Everything you wanted to know about being a Data Scientist

The term Data Science originated 30 years ago (then called Datalogy). It involves implementation of theories and techniques derived from different streams such as statistics, mathematics, operations research, pattern recognition, computer science, predictive analytics, database, data mining, visualization, data warehousing, artificial intelligence, and many more (Source: https://en.wikipedia.org/wiki/Data_science)

MVA-Blog_Data Scientists

Nowadays, with creation and exchange of large volume of data or Big Data, majority of the organisations around the globe are on the lookout for professionals who are good at Data Science (can also be referred as Data Mining). Such professionals are called Data Scientists.

Roles of Data Scientist

Data Scientists are Big Data crunchers who wrangle with big volumes of unstructured and structured data to discard irrelevant data and organize the required data. For the same, they should also be good at contextual understanding and analytics, and should possess a decent understanding about the concepts in statistics, mathematics, computer programming, and about their associated industry. Typically, following are some roles and tasks undertaken by Data Scientists:

  1. Conduct in-depth research and sift through a large amount of data from different sources – internal and external.
  2. Create a list of questions for discussion with different departments to segregate relevant data
  3. Implement statistical methods; sophisticated analytics programs, and mathematics for forecasting.
  4. Devise and employ new algorithms to create automated tool which can used for sorting complex data and find useful data
  5. Carefully check different types of data to retain valuable data for prediction purpose
  6. Invent solutions to handle large chunks of data
  7. Use visualizations and other techniques to represent findings and communicate them to senior management and IT department for result forecasting
  8. Suggest viable solution (considering economical cost and existing strategies) to enhance productivity

Areas of Applications

Data Science is used in a variety of fields. Some of the common areas where it is largely used are as follows:

  1. Search Engine: A search engine like Google® has to sift through humongous amount of data to provide results. In regards to fetching of data, search engine implements Data Science algorithms to deliver the most relevant output in few seconds based on user’s query.
  2. Product Recommendations: In websites where products are sold, it is usually noted that on the right-hand side of the web page, there are suggestions about similar products. These suggestions are listed based on user’s interest; previous searchers, and information relevance. To carry out such complex tasks, Data Science algorithms are implemented.
  3. Price comparisons: There are many websites which compare prices of similar products based on user’s query and display relevant results. This becomes possible due to implementation of Data Science algorithms again.

Data Scientist certification training

Data Science is a vast area which involves application of a variety of skills; deep knowledge about mathematics, statistics; various industries; software, etc. Thus, it will be a wise option to consider guidance such as Data Scientist certification training for getting acquainted with its concepts. Usually during Data Scientist certification training, candidates are introduced to software such as SAS®, R Programming, and Excel and statistical and mathematical theories to sort out relevant data from large pool of data. More importantly, if the training is based on Data Scientist certification, the candidates will not only understand all concepts about Data Science but will also be in a position to attempt a certification exam based on the learnt concepts.

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