Process of Data Science

In this article, I’ll walk you through the 5-step process of data science. Let me walk you through these steps first and then walk you through all the steps involved in the Data Science process. The data science process includes:

Capture the data
Process data
Analyze the data
Communicate the results
Maintain data

Now, let’s go through all this process of Data science Step by Step.

Also, Read – Image Segmentation with Python.

Five-Step Process of Data Science

Capturing the data

To have something to analyze, you have to capture data. In any real-life situation, you probably have several potential sources of data. Take inventory and decide what to include. To know what to include, you must have carefully defined your business terms and goals for the upcoming analysis.

Your goals can be vague as sometimes “you just want to see what you can get” from the data. If you can, integrate your data sources so it easily gets the information you need to find insights and create all those nifty reports you can’t wait to show management.

Processing The Data

In my humble opinion, this is the part of data science that should be easy, but it seldom is. I’ve seen data scientists spend months massaging their data so they can process and trust the data. You need to identify anomalies and outliers, eliminate duplicates, remove missing entries, and determine which data is inconsistent.

And all this should be done properly so as not to delete the important data for your next analysis jobs. But this is not so easy to do in many cases. If you have ambient temperatures of 170 degrees C, it’s easy to see that this data is wrong and inconsistent.

Care should be taken in cleaning and handling your data, otherwise, you will skew and possibly destroy the ability to make good inferences or get right answers down the line. In the real world, you should expect to spend a lot of time in this step.

Analyzing The Data

This step in the process of data science is what data science is for. By the time you’ve spent all the energy it takes to sift through the data to see what you can find, you’d think asking the questions should be relatively straightforward. It’s not. Analyzing large data sets to get ideas and inferences or even ask complex questions is the most difficult challenge, one that requires the most human intuition in all of data science.

Data Science requires skills and experience in statistical techniques such as linear and logistic regressions and finding correlations between different types of data using a variety of probability algorithms and formulas such as formulas and concepts incredibly named “Naïve Bayes”.

Communicating The Results

After you’ve analyzed and edited your data into the format you need, and then analyzed the data to answer your questions, you need to present the results to management or the client. Most people visualize information better and faster when they see it in a graphic format rather than just text.

Maintaining the data

This is the data science step that everyone ignores in the process of data science. Once you’ve asked your first set of questions, got your first set of answers, many pros will just close and move on to the next project.

The problem with this way of thinking is that there’s a good chance you’ll have to ask more questions about the same data, sometimes quite a long way into the future. It is important to archive and document the following information so that you can restart the project quickly, or even more likely in the future, you will encounter a similar set of issues and can quickly dust off models and get answers faster.

Also, Read – Spacy in Machine Learning.

I hope you liked this article on the five-step process of data science. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Data Science and Machine Learning.