Professional Certificate in DevOps Presentation for Data Science

Wednesday, 25 February 2026 11:24:56

International applicants and their qualifications are accepted

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Overview

Overview

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DevOps for Data Science: This Professional Certificate empowers data scientists.


Master CI/CD pipelines and automation. Learn to deploy and manage data science models efficiently.


This program is ideal for data scientists seeking to improve their workflow. It's also perfect for those wanting to enhance their cloud computing skills.


The DevOps certificate bridges the gap between data science and engineering. You'll gain practical skills in containerization and infrastructure management.


Become a more effective and valuable data scientist. Enroll today and transform your career!

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DevOps for Data Science: This professional certificate empowers you to automate and streamline your data science workflows. Gain in-demand skills in CI/CD, infrastructure as code (IaC), and containerization, bridging the gap between data science and IT operations. Accelerate your data science projects and deploy models faster. This intensive program features hands-on labs and real-world case studies, preparing you for high-impact roles like Data Engineer, MLOps Engineer, and DevOps Engineer. Boost your career prospects and command higher salaries with this sought-after certification in DevOps practices.

Entry requirements

The program operates on an open enrollment basis, and there are no specific entry requirements. Individuals with a genuine interest in the subject matter are welcome to participate.

International applicants and their qualifications are accepted.

Step into a transformative journey at LSIB, where you'll become part of a vibrant community of students from over 157 nationalities.

At LSIB, we are a global family. When you join us, your qualifications are recognized and accepted, making you a valued member of our diverse, internationally connected community.

Course Content

• Introduction to DevOps for Data Science
• Version Control with Git and GitHub for Data Science Projects
• CI/CD Pipelines for Data Science: Automation and Deployment
• Containerization with Docker for Data Science Applications
• Cloud Computing for Data Science (AWS, Azure, GCP)
• Infrastructure as Code (IaC) for Data Science Environments
• Monitoring and Logging in Data Science DevOps
• Security Best Practices in Data Science DevOps
• DevOps for Machine Learning Model Deployment

Assessment

The evaluation process is conducted through the submission of assignments, and there are no written examinations involved.

Fee and Payment Plans

30 to 40% Cheaper than most Universities and Colleges

Duration & course fee

The programme is available in two duration modes:
1 month (Fast-track mode): 140
2 months (Standard mode): 90

40% Cheaper

Our course fee is up to 40% cheaper than most universities and colleges.

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Awarding body

 The programme is awarded by London School of International Business. This program is not intended to replace or serve as an equivalent to obtaining a formal degree or diploma. It should be noted that this course is not accredited by a recognised awarding body or regulated by an authorised institution/ body.

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  • Start this course anytime from anywhere.
  • 1. Simply select a payment plan and pay the course fee using credit/ debit card.
  • 2. Course starts
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Got questions? Get in touch

Chat with us: Click the live chat button

+44 75 2064 7455

admissions@lsib.co.uk

+44 (0) 20 3608 0144



Career path

Career Role (DevOps Engineer, Data Science) Description
Senior DevOps Engineer (Cloud) Designs, implements, and maintains cloud-based infrastructure; automates deployment pipelines; ensures high availability and scalability of data science applications. High demand, excellent compensation.
Data Scientist with DevOps Skills Builds and deploys machine learning models into production environments; collaborates with DevOps engineers to optimize model performance and infrastructure. Growing field, strong earning potential.
DevOps Cloud Architect (Data Platforms) Designs and implements robust and scalable cloud infrastructure for data platforms; automates infrastructure provisioning and management; ensures data security and compliance. Competitive salary, leadership opportunities.
MLOps Engineer Focuses on the deployment and management of machine learning models in production; responsible for CI/CD pipelines for ML models; ensures model reliability and monitoring. Emerging role, rapid growth.

Key facts about Professional Certificate in DevOps Presentation for Data Science

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This Professional Certificate in DevOps for Data Science equips you with the essential skills to streamline your data science workflows. You'll learn to automate processes, improve collaboration, and deploy models efficiently, making you a highly sought-after data professional.


The program's learning outcomes include mastering CI/CD pipelines for data science projects, efficiently managing infrastructure using cloud platforms (like AWS or Azure), and implementing robust monitoring and logging systems. You'll also gain expertise in containerization (Docker, Kubernetes) and infrastructure as code (IaC).


This intensive program is designed to be completed within 12 weeks, offering a flexible learning schedule to accommodate busy professionals. The curriculum is meticulously crafted to ensure you gain practical, hands-on experience through engaging projects and real-world case studies.


DevOps practices are increasingly crucial in today's data-driven world. Organizations are actively seeking data scientists with strong DevOps skills to accelerate model deployment, enhance team collaboration, and improve overall efficiency. This certificate significantly enhances your career prospects and makes you a competitive candidate in the industry.


Graduates will be prepared for roles such as Data Scientist, Machine Learning Engineer, Data Engineer, and DevOps Engineer, all of which demand expertise in data science and DevOps practices. The certificate demonstrates your commitment to continuous integration, continuous delivery, and Agile methodologies, further strengthening your resume.

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Why this course?

A Professional Certificate in DevOps for Data Science is increasingly significant in today's UK market. The demand for skilled professionals who can bridge the gap between data science and efficient deployment is rapidly growing. According to a recent survey (hypothetical data for demonstration), 70% of UK tech companies report a skills shortage in this area. This necessitates professionals equipped with the expertise to streamline the data science workflow, from development to deployment, leveraging DevOps principles. This translates to enhanced efficiency, faster time-to-market for data-driven products, and improved overall ROI for businesses.

Skill Importance
CI/CD Pipeline Management High
Containerization (Docker, Kubernetes) High
Cloud Computing (AWS, Azure, GCP) Medium

Therefore, obtaining a DevOps certificate significantly enhances career prospects for data scientists in the competitive UK job market by equipping them with highly sought-after skills. This Professional Certificate proves expertise in essential DevOps practices, thereby improving employability and earning potential. The ability to efficiently deploy and manage data science models is a key differentiator in today's industry.

Who should enrol in Professional Certificate in DevOps Presentation for Data Science?

Ideal Candidate Profile Skills & Experience Career Aspirations
Data Scientists seeking to enhance their deployment skills Proficient in Python or R; experience with data analysis and modelling; familiarity with cloud platforms (AWS, Azure, GCP) is a plus. Faster, more efficient data product deployment; improved collaboration with engineering teams; increased earning potential—the average UK salary for a DevOps Engineer is £60,000+
Machine Learning Engineers aiming for end-to-end system responsibility Experience building and training ML models; understanding of CI/CD pipelines; knowledge of containerization (Docker, Kubernetes) is beneficial. Greater control over the entire ML lifecycle; improved model deployment and monitoring; leadership roles in data-driven organisations.
Data Analysts looking to automate workflows and improve efficiency Strong analytical skills; experience with data visualization tools; basic scripting skills are helpful. Streamlined data processing; improved data quality; increased productivity and reduced manual effort—saving valuable time for deeper analysis.