A Detailed Overview Of The Data Science Project Lifecycle

To successfully complete a Data Science project the Data Scientists require following a systemic approach. Now let’s have a clear look on the sequence of steps that are involved in the Data Science project lifecycle.

  • Data Acquisition

The primary requirement of a data analytics process is data. So the primary step involves identifying the source of data that has all the answer to the questions. This data can be collected from a variety of sources namely webservers, social media data, or even from online repositories & even through web scraping, The major problem that arise here is that tracking the source of the data & is accuracy.

  • Data Preparation

Data Preparation is also referred to as Data wrangling. This is regarded as the most time consuming task & uninteresting task for a Data Scientist. Usually the data which is collected from different sources is in usable format having missing entries, inconsistencies and semantic errors.  As a part of the Data Preparation process, Data Scientists will be following manual editing process either by editing on spreadsheet or writing code.

  • Hypothesis And Modelling

The is the most crucial aspect in the Data Science lifecycle process which includes writing, running and refining the programs to carefully analyze & extract insights from data. The most commonly used programming languages for this process are either Python or R.  Also, several Machine Learning models are also applied for accurate analysis of the data sets.

A Detailed Overview Of The Data Science Project Lifecycle

  • Model Design

This process makes use of closeting algorithms like K means or hierarchical clustering. This process determines the likelihood of the occurrence of outcomes that we need.

  • Building Thing Model  

Having finished on the model design then the next step involves building the model however, before beginning with the process you need to validate whether everything to know whether it produces the desired results for your business. If you get satisfactory results that you can work towards successfully implementing & deploying the model.

The final step involves communicating the results in the form of rich visuals along with the other team members. Get to know more in-depth about the Data Science project lifecycle with the help of Analytics Path Data Science Training In Hyderabad program.

Kevin