Skip to main content

6 Phases of the Data Science Project Life Cycle

  • Author:
  • Updated date:
All about the data science project life cyle

All about the data science project life cyle

The Data Science Project Life Cycle

Data science is a complex field, and it can be easy to lose track of your workflow. To avoid that, follow this handy six-phase life cycle to help you stay organized and efficient.

1. Define the Problem

The first step in the process of a data science project is to define the problem.

Define Your Goals

You must know what you are trying to achieve with your project and for whom. Goals should be defined as quantifiable metrics that can be used as indicators of success or failure. For example, if your goal were "increase sales by 10%," then you would define specific metrics such as the revenue generated per quarter or the total number of customers acquired over a given period (e.g., three months).

Define Your Data Requirements and Sources

What data will be needed from which sources throughout this project? This includes raw source files (e.g., Excel spreadsheets) and intermediary files (e.g., SQL database tables). These may need to be created during processing steps later in the pipeline before being cleansed into another format for final use at other stages in your workflows like visualization toolsets like Tableau Desktop or Power BI Desktop.

2. Gather the Data

The next step in the data science project life cycle is gathering data. Data can be collected in multiple ways, from many sources, by many people, and at many times.
Data is the foundation of any project, and it's essential to ensure that you have enough data for your analysis or machine learning model to get valid results when you analyze it later on.

Before starting your project, you need to figure out what kind of data you need from each source because this will help inform how it's collected. For example: If I want to analyze my customers' buying habits online but don't have access yet (or don't know how), then maybe I should start collecting information about what they might buy instead.

3. Prepare the Data

One of the most important and also critical parts of data science is data preparation. Data preparation must be done before you can use any analytical methods, visualize your insights and communicate them to others.

Data preparation should be done for two primary reasons.


You must ensure that it is clean, accurate, and complete. You also need to ensure that there are no missing values or outliers in your dataset. In addition, you also need to check if there are any correlations between different variables in your dataset, which may cause bias during statistical analysis (e.g., linear regression). If needed, fix these problems using imputation techniques like kNN imputation or Gaussian process regression (GPR).

Visualization Purposes

One of the most common mistakes people make is not organizing their datasets properly before plotting them on graphs or charts such as bar charts or scatter plots, etc.

4. Analyze, Model, and Visualize

The fourth phase of the data science project life cycle is Data Analysis, Modeling, and Visualization. All of your data should be transformed into a useable format and ready to be analyzed.

Now we look at different ways to analyze datasets using tools like RStudio or Jupyter Notebook. We can start by looking at correlations in our dataset and then move on to more complex modeling techniques like regression models. Finally, after we've built a model, we can visualize the results using Tableau or another visualization tool like ggplot2 or d3 with JavaScript plots.

5. Communicate Results and Insights

The next step in the data science project life cycle is communicating results. There are many ways to do this, including creating visualizations or interactive dashboards that help with visualization and analysis. You can also use a database to store your findings, which is helpful if you want to refer back to them later on or share them with others on your team.

6. Deploy, Monitor, and Maintain Models

The final stage of the data science project life cycle is when your model gets ready to be deployed.

After creating a successful model and deploying it, you need to maintain it by monitoring its performance and fixing any bugs that may arise due to changes in data, technology updates, or user requirements.

Finally, always keep good documentation of all this work so that when someone else comes along, they can easily understand what has been done before them, what models have been made, and what their business value is.

Photo by Sebastian Herrmann on Unsplash

Photo by Sebastian Herrmann on Unsplash

You Must Follow a Workflow, or Things Will Get Messy

If you use the six phases of the data science project life cycle, then there will be a process that guides your team through it. If not, then there will be confusion and chaos.

An excellent example is when someone creates an experiment by selecting random variables and running them through a model without considering what might happen in the future or how those variables might interact with each other later in the project cycle. It's common for people who don't know what they're doing to start working on their experiments before completing all life cycle phases because they're eager to get results back quickly (I've been there, too!).

Unfortunately, they'll run into problems later when they realize that their models aren't performing as well as they initially thought due to missing data or a lack of understanding about how specific algorithms work together under certain conditions.


The data science project life cycle is an important thing to understand. But, if you don't follow the workflow, your data science project will quickly become messy. It would be helpful to remember that it all comes down to clean data and sound analysis at the end of the day.

This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.

© 2022 Hassan