What is data science?

Data is all around us, and it runs in ever-increasing ways as the world increasingly interacts with the internet. Industries have now realized the tremendous power behind data and are discovering how it can change not only the way we do business but also the way we understand and experience things. Data Science refers to the science of decoding information from a particular set of data. In general, data scientists collect raw data, process it into data sets, and then use it to build statistical models and machine learning models. To do this, they need the following:

1. Data collection framework like Hadoop and programming languages ​​like SAS to write the sequels and queries.

2. Data modeling tools like python, R, Excel, Minitab, etc.

3. Machine learning algorithms like regression, clustering, decision tree, support vector mechanics, etc.

Components of a data science project

• Study Concepts: The first step involves meeting with the stakeholders and asking a lot of questions to find out the issues, the resources available, the conditions involved, the budget, the deadlines, etc.
• Data exploration: Many times the data can be ambiguous, incomplete, redundant, erroneous or unreadable. To deal with these situations, data scientists explore the data by looking at samples and trying ways to fill in the blanks or remove redundancies. This step may involve techniques like data transformation, data integration, data cleansing, data reduction, etc.
• Planning Model: The model can be any type of model, such as a statistical or machine learning model. The selection varies from one data scientist to another, and also depending on the problem at hand. If it is a regression model, then regression algorithms can be chosen, or if it is classification, then classification algorithms such as decision tree can produce the desired result.

Model Building refers to training the model so that it can be deployed wherever it is needed. This step is mostly done by Python packages like Numpy, pandas, etc. This is an iterative step, meaning a data scientist has to train the model multiple times.

• Communication: The next step is to communicate the results to the appropriate stakeholders. It is done by preparing simple charts and graphs showing the discovery and proposed solutions to the problem. Tools like Tableau and Power BI are extremely helpful for this step.
• Test and operation: If the proposed model is accepted, it goes through some pre-production tests, like A/B testing, which is about using, say, 80% of the model for training and the rest to check the statistics of how well it works. Once the model has passed testing, it is deployed to the production environment.

What must you do to become a data scientist?

Data Science is the fastest growing career of the 21st century. The work is challenging and allows users to use their creativity to the fullest. Industries are in dire need of qualified professionals to work on the data they are generating. And that is why this course has been designed to prepare students to lead the world in Data Science. Detailed training by renowned faculties, multiple assessments, live projects, webinars and many other facilities are available to train students according to industry needs.