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Understanding the Career Choice Between Data Science and Business Analytics

What is data?

How do we make use of data?

What is the real-world application of data?

Unlocking the world of data!

Ever since the invention of computer, the term data was used to denote the information in a computer. Before the development of computing devices, people used to collect data and analyse it, and impose pattern to it manually. Today, in the world of technology, computers are widely used for collecting, and sorting data in many disciplines like marketing, social services and scientific research. These sorted data are used for improving processes, products, services and software.

Table of Content
What is Data? Relationship between Data Scientist and Business Analyst The Data Science Hierarchy of Needs Difference between Data Scientist and Business Analyst How Hierarchy Pyramid Affect the Career of Individual Data Scientist? Industry Requirement of Data Science Top 10 Data Science Courses in India Top 10 Job Roles in the World of Data Science Frequently Asked Questions

What is Data?

Data is a set of values that exist in different forms like numbers or text on a piece of paper, bits and bytes in electronic memory and facts in human memory. Data has a meaning only when it is placed in a context. The terms data and information are used interchangeably but it has a vast difference in its meaning. Data is a concrete concept and information is a more abstract concept. For example, the depth of Indian Ocean is a data, wherein, the etymology of Indian Ocean is an information.

Computers use different types of data stored in digital format like text, numbers, multimedia. Data Scientist and Business Analyst deals with these data. A Data Scientist uses scientific methods and algorithm to extract knowledge and insights from structured and unstructured data. A business analyst guides in improving businesses by analysing business model data and its integration with technology.

Looking forward to becoming a Data Scientist? Check out the Data Science Bootcamp Program and get certified today.

Relationship Between Data Scientist and Business Analyst

The terms Data Scientist, Data Engineer and Business Analyst are deeply intertwined with each other.

The data scientist is an analytical expert who utilizes his skills to gather and analyse large set of structured and unstructured data.

Data engineer is the one who is in charge of managing data workflows, pipelines and ETL processes and provide a reliable infrastructure for data.

Business analyst is the one who bridges the gap between IT and the business. They analyse an organization, determine requirement and deliver data driven recommendations.

In other words, the science of data that uses statistics, algorithms and technology is called as Data Science. And, the statistical study of the structured data is known as Business Analyst.

The Data Science Hierarchy of Needs

The Data Science Hierarchy of Needs is a framework which helps in understanding the career role to choose from Data Scientist to Business Analyst. This is the key guide that connects your current skills and where you want to go with your career in future.

Data Collection

At the bottom of the pyramid is data collection. What data is available, what data do we need, what data is coming through and how? These questions are analysed and collected at this stage. A data driven organization starts with basic data collection like logging errors, recording transactions and digitizing analogue data.

Data Flow

Next stage analyses on how the data flow through the system. It also analyses where you store it and how easy it is to access and analyse those data. This stage starts with basic data-organization like transforming, cleaning and storing data. A matured company may build a data ETL pipeline, data warehouse or a data lake at this stage.

Data Transform

Exploring, transforming and data cleaning happens in the next stage. This stage helps you to identify the missing data, misinterpretations and unreliable data. It starts with basic data analysing tools like reports and dashboards to explain what is happening and why is it happening in the organization. Matured companies incorporate data mining, diagnostic analytics and descriptive analytics at this stage.

Data Label

The most interesting data stories are found in this stage. It defines the matrix to track, do user segmentation and start to predict or learn training data by generating labels. This stage will help you to predict what will happen in the future. Companies may incorporate predictive analytics and prescriptive analytics and machine learning at this stage in their data science pipeline.

Data Learning

Deep learning stage where you deploy a simple ML algorithm and think of new features and signals that might affect your results.

Data Automation

The final stage where you are ready to go ahead and explore your instrumented, organised and cleaned data. Your data are ready for AI. Data science loop is closed, and humans are removed from the process at this stage.

How this pyramid affects the career of individual data scientist?

Every company and every sector are different. Firms decide on advance research or machine learning based on the business they are in. Like research, companies differ in their staffing strategies as well.

Though many companies prefer generalist to own the large portion of the project, matured companies prefer specialist for each layer of the pyramid. The specialist model gets better results at each stage. This model requires huge communication overhead as each portion is covered by different people. Generalist model helps people to build data science products quicker. However, it is a challenge to have someone with such diverse background.

Difference Between Data Scientist and Business Analyst

From the above pyramid it is clear that both Business Analyst and Data Science involves data gathering, modelling and insight gathering, yet both are different. Below is a detailed comparison between data Science and Business Analytics.

The term

The term Data Science was coined by DJ Patil and Jeff Hammerbacher in 2008. The term Business Analytics has been used since the late 19^th century and the term was introduced by Frederick Winslow Taylor.

Concept

Data Science study about data using statistics, technology and algorithm. Wherein, Business Analytics is the study of business data.

Data Used

When Data Science use both structured and unstructured data, Business Analytics use mostly structured data.

Skills

A Data Scientist should be an expert in skills like computer science fundamentals, programming, and linear algebra. As the main role of a Business Analyst is to analyse the trends in business, the skills required are business simulations and business planning.

Coding

Data Science involves lot of coding but Business Analytics does not have much coding involved. It is mostly statistics oriented.

Tools

The common tools used by Data Scientist are R, Python, scikit-learn, and PyTorch. Business analyst use tools like Excel, Tableau, SQL and Python.

Use

Data Science results give insights but can’t be used for daily business decision. Business Analytics results are key decision makers.

Future Trend

The future trend of Data Science is Machine Learning and Artificial Intelligence. Wherein the future application of Business Analytics would be in Cognitive Analytics and Tax Analytics. 

Industry Requirement of Data Science

Data helps us achieve major goals which required a great deal of time and energy just a few years ago. Data Science is used in different fields for automation, classification, forecasting, recognition and recommendations. Below are few areas in which data science is used to innovate and create new products.

Cybersecurity

Data Science is used in almost every industry. But it is most important in cybersecurity. Data Scientists identify potential cybersecurity threats with the help of machine learning and predict the risk based on past behaviour patterns.

Healthcare

Medical practitioners are finding new ways to understand diseases, diagnose faster and develop preventive medicines and explore new treatment options.

Logistics

Data Science is used in logistics industry both internally along its delivery routes. They create optimal routes for drivers based on weather, traffic, construction, etc.

Entertainment

Entertainment apps and software like Netflix, Spotify and Instagram use data for finding out what their viewers might like and suggest them what to watch or listen next.

e-Commerce

e-Commerce giants like Amazon use data for improving customers shopping experience. They use collected data for determining customer behaviour and shopping pattern and suggests personalized product recommendation.

Top 10 Data Science Courses in India

Data Scientist and Business Analysts are currently the most in-demand professionals. A career in Data Science requires analytical, statistical and a set of unique soft skills. Data Science course will equip you with the skills and information to pursue a career in this field.

Job Roles in the World of Data Science

The hot new field of Data Science is revolutionizing industries from business to government, health care to academia. There is quite a wide variety of roles involved in data which include research oriented, engineering and business oriented. To sustain in today’s world of massive data, it is very essential to collect, clear and analyse data. Data literacy and strategic thinking are the key skills required for this field.

Students and professional need to be adaptable and need to constantly aim at learning new skills. Data Science and Business Analytics courses are considered as hot opening as the data and the learning trends are changing in a fast pace.

Data Scientists, as they have strong technical background prefer tech entrepreneur roles. On the other hand, Business Analyst prefer business, strategic and entrepreneurship roles.

Top job roles in Data Science
Data Analyst Data Engineer Data Scientist Database Administrator Machine Learning EngineerData Architect Business Analyst Statistician Data and Analytics Manager Product Analyst

There is a massive career scope in the field of data. Students who are curious about data and professionals who are thinking of making a shift to data roles can consider Data Science and Business Analytics. An Under Graduate or Post Graduate course in Data Science and Business Analytics helps you with hands-on practical learning with case studies and projects.  

No single role is better or more important than other. There are data scientists who can fill in nearly all parts of the pyramid. There are also specialists who have in-depth knowledge in one part of the pyramid. Think about what part of data pyramid will have the best impact on you and strive for the skills to fit in to that particular role.

“Data is the new oil. We need to find it, extract it, refine it, distribute it and monetize it” – David Buckingham

Frequently Asked Questions

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