A Data Science Project Lifecycle

This is an article about the lifecycle of a Data Science project, how you can create a project using different components, and how to re-use them in other projects. Learn how to use your skills as a Data Scientist and apply it to multiple projects.


Hassan Faheem

a year ago | 5 min read

Data science is a dynamic field, and the lifecycle of data science projects almost always differs from one project to another. However, if your project is large, then its more likely that it will follow this general pattern:

Uploaded by Eden on Unsplash

Hypothesis generation

The first phase in the data science project life cycle is to generate a hypothesis. The hypothesis should be testable, measurable, and not too narrow or too broad. It should instead have the right level of generality for the problem you are trying to solve. A reasonable hypothesis can be written down clearly and concisely, another requirement for any valid question. Finally, it must also be testable to know whether your solution works when implemented on real data sets with real business problems.

For your group’s project idea to come together successfully, make sure that everyone involved knows their role from day one.

Data collection

Data collection is the next phase in the data science life cycle. But, first, you should know that the information and the data are two different things: data is raw, unorganized, and comprehensive, while information has been processed and analyzed to be more beneficial for decision-making.

During this project phase, you need to gather all the necessary data from your sources (such as internal databases) by running surveys or other types of research (interviews, focus groups). You also have to select which sources you want to use for your analysis. A wrong selection can ruin your entire analysis.

You will also have to clean up your data by removing errors such as misspellings or duplicates that could skew any results based on new insights gained during this phase. Once cleaned up, prepare it by transforming it into usable formats like spreadsheets or database tables so we can later analyze it with algorithms designed specifically for each type of input (e.g., regression models). Finally, store those results somewhere safe, so they don’t get lost in case something happens during later stages when we need them again.

Cleaning and Preprocessing

As you start working with data, it’s essential to realize that the stage at which you begin cleaning and preprocessing your data can significantly affect the quality of results later.

It’s easy to think that if you have a lot of data, there is no need to clean it up because your results will be better than those using less data. However, this isn’t necessarily true. If you don’t clean your data well, more time will be spent on collecting accurate information and will not lead to better conclusions or predictions.

So what exactly do I mean by cleaning and preprocessing? It refers to two key steps:

  • identify wrong or missing values
  • Convert categorical variables into meaningful numeric ones with enough samples per category so as not to introduce bias due to overfitting on small sample sizes.


Exploratory Data Analysis (EDA) is looking at data and identifying patterns and trends to help make informed decisions. A lot of specialized knowledge is not required outside of being able to understand how different variables affect each other.

There are three main steps in EDA:

  • Visualization: plotting data on a chart, like a histogram or a box plot; using different colors and fonts to make it easier for people to digest
  • Descriptive statistics: calculating mean, median, standard deviation, etc., across all variables
  • Correlation analysis: finding out which pairs of variables tend to move together more than others.

Model selection

You might be wondering what types of models there are and how to select the best one for your problem. The two main categories are regression (or supervised) models and classification (or unsupervised) models.

  • Regression models: These predict continuous values such as sales or temperature.
  • Classification models: These predict categorical values such as whether a customer will purchase your product or not or whether an email received is spam or not.

It’s important to note that regression can also be used in classification problems. For example, if you have images of cats and dogs labeled as either “cat” or “dog” using deep learning techniques, you could build a model which predicts which image belongs in each category by looking at the pixels in it.


Deployment is putting a model into production or making it available to users. Deploying a model differs from deploying a software application because it involves several stakeholders and can be more complex than expected.

A common misconception is that deployment is simply switching on a switch or flipping over a toggle to “deploy” your model. In reality, deployment involves many stakeholders who may not be familiar with each other’s roles and responsibilities in ensuring that everything goes smoothly once you flip that switch or toggle.

The deployment also requires attention to detail and careful planning so that you don’t waste time debugging problems related to the deployment itself rather than focusing on other aspects of your project, such as training data collection or feature engineering.

Model maintenance

Model maintenance is a crucial part of any data science project. Model maintenance is keeping your model up to date with the latest data and features, allowing it to adapt over time and become more valuable. A good model can adapt to changing data and features, including how users interact with the system or new events.

In 2022, I expect many new model maintenance tools to come into play due to technological advancements. These include:

  • Machine learning algorithms that can extract insights from raw data without being explicitly programmed about what those insights might look like
  • Automated systems capable of updating models without human intervention

Uploaded by Christina on Unsplash

Project Lifecycle Duration

The duration of the project lifecycle will depend on the number of people you have working in your group. The amount of data you’re dealing with will also factor into how long it takes to complete your project. For example, if you’re working on a small project (i.e., one person who needs help building their dashboard), it’s possible to do this in just a couple of weeks. On the other hand, if the scale of your project is large (i.e., multiple people who require assistance with various aspects of their data science projects). A large project can take up to six months to get through all validation/iteration/finalization stages.


This is just a slight taste of what the process might look like. Remember that these steps are essential for a successful data science project, and each one requires careful consideration. Also, some projects may not need every step or may even have an entirely different life cycle. The point is that it’s essential to know when each action should be taken so you can make sure your team is always moving forward productively without getting stuck on any one step too long.


Created by

Hassan Faheem

Data Scientist by Day, Blogger by Night. Obtained my Master of Science in Data Science from Heriot-Watt University. I am interested in writing about Analytics, Blogging and Productivity.







Related Articles