What is data scientist ? How to become a data scientist ? Data scientist salary.

Data science :- 

                   The term data science process is an agile, iterative data science methodology to deliver predictive analytics solution and intelligent application efficiently. it helps improve team collaboration and learning by suggesting how term role works best together.

                                                    It is the study of data. it involves developing methods of recording,storing,and analyze data to effectively extract useful information.

Types of data science :-   

                              There are mainly two types of data science.

1.data analytics

2.data scientist

1.Data analytics :-

                           It is the science of examining  raw data with the purpose of drawing conclusion about that information . data analytics technologies and techniques are widely use in commercial industry to enable organisation to make more informed business decisions and scientist and researchers to verify or disprove scientific models, theories and hypothesis.

2.Data scientist :- 

                 Data science are a new breed of analytical data experts who have the technical skill to solve complex problems and the curiosity to explore what problems need to explore what problems need to be solved. many data scientist began their career as statistician or data analytics.

 

Various role of data science :-                                                                                                                    Data collection :- It is the process of gathering and measuring information on targeted variables in an established system which then enables one to answer relevant questions and evaluate outcomes.                        

Data firehose :-

                      A firehose if you will and that's exactly what a data firehose API does. the firehose API is a study stem of all available data from a source in real time a giant spigot that delivers data to any number of subscriber at a time.

Feature engineering :-

                   It is the process of using domain knowledge of the data to create features that make machine learning algorithm works feature engineering is fundamental to the application of machine learning and is both difficult and expensive.

Feature selection :-

                  It is the process where you automatically or manually select those features which contribute most to your prediction variable or out put in which you are interested in. having relevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

Model creation :- 

               It is the process of producing a descriptive diagram of relationship between various types of information that are to be stored in a database. one of the goals of data modeling to create the most efficient method of storing information while still providing for complete access and reporting.

Model deployment :-   

                     The concept of deployment in data science refers to the application of a model for prediction a new data building a model is generally not the end of the project.

 AI/ML :-

Artificial intelligence :-

                    Artificial intelligence sometimes called machine intelligence is demonstrate by machine in contrast  to the natural intelligence displayed by humans. leading AI textbooks define the field as the study of  "INTELLIGENT AGENTS" any devices that receives its chance to successfully  achieving its goal.

Machine learning :-

                    It is an application of artificial intelligence that provides system the ability to automatically learn and improve from experience without being explicitly programmed.

Python in data science :-

                       Python is a powerful language, python is used by programmers that want to delivers into data analysis or apply statistical technique. there are plenty of python scientific packages for data visualization, ml, natural language processing, complex data analysis and more.

IDE :- 

    Best IDE for programming data science is spyder. it is use to write some programming language for python program.

Math :-

     Mostly three types of mathematics are used in data science.

1.statistics

2.linear algebra

3.differential calculus

If these math are strong, then you best in data science.

Avg salary of data scientist by glassdoor :-

1.india :- 10,360,00/yr                5.japan :-$113,450/yr

2.australia :- $114,319/yr           6.china :-￥200,000/yr

3.uk :- ￡50,584/yr                    7.singapore :-$ 73,645/yr

4.usa :- $ 113,309/yr                  8.canada :- $ 83,000/yr