What is data science?
Data surrounds us and develops on a constantly increasing path as the world increasingly interacts with the Internet. The industries have now realized the enormous power behind the data and are discovering how it can change not only the way of doing 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 in data series and then use it to build statistical models and machine learning models. To do this, they need the following:
- Data collection framework like Hadoop and programming languages like SAS to write sequels and queries.
- Data modeling tools such as python, R, Excel, Minitab etc.
- Machine learning algorithms such as Regression, Clustering, Decision Tree, Vector Support Mechanics, etc.
Components of a Data Science project
- Studying concepts: The first step involves meeting with the interested parties and formulating many questions in order to understand the problems, the available resources, the conditions, the budget, the deadlines, etc.
- Data exploration: Many times the data can be ambiguous, incomplete, redundant, incorrect or illegible. To cope with these situations, Data Scientists explore data by examining samples and trying ways to fill in gaps or remove redundancies. This step can include techniques such as data transformation, data integration, data cleaning, data reduction, etc.
- Model planning: The model can be any type of model such as the statistical model or machine learning. The selection varies from one Data Scientist to another, and also based on the problem in question. If it is a regression model, then regression algorithms can be chosen, or if it is a matter of classification, then classification algorithms such as the decision tree can produce the desired result.
Model Building refers to the formation of the model so that it can be distributed where it is needed. This step is mainly performed by Python packages like Numpy, panda, etc. This is an iterative step, ie a Data Scientist must train the model several times.
- Communication: The next step is to communicate the results to the appropriate stakeholders. It is done by preparing simple graphs and charts showing the discovery and the solutions proposed to the problem. Tools like Tableau and Power BI are extremely useful for this step.
- Test and operation: If the proposed model is accepted, it is conducted through some pre-production tests such as the A / B test, which concerns the use, for example the 80% of the model for training and rest to verify the statistics of how it works. Once the model has passed the tests, it is distributed in the production environment.
What should 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 great need of qualified professionals to work on the data they are generating. And this is why this course was designed to prepare students to lead the world in Data Science. Detailed training courses are available for renowned faculties, multiple assessments, live projects, webinars and many other structures to shape students based on industrial needs.