Everyone is fascinated by data science in the 21st century, and it’s the most trending job to do. When the companies realised the potential of big data, ML and AI demanded Data Scientists and the role skyrocketed. Let’s dive into the details of Data Science and Decision Science, their job role and the connection between them and their usage in industry and finally, Data Science vs Decision Science. we’ll clear your confusion between the two and try to give you clarity in choosing a career path. If you are planning to pursue a course in Data Science course in Singapore then you are at the right place.
Definition of Data science
You have read many definitions of Data Science. Let me simplify it for you. Analysing Data is called Data Science. Yes, you heard it right. Data science analyses a lot of data with tools and techniques to uncover hidden information and help make effective business decisions. Complex machine learning algorithms are needed to create predictive models in Data science.
Data is the primary tool for a Data Scientist to develop and improve products. He analyses the Data statistically, and his primary goal is to understand and build better products. Therefore the quality of data, measurement perfection and statistical rigour are their trademarks. They conduct deep analysis and experimental statistics and think about data processing, patterns, and experimental statistics.
Definition of Decision science
Decision science is a modern field of study which gained Popularity in the last
10 years across the globe. It is the study of how to make decisions effectively with the information you have. It mainly concentrates on understanding the underlying business problems deeply. Decision science is built upon data science by integrating behavioural sciences, business context, and design thinking.
It is also a collaborative field focusing on psychology, economics, applied probability, machine learning, operations research, statistical decision theory and cognitive psychology.
Data science vs decision science
Decision science involves interpreting data and making decisions, while DataScience mainly involves analyzing business data. Data science and Decision Science combined solve business problems. Suppose a company needs to introduce a new product or update an existing product. In that case, the Data Scientist analyses the customer database and finds out what the customer needs are and which products are selling high. Decision scientists will provide the solution to get the best results.
Some of the fundamental differences between Data Science and Decision Science are:
- A Data Scientist will analyze business Data using statistical and analytical methods to find hidden patterns in the Data, which helps predict future events. Their primary role is to analyze and have the ability to communicate the analyzed data to others.
- A Decision Scientist will have a comprehensive view of the data, and his main priority is the business problem and a clear picture of it. Decision Scientists need to have analytical ability and business acumen.
Their Purpose and Application
- The Data Scientist will deliver insights into the unstructured/ structured data. Data science plays a major role in different sectors like education, finance, e-commerce, banking, manufacturing, etc. they generally work with big data.
- Decision Scientists will focus on revealing data-driven insights. Decision Science is applied in policy-making, business, health care, etc. Decision Science relies on small parts of data.
Their View on Data
- Here Data is important for both, but they differ in analysing and making decisions.
- Data Scientists consider data as a tool for creation. Whereas for Decision scientists, Data is used to make better decisions. They figure out numerous ways to analyse data and solve business problems.
- Data scientist mostly focuses on finding insights, and Decision Scientists focus on discovering those insights.
- A Data Scientist will focus on previous data trends to make better decisions and aims to create models for business problems. They will present the information using data visualisation tools.
- A Decision Scientist will start forming values which focus on the client’s needs and predict future outcomes.
Data science scope uses challenges
A recent report of WEF future of Work Report 2020 predicted that by 2025, the Data Scientists job will be the highest demanding and growing one. Let us look at the Data Science job roles and career growth.
For freshers, Data Science jobs include business analyst, statistician and Data architect. For experienced ones, roles include Big Data Engineer, Machine learning Engineer, Data Scientist etc. the salary for beginners will start around 2 Lakh and goes upto 20 Lakhs depending on the role and experience. Below is the career path from beginner to experienced level
- Data Analyst
- Data Scientist (entry-level)
- Associate data scientist
- Data Scientist (senior-level)
- Product Manager
- Lead data scientist
Challenges in Data science include
- Multiple Data Sources: using different CRM and ERPs for gathering data. They gather data depending on their requirements, and heterogeneous Data is difficult to understand, and it’s hard for a data scientist to reach a conclusion.
- Data Security: information theft has become a major hurdle for businesses, and securing their Data is a hectic task. Hence organisations use different methods like confidentiality, accessibility, integrity etc., to secure their data.
- Undefined KPIs and Metrics: If the management and Data scientist’s coordination often leads to a decrease in their performance, it ultimately may not reach their expectations. So every organisation must have appropriate business KPIs and Well defined metrics.
- Lack of skilled personalities: An organisation’s main challenge is finding a skilled Data scientist. Many companies have a misconception about Data scientists as companies think they should be the jack of all trades in maintaining data, but it is not an easy task for a data scientist. It should be done in teams rather than single-handedly.
Decision science scope uses challenges
The future is data-driven, and companies rely on data, so the need for Decision Scientists is abundant. The Decision Scientist’s role is to focus on customer needs and provide solutions for future needs. There is a need for complex knowledge of analysis, techniques applied, maths, lack of accurate data, and problems while dealing with complex data environments. The roles of Decision scientists in an organisation include
- Business Analyst
- Software Developer
- Data Scientist
- Data Analyst
Challenges in Decision Scientist include:
- Lack of skilled personality: A lack of skilled personalities is an obstacle for companies. Finding a perfect Decision Scientist for their company is an arduous task as there are limited resource personnel in the industry.
- Best methods: A Decision Scientist should have a 360-degree view of the company’s product and the user’s requirements. They should have a solid and analytical mindset. Sometimes business tactics will not work and can not rely entirely on measurement, so thinking out of the box will make them ahead of others, and they have to do the right things.
- Experimentation: A Data Scientist sometimes has to do experiments with data and sometimes not to get the desired results.
- Accuracy of the Data: When companies rely on different data sources, it’s a hectic task to conclude with multiple data sets. Obtaining reliable and accurate Data is always challenging. Data Scientists must have in-depth knowledge of their product.
In this blog, we have discussed Data scientists and Decision scientists roles, challenges, and scope in the industry so far. One thing to remember is if we need to run a successful business, we need both a Data scientist and Decision Scientist. Their contribution to the company is enormous. The roles of these are interconnected. Choose a career path that suits you, whether Data Scientist or a Decision Scientist, you will have a good role and career in the company and industry by using your skills effectively. Hope I made a clear understanding and distinction for you between data science and decision science.