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Add data scientist lifecycle guid
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src/data/guides/ai-data-scientist-lifecycle.md
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src/data/guides/ai-data-scientist-lifecycle.md
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---
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title: "Data Science Lifecycle 101: A Beginners' Ultimate Guide"
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description: 'Discover the Data Science Lifecycle step-by-step: Learn key phases, tools, and techniques in this beginner-friendly guide.'
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authorId: fernando
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excludedBySlug: '/ai-data-scientist/career-path'
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seo:
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title: "Data Science Lifecycle 101: A Beginners' Ultimate Guide"
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description: 'Discover the Data Science Lifecycle step-by-step: Learn key phases, tools, and techniques in this beginner-friendly guide.'
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ogImageUrl: 'https://assets.roadmap.sh/guest/data-science-lifecycle-eib3s.jpg'
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isNew: true
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type: 'textual'
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date: 2025-01-29
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sitemap:
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priority: 0.7
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changefreq: 'weekly'
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tags:
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- 'guide'
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- 'textual-guide'
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- 'guide-sitemap'
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---
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Developing a data science project, from beginning to production is not a trivial task. It involves so many steps and so many complex tasks, that without some guardrails, releasing to production becomes ten times harder.
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Here’s where the data science lifecycle comes into play. It brings a structured approach so that [data scientists](https://roadmap.sh/ai-data-scientist), data analysts, and others can move forward together from raw data to actionable insights.
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In this guide, we’ll cover everything you need to know about the data science lifecycle, its many variants, and how to pick the right one for your project.
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So let’s get going\!
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## Core Concepts of a Lifecycle
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To fully understand the concept of the lifecycle, we have to look at the core concepts inside this framework, and how they contribute to the delivery of a successful data science project.
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### Problem Definition
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Every data science project begins with a clear definition of the problem to be solved. This involves collaborating with key stakeholders to identify objectives and desired outcomes. Data scientists must understand the context and scope of the project to ensure that the goals align with business or research needs.
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### Data Collection
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In the data collection phase, data scientists and data engineers work together and gather relevant data from diverse data sources. This includes both structured and unstructured data, such as historical records, new data, or data streams.
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The process ensures the integration of all pertinent data, creating a robust dataset for the following stages. Data acquisition tools and strategies play a critical role in this phase.
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### Data Preparation
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This stage addresses the quality of raw data by cleaning and organizing it for analysis. Tasks such as treating inaccurate data, handling missing values, and converting raw data into usable formats are central to this stage. This stage prepares the data for further and more detailed analysis.
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### Exploratory Data Analysis (EDA)
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The exploratory data analysis stage is where the “data processing” happens. This stage focuses on uncovering patterns, trends, and relationships within the data. Through data visualization techniques such as bar graphs and statistical models, data scientists perform a thorough data analysis and gain insights into the data’s structure and characteristics.
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Like every stage so far, this one lays the foundation for the upcoming stages. In this particular case, after performing a detailed EDA, data scientists have a much better understanding of the data they have to work with, and a pretty good idea of what they can do with it now.
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### Model Building and Evaluation
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The model building phase involves developing predictive or machine learning models tailored to the defined problem. Data scientists experiment with various machine learning algorithms and statistical models to determine the best approach. Here’s where data modeling happens, bridging the insights gained during the exploratory data analysis (EDA) phase with actionable predictions and outcomes used in the deployment phase.
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Model evaluation follows, where the performance and accuracy of these models are tested to ensure reliability.
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### Deployment and Monitoring
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The final stage of this generic data science lifecycle involves deploying the model into a production environment. Here, data scientists, machine learning engineers, and quality assurance teams ensure that the model operates effectively within existing software systems.
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After this stage, continuous monitoring and maintenance are essential to address new data or changing conditions, which can impact the performance and accuracy of the model.
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## Exploring 6 Popular Lifecycle Variants
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The data science lifecycle offers various frameworks tailored to specific needs and contexts. Below, we explore six prominent variants:
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### CRISP-DM (Cross Industry Standard Process for Data Mining)
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CRISP-DM is one of the most widely used frameworks in data science projects, especially within business contexts.
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It organizes the lifecycle into six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment.
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This iterative approach allows teams to revisit and refine previous steps as new insights emerge. CRISP-DM is ideal for projects where aligning technical efforts with business goals is very important.
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**Example use case**: A retail company wants to improve customer segmentation for targeted marketing campaigns. Using CRISP-DM, the team starts with business understanding to define segmentation goals, gathers transaction and demographic data, prepares and cleans it, builds clustering models, evaluates their performance, and deploys the best model to group customers for personalized offers.
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### KDD (Knowledge Discovery in Databases)
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The KDD process focuses on extracting useful knowledge from large datasets. Its stages include Selection, Preprocessing, Transformation, Data Mining, and Interpretation/Evaluation.
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KDD emphasizes the academic and research-oriented aspects of data science, making it an ideal choice for experimental or exploratory projects in scientific domains. It offers a systematic approach to discovering patterns and insights in complex datasets.
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**Example use case:** A research institute analyzes satellite data to study climate patterns. They follow KDD by selecting relevant datasets, preprocessing to remove noise, transforming data to highlight seasonal trends, applying data mining techniques to identify long-term climate changes, and interpreting results to publish findings.
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### Data Analytics Lifecycle
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This specific data science lifecycle is tailored for enterprise-level projects that prioritize actionable insights. It’s composed of six stages: Discovery, Data Preparation, Model Planning, Model Building, Communicating Results, and Operationalizing.
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The framework’s strengths lie in its alignment with business objectives and readiness for model deployment, making it ideal for organizations seeking to integrate data-driven solutions into their operations.
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**Example use case:** A financial institution uses the Data Analytics Lifecycle to detect fraudulent transactions. They discover patterns in historical transaction data, prepare it by cleaning and normalizing, plan predictive models, build and test them, communicate results to fraud prevention teams, and operationalize the model to monitor real-time transactions.
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### SEMMA (Sample, Explore, Modify, Model, Assess)
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SEMMA is a straightforward and tool-centric framework developed by SAS. It focuses on sampling data, exploring it for patterns, modifying it for analysis, modeling it for predictions, and assessing the outcomes.
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This lifecycle is particularly useful for workflows involving specific analytics tools. Its simplicity and strong emphasis on data exploration make it an excellent choice for teams prioritizing rapid insights.
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**Example use case:** A healthcare organization predicts patient readmission rates using SEMMA. They sample data from hospital records, explore patient histories for trends, modify features like patient age and diagnoses, build machine learning models, and assess their accuracy to choose the most effective predictor.
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### Team Data Science Process (TDSP)
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TDSP offers a collaborative and agile framework that organizes the lifecycle into four key stages: Business Understanding, Data Acquisition, Modeling, and Deployment.
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Designed with team-based workflows in mind, TDSP emphasizes iterative progress and adaptability, ensuring that projects align with business needs while remaining flexible to changes. It’s well-suited for scenarios requiring close collaboration among data scientists, engineers, and stakeholders.
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**Example use case:** A logistics company improves delivery route optimization. Using TDSP, the team collaborates to understand business goals, acquires data from GPS and traffic systems, develops routing models, and deploys them to dynamically suggest the fastest delivery routes.
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### MLOps Lifecycle
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MLOps focuses specifically on machine learning operations and production environments. Its stages include Data Engineering, Model Development, Model Deployment, and Monitoring.
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This lifecycle is essential for projects involving large-scale machine learning systems that demand high scalability and automation.
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MLOps integrates seamlessly with continuous integration and delivery pipelines, ensuring that deployed models remain effective and relevant as new data is introduced.
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Each of these frameworks has its own strengths and is suited to different types of data science operations.
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**Example use case:** An e-commerce platform deploys a recommendation engine using MLOps. They engineer data pipelines from user activity logs, develop collaborative filtering models, deploy them on the website, and monitor their performance to retrain models when new user data is added.
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## How to Choose the Right Data Science Lifecycle
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Determining the most suitable data science lifecycle for your data science project requires a systematic approach. After all, not all lifecycles are best suited for all situations.
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You can follow these steps to identify the framework that aligns best with your goals and resources:
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1. **Define your objectives:** Clearly identify the goals of your project. Are you solving a business problem, conducting academic research, or deploying a machine learning model? Understanding the end objective will narrow down your choices.
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2. **Assess project complexity:** Evaluate the scope and intricacy of your project. Simple projects may benefit from streamlined frameworks like SEMMA, while complex projects with iterative requirements might need CRISP-DM or TDSP.
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3. **Evaluate your team composition:** Consider the expertise within your team. A team with strong machine learning skills may benefit from MLOps, whereas a diverse team with varying levels of experience might prefer a more general framework like CRISP-DM.
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4. **Analyze industry and domain requirements:** Different industries may have unique needs. For example, business-driven projects often align with the Data Analytics Lifecycle, while academic projects might find KDD more suitable.
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5. **Examine available tools and resources:** Ensure that the tools, software, and infrastructure you have access to are compatible with your chosen lifecycle. Frameworks like SEMMA may require specific tools such as SAS.
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6. **Match to key stakeholder needs:** Align the lifecycle with the expectations and requirements of stakeholders. A collaborative framework like TDSP can be ideal for projects needing frequent input and iteration with business partners.
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7. **Run a trial phase:** If possible, test a smaller project or a subset of your current project with the selected framework. This will help you assess its effectiveness and make adjustments as needed.
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Follow these steps and you can identify the lifecycle that not only suits your project but also ensures that your data science process is efficient and productive. Each project is unique, so tailoring the lifecycle to its specific demands is critical to success.
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## Generic Framework for Beginners
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While there are many different data science lifecycles and ways to tackle data science projects, if you’re just getting started and you’re trying to push your first project into production, relying on a beginner-friendly lifecycle might be a better idea.
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A generic framework for beginners in data science simplifies the lifecycle into manageable steps, making it easier to understand and implement. You can follow these steps to define your new framework:
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### 1\. Define the problem
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Start by clearly identifying the problem you aim to solve. Consider the objectives and outcomes you want to achieve, and ensure these are aligned with the needs of any stakeholder. This will help focus your efforts during development and set the right expectations with your stakeholders.
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### 2\. Collect and clean data
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Gather data from reliable and relevant sources. During this stage, focus on ensuring data quality by treating inaccurate data, filling in missing values, validating and removing potential data biases and finally, converting raw data into usable formats.
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### 3\. Analyze and visualize
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Explore the data to uncover patterns, trends, and insights. Use simple data visualization techniques such as bar graphs and scatter plots, along with basic statistical methods, to gain a deeper understanding of the dataset’s structure and variables.
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### 4\. Build and evaluate a model
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Develop a basic predictive model using accessible machine learning or statistical tools. Test the model’s performance to ensure it meets the objectives defined earlier during step 1\. For beginners, tools with user-friendly interfaces like Python libraries or Excel can be highly effective.
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### 5\. Share results and deploy
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Present your findings to stakeholders in a clear and actionable format. If applicable, deploy the model into a small-scale production environment to observe its impact and gather feedback for further improvement.
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**Tips for small projects:** Start with a problem you’re familiar with, such as analyzing personal expenses or predicting simple outcomes. Focus on learning the process rather than achieving perfect results. Use open-source tools and resources to experiment and build your confidence.
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Use this framework if this is your first data science project, evaluate your results, and most importantly, reflect on your experience.
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Take those insights into your next project and decide if for that one you would actually benefit from using one of the predefined standard lifecycles mentioned above.
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## Conclusion
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The data science lifecycle is a cornerstone of modern data science. By understanding its stages and principles, professionals can navigate the complexities of data science projects with confidence.
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Regardless of what you’re doing, dealing with unstructured data, creating models, or deploying machine learning algorithms, the lifecycle provides a roadmap for success.
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As data science experts and teams continue to explore and refine their approaches, the lifecycle framework remains a key tool for achieving excellence in any and all operations.
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Finally, remember that if you’re interested in developing your data science career, you have our [data scientist](https://roadmap.sh/ai-data-scientist) and [data analyst](https://roadmap.sh/data-analyst) roadmaps at your disposal. These roadmaps will help you focus your learning time on the really important and relevant topics.
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29
src/pages/ai-data-scientist/lifecycle.astro
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src/pages/ai-data-scientist/lifecycle.astro
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---
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import GuideContent from '../../components/Guide/GuideContent.astro';
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import BaseLayout from '../../layouts/BaseLayout.astro';
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import { getGuideById } from '../../lib/guide';
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import { getOpenGraphImageUrl } from '../../lib/open-graph';
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const guideId = 'ai-data-scientist-lifecycle';
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const guide = await getGuideById(guideId);
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const { frontmatter: guideData } = guide!;
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const ogImageUrl =
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guideData.seo.ogImageUrl ||
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getOpenGraphImageUrl({
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group: 'guide',
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resourceId: guideId,
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});
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---
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<BaseLayout
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title={guideData.seo.title}
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description={guideData.seo.description}
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permalink={`/ai-data-scientist/lifecycle`}
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canonicalUrl={guideData.canonicalUrl}
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ogImageUrl={ogImageUrl}
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>
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<GuideContent guide={guide!} />
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<div slot='changelog-banner'></div>
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</BaseLayout>
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