What is Data Science?

Data Science is the future of present generation. People should understand data science and they should imply in their business organizations.

Data science used to implement algorithms and technology in order to solve analytically complex problems.

The required skill sets:

There are two types of skills are required for data science

Technical skills Nontechnical skills

Technical skills:

Programming: We should be aware of programming languages like Python, C/C++, SQL, Java, dot.net etc. Python plays vital role in all among the programming languages. Most of the professional data scientist is used to run and execute the data with python only.

SAS and other analytical tools:

We should be aware on like SAS, Hadoop, Spark, Hive, Pig, and R. The R is the most popular data analytical tools that use in data science.

Machine Learning and AI:

The data scientist should be experienced in machine learning and some techniques such as decision trees, logistic regression etc. These techniques will used to predict the data and solve to easy manner.

Data Visualization:

There are some tools to visualize like d3.js and Tableau. It will help you out to convert from complexity to easy manner.

Non-Technical Skills

Business acumen

It does mean understand at what you’re working on and analyze how the problem you solve can impact on business.

Communication skills

For the data scientist, the communication plays very imperative level in their business because they should be approach to their clients in a straightforward direction and to make the right analysis of their reports.

Teamwork:

Essentially, you will be collaborating with your team members to develop the analysis of data and reducing the complexity of problems. It will help you to lead good growth in business.

Life cycle of Data Science:

Discovery:

Before you start we should have ability to comprehend the different particulars, necessities, needs and required spending plan. You should have the capacity to ask the correct inquiries.

Data preparation:

In this stage, you need to perform analytics for the entire duration of the project. You have to explore, preprocess and condition data prior to modeling.

Model planning:

There are different types of model planning. Some tools we need to use for model planning like

R has a complete set of modeling capabilities and it is used for building interpretive models.

SQL Analysis services used to perform in basics, predictive models.

SAS It is used for creating repeatable flow diagrams.

Model building:

It is used for training and testing purposes. You will analyze various learning techniques like classification, association, and clustering to build the model.

Operationalize:

In this phase, you convey last reports, briefings, code, and technical documents.

Communicate results:

It is necessary to evaluate the result that what you had worked upon. If any negative results occurred we need to try to resolve those problems.

Career opportunities in Data Science

As depends upon the job title the pay will be chosen. The national normal compensation for a Data Scientist is 6, 50,000 in India. In view of the area, the Data Scientist pay rates will be getting shafted.

Some of the prominent Data Scientist job titles are: