Lifecycle of a Data Science Project?

Lifecycle of a Data Science Project?

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4 min read

INTRO

Data Science is one Trending Domain and to gain practical exposure, doing Projects will definitely help. But before doing a Project you should be aware of the Lifecycle of a Data Science Project.

You may ask but hey Sumanth, We are aware of Data Science and how algorithms work but don't know the Lifecycle and How to do Projects? 🤔

But Don't Worry that's why I am here, by the End of this Blog you will be aware of the whole Lifecycle of a Data Science Project.

Note: I am writing this Thread from my experiences working with Projects at multiple startups. With that said, Let's go 🚀

Step 1: Defining the Problem Statement ⭐️

Before you start working on a project, You have to Clearly define the Problem you are Solving and the Value that is adding.

Step 2: Hypothesis Generation ⭐️

Once you Define your Problem, you have to understand What kind of Data you require and How that data will impact the Problem you are Solving.

Believe me, without a proper understanding of the data you require, you can't build a good Project!

Step 3: Data Collection ⭐️

Based on the Hypothesis you generated Collect the Data. But How can you Collect it?

So here are some ways:
• Scrapping the Websites • Collecting from the Databases • Pre-available Datasets in Kaggle

Step 4: Data Preprocessing ⭐️

Ok, this is the Main Part and Important Part of any Data Science Project. The Data you collected will be a complete mess unless and until you Preprocess that. And Without Proper Preprocessing you can't even expect better results.

Some preprocessing steps include: • Handling Missing Values • Feature Selection • Feature Engineering etc

Step 5: Data Visualization ⭐️

We, humans, interpret the data better visually rather than any other thing. So this Step includes Visualizing the Data into some Plots and Graphs. 📊

These are some Libraries for Data Visualization: • Matplotlib • Seaborn • Cufflinks

Step 6: Feature Selection ⭐️

Feature Selection also Plays a Vital Role in a Data Science Project. You should select the features which are useful and have an Impact on the Problem.

And also Removing the features which are Unwanted is very Important for the Best Results.

Step 7: Feature Engineering ⭐️

It's nothing but "Deriving New Features from the existing Ones."

Believe me, this is tough, unless you have a strong Domain Knowledge and Proper Understanding of the Problem you can't come up with New Features.

Step 8: Data Preparation ⭐️

Once you cleaned the Data, you have to prepare it to send it to the Model by dividing it into Train and Test.

Step 9: Model Building ⭐️

Finally, we are here 🙂 ! We Select the Model which best fits the Data then Train it on "TRAIN DATA" and evaluate it on the "TEST DATA".

You have to try different techniques like Hyperparameter Tuning, Cross Validations, Regularization, etc to come up with a good Model.

Step 10: Deployment ⭐️

Once you think your model is ready then try to deploy it.

Even if you create a model with 99% accuracy, and if you haven't Deployed it and No one is Using it, It's of No Use.

Deploy it using any Cloud Platform like: • Heroku • GCP • AWS etc

Step 11: Model Retraining ⭐️

Don't think that once you deployed it's done. It's not done yet. You have trained the model on the Current Data and Current Trends. Data trends will change with time and situation. So have to Evaluate the model continuously and Retrain it on the Current Data Trends.

Before We End...

I hope you enjoyed reading this article and found it insightful. If so, please like/comment/share it. It means a lot to me.

Let's connect. If you have further questions, doubts, or want to discuss anything you can connect with me on,

Will see you on the Next Blog Until then Keep learning 😍