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Big Data LDN
This course is no longer available. Find something similar by browsing:
Internet of Things (IoT)
Data Analytics
Machine Learning
Open Source Tools
Integrated End-to-End Platform
Register for this course and see more details by visiting:
OpenCourser.com/course/4r80ya/driving
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Shares techniques for gaining competitive advantages and decreasing costs
Emphasizes the advantages of combining data science, machine learning, and open-source tools
Suitable for learners seeking to enhance their IoT data handling and application development skills
In collaboration with Big Data LDN, recognized experts in data analytics
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Activities
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We're preparing activities for
Driving Faster, Smarter Actions on IoT Data with New Real-time Analytics Capabilities.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Driving Faster, Smarter Actions on IoT Data with New Real-time Analytics Capabilities will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
This course will teach you how to take advantage of data from IoT and online apps with data science, machine learning, and open source tools. This will be valuable for a Data Scientist that wants to leverage IoT and online app data for analysis and decision-making.
Data Analyst
Data Analyst
As a Data Analyst, you will learn from this course about how to act on customer and IoT data faster and smarter. You will also learn how to use data science, machine learning, and open source tools in an integrated end-to-end platform for fast data and event-driven application development.
Machine Learning Engineer
Machine Learning Engineer
This course will teach you how to use machine learning and open source tools to develop applications that can act on IoT and online app data in real-time.
Software Engineer
Software Engineer
This course can help you develop software that can act on IoT and online app data in real-time. You will also learn how to use data science and machine learning to improve the performance of your applications.
Product Manager
Product Manager
You will learn how to use data from IoT and online apps to better understand your customers and their needs. This will help you develop products and services that are more relevant and engaging.
Business Analyst
Business Analyst
As a Business Analyst, you will learn from this course how to use data from IoT and online apps to improve business processes. You will also learn how to use data science and machine learning to make better decisions.
Marketing Manager
Marketing Manager
This course will teach you how to use data from IoT and online apps to better reach and engage your target audience. You will also learn how to use data science and machine learning to improve the effectiveness of your marketing campaigns.
Operations Manager
Operations Manager
Through this course, you will learn how to use data from IoT and online apps to improve the efficiency and effectiveness of your operations. You will also learn how to use data science and machine learning to make better decisions.
Project Manager
Project Manager
This course can help you manage projects more effectively by teaching you how to use data from IoT and online apps to track progress and make better decisions.
Consultant
Consultant
You will learn from this course how to advise clients on how to use data from IoT and online apps to improve their businesses. You will also learn how to use data science and machine learning to provide more valuable insights.
Researcher
Researcher
This course can help you conduct research on IoT and online app data. You will learn how to use data science and machine learning to analyze data and draw meaningful conclusions.
Professor
Professor
If you are a Professor that specializes in data science, machine learning, or a related field, you may find this course helpful for your teaching and research.
Data Architect
Data Architect
This course may be useful for your career as a Data Architect by teaching you how to design and build data architectures that can handle the demands of IoT and online app data.
Database Administrator
Database Administrator
This course may be useful for your career as a Database Administrator by teaching you how to manage and maintain databases that store IoT and online app data.
Network Administrator
Network Administrator
This course may be useful for your career as a Network Administrator by teaching you how to manage and maintain networks that support IoT and online app data.
For more career information including salaries, visit:
OpenCourser.com/course/4r80ya/driving
Reading list
We haven't picked any books for this reading list yet.
Provides a comprehensive overview of the Internet of Things (IoT), covering the fundamentals, technologies, and applications. It is suitable for beginners and intermediate learners who want to gain a solid understanding of IoT.
Focuses on the design aspects of IoT systems, including architecture, protocols, and security. It is suitable for intermediate and advanced learners who want to develop and implement IoT solutions.
Provides a detailed overview of the technical aspects of IoT, including architecture, protocols, and applications. It is suitable for intermediate and advanced learners who want to gain a deep understanding of the underlying technologies.
Provides a comprehensive overview of data mining. It covers the basics of data mining, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about data mining.
Introduces the fundamental principles of data science and data-analytic thinking from a business perspective. It helps readers understand how to extract valuable knowledge and business value from data, covering various data mining techniques without getting overly technical. Based on an MBA course, it uses real-world business problems to illustrate concepts, making it highly relevant for business-oriented individuals and professionals.
Provides a practical introduction to statistical methods for data analytics. It covers the basics of statistics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about using statistics to analyze data.
Provides a comprehensive introduction to data analytics with Python. It covers the basics of Python, as well as more advanced techniques for data analytics. It valuable resource for anyone who wants to learn more about how to use Python for data analytics.
Provides a friendly introduction to data analytics for people who are new to the field. It covers the basics of data analytics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about data analytics without getting bogged down in technical details.
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Offers a practical and engaging approach to data science and analytics, focusing on using readily available tools like Excel to perform powerful analysis. It's a great resource for business professionals who want to leverage data without necessarily diving deep into programming. It provides a solid understanding of analytical techniques through relatable examples.
Provides a broad, introductory overview of data analytics concepts, making it ideal for beginners across various disciplines. It covers key data concepts and includes real-world examples and case studies to solidify understanding. Many universities use this book as a textbook for introductory data analytics courses. It serves as excellent background reading for anyone new to the field.
Offers a less technical introduction to statistical learning compared to its counterpart, 'The Elements of Statistical Learning.' It covers essential concepts and methods for statistical modeling and prediction, with practical applications in R. It is widely used as a textbook in universities and is suitable for those with a background in statistics or quantitative fields looking to deepen their understanding of the statistical foundations of data analytics.
Focusing on the crucial aspect of communicating insights, this book teaches the fundamentals of data visualization and how to tell compelling stories with data. It provides practical guidance and real-world examples to help readers create effective visualizations and presentations. is highly recommended for anyone who needs to present data-driven findings clearly and persuasively, regardless of their technical background.
Offers an accessible and engaging introduction to the fundamentals of statistics, a critical component of data analytics. It explains key statistical concepts using real-world examples and relatable anecdotes, making it an excellent resource for those without a strong mathematical background. It helps build a solid foundation in statistical thinking necessary for data analysis.
Written by the creator of the pandas library, this book practical, hands-on guide to data manipulation, cleaning, processing, and analysis using Python. It is an essential resource for anyone looking to use Python for data analytics, covering key libraries like pandas, NumPy, and Jupyter. It includes numerous real-world case studies and is widely used by students and professionals.
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Provides a comprehensive introduction to data science using the R programming language and the tidyverse package collection. It guides readers through the entire data analysis workflow, from importing and cleaning data to visualization and modeling. It's a widely recommended resource for those who prefer to use R for data analytics and is suitable for students and professionals.
Provides a guided tour of predictive analytics. It covers the basics of predictive analytics, as well as more advanced techniques. It valuable resource for anyone who wants to learn more about using predictive analytics to make better decisions.
Considered a classic in the field, this book provides a comprehensive and rigorous treatment of statistical learning methods. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and a variety of algorithms. While mathematically more demanding, it is an invaluable reference for graduate students and researchers seeking a deep understanding of the theoretical underpinnings of many data analytics techniques.
This practical guide focuses on machine learning concepts and techniques using popular Python libraries. It provides a hands-on approach with code examples, making it excellent for those who want to implement machine learning models as part of their data analytics workflow. It is suitable for individuals with some programming experience and valuable resource for deepening technical skills.
Provides a practical guide to big data analytics. It covers the challenges of big data, as well as the techniques and tools that can be used to analyze big data. It valuable resource for anyone who wants to learn more about big data analytics.
Provides a comprehensive introduction to data analytics with R. It covers the basics of R, as well as more advanced techniques for data analytics. It valuable resource for anyone who wants to learn more about how to use R for data analytics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4r80ya/driving
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Big Data LDN
This course belongs to a
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Big Data LDN 2019 . It's comprised of 101 courses:
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- The Next Generation of Data Architecture
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