Sheet Metal Fabrication Process in Electronics & Communication Parts Manufacturing

The Sheet Metal Fabrication Process holds an integral position in manufacturing electronic and communications components. This comprehensive guide takes you on a journey through...

Hair Care Essentials: Choosing the Right Shampoo for You

Are you searching for hair care products that won't leave your wallet crying but still give you that salon-fresh look? You're in the right...

Data Science V/s Business Analytics – Key Differences

Introduction

In today’s world, data is the driving force behind a successful business model. How a business can leverage and manage data determines the level of its success. Companies and organizations now have to perform extensive investigations and analysis of the data they generate to understand and serve their customers better. It also helps them understand how their customers interact with the products and services of their business. To achieve this, specific skill sets are required to understand data patterns and the ability to interpret them, figure out the contributions of the data towards the growth of a business, and how to adjust certain aspects of business. This can be carried out by both business analysis and data science professionals.

The terms data scientists and business analysts have been used interchangeably since both involve working with big data. While both these professions offer excellent career opportunities, their working methodologies are not the same. If you are interested in making a career in either of these professions, it is vital to know the difference between them.

In this article, we have discussed the differences between data science and business analytics to obtain deeper insights into the two professions. Before delving into the differences between data science and business analytics, let’s understand what both of these fields encompass.

Understanding Data Science

Data science involves integrating mathematical and statistical expertise with programming skills to extract valuable insights from unstructured data sets. Data science professionals, much like business analysis professionals, extract, format, analyze, and maintain large sets of data. However, they usually work at the entry point of the collection of data as well as its analysis. The ultimate goal of data scientists is to be able to provide well-informed and data-driven predictions through analysis of past trends. Attending some of the best online Data Science Bootcamp can not only help you understand data science in depth but also help you build solid tech skills and land a job as a skilled Data Scientist for a lucrative career and the numerous opportunities ahead!

Roles & Responsibilities of Data Scientists

Data scientists usually work with various algorithms, which range from assembly-level algorithms to algorithms of higher-level machine learning for identifying patterns in business data. These trends in data are beneficial for predicting future trends or customer behaviors. These can also be used to make different hypotheses, as well as test them and subsequently prove or reject the hypotheses based on test results. This helps data scientists make accurate predictions that help achieve business goals.

Some of the roles and responsibilities of a data scientist are:

  • They process and analyze Big Data to present valuable insights from such data.
  • They should be skilled in managing huge data to provide insights that will be helpful for businesses in making essential and smarter decisions.
  • They should also be able to deal with the shareholders of the company clearly and concisely.

Skills Required To Be A Data Scientist

A data scientist must have a strong understanding of various concepts of statistics, fundamentals of computer science, and the fundamental concepts of artificial intelligence and machine learning. The main skills necessary to become a data scientist include the following.

  • Statistics & statistical analysis: Hypothesis testing and analysis are important aspects of data analysis which use different statistical tools, likelihood estimation tools, etc. Data scientists should have knowledge of such statistical tools to be able to provide correct hypothesis analysis of data.
  • Multivariable statistical tools: Data scientists should be comfortable with using a Machine Learning (ML) model that necessitates their understanding of statistical and mathematical concepts.
  • Programming & computer science: Programming skills are essential for data science professionals as they have to work with various levels of algorithms. They may need to study or enhance these algorithms thoroughly from the perspective of computer science applications. Additionally, they need to analyze and assess business data to find patterns using their programming skills. Programming languages, such as R, Python, C, and SQL, are important for data scientists to be familiar with.
  • Machine learning: Machine learning has benefitted in unraveling useful information using data at an unprecedented rate, making ML an indispensable tool in data science. Data Scientists are required to have hands-on knowledge of machine learning and be able to work with different ML algorithms to analyze data whenever required.
  • Data visualization: Data visualization provides a way to represent useful data in a manner that a layman can understand. Due to this, data scientists must also have skills in data visualization for transforming unstructured data into easily comprehensible information.

Important Tools for Data Science

The most commonly used tools in Data Science:

  • R
  • Python
  • Tableau
  • Microsoft Office Suite
  • Matlab
  • SAS
  • BigML
  • Jupyter Notebook
  • Natural Language Toolkit, etc

Disciplines Under Data Science

With the help of data science, insights based on data can be generated that help increase the efficiency of business operations and identify new business opportunities, among other important business efforts. This helps data scientists earn a competitive edge over their peers.

The different disciplines involved in the field of data science include:

  • Machine Learning and Artificial Intelligence
  • Data Analytics
  • Statistical Analysis
  • Data mining
  • Predictive analytics
  • Data engineering and Warehouse engineering
  • Data Visualization

Career Opportunities for Data Scientists

The field of Data Science is rapidly gaining traction, due to which the number of opportunities in this field is plenty. A data scientist can work in an enterprise of any size or even offer their services independently as a freelancer. The common roles of a data scientist in the industry include:

  • Data analyst
  • Data Engineer
  • Product Manager

A data scientist may also be needed to make reports to help higher-level management to understand how well their business is performing financially in comparison to their competitors within the industry or even globally across all industries.

Salary Prospects of Data Scientists

The average annual salary of a data scientist may range anywhere between Rs. 7 lakhs and Rs. 19 lakhs [Ref]. This may vary depending on their knowledge and experience in programming languages like Python, SQL, etc. Their experience in the relevant industry may also be an important determinant of their salary. Therefore, learning programming languages may increase earning potential.

Understanding Business Analytics

Business analytics is a superset of disciplines and technologies that involve processes to solve problems of a business by using various statistical tools and models for analyzing data. It is the practice of analyzing, organizing, and making changes within a company by identifying problems and finding and suggesting solutions that help provide value to shareholders.

A business analyst leads the way within an enterprise by defining a plan at an enterprise to provide new business opportunities. They analyze data to develop actionable business plans and also find shortcomings in various business models that help decision-makers understand the performance of a company while also predicting future trends and performance. Their goal is to find the best ways to improve processes and enhance productivity by using data, technology, and analytics.

Roles & Responsibilities of Business Analysts

Business analysts serve as a bridge between the information technology department and the company’s business by using their skills in data analysis. They work at an organizational level, and they are responsible for developing strategies as well as building enterprise architecture. They also serve as leaders in the design of programs as well as in the process of laying down objectives and setting organizational goals for a project.

Business analysts use advanced quantitative tools along with various modeling tools to generate future predictions in business. They use approaches like data mining, data prediction, and statistical analysis tools for generating insights, anticipating trends, and making important business decisions.

The roles and duties of a business analyst include the following.

  • They identify and resolve any risks that may arise during business operations.
  • They are responsible for creating an elaborate business plan that documents the problems, and solutions of an enterprise.
  • They also support the process of implementation testing and ensure that they meet the requirements and expectations of clients.

Skills Required to be a Business Analyst

Business Analysts should be able to provide actionable plans for the growth of a business. Here are some of the top skills required to become a business analyst.

  • Data interpretation: Business analysts must understand and interpret the ever-increasing pile of data that businesses generate, obtain, and manage on a daily basis. A business analyst should be capable of processing and representing data in a manner that helps to find insights.
  • Analytical reasoning: Business analysts should be able to make quick decisions to address the various challenges in a business by using various processes of analysis of use cases in business. To be able to do this, business analysts require critical thinking, logical thinking, analytics, etc.
  • Statistical and mathematical skills: The ability to properly describe the data is important for business analytics. This requires knowledge of relevant statistical and mathematical tools. This skill is also required for modeling, inferring, estimating, or forecasting based on the current data.
  • Communication skills: Business analysts should also have good interpersonal communication skills since they fill the gap between two important domains. With good speaking skills, it becomes easier for business analysts to persuade decision-makers to make modifications and enhance business operations.
  • Storytelling and visualization: Business analysts need the capability to utilize visual elements such as charts, graphs, and maps for effectively conveying data trends, outliers, and patterns to individuals. Serving as a liaison between IT and business, they facilitate seamless communication of findings and conclusions to all relevant parties.

Important Tools For Business Analysis

The tools used for modeling, and analysis purposes by business analysts include the following.

  • Microsoft Office Suite
  • Microsoft Visio
  • SWOT
  • R
  • SAS
  • Pencil
  • Smart Draw
  • Python
  • Rational Requisite Pro
  • Jira
  • Trello
  • Balsamiq, etc.

Disciplines Involved in Business Analysis

Business analytics includes identifying business needs using historical data to come up with new solutions for a business. These solutions involve new system development, optimizing processes, strategic planning, etc.

The different disciplines involved in the field of business analytics:

  • Requirements Elicitation and Analysis
  • Business Modeling
  • Solution assessment
  • Workflow Modeling
  • Data Analysis, etc.

Career Opportunities for Business Analysts

Business analysts often work closely with members of other departments within a company, such as marketing, sales, IT, finance, and human resources, to deliver high-quality products and services to the enterprise’s customers. The jobs of a business analyst are available at almost all levels of an organization. Some of the common positions in which a business analyst may be able to work include:

  • Junior Analyst
  • Enterprise Analyst
  • Enterprise Architect

Some companies may even offer leadership development programs to help employees scale up their careers by moving up through various management-level positions in the company.

Salary Prospects of Business Analysts

In India, the average salary of business analysts may be in the range of Rs. 5 lakhs to Rs. 12 lakhs per annum, with an annual average of Rs 8 lakhs[Ref]. This salary of a business analyst may vary depending on the company they are working at, years of experience they have, knowledge of any particular programming languages or techniques, etc.

Data Science vs Business Analytics – Major Differences

Data Scientists and Business Analysts need to have a deep understanding of data gathering, modeling, and insight representation. The difference between the two is that business analysis relates to specific business-related problems like cost, profit, etc. At the same time, data science helps answer questions like seasonal factors, the influence of geography, and customer preferences on the business. In recent developments, data science can be taken to the next level using Machine Learning and Artificial Intelligence. Business analytics, on the other hand, is the analysis of company data using statistical concepts to get new insights and solutions.

Here is a tabular representation of the key differences between data science and business analytics.

Aspect Data Science Business Analytics
Primary Focus Using advanced statistical analysis techniques to find patterns in the data they collect. Making decisions based on their knowledge of an organization’s operations and needs.
Goals Provide well-informed, data-backed predictions by studying previous trends. Best ways to improve processes and enhance productivity.
Tools & Technologies R, Python, SQL, C, SAS, Apache Spark, etc. R, SAS, Python, Microsoft Office Suite, etc.
Skill Sets Required 1.     Statistics & statistical analysis

2.     Machine learning

3.     Programming & computer science

4.     Linear algebra & Multivariable calculus

5.     Data visualization

1.     Data interpretation

2.     Storytelling and visualization

3.     Analytical reasoning

4.     Statistical and mathematical skills

5.     Communication skills

Educational Background ●      3+ years’ experience in data analytics, data science, or a bachelor’s degree in mathematics, computer science, or statistics.

●      Experience in machine learning models.

●      Having a degree in computer applications like MCA, or a degree in management, like an MBA from a reputed institution.

●      Having at least 4-5 years of experience in a related field.

Decision-making Framework Decisions can range from customer segmentation strategies to optimize manufacturing processes. Data visualization for future outcome projecting helps in decision-making and planning for the future.
Use Cases ●      Healthcare

●      Fraud Detection

●      Operational Efficiency

●      Entertainment

●      Customer Relationship Management (CRM)

●      Manufacturing

●      Marketing

●      Finance

Career Trajectory Data Scientist, Sr. Data Scientist, Lead Data Scientist, Chief Data Scientist Business Analyst, Analytics Lead, Analytics Manager and Sr. Business Analyst
Industry Application ●      Technology

●      Financial

●      Mix of fields

●      Internet-based

●      Academics

●      Financial

●      Technology

●      Mix of fields

●      CRM/Marketing

●      Retail

Collaboration With IT team With IT team
Challenges ●      Business decision-makers may not use Data Science results.

●      Inability to apply findings to the organization’s decision-making process.

●      Lack of clarity on the questions that need to be answered with the given data set.

●      Unavailability of data or difficulty in accessing data.

●      Lack of significant domain expert input.

●      Biased data.

●      Inability to access significant data or unavailability of such data.

●      Privacy concerns

●      Lack of funds to source useful datasets from external sources.

●      Limitations of business analytics tools.

●      Need to coordinate with IT.

Future Trends Machine Learning and Artificial Intelligence Cognitive Analytics, Tax Analytics

 

Conclusion

Given the recent developments in the field of data analysis, a major shift can be expected in the fields of data science and business analytics. With the rapidly growing influence of Big Data, businesses will have the opportunity to explore different data to help management make important decisions. Not only financial analysis but also the analysis of customer behaviors contribute to the growth of a business. Data forecasting is another important aspect of a business that can help management understand where they may stand in the near future to make confident business decisions. With changing data and learning trends, both Data Science and Business Analytics are extremely inviting and innovative fields. Both Data Science and Business Analytics can offer prosperous career opportunities. To begin a career in these fields, you must obtain a certification by undergoing training.

FAQs

  • Which has more scope: data science or business analytics?

Business Analytics mainly focuses on business-oriented problems by using well-known and established methods to solve those problems. Data Science, on the other hand, involves finding the best way to predict future results using various algorithms. Data Science can be considered to be a superset of Business Analytics. Thus, there is greater scope in Data Science than in Business Analytics.

  • Who gets paid better: business analyst or data analyst?

Data scientists have higher education and a higher degree of specialization. Therefore, they demand a higher salary than business analysts.

  • Does business analytics involve coding?

To practice as a business analyst, knowledge of coding is not mandatory.

Latest Posts