Want To Be A Certified Data Science Professional?

Data science is an extraction process. The extraction of usable data from a set of unstructured or structured data is called data science. Data science involves collecting unstructured data, cleaning the data, analysing that data, modelling it, deploying it, and then using it as knowledge. Data science has many components like mathematics, machine learning, computer science, data processing, statistical research, analysis, big data, artificial intelligence, tools, data extraction techniques, etc. Image recognition, speech recognition, internet search, digital advertisements, fraud, and risk detection are data science applications.

Data science is a versatile field. It is in high demand in the job market. There are a vast amount of job positions available in the data science field. As there is a lack of skilled professionals, data science is exciting and offers high paying jobs. If you want to boost your career, you can think about choosing a data science certification course.

When learning about Data Science, you are most likely to know some of the concepts and topics listed below:

• Data Science – Data Science is the process of extracting knowledge from structured and unstructured data using algorithms, scientific methods, processes, and tools. Big data, data mining, data analysis, and machine learning are vital components of data science.
• Statistical Analysis – The process of collecting, organising, analysing, interpreting, and presenting data is known as statistical analysis.
• Machine Learning – Machine learning is a subset of artificial intelligence. It is the study of machines that learn and evolve from the experience of algorithms and programs provided.
• Python Programming – Python is a high-level programming language and is essential in the field of data science.
• Business Intelligence – Business Intelligence is used by businesses and enterprises to extract knowledge and profit-oriented strategies from unstructured or structured data sets.
• Data Analysis – The procedure of extracting knowledge from a given data set by cleaning, transforming, and modelling it to help the decision making process is known as data analysis.
• Pandas – A library in Python.
• Numpy – A library in Python.
• Cloud Databases – Cloud database is a database that generally runs on a cloud computing platform.
• Relational Database Management System (RDBMS) – RDBMS is a relational database management system.
• SQL – SQL or structured query language is designed to manage the relational database management system.

The professionals in the field of data science can apply for a job as data scientists. Some of the responsibilities of a data scientist are as follows:

• Extracting useful insights by mining a vast amount of data, using business intelligence.
• Using the extracted insights to determine the approach of an organisation in business challenges.
• Being able to solve issues in the business by combining the strategies from computer science, applications modelling, statistical analysis, and mathematics.
• Using multiple sources for data extraction.
• Searching for weaknesses or problems in the data set by sifting and analysing it.
• Update existing business strategies for better performance.

Data scientists usually get hired by employers like:

• Universities and research institutions
• The government
• Manufacturers
• Banks
• Airlines
• Large retailers etc
• Big data companies
• Marketing agencies

Data science certification courses are beneficial for anyone who wants to learn about data science, the implementation of data science, and get a job in data science. The certificate is essential in getting a job as it indicates that the candidate has completed the course and is eligible to work in the field. The certification process is usually done at the end of the course. Most courses have exams or tests to check how much students have learned and whether they can implement the knowledge in real-life projects or not. Certification shows that the candidate is employable and is suited for the job description.