Management science (MS) is the broad interdisciplinary study of problem solving and decision making in human organizations, with strong links to management, economics, business, engineering, management consulting, and other fields. It uses various scientific research-based principles, strategies, and analytical methods including mathematical modeling, statistics and numerical algorithms to improve an organization's ability to enact rational and accurate management decisions by arriving at optimal or near optimal solutions to complex decision problems. Management science helps businesses to achieve goals using various scientific methods.
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The field was initially an outgrowth of applied mathematics, where early challenges were problems relating to the optimization of systems which could be modeled linearly, i.e., determining the optima (maximum value of profit, assembly line performance, crop yield, bandwidth, etc. or minimum of loss, risk, costs, etc.) of some objective function. Today, management science encompasses any organizational activity for which a problem is structured in mathematical form to generate managerially relevant insights.
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Management science is concerned with a number of areas of study:
Management science research can be done on three levels:[2]
The management scientist's mandate is to use rational, systematic, science-based techniques to inform and improve decisions of all kinds. The techniques of management science are not restricted to business applications but may be applied to military, medical, public administration, charitable groups, political groups or community groups.
The origins of management science can be traced to operations research, which became influential during World War II when the Allied forces recruited scientists of various disciplines to assist with military operations. In these early applications, the scientists used simple mathematical models to make efficient use of limited technologies and resources. The application of these models to the corporate sector became known as management science.[3]
In 1967 Stafford Beer characterized the field of management science as 'the business use of operations research'.[4]
Some of the fields that management science involves include:
as well as many others.
Applications of management science are abundant in industries such as airlines, manufacturing companies, service organizations, military branches, and in government. Management science has contributed insights and solutions to a vast range of problems and issues, including:[1]
Management science is also concerned with so-called soft-operational analysis, which concerns methods for strategic planning, strategic decision support, and problem structuring methods (PSM). At this level of abstraction, mathematical modeling and simulation will not suffice. Therefore, since the late 20th century, new non-quantified modelling methods have been developed, including morphological analysis and various forms of influence diagrams.
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Data Science (DS) has given us a unique insight into the way we look at data. There is a huge demand for Data Scientists who can extract useful insights out of large and complex datasets to influence business decisions. This is the right time to make the career transition from Software Developer to Data Scientist. You are at leverage for your next role with your passion and vision for data, backed up by your programming background and problem-solving attitude to business challenges.
“A career transition from Software Developer (SD) to Data Scientist (DS) requires 3 aspects:
A Software Developer builds an enterprise software program. Manages end-to-end Software Development Life Cycle (SDLC) in a cross-platform agile environment.
Job responsibilities:
Skills:
Many of the tasks already mimic that of a Data Scientist.
Data Scientist is a nerd who uses their analytical, statistical, and programming skills to collect, analyze, interpret and visualize large data sets.
They develop data-driven solutions to complex business challenges and make future predictions that affect business decisions.
They usually have a degree in Math, Statistics, Computer Science, or the research field. A Master’s or Ph.D. is a plus.
As a Software Developer, you already have 2/3rd of the equation in place to become a Data Scientist, you:
Shweta Bhatt, a Data Scientist at Youplus, talks about how her Software Developer background helped her career transition –
“As a Developer, your programming skills are going to be valuable, as you would be integrating your ML models (solutions) with the product. Knowledge of how the industry works using SDLC is an advantage.”
It’s essential to question yourself why you intend to be a Data Scientist? Is it the hype on various business magazines and job sites? Or is it the salary and career growth? Or does the nature of work excite you? The answer might be a collective yes, however, staying focused and consistent is the key.
For the remaining 1/3rd part of the equation, you need to –
A shift from C programming to Python helped Shweta develop insights and interest towards datasets that inspired her to indulge in ML and DS courses. She further did a Master’s in AI and at present works on ML and NLP.
Shweta says for non-technical professionals, domain knowledge is an added advantage, as DS is a multidisciplinary field.
It is not tough to shift career from a Sofware Developer to Data Scientist, says Shweta. Clearing the myth about tools she says – “DS or AI is not all about tools. It is essential to understand and apply the concepts. Tools are essential to implement solutions and integrate them into your product.” You must put your knowledge into practice by solving problems with real-time datasets on popular sites like Kaggle and KDnuggets. Companies like Google and Facebook conduct competitions to prove your Data science skills, and bag the job based on your scores. The Data Science projects are evidence of your knowledge that makes your CV stand out. Proceed by applying for jobs on Company websites and popular job sites like LinkedIn, Glassdoor, Monster, Indeed, and Kaggle.
While being interviewed, you must be prepared to justify your resume.
She says – “If you are from a technical background, you must be good at programming, ML concepts and must have proven knowledge in complex Data Science projects.” Business expertise with good communication and visualization techniques is also mandatory.
“As a Software Developer you inherently connect with product design, architecture, and infrastructure that you will deal with in a Data Scientist role,” says Shweta
“Breaking into DS requires you to be passionate about the field, have a stronghold of DS fundamentals. Choose an industry that interests you. You must be willing to constantly learn and upgrade your knowledge as it is an ever-evolving field. Your curiosity and the drive-in you is the right path towards Data Science.” Check out Springboard’s Data Science career track course to help you build your skills, develop a professional portfolio to grab your dream job. The Career Track is a self-paced, 1:1 mentoring-led and project-driven program that comes along with a job guarantee.