The Technique of Working on Data
Data Science is a broad term which encompasses everything that can be done with the data i.e. analyzing, modeling, visualizing etc. Initially, industries used simple tools like Business Intelligence for Data Mining. Most of the stored data was structured data such as data warehouses, and the primary reason why industries worked on them was to create reports such as sales reports or understanding if a particular product was a success or not.
Later on, as websites became more interacting and the amount of data exploded, Big Data was introduced to the world and development advanced algorithms and statistical tools paved way for Data Science. Industries now needed to deal with data on a huge level, and Data Science provided to work not only on structured data, but also unstructured data such as web logs and user feedbacks. The insights behind the data too became useful for not just creating historical charts, but to also predict the future trends and to understand certain scenarios. The professionals who can do this job are called Data Scientists.
Applications of Data Science
- Solving Problems: Based on the available data, Data Scientists are expected to solve or propose a logical solution to tackle business problems such as delay in flights, or wastage of money and resources etc.
- Analytics and Metrics: It provides clear analytics and metrics about what is happening in the industry and it gives Data Scientists an insight of how to improve the condition.
- Machine Learning: It is a very important aspect which helps making machines more accurate through a data-driven approach.
- Deep Learning: It is actually a part of Machine Learning and is related to working with representative algorithms of the brain called Neural Networks.
- Artificial Intelligence: It is also the base of Artificial Intelligence for creation of machines which work like humans.
Prerequisites of Data Science
- Curiosity and Creativity: A Data Scientist has to ask so many questions in order to understand the problem well, and he has to think creatively to frame out multiple approaches while creating statistical models.
- Programming Languages: Most of the coding is done by SQL and Python. SQL is handy in writing sequels and queries, while Python is a powerful language for Machine Learning.
- Tools: Tools are very important part of. A Data Scientist has to work on many different tools like Hadoop, SAS, Minitab, Tableau etc while carrying out the project.
- Communication: This doesn’t sound like much in the first place, but when it comes to explain the model to customers and other peoples, good communication skills like public speaking and representation skills become very important.
How Can You Become A Data Scientist?
Data Science brings together mathematics, technology and computing tools in one place. And this is why this training has been designed to make students expert in all these fields. The students get lifetime access to 160+ hours of trainings and more than 100 hours of rigorous assignments along with multiple live projects. They are also provided interview preparation so as to help them in grabbing their dream Data Scientist job in leading companies.