DATA ANALYTIC LIFE CYCLE

               
                Hi friends, we heard about the many life cycle events especially Life cycle of Butter fly, The Structure of Caterpillar change into Butter Fly. Even in a Software Development Life Cycle
( SDLC ) the whole process are the most important aspects for the project implementation.

                                                                                     
Project Planing -------Analysis ------ Design ------- Implementation

                                                                               |

                                                                                | 

 Maintenance ------------------ Testing & Integration                      

The main important phases are the data analytics process in a big data. A data scientist must know the phases, that would success their analytic design project.

 The Success Analytic Report contains many phases:

1. Business User
2. Project Sponsor
3. Project Manager
4. Business Intelligence
5. Data Engineer
6. Data Base Administrator
7. Data Scientist

Business User:
          He is the end user or client of the project.The client should tells the requirements what type of data he want, what kind of prediction the data must have, etc....

Project Sponsor:
          A Project sponsor have a little much idea whether the funding project is success or not. The sponsor must have a marketing team and what period to sell the project. He links the project between business community,decision making groups.

Project Manager:
          The project manager has huge burden over the project. he was the lead the team to a sufficient path. He managing the team in a good manner. He check out the every module in a project is correct or not. The project manager continuously reports to the project sponsor. He enthusiast his team to make the project successful.

Business Intelligence:
          He gives the data of past and present algorithm to the data engineer.

Data Engineer:
          He supports the extraction & data ingest to analyst sandbox.(retrieves the data from sandbox.)

Database Administrator:
          He provides the access to the data,whether it was public user or private user.

Data Scientist:
          He was responsible for all the manager,Business Intelligence, Data Engineer and Administrator.
         

 DATA ANALYTIC LIFE CYCLE:

Discovery:
                  First we need to identify the data whatever it should be used for and what kind of data it requires,then we need to prepare the data in next phase

Data Preparation:
                   The preparation reduces the unwanted and noisy data, and make the efficient way of using the data.

Model Planning: 
                   Select Particular algorithm to make the data alive.
example: k means Clustering, Recursive, Association Rules, Classification, etc..

Model Building:
                   It prefers to Build the data to configure for the end users environment

Communicative Results:
                    It gives the samples outputs for the end users. If it was not convenience
the data scientist want to rebuild the same module.

Operationalize:
                    Final Completion of the projects, it refers the worth of the project and tells what kind efforts the data scientists do.

KEY ROLES OF DATA ECOSYSTEM:

1. Deep Analytical Talent
2. Data Savvy Professional
3. Technology & Data Enablers

PROFILE OF DATA SCIENTIST:

Quantitative:

                Problem solving techniques in mathematical or statistical analysis.

Curious & Creative:

                The data scientist should be enthusiast about the idea of a data and explore concept as big deal to the end user.

Communication & Collaborative: 

                The communication between the social communities would be very clear. It must develop their data architecture to the data sets. The data scientist would discuss with the project sponsor, that the sponsor thought he spending money for the reasonable projects. 

Skeptical: 

                The skeptical analysis is diagrammatic solution for the developers, it is easy to analyse better than see a data.

Technical: 

                The data should related to the technical words, that relates the convenient usage of end user.The data scientist must updates their technical analysis every time. It refers to a good data scientist.

R-STUDIO ENVIRONMENT:

                 

                   R Studio is a programming platform for analysing the Big data . It contains the programming language of R. Globally 12% of developers are working on the BIG DATA.

R programming is a interpreted language that you executes it with line by line coding.If you believe it or not,it is very easy to understand the code.

                   Let's we move to the installation of the software in windows. You need to click the link for downloading the package for Rstudio Download Package

                   After, downloading the package. Install the application on your root directory (C:\\)

                   When completing the install you are enter to the R studio environment.

                    Let's we see about How to use the R studio in my next post. I'm going to explain the R language with my hands on dirty.

                   If you have a queries or about those contents comment or contact me in kamalkk54321@gmail.com