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A Detailed Overview Of The Project Life Cycle In Data Science
The prominence of Data Science isn’t new to anyone. Having been termed as the sexist job of 21st century the job role of Data Scientist has gathered everyone’s attention. Well, if you are a Data Science career enthusiast then let us help you in understanding the Data Science project life cycle.
The whole concept of Data Science project life cycle can be divided into six different phases
- Phase 1: Determining The Goals Or Objectives
The objective of this preliminary stage is to understand or determine what exactly the objectives are & then set the goals you need to achieve. To achieve this it’s significant that the information researcher and the representative head are cooperating. A couple of inquiries they may pose to each other are
- What is the primary target of the business?
- What are the ideal results the business needs to have occur?
- What are the torment purposes of the business?
Having characterized the goals & objectives it’s time to move towards the next time
- Phase 2: Data Discovery
After deciding on the goals, the next step involves identifying the right ingredients that would help you towards reaching your goal. Data Scientist has the knowledge to know which ingredients are required, how to source and collect them, and how to prepare the data to meet the desired outcome.
- Phase 3: Data Preparation
After obtaining data, the immediate step that follows is preparing the data. Data preparation refers to cleaning & arranging the data. Most of the data which is collected will be in unstructured & unorganized format. Without cleaning & filtering the data, it becomes impossible to avail accurate results from the overall process.
- Phase 4: Exploratory Analysis
This process involves transforming the business problem into a Data Science problem. To do this you need to carefully explore the data & its properties. Different data types like numerical data, categorical data, ordinal and nominal data etc. require different treatments. This is followed by applying descriptive statistics from testing the significant variables. And finally with the application of Data Visualization it helps in getting a clear idea about the insignificant trends & patterns in the data.
- Phase 5: Model Design
This is where the real magic happens. Model design is the application where we truly put our intuition caps on, as we need to comprehend or characterize the scientific methodology that will in all likelihood give us the outcomes that we need. This process involves use of clustering algorithms like k-means or hierarchical clustering.
- Phase 6: Building Thing Model
Before building the model you need to validate whether everything to know whether it produces the desired results for your business. After making sure that the results are satisfying, you are all good to go! After successfully implementing the model you can observe the business making a difference like never before. Get to know more about such interesting applications of Data Science by signing up for our Kelly Technologies Data Science Training In Hyderabad.