Introduction to Data Science
In this world of Digitalization, Data has gained multifold importance. A lot of data is being generated regularly, and it has become important for business homes to analyze this data to prepare a meaningful analysis out of it. Data Science deals with an enormous amount of data to build insights into its relevance for the decision-making of the entity. It works towards selecting areas within the data that can help in some meaningful concepts through a questioning and inquisitive mindset, especially with a data scientist certificate.
Future Scope of Data Science
- Data Privacy Norms: Due to changing Rules and Regulations and the introduction of the new Data Privacy Norms, restrictions have been imposed on the free flow of data from entity to entity. Information gathered by one source has to keep a check on the privacy policies before using it for any other purposes.
- Data-Driven Decisions: With the advent of technology, organizations focus more on making decisions that positively affect their business. These decisions are majorly based on the data collected. Data Scientists are engaged by businesses to collect, store, analyze, interpret the data and draw meaningful conclusions for future business actions.
- Specializations: Technological revolution and digitalization have increased the scope of Data Science to a great extent. The smallest of the areas are being developed within the field, and courses are framed to achieve minimal specializations.
- Artificial Intelligence and Machine Learning: AI and ML are majorly dependent on Data Science. The scope of the function of AI and ML is framed based on the Data collected and analyzed. Data Science becomes the core for the functioning of AI and ML.
- Increase demand for Data Scientists with the increase in Data: As there is an increase in data, there is an increased demand for data scientists. Data Scientists have the problem-solving attitude that the organization needs for their development.
Careers in Data Science
The following are sought-after careers in Data Science with specializations of their own. Each one of them has a specific role to play concerning the data.
- Machine Learning Scientist/Engineer
- Data Analyst
- Data Engineer
- Data Scientist
- Applications Architect
- Enterprise Architect
- Statistician
- Infrastructure Architect
- Business Intelligence Developer
Real-Life Applications of Data Science
- Chatbots: A Chatbot is an automatic Chat System developed on websites that answers users’ queries. This system is developed using Data-driven facts.
- Robotics: Robotics involves a lot of designing and engineering of data to set commands to be followed by the machine. Such data is fed into the machine to function in a specific way.
- Medical: The field of medicine has immense use of data to conclude about various health possibilities and concerns. Medical equipment is designed based on Data science techniques.
- Optical Character Recognition: OCR is a machine reading process through which complex codes can be scanned and read by the machine. This shows how complex data can be compiled into a single design and how the machine is fed with data to read such characters.
- Auto-correct: The auto-correct feature that we regularly face on our systems and browsers majorly works based on the data collected. It reads the user specifications and responds by correcting the same on the basis of data available with it that collides with the user’s needs.
- Email Spam Filtration: The Spam messages are automatically set apart from our regular authentic messages in the Email inbox. This happens due to Data Science which allows users to mark messages as Spam, and such data is used to differentiate between authentic and spam messages.
Conclusion
One can achieve specializations in Data Science by applying for the business analytics online certification, like the one offered by Great Learning. You can also opt for data scientist certificate courses from Great Learning for a career in Data Science and related fields.
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