What is Data Science Life Cycle? | Updated 2025

Introduction

You have heard about data science. You know it’s powerful. But how do you actually do a data science project from start to finish? It’s not just about using algorithms. There’s a whole journey involved. It’s a series of steps that help you head in the right direction. In the end, you build something useful. We call this series of steps as the Data Science Life Cycle. Think of it as a roadmap for a project. It takes you from complex question to a real outcome. This outcome can make a real difference. Understanding life cycle in data science is like having a good plan before you build something complex. It keeps things organized and on track.

If you’re looking to learn this process the right way, a data science course with placement guarantee can be a smart starting point it not only teaches you the steps but also helps you land a role where you can put them into action.

In this blog, we will look into some of the best steps to follow in data science life cycle. We will talk about what happens at each step. We will also explain why it’s important.

Before getting into more details, lets us first understand what data science life cycle is.

What is Data Science Life Cycle?

The data science life cycle is a structured approach in solving problems with data from problem definition, data collection and cleaning to model deployment. It starts when teams identify a business challenge. They then collect relevant data from various sources and clean it for use. Next, they explore this data to find useful patterns. Teams build computer models that can make predictions based on these patterns. They test these models to ensure accuracy. Finally, they put the best model to work and watch its performance. This structured approach helps teams stay organized and avoid mistakes. Each step builds on the previous one, creating reliable solutions for business needs.

Let’s now understand why it is important to follow a structured data science life cycle for any given project.

Why a “Data Science Life Cycle” Matters for Your Project?

You might wonder: “Do I really need a ‘Data science life cycle’? Can’t I just get the data and start analyzing?” It’s a fair question. Starting right away can feel productive. But a structured approach offers serious advantages. This is especially true as projects get bigger or more complex.

Without a clear process, it’s easy to:

• Try to solve a problem that isn’t clearly defined
• Waste time cleaning data that might not be right for your goal
• Build a model that looks good on paper but doesn’t work in the real world
• Struggle to explain your results or how you got there
• Find it hard for team members to work together well

Using a life cycle of data science helps you avoid these problems. It makes you think through each step carefully. It ensures you ask important questions at the right times. It makes your project more manageable and transparent. It increases your chances of achieving a meaningful outcome. It’s about being systematic and thoughtful. This usually leads to better, more reliable results. It’s less about rigid rules and more about a smart way to work.

Steps Involved in Data Science Life Cycle

Below, we have discussed some of the steps involved in life cycle of data science.

Step I: Understanding Business Problem

The first step of data science life cycle is understanding the business problem. This is where every data science project should begin. Before you touch any data or write any code, you need to understand what you are trying to achieve in terms of business. This step is about defining the problem you are trying to solve. It’s about understanding the business context or research goal.

What does this really mean? It means getting clear on:

• The Core Problem: What specific issue needs addressing? Is it about increasing efficiency? Understanding customer behavior? Predicting future trends? Or something else?
• The Desired Outcome: What does success look like? What do you want to achieve? For example, instead of “improve marketing,” a better goal might be “identify customer groups most likely to respond to our new campaign.”
• Success Metrics: How will you measure if the project works? What numbers will tell you if you’ve hit the mark? For instance, “reduce customer complaints by 15%” or “increase website sales by 5%.”

Real – world Case Scenario

Let’s say you work on a project for an online store. The initial request might be “use data to boost sales.” That’s a start, but it’s too broad. You need to get into more details. Are they looking to attract new customers? Get existing ones to buy more? Reduce shopping cart abandonment? A more focused goal could be: “Build a system to predict what products a customer will buy next. Use this to personalize recommendations and increase order values.” This is much more specific and actionable.

Key activities in this step:

• Talk with stakeholders: Have detailed conversations with business users and experts. Talk to anyone who has an interest in the project’s outcome.
• Write clear objectives: Create specific, measurable goals for the project. Make sure they’re achievable and have deadlines.
• Define what you’ll deliver: What will the project produce? A report? A dashboard? A prediction system built into an app?
• Check if it’s doable: Consider the resources you have. Think about time, budget, people, data, and technology. Note any limits.

Don’t rush this step. A solid understanding of the project’s purpose is the foundation for everything else. If you get this wrong, even the best data analysis might be a waste of time. Clear goals guide your data collection, analysis, and model building. They ensure everyone works toward the same goal.

Step II: Data Collection

Once you understand business’s goals, the next step in data science life cycle is to get the data you need. Data is crucial for any data science project. Without it, you can’t do much. This step is about finding, sourcing, and gathering the necessary information.

Data can come from many places:

• Internal Systems: These are often the first place to look. Think company databases, sales records, customer systems, inventory logs, or internal documents.
• Public Datasets: Many government agencies and research groups publish free data. Examples include census data, economic indicators, and health statistics.
• Third-Party Data Providers: Sometimes companies buy data from vendors who collect datasets on specific topics. This might include market research or financial data.
• APIs: Many web services offer APIs. These let you access their data through code. Examples include social media platforms, weather services, and stock market feeds.
• Manual Collection: For some projects, you might collect data yourself. This could be through surveys, experiments, or observations.
• Web Scraping: This involves extracting data from websites. Be ethical and respect website terms if you use this method.

During data collection, consider:

• Relevance: Does the data relate to the problem you’re solving?
• Amount: Do you have enough data? Many machine learning models need lots of data to learn well.
• Access: Can you actually get the data? Are there permission issues, technical barriers, or costs?
• Format: What format is the data in? Will it take much effort to convert it to a usable form?
• Quality: While detailed cleaning comes next, get an early sense of the data’s reliability.

For our online store project (predicting next purchases), we would need data like:

• Customer purchase history (items bought, dates, prices, amounts)
• Product information (categories, descriptions, features)
• Customer browsing history (pages viewed, items clicked, time spent)
• Customer demographics (if available and appropriate)

Getting data can range from running a simple database query to setting up complex data pipelines. Document where your data comes from and how you got it. This helps with reproducing your work and understanding any limits or biases in your dataset.

Step III: Data Preparation & Preprocessing

You have got your data. Great! But it’s probably not ready to use yet. Real-world data is often messy, incomplete, or in the wrong format. Next step in data science life cycle is about transforming your raw data into a clean, usable dataset. People call this data preparation, data cleaning, or data wrangling.

Many data scientists say this is the most time-consuming part of the whole process. It can take up 60-80% of project time. But it’s critical. If you feed poor data into your analysis or models, you will get poor results.

What kinds of issues do you typically face?

• Missing Values: Some data points might be missing. You need to decide what to do. Remove the records? Fill in the missing values with averages? Use more advanced methods?
• Wrong or Inconsistent Data: Typos and data entry errors are common.
• Outliers: These are extreme values far outside the typical range. Check if they are real data points or errors. Then decide how to handle them.
• Irrelevant Data: Some columns or rows might not help your project. You can remove these.
• Data Transformation Needs: You might need to change your data’s structure or format. This includes:
  • Feature Engineering: This is important and often creative. It means creating new, useful features from existing data. From a birth date, you could create an “age” feature. From transaction dates, you could create “days since last purchase.” Good feature engineering can greatly boost model performance.
  • Scaling: Bringing number features to a similar scale can be important for some algorithms.
  • Encoding Categories: Machine learning models work with numbers. Text categories (like “red,” “green,” “blue”) often need to become numbers.

Think of this step as getting data ready before EDA:

• finding and fixing errors
• transforming data into the right format
• standardizing and scaling
• dealing with outliers or bad data

This step needs patience, attention to detail, and domain knowledge. The cleaner your data, the more reliable your analysis and models will be. Document all your cleaning and transformation steps. This is vital for reproducing your work and understanding what you did.

Step IV: Exploratory Data Analysis – EDA

With your data cleaned and ready, it’s time to dig in and understand what you have. Next step in data science life cycle is Exploratory Data Analysis, or EDA. You are examining your dataset to find patterns, spot oddities, test assumptions, and find interesting relationships.

EDA is crucial because it helps you:

• Gain a deep understanding of your data’s structure and content
• Find trends, correlations, and distributions in the data
• Detect any remaining outliers or issues you might have missed
• Develop ideas that you can test later with statistical models
• Make better choices for feature selection and model building

What techniques do you use in EDA?

• Summary Statistics: Calculate basic stats for your variables.
  • For numbers: average, middle value, most common value, spread, min/max values
  • For categories: counts and proportions
• Data Visualization: This is key to EDA. Seeing your data can reveal insights that numbers alone can’t show. Common plots include:
  • Histograms: Show how a single number variable is distributed
  • Bar Charts: Compare counts or values across categories
  • Pie Charts: Show proportions (use carefully, bar charts are often better)
  • Scatter Plots: Show relationships between two number variables
  • Box Plots: Great for comparing distributions and finding outliers
  • Line Charts: Show trends over time
  • Heatmaps: Show correlations between many variables

For our online store project (predicting next purchases), during EDA we might:

• Plot how many items are in each order
• Make scatter plots to see if customer age relates to spending
• Use bar charts to see which product categories are most popular
• Analyze purchase patterns over time

EDA isn’t a one-time thing. It’s often a back-and-forth process. You make some plots, which lead to new questions. These lead to more exploration. You might even go back to data preparation if you find new issues. The insights you gain here are invaluable. They help you build intuition about your data. They help you make better decisions when building models.

Step V: Model Development

This is where the “science” in data science often shines. You understand the problem. You have gathered and prepared your data. You have explored it to gain insights. Next step in data science life cycle is Model development. All the insight you have explored is used as a foundation to build a model. A model is a mathematical representation that learns patterns from your data. It makes predictions, classifies items, or uncovers deeper structures.

What does building a model involve?

• Choosing an Algorithm: There are many machine learning algorithms available. Each has strengths and weaknesses. Each suits different problems and data types. Your choice depends on your project goal (from step I) and what you learned about your data (from Step IV).
  • Regression Algorithms: Predicting a number (like sales amount)? Use regression algorithms (Linear Regression, Random Forest)
  • Classification Algorithms: Predicting a category (will a customer click this ad)? Use classification algorithms (Logistic Regression, Decision Trees, Neural Networks)
  • Clustering Algorithms: Grouping similar items without labels? Use clustering algorithms (K-Means, DBSCAN)

You might try several algorithms to see which works best.

• Splitting Your Data: This is critical for honest evaluation. Don’t use all your data to train the model. Instead, divide it:
  • Training Set: Most of your data. The model learns patterns from this set.
  • Test Set: Keep this completely separate. Don’t show it to the model during training. Use it at the end to see how well your model works on new data.
  • Validation Set: Sometimes you create a third split. Use this during development to tune your model without touching the test set.
• Training the Model: The algorithm processes the training data and learns patterns. It adjusts its internal settings to minimize errors or best represent relationships in the data.
• Tuning Settings: Most algorithms have “hyperparameters.” These are settings you configure before training starts. Finding the best settings can greatly improve performance. This often means trying different combinations and checking them on the validation set.

For our online store project, we would likely choose a classification or recommendation algorithm. We would train it on customer purchase histories and product features. The model would learn patterns that suggest which products a customer might want next.

This step in life cycle of data science is often repetitive. You train a model, evaluate it, find it’s not good enough, and loop back. You might try different features, different algorithms, or tune your settings more.

Step VI: Model Evaluation

You have built a model! But now comes the crucial question: how good is it? Does it actually work well for its intended task? This is where model evaluation comes in. You need to test your model’s performance, especially on data it has never seen (your test set).

Why is thorough evaluation so important?

• It measures how well your model will likely perform in the real world
• It lets you compare different models objectively
• It helps you see if your model is overfitting (great on training data but poor on test data) or underfitting (poor on both)
• It gives you confidence to deploy the model or tells you to improve it

How do you evaluate a model? The metrics depend on the problem type:

For Classification Models (like predicting if a customer will leave):

• Accuracy: The percentage of correct predictions. Simple but can mislead if classes are unbalanced.
• Precision: Of all the “yes” predictions, what fraction was actually “yes”?
• Recall: Of all the actual “yes” cases, what fraction did the model find?
• F1-Score: A balance between precision and recall.
• Confusion Matrix: A table showing right and wrong predictions in detail.

For Regression Models (like predicting sales figures):

• Mean Absolute Error: The average difference between predicted and actual values.
• Mean Squared Error: The average of squared differences. Penalizes big errors more.
• R-squared: Shows how much variance the model explains.

Beyond just metrics, interpret them in context of the original business problem. Is 80% accuracy good enough? It depends on what happens when predictions are wrong. If the model is not good enough, revisit earlier steps. Maybe you need better features, a different algorithm, or more data. Evaluation ensures your model is truly valuable.

Step VII: Model Deployment

Now, the next step in data science life cycle is model deployment. Testing shows it works well and meets project goals. Fantastic! But a model sitting on your computer isn’t creating value. The deployment step is about putting your model into production. There, people can use it to make decisions, automate tasks, or get insights.

What does deployment look like? It can take many forms:

• Adding to an existing app: Your model becomes a feature in a website or mobile app (like product recommendations)
• Creating an API: Very common. Your model gets wrapped in an API. Other systems send it data and get predictions back.
• Building a dashboard: Model outputs get visualized for business users to monitor and act on.
• Batch scoring: The model runs regularly (like nightly) on new data batches. Results get stored or update other systems.
• Edge deployment: For real-time needs (like smart cameras), models go directly on hardware devices.

Key things to consider during deployment:

• Can it scale? Can the system handle expected data volumes?
• How fast? How quickly can the model give predictions? Critical for real-time uses.
• Is it reliable? The system needs to be stable and handle errors well.
• Does it fit? How will the model work with current IT systems?
• Is it secure? Ensure the model and its data stay protected.

For our online store project, deploying the “next product” model might mean creating an API. The website could call this API for each user, get recommendations, and show them on the page.

Deployment often needs different skills than model building. It involves data scientists, software engineers, and IT teams working together. This step makes your data science work operational. It ensures ongoing value delivery.

Step VIII: Monitoring & Maintenance

Your model is deployed and being used. Is the project done? Not quite. The world changes. Data patterns shift. Customer behaviors evolve. Business environments transform. A model that worked great at first might get worse over time. This is why the final step in data science life cycle is monitoring and maintenance. And it never really ends.

Why is ongoing monitoring essential?

• Model Drift: The data your model sees can change over time. It might differ from training data. This can reduce accuracy. If new competitors enter the market, factors affecting purchases might change. This makes older models less effective.
• Data Quality Issues: Problems with incoming data (new formats, missing data) can hurt performance.
• Changing Goals: Original project objectives might evolve. This requires adjustments or rebuilding.

What should you monitor?

• Performance Metrics: Track key metrics (accuracy, precision, etc.) on live data. Set alerts for when performance drops too low.
• Data Changes: Watch input features for big changes compared to training data.
• System Health: Monitor operational aspects like uptime, speed, and error rates.

Maintenance activities include:

• Retraining: Regularly retrain the model with fresh data. How often depends on how fast things change.
• Rebuilding: Sometimes retraining isn’t enough. Big changes might require going back to earlier steps. You might need new data types, new features, or a completely new model.
• Updates and Fixes: The software supporting your model needs maintenance, updates, and bug fixes too.

Think of this step in life cycle of data science as regular check-ups for your model. It ensures your solution stays accurate and relevant. It continues delivering value over time. Insights from monitoring can trigger new projects or improvements to existing ones.

Frequently Asked Questions

Q1. What are the 5 steps in data science lifecycle?

Five steps in life cycle of data science are:

• Understanding the business problem
• Data collection, preparation, and preprocessing
• Exploratory Data Analysis – EDA
• Model Development, Evaluation, and Deployment
• Monitoring & Maintenance

Q2. What are the 7 steps of the data science cycle?

Seven steps in life cycle of data science are:

• Understanding the Business Problem
• Data collection
• Data Preparation, and Preprocessing
• Exploratory Data Analysis – EDA
• Model Development & Evaluation
• Model Deployment
• Monitoring & Maintenance

Q3. What is the data science life cycle model?

The data science life cycle is a step-by-step process. It starts with understanding the business problem. Then you collect data, clean it, analyze patterns, build models, and share results.

Q4. What is a data lifecycle?

A data lifecycle shows how information moves through different steps. First, data gets created or collected. Then it’s stored, used, shared, and finally deleted when no longer needed.

Conclusion

Data science life cycle is a complete process that guides a project from initial idea to working solution and beyond. It’s not always a perfectly straight line from start to finish. Often, you will jump back and forth between different steps. Insights from data exploration might send you back to data preparation. Poor evaluation results might make you revisit your algorithm choice or even your problem definition.

For students and professionals, life cycle of data science provides a solid framework for data science projects. It turns the complex task of data science into something more manageable.