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Big Data LDN
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DataOps
AI
Digital Transformation
Big Data
Data Management
Machine Learning
Deep Learning
Register for this course and see more details by visiting:
OpenCourser.com/course/0om011/dataops
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Strengthens an existing foundation for intermediate learners
Provides automated, curated, and trusted data pipeline
Develops professional skills or deep expertise in DataOps
Teaches DataOps methodology with demonstrations
Helps learners accelerate journey to AI and digital transformation
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DataOps for AI and Digital Transformation.
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Career center
Learners who complete DataOps for AI and Digital Transformation will develop knowledge and skills
that may be useful to these careers:
Data Architect
Data Architect
Data Architects design and build the data architecture for an organization. This course helps build a foundation for this role by teaching DataOps methodology, which is essential for getting to business-ready data and providing an automated, curated, and trusted data pipeline.
Data Engineer
Data Engineer
Data Engineers build and maintain the data infrastructure that supports an organization's data needs. This course can help build a foundation for this role by teaching DataOps methodology, which is essential for getting to business-ready data and providing an automated, curated, and trusted data pipeline.
Data Scientist
Data Scientist
Data Scientists use data to extract insights and knowledge to help organizations make better decisions. This course may be useful for this role by teaching DataOps methodology, which can help ensure that the data used for analysis is accurate and reliable.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models that help organizations automate tasks and make better decisions. This course may be useful for this role by teaching DataOps methodology, which can help ensure that the data used to train machine learning models is accurate and reliable.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns that can help organizations improve their operations. This course may be useful for this role by teaching DataOps methodology, which can help ensure that the data used for analysis is accurate and reliable.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help organizations make better decisions. This course may be useful for this role by teaching DataOps methodology, which can help ensure that the data used for analysis is accurate and reliable.
Data Governance Analyst
Data Governance Analyst
Data Governance Analysts develop and implement data governance policies and procedures to ensure that data is used in a consistent and ethical manner. This course may be useful for this role by teaching DataOps methodology, which can help organizations ensure that their data is managed in a way that supports their business objectives.
Data Privacy Analyst
Data Privacy Analyst
Data Privacy Analysts develop and implement data privacy policies and procedures to protect the privacy of individuals. This course may be useful for this role by teaching DataOps methodology, which can help organizations ensure that their data is used in a way that complies with privacy regulations.
Data Quality Analyst
Data Quality Analyst
Data Quality Analysts evaluate the quality of data and develop and implement data quality processes to improve data accuracy and reliability. This course may be useful for this role by teaching DataOps methodology, which can help organizations ensure that their data is of high quality.
Data Security Analyst
Data Security Analyst
Data Security Analysts develop and implement data security policies and procedures to protect data from unauthorized access. This course may be useful for this role by teaching DataOps methodology, which can help organizations ensure that their data is secure.
Data Warehousing Architect
Data Warehousing Architect
Data Warehousing Architects design and build data warehouses that store and manage large amounts of data. This course may be useful for this role by teaching DataOps methodology, which can help organizations ensure that their data warehouse is efficient and effective.
Data Visualization Analyst
Data Visualization Analyst
Data Visualization Analysts use data visualization tools to create visual representations of data that can be used to communicate insights to stakeholders. This course may be useful for this role by teaching DataOps methodology, which can help organizations ensure that the data used for visualization is accurate and reliable.
Database Administrator
Database Administrator
Database Administrators design, build, and maintain databases that store and manage data. This course may be useful for this role by teaching DataOps methodology, which can help organizations ensure that their databases are efficient and effective.
IT Manager
IT Manager
IT Managers plan, organize, and direct the activities of an organization's IT department. This course may be useful for this role by teaching DataOps methodology, which can help organizations ensure that their IT infrastructure is aligned with their business objectives.
Product Manager
Product Manager
Product Managers develop and manage products that meet the needs of customers. This course may be useful for this role by teaching DataOps methodology, which can help organizations ensure that their products are based on accurate and reliable data.
For more career information including salaries, visit:
OpenCourser.com/course/0om011/dataops
Reading list
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A comprehensive overview of DataOps for IT practitioners, covering topics such as data architecture, data governance, and data security.
A technical guide to implementing DataOps in the financial industry.
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A technical guide to implementing DataOps in the manufacturing industry.
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A comprehensive reference on deep learning, covering theory, algorithms, and applications. Suitable for researchers and advanced students.
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Examines the transformative potential of AI in healthcare, discussing applications such as diagnostics, treatment planning, drug discovery, and personalized medicine.
Introduces the fundamental concepts and algorithms of reinforcement learning. Suitable for beginners with a background in probability and calculus.
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Provides insights into the global AI landscape, focusing on the competition between China and the United States. Suitable for general readers interested in the geopolitical implications of AI.
Provides a comprehensive overview of computer vision, covering image processing, feature extraction, and object recognition. Suitable for researchers and advanced students.
Offers a comprehensive introduction to pattern recognition and machine learning from a Bayesian perspective. It foundational text for those seeking a deeper theoretical understanding of the subject. While more mathematically rigorous, it valuable resource for advanced undergraduate students, graduate students, and researchers. It provides essential background for many AI techniques.
Explores the potential impact of AI on society and human civilization. Discusses ethical considerations and the future of work, education, and healthcare.
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Provides an overview of the ethical issues raised by AI, such as bias, privacy, and accountability. Suitable for anyone interested in the responsible development and deployment of AI.
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Delves into the safety and security challenges associated with AI, including adversarial attacks, hacking, and malicious use. Discusses potential solutions and policies.
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Provides a comprehensive overview of algorithmic game theory, covering both theoretical foundations and practical applications in AI and computer science.
Provides an introductory overview of AI, covering topics such as search algorithms, knowledge representation, and machine learning. Suitable for beginners with no prior knowledge of the field.
Covers various aspects of natural language processing, including text classification, sentiment analysis, and machine translation. Suitable for beginners with a background in Python.
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Definitive resource for understanding deep learning, a critical subfield of modern AI. It covers theoretical concepts, algorithms, and practical applications. It is often used as a textbook in graduate-level courses and is highly valuable for researchers and professionals looking to deepen their understanding of neural networks and deep learning techniques. A strong mathematical background is recommended for this book.
This concise book provides a solid introduction to the fundamental concepts of machine learning. It is praised for its clarity and practicality, making it a good starting point for those with a STEM background. It serves as a useful reference for quickly reviewing key machine learning algorithms and principles. While not exhaustive, it helps solidify understanding of core ML techniques relevant to AI.
Focuses on the practical aspects of machine learning, emphasizing intuition and understanding over mathematical rigor. Suitable for beginners with a background in programming.
Provides a comprehensive overview of AI, covering fundamental concepts, algorithms, and applications. Suitable for both beginners and experienced researchers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0om011/dataops
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Big Data LDN
This course belongs to a
collection
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Big Data LDN 2019 . It's comprised of 101 courses:
- The Database Is Only Half Done
- Your AI Is Wrong! Lessons from Decision-making with Imperfect AI
- Multiparadigm Data Science: The Next Frontier in Decision Making
- Achieving 100% Adoption of Analytics in the Enterprise
- Data Skills: How Mondi Does Digital
- Building a Modern Data Architecture for the Data Driven Enterprise
- Harnessing the Power of Data to Improve Public Health
- Real-time Data Analytics at Scale Using Cloud Services
- Quick and Easy Access to Data through a Logical Data Warehouse Environment
- The Promise of Data Mastering at Scale
- Research vs. Reality in AI: Would You Trust Your Model with Your Life?
- Modernise Your IT Infrastructure for AI
- Scaling Machine Learning at Holiday Extras
- Why Do Some Machine Learning Models Fail?
- AI: Lessons Learned from the Front Line
- Machine Learning in Real-time: Predicting Taxi Fare in NYC
- How AI and Automated Machine Learning Will Disrupt the Organisational Alignment of Businesses
- Multi-model Powered Machine Learning
- ML in Production: Serverless and Painless
- Leveraging Data Virtualization to Maximize Advanced Analytics and Machine Learning Potential in Your Data Architecture
- Bring the Power of Watson to AWS and Azure: Watson Anywhere Powered by Cloud Pak for Data
- Modern Data Warehousing at Scale Using Azure Data Factory
- Balancing Performance, Scalability, and High Availability in Your Database Environment
- Enabling Microservices for Big Data On-demand in the Cloud
- From ETL to ELT: Building a Scalable Data Stack with SQL
- Data, Governance, and Freedom
- How to Get the Technology Stack Right for Your Business
- Data Modelling with Apache Cassandra
- How to Orchestrate Your ML Pipelines in Hybrid Cloud
- Full Stack Metadata Driven Thinking to Drive Business Agility
- Enhance Developer Experience Using OpenShift on Azure
- Fast Data for Faster Business
- Driving Faster, Smarter Actions on IoT Data with New Real-time Analytics Capabilities
- Introduction to Adept and How Big Data and Analytics Is Transforming the Connected Car Ecosystem
- Bridge to Cloud
- How to Train Your AI Models at Scale
- 99% of Your IoT Data Is Worthless, but Which 99%?
- Data Engineering in MagicLab: The Birth and Life of an Event
- RNA - Event Stream Processing Made Simple
- When Data Leads, Transformation Follows - A Look at Qlik's Data Integration Portfolio
- Saving Energy in Homes with a Unified Approach to Data and AI
- Building Stream Processing Applications with Apache Kafka Using KSQL
- Real-world, Real-time Digital Transformation
- Machine Data in Industry 4.0: How to Handle It Better
- Why CockroachDB in the Kindred Platform?
- The Hardest Part of AI & Analytics Is Not AI, It Is Data Management
- Effective Cloud Migration Strategies for Your Modern Data Applications
- Snowflake? Why Should I Care?
- The Future of Business Intelligence Isn't a Tool
- AI-driven Retail for the 21st Century
- The Next Generation of Data Architecture
- Modern Enterprise Data Engineering
- Taking Control of User Analytics with Snowplow
- Flex Your Data(Ops) Muscles
- You Love Big Data, so Does R
- Building Predictive Maintenance Solutions
- Quickly, Codelessly Connect Any Application to Any REST API
- Rethinking Data Warehouse Modernization in the Age of Hybrid Cloud/On-premise Data Warehouses
- From Waterfall to Agile – A Data Warehouse Modernisation Story
- Freeing up Engineering and Infrastructure Resources to Scale with DataOps
- Unit Testing with pytest
- Credit Card Fraud – Why the Database Matters
- Data Science at SEGA
- British Telecom: Data-driven Digital Transformation
- Using Cloud Solution Platforms to Drive Business Value and Cost Optimization
- Data Analytics & IoT Innovations at Bosch
- Converting the CDO to a Profit Centre
- Why Self-service Data Analytics Usually Fails & Ideas for Succeeding
- Governed Data Discovery
- Journey to Create a Great Analytic Culture
- Something Amazing for Everyone: Embracing Inclusive Design to Deliver Accessible Experiences
- Democratising Data – How We Built a Self-serve Data Platform
- Discover the “Why” You Didn't Know Existed
- Selecting Your Fantasy Premier League Team with Advanced Analytics
- Decouple Storage from Compute
- Data Skills: Building a Data Team Fit for a Data-driven Enterprise
- Consent, Compliance, and the Impact on Customer Experience
- AI & Communication: Meaningful Data from New Sources
- Real-time Personalised Customer Interaction at Scale
- Data: The Musical, with LW Theatres
- Self-service and Tableau Community at Lloyds Banking Group
- The Digital Hero’s Journey: Using Big Data to Uncover Business Value and Meaning at Work
- Using Location Intelligence to Place Solar Panels in Rwanda
- Embedding Intelligence into the Heart of Customer Operations
- The Art of Customer Conversations
- How to Build a Fully Autonomous Card Fraud Detection System
- At Last! The Real-time Customer Experience
- Data Privacy Mythbusting
- Demystifying Data – What They Don’t Tell You at Data School
- Optimizing Your Data Value Chain with Agile Data Management
- Data at SEGA Europe
- Keep Your Data Lake from Getting Hadooped
- Top 10 Privacy Tips & Tricks in Big Data
- Data Governance as a Customer Service
- 7 AI Quick Win Projects
- Delivering Value with Intelligent Data Governance
- The Culture Competition – Personalisation vs. Privacy
- DataOps for AI and Digital Transformation (viewing)
- Big Data LDN: A GDPR Retrospective
- GDPR: A Sword Not a Shield
- Drive Business Growth While Satisfying GDPR and CCPA
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