While working on a data science project, we are analyzing a dataset based on various features and patterns to gain insight or train a machine learning model that can add value to an organization. As a beginner, you must be facing some difficulty in starting a data science project or what should be your first step after getting a dataset. So, in this article, I am going to take you through how to start a data science project.
Steps to Start a Data Science Project
Below are the steps that you need to follow to start a data science project:
- Understand the use case
- Obtain data
- Explore data
So these were the steps that you need to follow to start a data science project. Now let’s go through all the steps briefly one by one.
Understand the Use case:
The ultimate goal of a data science project depends on an organization’s use case. The problem statement or use case tells you what you need to achieve with this project and how your insights or a machine learning model should add value to an end-user or an organization.
So it is very important to understand the problem statement or the given use case to start a data science project. It helps in understanding:
- What is the need for working on the project?
- What kind of data you will require to add values to an organization from this project?
Obtain Data:
The next step to start a data science project is to obtain a dataset. To get the most appropriate data for your data science project you should go through some data sources like:
Explore Data:
Exploring a dataset is the most important step while working on a data science project. Once the data is available you just cannot start finding the final output. You first have to clean the data by removing or filling the missing values and the outliers. Then you can start exploring your data to find patterns.
To explore your data, you can ask questions from the dataset to understand how you should prepare your data to train machine learning models or gather insights from the data to add value to an organization. Here are some of the questions you should always ask about your data when working on a data science project:
- Is the dataset skewed towards a range of values?
- Are there any missing values in the dataset? If yes, what approach you should choose to fill the missing values?
- Are there any outliers in the data? If yes, then how you will handle those outliers?
- What approach you should use to rescale the data?
- How to handle the long tail of categorical features?
These are some questions that I always ask while working on a data science project. You can also frame more questions that you should always solve as it will help you to find a way to the next steps that you need to follow to add value from your data science project.
Summary
I hope you now have understood the steps you should follow for starting with a data science project. Once you get a start you can easily apply your knowledge and programming skills to add value to an organization or an end-user with your data science project. To understand more you can go through some of the data science and machine learning projects mentioned here. I hope you liked this article on how to start a data science project. Feel free to ask your valuable questions in the comments section below.
