Data science has picked up traction and has become inseparable for many businesses. Until two decades ago, data used to multiply in ten to fifty years’ time. However, today the amount of data the world is generating is so humongous that it is doubling every 2 years, hitting 45,000 Exabytes in 2020.
Traditionally, data was analyzed by mathematicians and statisticians. However, with the loads of data we are producing today, it is not practical to employ traditional methods anymore. This has created a demand for data scientists and related technologies in the new world reality. Moreover, we are living in a world led by data-narratives.
Data science is a combination of multiple disciplines used to analyze chunks of data and finding the best possible solutions based on them. Inducing optimization and using computer science to an array of structured and unstructured data for their conversion into a human-readable format is data science.
A lot of times people confuse the words Artificial Intelligence, Big Data, Machine Learning and Deep Learning with Data Science. As a beginner and an aspirant in the field, it is important to make the right distinction between these disciplines and identify how and where these disciplines overlap.
Artificial Intelligence: It is the realm focusing on the creation of intelligent machines that work and react like humans. Despite quite a long history, today AI in most areas is not yet able to completely replace a human.
Big data: It deals with huge loads of largely unstructured data. It uses tools and systems to process high loads.
Machine learning: It is a creating tool for extracting knowledge from data.
Deep learning: It is the creation of multi-layer neural networks in areas where more advanced or fast analysis is needed and traditional machine learning cannot cope.
To put in perspective what actually is done through data science, the following are some of the fields where the use of data science is rapidly growing.
It sure seems fascinating to be able to predict market trends, customer choices, running cars with intelligent auto-drive systems, etc. However, to be competent enough to be a data scientist or a management professional dealing with data-led narratives there are certain things you need to learn in order to operate in such an environment.
There is a huge gap between the demand and availability of professionals who can manage industries where data is becoming inevitable. This also means that there exists an open opportunity that can be utilized. To be competent to survive and grow in these environments it is best to equip yourself with the basic concepts of data sciences even if you don’t want to dwell into programming and other software management.
There are open resources that you can access to learn the whole process on your own but it is highly unlikely that it will be a practical and time-saving process for you. It is best to take a structured course in data science which is committed to offering a comprehensive framework. Especially if you are in the middle of your career then taking an executive course is the best possible solution.
Hughes Global Education offers one of the best data science courses in India in collaboration with Columbia University’s Data Science Institute. The Executive Programme in Data Science is rigorously designed to entail all the depth of the field. You will gain insight into the latest data science tools and their application in finance, health care, product development, sales and more. With real-world examples, it demonstrates how data science can improve corporate decision-making and performance, personalize medicine and advance your career goals.
Comprising of module 1, 2, & 3 will be taught by a distinguished team of professors at Columbia University’s Data Science Institute, this program is perfect for anyone who wants to understand basic concepts in data science without getting into the weeds of programming.
An interesting and important part of the course is a 72-hour live capstone project that the students have to take on. It offers an opportunity to work on tools like R, Python, Power BI, Excel, Tableau, Hadoop Ecosystem. It offers a practical understanding of the fundamental methods used by data scientists including; statistical thinking and conditional probability, machine learning and algorithms, and effective approaches for data visualization.
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