Steps In A Data Science Project

Overview

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Objectives

• Understand the steps in Data science project

What is Data Science?

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data scientists apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificially intelligent systems to perform tasks. These systems generate insights which analysts and business users can translate into tangible business value.

What are the steps in a Data Science Project?

The following are the steps that are generally followed in Data Science projects.

Step 1 : Obtain Data

In this step, we obtain the data that we need from available data sources.

Step 2 : Scrubbing Data

Once we obtain the data from various sources, we need to clean it. The reason for this is that performing analysis or modelling unclean data gives results that are not useful and are not accurate. This step includes handling missing values, data encoding etc. This step is also know as “Data Pre-Processing”

Step 3: Explore Data

Once the data has been cleaned, we examine the data. in this step, we try to make sense of the data. what does this data represent?. What questions can be answered using this data?. What needs to be predicted using this data?. These are some of the questions answered in this step. We also try to identify significant patterns and trends in our data using data visualization.

Step 4 : Model Data

In this stage, we use the cleaned data to train machine learning models. These trained models then can be used to predict the outcome when a new entry of data is presented to it. For example: Train a spam detector using the mails in your inbox. When a new mail arrives, the trained model identifies if this mail is spam or not.

Step 5 : Interpreting Data

In this step, We deliver the results in to answer the business questions we asked when we first started the project, together with the actionable insights that we found through the data science process.

Key Points