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Big Data LDN
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Energy Efficiency
Databricks
Data Analytics
AI
IoT
Machine Learning
CI/CD
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Examines the full Databricks stack, which is industry standard for large scale data science projects
Emphasizes the importance of model performance tracking, which is crucial for data science projects
Stresses the practical aspects of data science by including production-grade pipelines and monitoring
Teaches large scale data science on IoT data, which is highly relevant in industry
May require prior knowledge in data science and machine learning
Taught by Erni Durdevic, a solutions engineer at Quby, who has experience in production-grade data science
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Saving Energy in Homes with a Unified Approach to Data and AI.
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Career center
Learners who complete Saving Energy in Homes with a Unified Approach to Data and AI will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze data to establish and achieve organizational goals. They apply mathematical and statistical techniques to model data and predict trends. This course introduces the technical workflow of a Data Science project from end to end, starting from developing a notebook prototype and tracking model performance with MLFlow, we move towards production-grade Databricks jobs with a CI/CD pipeline and monitoring system in place. This foundational knowledge in data science may help you establish a strong foundation for a career as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. They work with data scientists to understand the business problem and develop a machine learning solution. This course may be helpful for Machine Learning Engineers as it goes over the technical workflow of a Data Science project from end to end, starting from developing a notebook prototype and tracking model performance with MLFlow, we move towards production-grade Databricks jobs with a CI/CD pipeline and monitoring system in place. By taking this course, you may gain valuable insights into the development and maintenance of machine learning models.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make better decisions. They use statistical and data mining techniques to identify trends and patterns in data. This course may be helpful for Data Analysts as it goes over the technical workflow of a Data Science project from end to end, starting from developing a notebook prototype and tracking model performance with MLFlow, we move towards production-grade Databricks jobs with a CI/CD pipeline and monitoring system in place. This foundational knowledge in data analysis may aid you in successfully carrying out your responsibilities as a Data Analyst.
Business Analyst
Business Analyst
Business Analysts help businesses improve their performance by analyzing data and identifying opportunities. They work with stakeholders to understand the business problem and develop solutions. This course may be useful for Business Analysts as it provides an overview of how data science can be used to solve business problems. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better recommendations to stakeholders.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring a product to market. This course may be useful for Product Managers as it provides an overview of how data science can be used to improve product development. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better decisions about product development.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. They work with data analysts to understand the target audience and develop effective marketing strategies. This course may be useful for Marketing Managers as it provides an overview of how data science can be used to improve marketing campaigns. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better decisions about marketing strategy.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. They work with data analysts to understand the sales pipeline and identify opportunities for growth. This course may be useful for Sales Managers as it provides an overview of how data science can be used to improve sales performance. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better decisions about sales strategy.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. They work with data analysts to understand the business processes and identify opportunities for improvement. This course may be useful for Operations Managers as it provides an overview of how data science can be used to improve business operations. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better decisions about operational strategy.
Financial Analyst
Financial Analyst
Financial Analysts evaluate the financial performance of companies and make recommendations to investors. They use data analysis to identify trends and patterns in financial data. This course may be useful for Financial Analysts as it provides an overview of how data science can be used to improve financial analysis. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better recommendations to investors.
Risk Manager
Risk Manager
Risk Managers identify and assess risks to a business. They work with data analysts to understand the risks and develop mitigation strategies. This course may be useful for Risk Managers as it provides an overview of how data science can be used to improve risk management. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better decisions about risk management strategy.
Consultant
Consultant
Consultants provide advice to businesses on a variety of topics, including data science. They work with clients to understand their business problems and develop solutions. This course may be useful for Consultants as it provides an overview of how data science can be used to solve business problems. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you provide better advice to clients.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. They use data analysis to understand the market and develop products and services that meet the needs of customers. This course may be useful for Entrepreneurs as it provides an overview of how data science can be used to improve business performance. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better decisions about your business.
Teacher
Teacher
Teachers educate students in a variety of subjects, including data science. They use data analysis to understand student performance and develop effective teaching strategies. This course may be useful for Teachers as it provides an overview of how data science can be used to improve teaching and learning. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better decisions about your teaching practice.
Researcher
Researcher
Researchers conduct research to advance knowledge in a variety of fields, including data science. They use data analysis to understand the world around us and develop new theories and models. This course may be useful for Researchers as it provides an overview of how data science can be used to conduct research. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better decisions about your research.
Doctor
Doctor
Doctors diagnose and treat patients. They use data analysis to understand patient symptoms and develop effective treatment plans. This course may be useful for Doctors as it provides an overview of how data science can be used to improve patient care. By taking this course, you may gain valuable insights into data analysis and modeling, which can help you make better decisions about patient care.
For more career information including salaries, visit:
OpenCourser.com/course/t67p9c/saving
Reading list
We've selected three books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Saving Energy in Homes with a Unified Approach to Data and AI.
Provides a comprehensive overview of smart grid technologies and their applications in energy management. It covers topics such as smart metering, demand response, and distributed generation.
Provides a comprehensive overview of energy efficiency measures for the built environment. It covers topics such as building design, construction, and operation.
Save
Provides a comprehensive overview of energy efficiency standards for buildings. It covers topics such as building envelope, HVAC systems, and lighting.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/t67p9c/saving
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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 (viewing)
- 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
- Big Data LDN: A GDPR Retrospective
- GDPR: A Sword Not a Shield
- Drive Business Growth While Satisfying GDPR and CCPA
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