Data Science and Businesses
Data Science is all about everything that can be done with the data in order to study and exploit hidden insights. This profession arose when simple Data Mining techniques like Business Intelligence and Hadoop were combined with technical statistics and complex computing. In general, Data Scientists are given some data and are expected to use the data and guide the company in the right direction. In today’s world, as more and more people are coming online and leaving a huge amount of Data on the internet, the need of Data Scientists has become acute in almost every industry we can think of.
Industrial Needs of Data Scientists
What a particular Data Scientist does depends on the type and size of the company he’s working for. A start-up company having a lack of resources cannot hire a lot of people, so only one or two people have to do all the job, namely collecting data, interpreting, transforming, modeling, testing and visualizing. Big companies, which enjoy abundant resources and can hire a lot of professionals, generally distribute the entire process among Data Engineers, Software Engineers and Data Scientists. Here, Data Scientists are mainly focused on Analytics, modeling, testing, Machine learning and Artificial Intelligence.
Things to Focus On In Order to Become a Data Scientist
The core facets of Data Science are statistics, computer science and business, so one has to become an expert in these three fields before starting.
- SQL: A Data Scientist may have to write so many sequels. Many companies have set up Data Infrastructures from where a Data Scientist can collect data using SQL. It is an easy programming language and also helpful in writing queries.
- Metrics: He has to interpret various types of metrics like success metrics, tracking metrics etc and understand how to construct models according to these metrics.
- Tools: During a project, he has to use complex algorithms and multiple computing tools like Python (for Machine Learning), Hadoop (for collecting data), Excel and R (for analytics and modeling), Tableau (for visualization) and others like SAS, Minitab, Spark etc.
- Testing: Testing is important to check if a model will work out as expected or not. A/B testing allows him to experiment multiple models at a time and see which works the best.
- Communication: He must have good communication skills like public speaking and technical writing so as to explain the model to customers and other team members. The point is not just in creating advanced models, but also to make others understand them.
How This Data Science Online Course Will Help You?
The online training on Data Science has been designed keeping the above-mentioned points in mind. Students are provided life time access of detailed and practical knowledge of all the important concepts by industry-experienced faculties. Quizzes, assessments, webinars and live projects help students become job ready, and to help them get placed in the right companies, a well-organized placement cell with excellent record is also available.