Data analytics has a big impact on business and management, and it has several advantages for students studying these fields. Here are seven justifications for why data analytics is crucial for management and business students.
Data analytics provides students with the ability to analyze and interpret data to make data-driven decisions. It equips them with the skills to evaluate information, identify trends, and draw insights that can guide strategic decision-making in business and commerce.
In today’s data-driven world, organizations that leverage data analytics gain a competitive edge. Students proficient in data analytics can help companies analyze market trends, consumer behavior, and competitors, enabling them to develop effective strategies and stay ahead of the competition.
Data analytics helps students identify inefficiencies and bottlenecks in business operations. By analyzing data, students can identify areas for improvement, streamline processes, and optimize resource allocation, leading to increased operational efficiency and cost savings.
4.Market Research and Customer Insights:
Data analytics allows students to analyze customer data, conduct market research, and gain insights into consumer preferences, buying patterns, and trends. This information is invaluable for businesses to tailor their products, services, and marketing strategies to meet customer needs and preferences.
Data analytics helps students in managing risk by analyzing historical data, identifying patterns, and predicting potential risks and challenges. This enables businesses to develop risk mitigation strategies, enhance decision-making under uncertainty, and minimize potential losses.
Data analytics is essential for students studying finance and commerce. It helps in financial analysis, forecasting, and budgeting by analyzing financial data and identifying key performance indicators. This information assists in making sound financial decisions and optimizing financial performance.
7.Supply Chain Optimization:
Students in management and commerce can use data analytics to optimize supply chain operations. By analyzing supply chain data, they can identify areas for improvement, optimize inventory levels, reduce costs, and enhance overall supply chain efficiency.
8.Marketing and Sales Optimization:
Data analytics provides students with the tools to analyze marketing and sales data, measure campaign effectiveness, and optimize marketing strategies. It helps in identifying target markets, evaluating marketing channels, and personalizing marketing efforts to drive sales and customer engagement.
9.Entrepreneurship and Startups:
For students interested in entrepreneurship or starting their own business, data analytics is crucial. It helps in market analysis, identifying potential opportunities, understanding customer needs, and making informed business decisions to create a successful venture.
Proficiency in data analytics opens up a wide range of career opportunities for students in management and commerce. There is a growing demand for professionals who can effectively analyze and interpret data to drive business growth, leading to diverse career paths in fields such as business analytics, data science, market research, financial analysis, and consulting.
In summary, data analytics offers students of management and commerce the ability to make data-driven decisions, gain a competitive advantage, improve efficiency, and enhance various aspects of business operations. It equips them with valuable skills that are highly sought after in today’s data-centric business landscape, opening up numerous career opportunities.
A data Analytics for commerce students
A Big Yes, Commerce students could do data analytics. It will require some investment, yet with little exertion reliably, anybody ready to learn Data analytics could learn it. Data Analytics is the field where huge amounts of data (both organized and unstructured) are gathered, refined, handled to bring significant information for stakeholders. A Commerce student could turn into a data analytic. For that, there are sure prescribed things that should be done to find some work as a Data Analylist.
Data science is all about working with the existing data and identifying trends and patterns to comprehend the data so that the company can make the best decisions possible. Even if your background is in business, you may pursue a career in data science by attending Vista Academy and obtaining all the required skills.
A B.Com. student can learn Python, right?
Anyone may enrol in a Python course. It is much more helpful for those studying business. We must have the tools necessary to extract information from the growing amount of data being created.
Can a student studying commerce use machine learning?
Students studying commerce may also apply for these. The information and abilities needed for use in the workplace will be provided to the students. These involve using AI-driven technologies, data analytics, machine learning, deep learning, design, and programming.
What is the starting pay for a data scientist in India?
What does a Data Scientist make when they first start out in India? In India, the beginning salary for a data scientist is around 4.5 Lakhs (or 37.5k) per year. For a Data Scientist, one year of experience is the absolute minimum.
Skill set and education
- To begin with, you should be talented, and that implies you want to have strong information in Excel, SQL, programming progarmming like R, Python, and so forth and information perception programming like Tableau, and so on
When you get gifted, the following thing is to do as numerous modern standard tasks, you could do.
- Then, getting a degree in an important field like math, statistics, IT, and so on will give you an edge while going for the Entry level employment, in the vast majority of the organization.
Data analytics gives students from any stream to explore new career opportunities. Now the need of skill is more important than degree and practical training over rote learning .
Even a simple job need a computer skill.
Data is the future. Businesses today heavily rely on data-driven facts and statistics to make
In today’s highly competitive world, there is no longer scope for assumptions. So businesses base their decisions on the data that is engineered and analyzed by market experts.
Data analytics allows affordable and accessible solutions to those who aim to learn new skills while managing their regular study or work schedule.
Most of the non-tech students enrolled in our training have either pursued or were pursuing a Bachelor of Business Administration (B.B.A.) or Bachelor of Commerce (B.Com).
Why to learn Data analytics ?
Data analysis is the need of the hour -It is very important for every business to run smoothly, a data-driven approach is a must.
In future every company will be tech company and data is engine to run it.
Finance professionals and commerce students have an edge in learning analytics. They have strong quantitative
skills and a solid understanding of statistics. They also have a robust understanding of business operations and the financial health of companies. Their understanding of finance and management gives them a leg up
on the competition. Numeracy does, after all, lie at the heart of analytics as well as business development.
Advantage of being a commerce student for studying data analytics
- Your finance background can be an invaluable asset if you become a data scientist.
- Your familiarity with numbers and business can be your secret weapon.
- They can help you identify correlations that could escape others.
- Your understanding of financial data is a big plus too
Analytics is deployed extensively in the field of finance.
what are data analytics key skills ?
In general, investment professionals in data science and other fintech careers need the following skills
to be successful:
A problem-solving mindset is an asset to any organization. It is more than just a skill.
A person with a problem-solving mindset sees a problem as an opportunity to grow and is motivated to
find solutions, thereby focussing on growth and achieving positive results.
A business strategy is a set of competitive moves and actions that a business uses to attract customers,
compete successfully, strengthening performance, and achieve organizational goals.
Communication skills involve listening, speaking, observing and empathizing.
Quantitative analysis (QA) is a technique that uses mathematical
and statistical modeling, measurement, and research to understand behavior.
Data anaytics technical skills?
Python is especially popular among data scientists. … There are countless libraries like NumPy, Pandas, and Matplotlib available in Python to make data cleaning, data analysis, data visualization, and machine learning tasks easier
As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data. R is used for data analysis. R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling. R is an environment for statistical analysis
Data visualization is defined as a graphical representation that contains the information and the data. By using visual elements like charts, graphs, and maps, data visualization techniques provide an accessible way to see and understand trends, outliers, and patterns in data.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Artificial intelligence (AI)
AI-powered systems can analyze data from hundreds of sources and offer predictions about what works and what doesn’t. It can also can deep dive into data analytics about your customers and offer predictions about consumer preferences, product development, and marketing channels.
SQL is the most commonly used data analysis tool for data analysts and data scientists. The majority of the world’s data is stored in databases, and learning SQL will enable you to access and analyze this data with ease.
What is data architecture in data analytics?
Image result for database architecture for data analytics
Data architecture is a framework for how IT infrastructure supports your data strategy. The goal of any data architecture is to show the company’s infrastructure how data is acquired, transported, stored, queried, and secured. A data architecture is the foundation of any data strategy. … Data standards.
Role of accountant in Data analysis .
You will have an impact on how the business makes decisions.
While the majority of employees feel that their lack of decision-making authority contributes to job discontent, data professionals do not.
It will be a very gratifying career because you will play a special function inside the organisation and be a crucial component of corporate policies and future strategies.
Professionals in data analytics are earning more money.
Prospects estimates that entry-level pay for data analytics specialists is in the region of £24,000 to £25,000. Salaries can increase to between £30,000 and £35,000 after a few years of experience, and high-level consultants and professionals may make £60,000 or more.
Role of Accountant
The role of accountants in data analytics is evolving as businesses increasingly rely on data-driven decision-making. Accountants play a crucial role in analyzing financial data, identifying patterns and trends, and providing valuable insights to drive business performance. Here are some key roles and responsibilities of accountants in data analytics:
Data Analysis and Interpretation:
Accountants are responsible for analyzing financial data to identify trends, anomalies, and patterns that can provide insights into business performance. They use data analytics tools and techniques to interpret the data and extract meaningful information for decision-making.
Financial Planning and Forecasting:
Accountants utilize data analytics to forecast financial outcomes, assess risks, and develop financial plans. They analyze historical financial data, market trends, and other relevant factors to create accurate financial projections and assist in strategic planning.
Cost Analysis and Optimization:
Accountants use data analytics to analyze cost structures, identify cost drivers, and optimize resource allocation. By analyzing data, they can identify areas of cost savings, recommend efficiency improvements, and assist in budgeting and cost control efforts.
Fraud Detection and Risk Management:
Accountants leverage data analytics to detect fraudulent activities and manage financial risks. They use techniques like anomaly detection, data visualization, and trend analysis to identify unusual patterns that may indicate fraud or financial irregularities.
Performance Measurement and Key Metrics:
Accountants analyze data to measure and assess the financial performance of an organization. They develop and monitor key performance indicators (KPIs) to evaluate business performance, assess profitability, and track financial health.
Compliance and Regulatory Reporting:
Accountants use data analytics to ensure compliance with financial regulations and reporting requirements. They analyze financial data to identify discrepancies, ensure accuracy, and prepare reports in accordance with applicable accounting standards and regulations.
Process Improvement and Automation:
Accountants apply data analytics to improve financial processes and enhance efficiency. They identify opportunities for automation, streamline workflows, and leverage technology to reduce manual effort and increase accuracy in financial reporting and analysis.
Business Insights and Decision Support:
Accountants provide valuable insights to support strategic decision-making. By analyzing financial and operational data, they help management understand the financial implications of different courses of action, assess investment opportunities, and evaluate the financial viability of projects.
Collaboration with Data Analysts and IT Professionals:
Accountants collaborate with data analysts and IT professionals to leverage their expertise in data analytics. They work together to access, analyze, and interpret relevant data, ensuring data quality, and implementing appropriate data analytics techniques.
Continuous Learning and Skill Development:
Accountants need to stay updated with advancements in data analytics tools and techniques. They continuously develop their skills in data analysis, data visualization, and data manipulation to effectively utilize data analytics in their accounting roles.
Overall, accountants who embrace data analytics are well-positioned to provide valuable insights, improve financial decision-making, and contribute to the overall success of the organization. By leveraging data analytics techniques, accountants can enhance their traditional accounting responsibilities and become strategic partners in driving business performance and growth
Data analytics for bankers
Data analytics is essential to the banking sector because it enables bankers to use data for better risk management, operational efficiency, and decision-making. The following are some essential uses of data analytics for bankers:
Customer Segmentation: Data analytics helps bankers analyze customer data to identify segments based on demographics, behavior, and needs. This segmentation enables personalized marketing campaigns, targeted product offerings, and improved customer experiences.
Customer Lifetime Value (CLV) Analysis: By analyzing historical customer data, bankers can determine the CLV for individual customers. This information helps prioritize high-value customers, develop retention strategies, and allocate resources effectively.
Fraud Detection: Data analytics helps identify suspicious patterns and anomalies in transactional data, enabling bankers to detect and prevent fraudulent activities. Advanced analytics techniques can identify potential fraud in real-time, minimizing financial losses.
Credit Risk Assessment: Data analytics enables bankers to assess creditworthiness by analyzing various factors such as credit history, financial ratios, and borrower characteristics. Predictive models and machine learning algorithms help make accurate credit risk assessments for loan applications.
Anti-Money Laundering (AML) Compliance: Banks are required to monitor transactions for potential money laundering activities. Data analytics helps in identifying unusual patterns, transaction flows, and suspicious activities, ensuring compliance with AML regulations.
Regulatory Compliance Reporting: Data analytics automates the process of collecting, analyzing, and reporting data required for regulatory compliance. It ensures accuracy, reduces manual effort, and helps banks meet reporting deadlines effectively.
Risk Management: Banks analyze large volumes of data to assess and manage various risks, such as market risk, credit risk, and operational risk. Data analytics provides insights into risk exposures, portfolio performance, and risk mitigation strategies.
Operational Efficiency: By analyzing operational data, banks can identify bottlenecks, streamline processes, and improve operational efficiency. Data analytics helps optimize resource allocation, reduce costs, and enhance overall productivity.
Fraudulent Claim Detection: Insurance banking operations often involve processing claims. Data analytics helps identify potentially fraudulent claims by analyzing claim patterns, historical data, and other relevant factors. It helps reduce fraudulent payouts and improves claim processing efficiency.
Financial Forecasting and Planning: Data analytics assists bankers in financial forecasting, budgeting, and strategic planning. By analyzing historical financial data, market trends, and economic indicators, banks can make accurate predictions and informed financial decisions.
In summary, data analytics empowers bankers to make data-driven decisions, manage risks effectively, detect fraud, improve operational efficiency, and enhance customer experiences. By leveraging the power of data, bankers can gain valuable insights that drive profitability, improve compliance, and create a competitive advantage in the dynamic banking industry.
Vista Academy analytics programme
This course is designed for people with absolutely no background in analytics, statistics or programming. It will introduce you to the wonderful world of analytics in a fun and easy to learn manner.
Q AND A
Data analytics equips students with the skills to analyze business data, make informed decisions, and develop strategies for better outcomes in areas like marketing, finance, and operations.
Data analytics helps students understand consumer behavior, market trends, and competition, aiding in designing effective marketing strategies and identifying growth opportunities.
By analyzing financial data, students can make accurate forecasts, assess investment opportunities, manage risk, and optimize budgeting and resource allocation.
Students can use data analytics to optimize inventory levels, predict demand fluctuations, enhance logistics efficiency, and reduce operational costs.
Data-driven insights help students identify strengths, weaknesses, opportunities, and threats in the market, leading to well-informed strategic decisions.
Students can analyze customer data to personalize offerings, improve customer satisfaction, and predict customer needs, leading to stronger brand loyalty
They acquire skills in data interpretation, statistical analysis, data visualization, and using analytical tools, making them proficient in translating data into actionable insights.
Certainly. For instance, Netflix uses data analytics to recommend personalized content, and Amazon employs it for inventory management and targeted marketing.
Yes, many employers seek candidates with data analytics skills, as businesses increasingly rely on data-driven decision-making to stay competitive
They can take VISTA AC ADEMY courses, workshops, or pursue degrees that offer data analytics modules. Hands-on experience with tools like Excel, Python, or specialized software is beneficial.