Complete Lifecycle of a Data Science Project Explained Simply Here

By POOJA BISHT |Email | Apr 15, 2019 | 14853 Views

Data Science Project is one of the most looked for as a skill by the potential employers in the candidates. It is the practical skill that tells whether a candidate is able to handle complex projects or not. Addition of a Data Science Project in your Resume is what separates your Resume from the rest. Because you are the one who has already flair in working with projects, you are the one who will be given a priority by the recruiters. It, therefore, becomes important to know the complete lifecycle of a Data Science Project so that following step by step you ultimately reaches to the final successful completion of your project.  The article explains the complete lifecycle of a Data Science Project so that every beginner who is starting to undertake a project gets the guidance and any experienced who is already into the process can cross check whether he is following the right steps or not.

Step 1: Gather Data
The first and very foremost step of a Data Science Project is to gather the required data from different available and trusted sources. There are many sources that an organization depends on while collecting data. The data collected through various platforms during this stage is of raw form and is stored at a central place. The process of collecting data at a central place is called Data Warehousing. 

Step 2: Clean Data
The Data collected in the first step is raw and contain irrelevant information as well. Because in the first step we are more focussed in collecting data, a certain amount of irrelevant information also gets collected. This raw data needs to be cleaned now. Cleaning here simply implies letting go of all the irrelevant information and storing only relevant information for further use. This is one of the most important steps in the project as collecting relevant data is more important than collecting useless data.

Step 3: Analyze Data
Now we have reached into the analyzing step and by using certain tools and skills you will be required to analyze your cleaned data. You will be required to find hidden patterns involved in data, finding answers to major business questions and generate that relevant information which is required by your business to grow. What is the market trend now? Which are the products customers are getting most attracted towards? What is the reason behind the downfall in the last year?- These are some of the questions that your business might be seeking answers for and you need to provide the answers by looking and analyzing your data.

Step 4: Interpret Results
Now you are with your insights having the answers to your business questions and strategies to tackle major challenges. It is the time to interpret these insights to your audience. Your audience could be the key decision makers in your organization, the non-technical team of your organization or the stakeholders. You need to present your insights in a meaningful way by using statistical tools, excellent communication skills, and good presentation skills. You cannot take this step lightly. It is the fruit of all your work. Ambiguity in delivering your presentation will do no good for your work. Develop the skills required to clearly interpret your results in this step.

This is the Complete Lifecycle of a Data Science Project Explained Simply Here. I hope you find meaning and purpose after reading it. Hope your upcoming Project seems easier to work on after reading this article and the article provides you a clear mindset for your future endeavor.

Source: HOB