Data Analytics: intermediate Data analytics 2022
Expertise:
Technology:
- 1 Course | 34m 22s
- 5 Books | 32h 54m
- 6 Courses | 4h 29m 19s
- 5 Books | 33h 57m
Expand your insights and gain understanding of your business data with Data Analytics.
GETTING STARTED
Developing an AI/ML Data Strategy: The Data Analytics Maturity Model
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45s
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5m 45s
COURSES INCLUDED
Techniques for Big Data Analytics
Big data analytics provides a way to turn the vast amounts of data available in today's digital world into valuable insights. For this reason, big data analytics techniques have taken a central place in many businesses' IT infrastructure. These comprise complex processes and multiple stack layers that allow you to transform raw data into visualizations that demonstrate trends or other phenomena. Use this course to explore the basic principles and techniques of big data analytics in a business context. Go through each step of data processing to fully comprehend the big data analytics pipeline. Furthermore, explore various use cases of big data analytics through real-world examples. When you're done with this course, you'll have a foundational comprehension of some of the technologies behind big data and how these can drive business decisions for the better.
12 videos |
34m
Assessment
Badge
COURSES INCLUDED
Developing an AI/ML Data Strategy: The Data Analytics Maturity Model
Data analytics is used across various industries to help companies make better-informed business decisions. Data analysts capture, process, and organize data in addition to establishing the best way to present that data. Through this course, learn about the uses and benefits of data analytics and the tools to leverage it. Examine the data analytics maturity model and compare the descriptive, diagnostic, predictive, and AI types of data analytics. Next, discover how data analytics can be used across teams and the benefits it offers. Finally, discover the different types of tools designed for data storage, cleaning, visualization, analysis, and collaboration. Upon completion, you'll be able to outline what data analytics is and list common data science tools.
10 videos |
39m
Assessment
Badge
Developing an AI/ML Data Strategy: Building an AI-powered Workforce
Building a successful data team is a key part of a data strategy. To build proper data teams, it's important to know how they are structured and the roles of each member. Through this course, learn how to build an AI-powered workforce with a data team. Discover the need for an AI-powered workforce and three main structure types of a data team. Learn how to determine which strategy is preferable for a data team. Next, explore the potential shifts in job roles and responsibilities due to AI integration, the role of managers in driving AI adoption and change management, and strategies for fostering a culture of innovation and AI awareness within the workforce. Finally, explore the roles of data team members, how to evaluate an organization's strategy, and how to move an organization toward a data-driven culture. After course completion, you'll be able to outline the functions and best practices for a data team.
11 videos |
45m
Assessment
Badge
Developing an AI/ML Data Strategy: Data Analytics & Data Ethics
Growing fields of data analytics and artificial intelligence (AI) provide many benefits to individuals and society, but also raise ethical concerns regarding privacy, transparency, and bias. How can organizations collect, store, and use data ethically, and what ethical safeguards must be maintained? Through this course, learn about data ethics and its importance in AI. Explore the concept of data ethics and a manager's role and responsibility to maintain ethical standards on their team. Next, discover the key principles and considerations for data ethics in AI. Finally, learn about data ethics frameworks that are used across a variety of industries. After course completion, you'll be able to identify the importance of data ethics and its concerns and best practices.
8 videos |
38m
Assessment
Badge
Developing an AI/ML Data Strategy: Aspects of a Robust AI Strategy
In today's artificial intelligence (AI)-driven landscape, it's vital to recognize the ethical dimensions of AI implementation. This course covers essential aspects of AI strategy, including defining its components, understanding the leadership role, and conducting readiness assessments. You will also explore implementation challenges, use case identification, alignment with existing technology, and setting success metrics. You will discover how to leverage agile methodologies and business model impacts. Finally, you will learn to analyze case studies, clarify managerial responsibilities, and promote cross-functional collaboration in AI initiatives.
14 videos |
48m
Assessment
Badge
Developing an AI/ML Data Strategy: Data Bias & Ethical Considerations in AI
This course focuses on the ethical aspects of data analytics and artificial intelligence (AI) in our rapidly evolving world. It explores how AI and data analytics impact our society and emphasizes the importance of addressing ethical concerns, particularly data bias. In this course, students will learn to identify and address data bias in AI, exploring its real-world implications. Additionally, students will delve into the ethical aspects of AI, including transparency, fairness, and regulatory compliance, while considering the manager's role in ensuring ethical AI practices. By the end of this course, students will have a thorough grasp of the ethical issues that arise in AI and data analytics and be able to identify, assess, and mitigate bias in AI systems.
13 videos |
35m
Assessment
Badge
Developing an AI/ML Data Strategy: Data Management & Governance in AI
The rapidly growing fields of data analytics and artificial intelligence (AI) offer immense advantages to individuals and society. Nevertheless, there are also challenges related to data management and governance within the context of AI. Begin this course by exploring the practical knowledge and skills necessary for effective data management and governance in the context of AI projects. Discover how data quality, integrity, availability, and adherence to governance frameworks are crucial in AI projects. Next, examine data lineage, data privacy regulations, and data accessibility. Then focus on the risks of incomplete or biased data and methods for handling large and complex datasets. Finally, investigate metadata management, managerial responsibilities in data governance, and ethical considerations in data usage. At course completion, you will be able to effectively manage data in AI projects and navigate the complex landscape of AI and data analytics with a strong foundation in data management and governance principles.
14 videos |
1h 1m
Assessment
Badge
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EARN A DIGITAL BADGE WHEN YOU COMPLETE THESE COURSES
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.BOOKS INCLUDED
Book
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and ChallengesThis book is intended to present the state of the art in research on machine learning and big data analytics.
10h 21m
By Aboul Ella Hassanien, Ashraf Darwish
Book
Big Data Analytics for Internet of ThingsThis book delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security.
6h 50m
By Mohammad Ahsan Chishti, Tausifa Jan Saleem
Book
Big Data Analytics for Large-Scale Multimedia SearchIncluding numerous tables, illustrations, and figures, this book presents a timely overview of cutting-edge technologies for multimedia retrieval with a special emphasis on scalability.
7h 42m
By Benoit Huet, Edward Y. Chang, Ioannis Kompatsiaris (eds), Stefanos Vrochidis
Book
Distributed Computing in Big Data Analytics: Concepts, Technologies and ApplicationsAddressing the key aspects of distributed processing in big data analytics, this valuable book tackles the essential concepts and patterns of distributed computing widely used in big data analytics.
3h 10m
By Ganesh Chandra Deka (eds), Robin Singh Bhadoria, Sourav Mazumder
Book
Big Data Analytics for Cyber-Physical SystemsThis book highlights research and survey articles dedicated to big data techniques for cyber-physical system (CPS), which addresses the close interactions and feedback controls between cyber components and physical components.
4h 51m
By Bei Yu, Shiyan Hu
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BOOKS INCLUDED
Book
A General Introduction to Data AnalyticsAn essential guide to understand and use data analytics, this highly accessible book is written using easy-to-understand terms and does not require familiarity with statistics or programming.
5h 10m
By Andre Carvalho, João Mendes Moreira, Tomáš Horváth
Book
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and ChallengesThis book is intended to present the state of the art in research on machine learning and big data analytics.
10h 21m
By Aboul Ella Hassanien, Ashraf Darwish
Book
Data Analytics: Principles, Tools, and Practices: A Complete Guide for Advanced Data Analytics Using the Latest Trends, Tools, and TechnologiesThe book introduces the essentials of data science and the modern ecosystem, including the important steps such as data ingestion, data munging, and visualization.
6h 51m
By Chitra Lele, Dr. Gaurav Aroraa, Dr. Munish Jindal
Book
Deep Learning for Data AnalyticsThis book provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments.
3h 53m
By Chattaranjan Pradhan, Himansu Das (eds), Nilanjan Dey
Book
Big Data Analytics for Large-Scale Multimedia SearchIncluding numerous tables, illustrations, and figures, this book presents a timely overview of cutting-edge technologies for multimedia retrieval with a special emphasis on scalability.
7h 42m
By Benoit Huet, Edward Y. Chang, Ioannis Kompatsiaris (eds), Stefanos Vrochidis
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