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.
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.
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.
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.
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.
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.
Deep learning stage where you deploy a simple ML algorithm and think of new features and signals that might affect your results.
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 Data Science was coined by DJ Patil and Jeff Hammerbacher in 2008. The term Business Analytics has been used since the late 19th century and the term was introduced by Frederick Winslow Taylor.
Data Science study about data using statistics, technology and algorithm. Wherein, Business Analytics is the study of business data.
When Data Science use both structured and unstructured data, Business Analytics use mostly structured data.
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.
Data Science involves lot of coding but Business Analytics does not have much coding involved. It is mostly statistics oriented.
The common tools used by Data Scientist are R, Python, scikit-learn, and PyTorch. Business analyst use tools like Excel, Tableau, SQL and Python.
Data Science results give insights but can’t be used for daily business decision. Business Analytics results are key decision makers.
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.
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.
Medical practitioners are finding new ways to understand diseases, diagnose faster and develop preventive medicines and explore new treatment options.
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 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 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.
- Post Graduate Diploma in Business Analytics (PGDBA)
PGDBA is 11 months to two-year full-time diploma course. The course is designed for those who have an analytical mindset, are interested in tackling exciting business challenges, and have an inclination towards numbers. The course is open for fresh graduates and experienced working professionals. Even Though the course is open for graduates, experts with minimum 2-10 years’ experience will be preferred to understand how managers use business analytics to solve problems and challenges in business. Major papers covered in the course includes Business Analytics, Data Mining and Predictive Analysis, Analytics with R, Business Analytics using SAS, SQL, Excel, and Python.
- Post Graduate Diploma in Data Science
PGDDS is a job-ready course spread over 11 months to 2 years. Both graduates and professionals can apply for this course. The course is designed to provide broad understanding of the basic and advanced concepts of data science with hands-on training on Big Data techniques using Excel, R, SQL, NoSQL, Tableau, Hadoop, and Hive. It includes instructor led classroom session accompanied with assignments and case studies followed by a three-month internship. During the period students learn about exploratory Data Analysis, Machine Learning, Data Visualization, Artificial Intelligence and Big Data Technologies.
- Master of Science in Data Science
A Master of Science in Data Science is an interdisciplinary program to provide scientific methods and process to extract knowledge from data. The eligibility requirement to the programme is a Bachelor degree in Science, Technology, Engineering or Mathematics (STEM) stream. Prior quantitative coursework is an added advantage for the process of admission. The advanced degree programme teaches concepts like Applied Statistics, Database System and Data Preparation and Practical Machine Learning. It also covers programming languages like Python, SQL and R.
- Master of Science in Business Analytics
MS in Business Analytics help students to develop skills needed to transform large amount of data into actionable business strategy insights. The course is open for fresh graduates from IT, Engineering and Mathematics background and working professionals in IT, Analytics, Big Data, Machine Learning and Analytics. The course is apt for professionals looking for developing their skills in statistical analysis to support decision-making. The main topics covered are Programming, Data Analytics and Management and Data Models and Visualization. The course is recommended for students who are interested in building their career in intelligent strategies and technologies to support collection, analysis, presentation and distribution of business data in enterprises.
- Master’s in Information System
Master’s in Information System equips you with new technologies and skills needed to manage information system, and evaluate technical approaches and risks. The topics covered in this course includes Information Systems Analysis and Design, Business Telecommunication and Managing Emerging Information Technology.
- Post Graduate Program in Data Science and Engineering
PG Data Science and Engineering course duration varies from 5 months to 11 months. The course is designed for fresh graduates looking to build a career in data science and analytics. They learn relevant data science techniques and tools for roles such as business analyst, data analyst and data engineer. They are also trained on programming languages like Python and SQL.
- MTech Data Science and Artificial Intelligence
MTech Data Science and Artificial Intelligence is a 2-year full-time course designed to make professionals in the domain of Data Science and AI. The course is open for BTech / BE / MCA graduates and for graduates with PG Diploma in Data Science. Candidates gain practical knowledge in programming and statistical techniques for Data Science along with Data Scrapping and Data Wrangling. They also expertise in Cloud Computing, Machine Learning, Deep Learning, Advance AI and Big Data Technologies.
- PGDM Big Data Analytics
PGDM Big Data Analytics is a two-year full-time course offered to students with flair for numbers and computers. The programme is designed to create future-ready data managers who are equipped to manage the new model of data-driven decision making. The main topics covered are business intelligence systems like machine learning, artificial intelligence, deep learning and data management including machine learning.
- MBA Data Science and Data Analytics
MBA in Data Analytics is a two-year full-time course. Unlike most of the other Data Science Course, MBA Data Science is open for Arts and Commerce graduates as well. The eligibility criteria is a bachelor’s degree in any stream but, the students should be comfortable in subjects like Mathematics and Computer Programming. The course focuses on skills needed to handle data analytics life cycle and business problems through visualization. The main covered in this course are Information Security in Business, Predictive Analytics, Big Data, Cloud Computing, E Business and Data Visualization.
- MBA in Business Analytics
MBA in Business Analytics is a two-year full-time multidisciplinary degree. It focusses on functional process of business such as operating day to day business and planning and executing it through market analysis. The course train candidates on how to study and interpret data and how to use this data in business operation.
Considering the hike in number of professionals taking Data Science Course, many online short courses has been introduced. These courses provide professionals an opportunity to undergo non-credit based courses to earn a certificate. Similar to the long-term courses, these courses offer sequential and progressive content. Main advantage of a short-term course is being able to work at individual’s pace, with flexibility to complete coursework along with work schedule.
Some of the short-term online course include Artificial Intelligence, Machine Learning, Cybersecurity, Business Analytics, Data Analytics, Marketing Analytics, etc.
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|
Machine Learning EngineerData Architect
Data and Analytics Manager
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
- What is the difference between MS in Data Science and MBA in Data Science?
The difference between both lies in focus. MS concentrates on technical side and MBA concentrates on the applied side of data analysis. If you are IT professional, MS prepare you to build Something new. Wherein, MBA is for the one who enjoys client facing and who measures something which is already there and try to optimise it better.
- Can I learn Data Science online?
There are large number of business schools offering online courses both in Data Science and Business Analytics. The curriculum offered are very much similar to the full-time on-campus courses. Professionals who are looking for a certificate course along with their work life can opt for an online course, which is a better option for them.
- Can an Arts Graduate pursue a career in Data Science?
Yes, Arts and Commerce Graduates can do an MBA in Data Science and Business Analytics provides he has a strong interest towards Mathematics and Computer.
- Is it mandatory to have work experience to learn Data Science?
No, it is not mandatory to get enrolled in a Data Science course. But most of the Institutions prefer candidates with minimum two-year work experience. A candidate with work experience can understand the concepts in a better way than a fresh graduate.
- How long does it take to be a data scientist?
The duration to become a data scientist depends on your career goals and the amount of time and money you are ready to spend on your education. There are many options available for Data Science courses like three-month bootcamps, three to four yearlong bachelor degree, and one to two yearlong post-graduate degree.