithm is designed as according to the optimal number of selection of clusters for K-means.

    Bee Colony Algorithm

    The population of ABC is initialized by equation 1. in which i=1,2,3,…….,BN, here BN defines the total number of food sources and value of j=1,2,3,………,D. D is the number of dimensions. The upper and lower bounds of the variable j is  xmin,j and xmax,j.

    Updation of the bees location is as given below

    Bee Colony Algorithm

    In above equation r ∈ 1, 2,3, ·····,BN and j ∈ 1, 2, ·····, D are indexes and Φ is a random generated number in between [−1, 1]. If new solution is better than old solution i.e. equation (2), than old solution will replaced by new one.

    Bee Colony Algorithm

    The of each solution is computed by where f iti is a probability fitness value of the i th solution. If fitness of new solution is higher than old solution than old will replaced by new solution.

    Proposed methodology:

    In our proposed methodology we hybridized the K-mean with ABC (ABK) comes up with the plan that K-means algorithm provide the new solution of scout bees in every iteration. The K-means generate the new solutions as according to the employed bee and onlooker bee steps. In this way we can get more optimized results. The new solution of K-means will be added in every iteration improve the accuracy for reaching ABC to higher level.

    The new solution from the K-means is generated according to the solutions of the employed bee and the onlooker bee phases. This process may increase the chances of giving more suitable solutions for the optimization problem. The addition of new solution from K-means after every cycle may enhance the reach of ABC algorithm to a different level. Our proposed idea finds the fi values from the given below distance formula.

    distance=min(ii,jj)         (4)

    The fitness function is the calculated be the given equation as the sum of all the distance i values.

    Bee Colony Algorithm
    Bee Colony Algorithm

    In the above equation the population will be survived according to the better fitness otherwise it will reject [5].

    TABLE 1[4]   COMPARATIVE ANALYSIS BASED ON INTRA CLUSTER DISTANCE

    Bee Colony Algorithm

    Reference

    1. Ilango, S. S., Vimal, S., Kaliappan, M., & Subbulakshmi, P. (2019). Optimization using artificial bee colony based clustering approach for big data. Cluster Computing, 22(5), 12169-12177.
    2. Alam, A., Muqeem, M., & Ahmad, S. (2021). Comprehensive review on Clustering Techniques and its application on High Dimensional Data. International Journal of Computer Science & Network Security, 21(6), 237-244.
    3. Saini, G., & Kaur, H. (2014). A novel approach towards K-mean clustering algorithm with PSO. Int. J. Comput. Sci. Inf. Technol, 5, 5978-5986.
    4. Krishnamoorthi, M., & Natarajan, A. M. (2013, January). A comparative analysis of enhanced Artificial Bee Colony algorithms for data clustering. In 2013 International Conference on Computer Communication and Informatics (pp. 1-6). IEEE.
    5. Bharti, K. K., & Singh, P. K. (2014, December). Chaotic artificial bee colony for text clustering. In 2014 Fourth International Conference of Emerging Applications of Information Technology (pp. 337-343). IEEE.
    6. Enríquez-Gaytán, J., Gómez-Castañeda, F., Moreno-Cadenas, J. A., & Flores-Nava, L. M. (2020, November). A Clustering Method Based on the Artificial Bee Colony Algorithm for Gas Sensing. In 2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) (pp. 1-4). IEEE.
    7. Alam, A., Rashid, I., & Raza, K. (2021). Application, functionality, and security issues of data mining techniques in healthcare informatics. In Translational Bioinformatics in Healthcare and Medicine (pp. 149-156). Academic Press.
    8. Alam, A., Qazi, S., Iqbal, N., & Raza, K. (2020). Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use. Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications, 1-26.

  • Python for Financial Analysis

    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

    UnitTitleDetails of Topic
    Unit IGetting Started with PythonThis 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 IINumpy, General Overview of Pandas and MatplotlibNumpy for financial analysis, General overview of pandas and visualization with matplotlib and pandas. Pandas with timeseries data. Capstone stock market analysis project.
    Unit IIITime series analysisTime Series basic, Introduction to basic Stat Model, ETS theory, EWMA Theory, ARIMA theory, ACF and PACF theory, ARIMA with stat model.
    Unit IVPython finance fundamentals, Calculating and Comparing Rate of Returns, Measuring Investment RiskIntroduction 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 VUsing 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. NoTitleDetails of TopicDuration
    Unit 1Getting Started with PythonInstalling 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 2Numpy, General Overview of Pandas and MatplotlibIntroduction 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 
      Pandas visualization overview, Pandas Timeseries visualization 
    Unit 3Time Series AnalysisIntroduction 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, 
      ETS theory, EWMA Theory, EWMA Code along, ETS Code along. 
      ARIMA theory, ACF and PACF, ARIMA with Statmodels, Discussion choosing PDQ. 
    Unit 4Python Finance Fundamentals, Calculating and Computing Rate of Returns. Measuring Investment RiskWelcome 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 5Using regression in python, Markowitz portfolio optimization, Capital Assets Pricing Model, Multivariate regression analysis, Monte Carlo SimulationsThe 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.

    S.No.NameCertificate Link
    1. Mohamed Hareesudeen
    email: mohamedharees007@yahoo.com    		Link
    2. Dr. Mohammed Mujahed Ali
    email: mubarak_mujahed@yahoo.co.in    		Link
    3. Md Abrar Alam
    email: mdabrar1994@gmail.com    		Link
    4. Abuzar Nomani
    email: abuzarnomani88@gmail.com    		Link
    5. Rithi S R
    email: rithi.satheesan@gmail.com    		Link
    6. HARISH R
    email: krharish2727@gmail.com    		Link
    7. Monika Mishra
    email: monikabarsha200@gmail.com    		Link
    8. Ali Thabit Yahya Al Qaser
    email: ali.ust77@gmail.com    		Link
    9.Tamer Elsheikh
    email: tamer.elshiekh@com.kfs.adu.eg    		Link
    10.Faozi Abduljalil Gazem Al-Maqtari
    email: faozi@umt.edu.my    		Link
    11.Waleed Mutahar Al-ahdal
    email: wm.alahdal2011@gmail.com    		Link
    12.Ayesha Siddiqui
    email: ayesha.siddiqui91@gmail.com    		Link
    13. Najib Hamood Saif Farhan
    email: Najib720000@gmil.com    		Link

  • How to Become a Business Analyst? What is the difference between Business Analytics and Data Analyst.

    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.

     

    Read Also: Sentiment Analysis

    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:

    Software Engineer: ₹267k–1,000k/year

    Software Developer: ₹215k–1,000k/year

    Business Analyst: ₹274k–1,000k/year

    Senior Software Engineer: ₹495k–2,000k/year

    Senior Software Developer: ₹438k–2,000k/year

    IT Consultant: ₹391k–2,000k/year

    Data Scientist: ₹342k–2,000k/year