What is Data Science?
Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It is a “concept to unify statistics, data analysis, machine learning, and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science.
Why is Data Science a Multidisciplinary Field?
Data Science is said to be multidisciplinary because it combines different fields of study like Statistics, Computing, Internet Technology, Media Studies, Programming, Data Security, Analytics, AI, Mathematics, Machine Learning, Data Visualisation, Neural Networks, Deep Learning, Deep Active Learning, Cluster Analysis, Big Data, Cognitive Computing, Data Mining, and Simulation.
What do Data Scientists do?
Data Scientists first collect data from an unending list of sources like
- Databases
- ERP
- POS systems
- CRM
- Surveys
- Logged data
- Software and applications
- Data selling companies
- Cookies
- Customer information
- Stock details, etc.
Collected data is then cleaned-up, sorted, and labeled using different tools and techniques like statistics and programming to make it analyzable. Sometimes user data is masked to preserve users’ privacy. This digestible data is then explored at a very deep level using machine learning, neural networks, cutting-edge algorithms and clustering among other ways. The results are made available using data visualization tools so it’s easily comprehensible by everyone. Patterns are discovered using many methods and based on them, predictions are made. This enables a client to make better-informed decisions.
Simply put, Data Scientists analyze large sets of data in multiple ways and arrive at valuable and actionable insights.
Where is Data Science Used?
With the right set of data, Data Science can be used in virtually any field. Actionable insights provided by data analysis have greatly enhanced:
- Search Engine Results
- Ad Personalisation
- Banking and Finance Operations
- Custom Manufacturing
- Healthcare
- Fraud and Risk Detection
- Virtual Assistants
- Simulations
- E-commerce
- Speech and Image Recognition
- Sports
- Recommendation Systems
There’s no end to the applications of Data Science, to be honest. However, these are all in high-demand and therefore high-value industries, which means there is enough capital to invest in research. It also means that these kinds of jobs pay very well. That is a large part of why it is this popular today.
Why is Data Science Becoming so Popular?
Data Science established itself fairly recently but it appeared in many discussions for the past few decades. It has now become more important than ever because it automates and supports decision making which is a core part of any business. Companies have been using this science to:
- Gather and retain customers,
- Calculate their KPI,
- Target relevant people,
- Innovate
- Manage risks and anticipate failure
- Improve customer experience
As mentioned before, with the right set of data, the utilities are infinite.
How You Can Become a Data Scientist
You can either get a degree in Data Science or do online courses or take your own time to master elements that make up the toolkit of a Data Scientist. These elements include:
- Mathematics
- Statistics
- Programming
- Domain Knowledge
- Database Management
- Creative Outlook
- Out-of-the-Box Thinking
- Data Visualisation
- Machine Learning
- Data Analytics
- Big Data
- Algorithms
This field is fairly hard to crack but if you’re passionate and willing to take this up it can be greatly rewarding. With enough freelance experience and expertise in all the right tools, you can create a portfolio to begin your journey as a Data Scientist.
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