Information is the oil of the 21st century, and analytics is the combustion engine.
– Peter Sondergaard
Are you looking for a career in Big Data? Or do you simply want to understand Data Science vs Business Analytics? This question is important and needs to be addressed because there are a lot of organizations that define these roles and use them interchangeably. Data Science is more widely known than Business Analytics yet its definition is blurred due to the practice of working with the wrong definition.
In this article, we will understand the comparative analysis of Data Science and Business Analytics. So, read along to gain deep insights into these two fields.
Table of Contents
What is Data Science?
Data Science comprises various fields, including statistics, scientific techniques, Artificial Intelligence, and data analysis, to extract valuable information from data. The individuals who practice Data Science are called Data Scientists, and they join a scope of abilities to analyze gathered data (web data, cellular data, clients data, sensor data, or different sources) to significant and actionable data-driven insights.
Data Science involves preparing data for analysis (cleansing, aggregating, and manipulating the data) to perform efficient data analysis. Analytic applications and Data Scientists would then be able to audit the outcomes to uncover patterns and empower business leaders to draw educated insights.
What is Business Analytics?
Data is what you need to do analytics. Information is what you need to do business.
– John Owen
Business Analytics is all about driving the business value of an organization and focuses on identifying the changes to an organization that is required to achieve strategic goals. Business Analytics includes Data Management and Business Intelligence. Business Analytics uses data mining, predictive analytics, and statistical analysis techniques to analyze and transform data into helpful information, recognize and expect trends and outcomes, and at last make smart data-driven business choices.
The essence of Business Analytics comprises descriptive analytics– which analyzes historical data to decide how a unit may react to a set of variables, predictive analytics– which takes a gander at historical data to decide the probability of specific future outcomes, or prescriptive analytics– the blend of the descriptive analytics and predictive analytics measure, which gives understanding on what occurred, what may occur, using which you can expect what will occur when it will occur, and why it will occur.
Read along to gain a basic understanding of the differences between Data Science and Business Analytics.
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Data Science vs Business Analytics
1. Data Science vs Business Analytics: Definition
Data Science
Data science is a field of study that combines domain knowledge, programming skills, and knowledge of mathematics and statistics to derive meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to build artificial intelligence systems that perform tasks that normally require human intelligence. These systems provide insights that analysts and business users can turn into tangible business value.
Business Analytics
Business Analysis is a collection of disciplines and technologies for solving business problems using data analysis, statistical models, and other quantitative methods. This includes an iterative and systematic survey of your organization’s data, with an emphasis on statistical analysis to drive decision-making.
2. Data Science vs Business Analytics: Disciplines
Data Science
Data Science generates data-driven insights that help organizations increase their operational efficiency, identify new business/market opportunities, improve their marketing and sales efforts, etc. This always gives you a competitive edge in the market.
Disciplines involved in Data Science:
- Data engineering and Warehouse engineering
- Data mining
- Predictive analytics
- Machine Learning and Deep Learning
- Data and Database Management
- Data Visualization
- Business Intelligence
- Statistical Analysis
- Data Analytics, etc
Business Analytics
Business Analytics will include identifying business needs, using historical data, coming up with solutions, etc. These solutions usually are new system development, optimizing processes, strategic planning, etc.
Disciplines involved in Business Analytics:
- Requirements Elicitation and Analysis
- Business Modelling
- Solution assessment
- Workflow Modelling
- Data Analysis, etc
To be more clear, Business Analytics uses all the technologies/disciplines that help in identifying requirements, analyzing data, discovering insights, and developing an action plan/model and implementation.
3. Data Science vs Business Analytics: Use Cases & Problem Solved
Data Science
- Healthcare: Data Science uses different approaches to help you comprehend the disease, practice preventive medicine, analyze diseases quicker, and investigate new treatment choices. Data Science also assists you in investigating clinical trial data and symptoms so specialists can analyze and predict diseases quicker which saves a lot of time required for diagnosis.
- Fraud Detection: Traditional fraud detection tasks include fraud screening groups working to understand the credibility of a person/process through verifiable data. Data Science and algorithms and AI-powered software have empowered industries by limiting fraud-related activities.
- Operational Efficiency: Data Science equipped with factual demonstration and algorithms make optimal routes for personnel/drivers dependent on climate, traffic, development, and so on. Data Science is saving logistic organizations up to 39 million gallons of fuel and in excess of 100 million conveyance miles every year.
- Entertainment: Using Data Science, the music streaming monster Spotify can predict and curate personalized song suggestions dependent on the music genre or band you’re at present into. Likewise, Netflix data mines the film you are watching to suggest movies that might be of your interest.
Business Analytics
- Customer Relationship Management (CRM): Incredible customer relations are basic for any organization to retain customers in business for the long haul. CRM systems collect significant personal customer data like demographics, buying patterns, socio-economic information, and lifestyle.
- Manufacturing: Business analysts work with information to assist stakeholders with understanding the things that influence operations and their primary concerns. Recognizing things like equipment downtime, inventory levels, and maintenance costs assist organizations with streamlining their inventory management and production.
- Marketing: The efficiency of promotion efforts, the amount of social media presence, etc can be understood by Business Analysts and they assist by estimating marketing and promoting metrics, distinguishing consumer behavior and the target audience, and dissecting market patterns.
- Finance: The financial world is very unstable. Business Analysis assists with removing factors that lead to precarious territories and helps businesses move cautiously. It also helps organizations to enhance planning, banking, financial planning, estimating, and portfolio management.
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4. Data Science vs Business Analytics: Type of Data
Data Science
Data Science deals with two types of data: traditional, and big data. Traditional data refers to structured data and data stored in databases(numeric or text values). Big data includes a variety of data(numbers, text, but also images, audio, mobile data, etc.), velocity (retrieved and computed in real-time), and volume (measured in Terabytes, Petabytes, and Exabytes).
Business Analytics
The data used by Business Analytics is mostly structured data. This data is historical business data that helps in understanding the factors that affected/might affect your business.
5. Data Science vs Business Analytics: Roles & Responsibilities
Data Science
When data is processed properly, it can be a blessing to businesses. Data Science is the study of data, its origins, value, and transformations in order to derive useful insights. Businesses rely on massive volumes of data, which traditional Business Intelligence tools can’t handle all at once. This is where Data Scientists come into the picture.
A data scientist does model-driven analyses of our data; analyzes to improve our planning, increase our productivity, and develop our deeper levels of subject matter expertise. A data scientist works at the tactical, operational, and strategic levels, sharing insights with the business.
– Chris Pehura, Practice Director, Management Consultant at C-SUITE DATA
Data Scientists are Analytical Experts with a strong background in technology who work with data to analyze it and extract meaningful information. Data Scientists are in charge of data gathering, analysis, and interpretation. They analyze large volumes of data, form theories, and look for patterns in the data.
Some of the roles & responsibilities of a Data Scientist are:
- Responsible for evaluating Big Data and presenting insights.
- Should be able to deal with large amounts of data to unearth insights that will help businesses make smarter decisions.
- Interact with company stakeholders in a clear and concise manner.
Business Analytics
By using Data Analysis, Business Analytics serves as a link between IT Services and Businesses. Business Analysts work at all levels of a corporation, and their duties range from developing a strategy to building the Enterprise Architecture, as well as serving as a leader in the designing of programs and setting project goals and objectives.
To generate future predictions, Business Analysts employ complex quantitative tools and numerous modeling methodologies. To produce insights, anticipate trends, and make business choices, Business Data Analytics uses approaches such as Data Mining, Predictive, and Statistical Analysis.
Some of the roles & responsibilities of a Business Analyst are:
- Creating a thorough business study that outlines a company’s issues, prospects, and solutions.
- Identifying and resolving any risks that may arise.
- Supporting the testing process and ensuring that the implementation fulfills client requirements.
6. Data Science vs Business Analytics: Skills
Data Science
A Data Science professional must have a stronghold on programming, linear algebra, computer science fundamentals, and basics of Machine Learning concepts. The fundamental skills required by Data Science professionals:
- Statistical Analysis: For a great sense of pattern and anomaly identification, you need to be conversant with statistical tests and likelihood estimators.
- Multivariable Calculus & Linear Algebra: Building a Machine Learning model necessitates this extensive mathematical understanding.
- Computer Science & Programming: Massive datasets are a normal sight for Data Scientists to work upon. You’ll need to build software applications to resolve problems. You should be familiar with programming languages like Python, R, and SQL.
- Machine Learning: You should be conversant with Machine Learning algorithms and statistical models that automatically enable a computer to learn from data.
- Data Visualization & Storytelling: After you’ve collected your data, you’ll need to present your results. Data Scientists convey and describe meaningful findings to both technical and non-technical audiences using visuals and dashboards.
Business Analytics
Professionals in the field of Business Analytics must be able to provide business simulations and plans. Analyzing business trends would be a big part of their job. The following are the main skills necessary for Business Analytics professionals:
- Interpretation: Businesses are in charge of handling a large quantity of data. You should be able to clean data and make it usable for interpretation as a Business Analyst.
- Analytical Reasoning Ability: A Business Analyst needs logical reasoning, critical thinking, communication, investigation, and data analysis to address business challenges by using different analytic techniques in business use cases.
- Mathematical & Statistical Skills: In Business Analytics, the competence to gather, organize, and understand numerical data is utilized for modeling, inference, estimating, and forecasting.
- Data Visualization & Storytelling: A Business Analyst employs visual components such as charts, graphs, and maps to help individuals see and comprehend data trends, outliers, and patterns.
- Written & Communication Skills: If you have good communication skills, it becomes easy to influence the management team to make improvements and increase business opportunities.
7. Data Science vs Business Analytics: Programming Language
Data Science
R and Python are popular basic programming languages in data science, but the choice of the right language to learn depends on your level of experience, role, and/or project goals. Apart from these two other programming languages such as Scala, Julia, Java, JavaScript, Matlab, C/C++, and SQL are also used in the Data Science field.
Business Analytics
The two most common programming languages for analysis are R used for statistical analysis and Python used for general programming. Knowledge of any of these languages can be advantageous when analyzing large amounts of data, but it is not required. In addition to the languages listed above, for managing and analyzing the data statistical software such as SPSS, SAS, Sage, Mathematica, and even Excel are used.
8. Data Science vs Business Analytics: Tools
Data Science
The most commonly used tools in Data Science:
- Apache Spark
- SAS
- BigML
- D3.js
- Matlab
- Excel
- Tableau
- Jupyter Notebook
- Tensor Flow
- R
- Python
- Natural Language Toolkit, etc
Business Analytics
Business Analytics uses the following tools for business assessment, analysis, modeling, and collaboration:
- Microsoft Office Suite
- Rational Requisite Pro
- SWOT
- Pencil
- Trello
- Smart Draw
- Balsamiq
- Jira
- Microsoft Visio
- Google Docs
- Python
- SAS
- R
- Tableau, etc
9. Data Science vs Business Analytics: Career Path
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Data Science
Data scientists have strengths in programming, math, and research skills that require continuous learning along their career paths. Business analysts, on the other hand, need to be more strategic in thinking and have strong project management skills. See the following Career Paths to see more detailed paths from the beginning of your data science.
Business Analytics
Business analysts tend to move to business roles, strategic roles, and entrepreneurial roles throughout their careers, while data scientists have a strong technical background that makes them more technically inclined. See the following Career Paths to see more detailed paths from the beginning of your business analytics journey.
10. Data Science vs Business Analytics: Job Opportunities
Data Science
Data Science experts are needed in almost every job sector and not just in technology domains- this you can understand by looking at the use cases. However, in order to get employed in these high-paying, in-demand roles at Tech giants an advanced education/degree is generally required.
In-demand Roles:
- Data Scientist
- Machine Learning Engineer
- Applications Architect
- Data Engineer
- BI developer
- Data Analyst, etc
Business Analytics
Business analysts recruiters generally looking to fulfill various specialists like- IT business analysts, data analysis scientists, system analysts, business analyst managers, and computer science data analysts.
In-demand Roles in the Business Analytics domain:
- IT Business Analyst
- Data Analysis Scientist
- Business Analyst Manager
- Quantitative Analyst
- Data Business Analyst
Hope you have finally gained a detailed understanding of the differences between Data Science and Business Analytics. You can also refer to the Data Science and Business Analytics Simplified 101 guide for deeper insights.
Data Science vs Business Analytics Summary
| Parameter | Data Science | Business Analytics |
| Definition | Data science is a field of study that combines domain knowledge, programming skills, and knowledge of mathematics and statistics to derive meaningful insights from data. | Business Analysis is a collection of disciplines and technologies for solving business problems using data analysis, statistical models, and other quantitative methods. |
| Disciplines | Disciplines involved in Data Science are Data engineering and Warehouse engineering, Data mining, Predictive analytics, Machine Learning, and Deep Learning, Data and Database Management, Data Visualization
Business Intelligence, Statistical Analysis, Data Analytics, etc. | Disciplines involved in Business Analytics are requirements elicitation and analysis Business Modelling Solution assessment, Workflow Modelling,
Data Analysis, etc. |
| Use Case & Problem Statement | Data Science can be used in Healthcare, Fraud Detection, Operational as well as in Entertainment sectors. | Business Analytics can be used in Marketing, Customer Relationship Management, Manufacturing, and Finance sector. |
| Type of Data | Data Science deals with both structured and non-structured data. | Business Analytics deals with mostly structured data. |
| Roles and Responsibilities | Data Scientist is responsible for evaluating Big Data and presenting insights, dealing with large amounts of data to unearth insights that will help businesses make smarter decisions. | A Business Analyst is responsible for creating a thorough business study that outlines a company’s issues, prospects, and solutions, identifying and resolving any risks that may arise. |
| Skills | The fundamental skills required by Data Science professionals are Statistical analysis, Multivariable Calculus & Linear Algebra, Computer Science & Programming,
Machine Learning,
Data Visualization & Storytelling. | The key skills required by Business Analyst are Interpretation, Analytical Reasoning Ability, Mathematical & Statistical Skills, Data Visualization & Storytelling, and Written & Communication Skills. |
| Programming Language | R and Python are popular basic programming languages in data science. Apart from these two other programming languages such as Scala, Julia, Java, JavaScript, Matlab, C/C++, and SQL are also used in the Data Science field. | The two most common programming languages for analysis are R used for statistical analysis and Python used for general programming. In addition to the languages listed above, for managing and analyzing the data statistical software such as SPSS, SAS, Sage, Mathematica, and even Excel are used. |
| Tools | The most commonly used tools in Data Science are
Apache Spark, BigML,
Matlab, Tableau
Jupyter Notebook, Tensor Flow, R, Python, and Natural Language Toolkit. | The most commonly used tools in Business Analytics are Microsoft Office Suite, Smart Draw, Balsamiq, Jira
Microsoft Visio, Google Docs, Python, SAS, R, and Tableau.
|
Career Path
| Different Career Path
in the Data Science field are Data Analysts, Data Engineers, Business Intelligence analysts, Data Mining engineers, Data architects, and Data scientists. | Different Career Paths for
Business Analysts is Sr. Business Analyst, Analytics Manager, Analytics Industry Leader, Strategy Leader, etc. |
| Job Opportunities | In-demand roles in the field of Data Science are Data Scientist, Machine Learning Engineer, Applications Architect
Data Engineer, BI developer, Data Analyst. | In-demand Roles in the Business Analytics domain are IT Business Analyst, Data Analysis Scientist, Business Analyst Manager,
Quantitative Analyst,
Data Business Analyst. |
Conclusion
In this blog post, you have discussed in detail Data Science and Business Analytics. You now have a good understanding of Data Science vs Business Analytics.
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