End to End Data Science Project

by mahaveer rulaniyajan 25th, 2021

"Data has ENORMOUS power if it is used wisely"

Table of Contents

  • Steps involved in End to End Data Science Project
  • Business Domain Knowledge
  • Data Wrangling
  • Exploratory Data Analysis

We come across various resources explaining various concepts of Data Science. But have you ever actually get to know what an end to end Data Science project is?
In this session, we will discuss several steps at the End to End Data Science project and look briefly at each step.

We will overlook what a data science project looks like and then move each step ahead to complete a project.

Steps involved in End to End Data Science Project

While doing a Data Science Project, we generally do some random things without knowing the exact order or path.
Basically, there are majorly 5 steps that are involved in an End to End Data Science project.

1. Business Domain Knowledge
2. Data Munging
3. Exploratory Data Analysis
4. Model Building
5. Deployment of Model

As we can have a basic idea by reading these of what a particular step includes. We will discuss each step briefly below.

Business Domain Knowledge

What is meant by saying that to start a Data Science Project, you must know Business domain? As we have explained in the previous session CAREER PATHS FOR DATA SCIENCE  about the different profiles and jobs in companies related to data science and explained every profile, we must know the business domain to start working on a problem or project. What is the purpose of our problem? What solution is it looking for?
Here are some of the common understanding we may use while interpreting the Problem Statement are-

1. How much or How many ? (Regression)
2. Which Category? (Classification)
3. Which Group? (Clustering)
4. Is something unusual in the pattern? (Anomaly detection)
5. Which option Should be taken? (Recommendation)

These are some of the basic understanding we must have while trying to solve the problem. If we figure out the problem based on the above cases mentioned, we can move ahead on the next step.
After this step, we understand the problem and what our business or enterprise expect to solve.

Data Wrangling

Data Wrangling or Data Munging is one of the most important steps of the project. It is the process of extracting, cleaning, manipulating and organizing of data. We can categorize this step in three parts -

1. Cleaning Data
2. Manipulating Data
3. Organizing data

In the first step, basic cleaning is performed, such as removing the features or columns from data which are of no use. While in the second step, basic manipulation is done for the better interpreting the data. And then we organize the Data so that it is easier for us to perform the next step of exploring the data.

Exploratory Data Analysis

Exploratory Data Analysis or EDA is the process of statistically exploring and analyzing the data-set to find the dataset's hidden patterns and trends, often employing data visualization methods.
This is the part of Data Science project where statistics is used maximum.
Some of the major steps involved in EDA are-

1. Variable Identification
2. Uni-Variate analysis
3. Bi-variate analysis
4. Missing value Treatment
5. Outlier treatment

We will discuss all these steps of EDA in-depth in the upcoming sessions.
After performing these steps in EDA, we make Feature selection. What is Feature selection?
Feature Selection is the process in which we automatically or manually select those most relevant features and contribute significantly in predicting the output.
Feature selection is done on the basis of EDA.

Machine Learning Model Building

Now we have organized data and important features that contribute towards predicting the output. Now we build a Machine Learning model and select the best algorithm for our data-set, and then we train the model to get the prediction.

Deployment of Model

If we make a model and it is only limited to our computer, what is its use?
If we are doing a project, then it should be helpful or accessible to other people as well to make the best use of it. So the model is now deployed in the cloud platform by building a web-application using some of the frameworks such as Flask, Django etc.
After deploying the model, we get a public link that can be shared with anyone.
All the steps discussed above will be discussed in-depth in the upcoming sessions.

This blog was to give a brief overview of the Data Science project and steps involved in it.

In the next session we will discuss about the Tools and Technologies required in steps involved in End to End Data Science Project and then we will proceed in depth to all the steps.

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