Data for Decision-makers and Leaders

Dealing with large amounts of data is essential to any modern business and to become a data-driven organization, leaders and decision-makers must establish a deeply ingrained data culture. Use this course to understand the underlying principles of analyzing data and get familiar with terms related to data in order to properly deliver data-related projects. This course will help you identify the basic concepts and processes related to data analysis, modern data sources, and data pipelines. You'll also discover fundamental principles of data storage, migration, and integration, along with common methods for data visualization and reporting. Having completed the course, you'll be well versed in foundational concepts of data, related terminologies, and various data processing methods.

Databases are essential in working with large amounts of data. Managers, leaders, and decision-makers need to choose the right approach when working on a large data project, distinguishing among multiple database types and their use cases. A relational database is a primary traditional data architecture commonly used by most businesses. Working with relational databases has some key advantages but also poses certain limitations. In this course, learn how critically evaluate and work with relational databases. Explore normalization and denormalization of datasets along with specific use cases of these opposite approaches. Examine two main online information processing systems, Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) systems. Finally, investigate the concepts of data warehousing, data marts, and data mining. Upon completion, you'll be able to identify when and how to use a relational database.

Data warehouses are actively used for business intelligence and, because they integrate data from multiple sources, are advantageous to simple databases in many instances. Considering modern companies often have ETL-based data warehousing systems, decision-makers need to comprehend how they operate and are appropriately managed. In this course, learn the necessary concepts and processes required to work with and manage projects related to data warehousing. Study data warehousing architectures and schemas and investigate some core data warehouse elements, such as dimension, fact tables, and keys. Furthermore, examine the extract, transform, and load (ETL) approach for working with data warehouses, specifying process flow, tools, and software as well as best practices. When you're done, you'll know how to adopt data warehousing and ETL systems for your business intelligence and data management needs.

As technology advances, new ways to store, process, and analyze data emerge. For example, large database systems, which require a lot of storage space, have been moved to the cloud and made remotely accessible to many users. These kinds of data infrastructures require business leaders to understand modern data systems and their working principles fully. Use this course to get to grips with the key differences between legacy data systems and modern infrastructures and explore crucial concepts related to modern data infrastructures. By the end of the course, you'll be able to argue why new age data infrastructures are necessary and traditional data systems are limited.

Final Exam: Data Primer will test your knowledge and application of the topics presented throughout the Data Primer track of the Skillsoft Aspire Data for Leaders and Decision-makers Journey.

Big data analytics has become an essential part of any business dealing with the digital world. The ability to collect large amounts of data and turn it into insights has transformed the world's business landscape. To properly manage projects using such technologies, leaders should at least have a foundational understanding of big data. Use this course to get to grips with the necessary concepts and terminologies you'll need when discussing big data projects. Learn about the primary sources and characteristics of big data. Then, dive into the world of big data analytics - exploring its main advantages, use cases, and significant challenges. When you've finished this course, you'll be able to speak about data-related projects, discussing relevant data infrastructures and architectures confidently.

Big data analytics, collecting vast amounts of data and transforming it into insights, drives major business decisions everywhere. Managers, decision-makers, data technicians, and data enthusiasts alike benefit from knowing how various systems and technologies are used in big data projects. Use this course to progress from a foundational comprehension of big data analytics to grasping more advanced concepts, like parallel and distributed computing systems and horizontal and vertical scaling. Take an in-depth look at Hadoop's main components and characteristics and how it's used for big data analytics. Then, delve into the various kinds of storage systems used in big data. Upon completing this course, you'll have a greater comprehension of the tools and methods used to execute big data projects.

Non-relational (NoSQL) databases are attractive for working with Big Data because they provide a way to store data from different sources in the same document and organize large amounts of diverse and complex data. Use this course to discover the principles behind non-relational databases and NoSQL, explore their benefits, and examine different types of non-relational architectures, such as document, key-value, graph, columnar, and multi-model databases. You'll also get familiar with HBase and NewSQL. After finishing this course, you will be able to identify the suitable NoSQL database required for any given business problem.

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.

Spark is an open-source, massively parallel, in-memory solution that allows you to run big data analytics pipelines at high speed. Use this course to learn how Apache Spark works and gain an understanding of its architecture. As you progress, investigate the industry-leading examples of Uber and Alibaba to recognize how Spark can add business value to data in many industry types. Moving along, compare the functionality of Spark and Hadoop in relation to use cases, identifying when using Spark is most advantageous. Finally, explore fundamental Spark characteristics, optimization techniques, and best practices. When you've completed this course, you'll have a solid theoretical understanding of how and when to use Apache Spark for specific big data analytics tasks.

Final Exam: Big Data Infrastructures will test your knowledge and application of the topics presented throughout the Big Data Infrastructures track of the Skillsoft Aspire Data for Leaders and Decision Makers Journey.

Data mining and data science are rapidly transforming decision-making business practices. For these activities to be worthwhile, raw data needs to be transformed into insights relevant to your business's goals. In this course, you'll walk through each stage of the data mining pipeline covering all requirements for reaching a conclusive and relevant business decision. You'll examine data preparation, descriptive and predictive analytics, and predictive modeling. You'll also investigate the role of model validation and implementation in machine learning. On completion, you'll have a solid grasp of how data-driven decision-making has helped other businesses succeed and how it can help yours, too, if you employ the right methods.

Data preparation transforms raw data into datasets with stable structures suitable for predictive analytics. This course shows you how to produce clean datasets with valid data to ensure accurate insights for sound business decision-making. Examine the role data sources, systems, and storage play in descriptive analytics. Explore best practices used for data preparation, including data collection, validation, and cleaning. Additionally, investigate some more advanced data exploration and visualization techniques, including the use of different chart types, summary statistics, and feature engineering. Upon completing this course, you'll know how to gather, store, and analyze data to make reliable predictions and smart business decisions.

The data mining process provides the opportunity for businesses to collect additional information and insights that are unavailable through other everyday operations of the company. Use this course to learn more about how utilizing data mining effectively may provide a competitive advantage and additional knowledge about the market and competitors. Start by examining the essential concepts in data exploration using summary statistics and visuals and discover different data mining techniques. This course will also help you develop an understanding of the complete data mining process - data gathering, cleaning, exploration, and mining. After completing this course, you'll be able to use data mining to answer in-depth questions about any business.

Predictive analytics enable modern businesses to obtain important insights about the market and gaining a competitive advantage. Use this course to explore how predictive analytics could provide insights about the future, empowering businesses to make informed decisions. Start by delving deeper into the concepts of deep learning, machine learning, and data. You'll then examine neural networks theory of operation and discover natural language processing (NLP) and computer vision applications. You'll wrap up the course by developing a deeper understanding of the future of analytics. Once you've completed this course, you'll be able to identify how regression, classification, time-series analysis, and recommender engines can be used to drive business decisions. You'll also have a solid grasp of using machine learning and deep learning methodologies for predictive analytics.

Final Exam: Raw Data To Insights will test your knowledge and application of the topics presented throughout the Raw Data To Insights track of the Skillsoft Aspire Data for Leaders and Decision Makers Journey.

Cloud computing has revolutionized the way we build, deploy, and maintain applications. Due to this paradigm shift, many businesses now rent rather than own IT infrastructure. This new way of approaching infrastructure requirements has given way to multiple cloud computing models, such as Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Function as a Service (FaaS). Knowing what your options are for cloud computing can help you choose the best models for your needs. Use this course to gain a solid foundational understanding of cloud computing. Compare various cloud service providers based on services, available tools, and pricing. Learn about real-life use cases for cloud computing and how businesses can benefit from this ever-evolving technological feat. When you've completed this course, your newfound knowledge of cloud computing will help you decide how it can help your business.

Cloud-based applications have now become a norm due to zero upfront costs and ease of management. Deployment of cloud-based applications can get a digital product to a wide market without the need to purchase costly server equipment and hire hardware professionals. Use this course to learn about the characteristics of cloud-ready applications and the different concepts, tools, and techniques related to deploying and managing cloud applications. Start by discovering key concepts related to cloud storage technologies and platforms. You'll also examine and compare Hadoop Distributed File System (HDFS) and Amazon Simple Storage Service (S3). Finally, you'll get a chance to explore data centers and their major types. After you've finished this course, you'll be able to identify cloud-ready applications and implement tools and solutions to make them ready for cloud deployment.

In order to utilize cloud computing, systems need to be connected to cloud data platforms. AWS, Azure, and GCP are the three big competitors in the cloud data platform space. Furthermore, cloud computing also has its own set of challenges that need to be understood, in order to apply it properly and without issues. Use this course to learn about some of the available cloud data platforms. Discover cloud analytics, available cloud analytics tools, and the pros and cons of each platform. Explore the current challenges of cloud computing, along with what the future looks like for cloud computing technology. When you've completed this course, your newfound knowledge of cloud data platforms, analytics tools, and the challenges related to cloud computing will help you decide on the right fit for your organization.

Data lakes are a useful way of storing all your structured and unstructured data in a single repository. They're widely used in the data industry to quickly retrieve data in raw formats and expose them to data pipelines. Anyone working with data technologies would benefit from appreciating the power and intricacies of data lakes. Use this course to explore the different aspects of data lakes, including their evolution, architecture, and maturity stages. Examine the advantages of governed data lakes. Learn about different data lake platforms. Identify the risks and challenges associated with data lakes and distinguish between a data warehouse and a data lake. Upon completion of this course, you'll fully comprehend why and how data lakes are used.

In today's world, data warehouses have become necessary for making informed business decisions. The wide availability of data comes at an increased cost of storing it efficiently - a necessity for any business working with large amounts of data. Learn more about the key concepts, architecture, stages, use cases, and available solutions for data warehouses using this course. You will examine data warehousing solutions, architecture, and techniques, discover Amazon Redshift and Google BigQuery, and explore the concepts, such as batch, stream, and real-time analytics. This course will also help highlight the considerations for implementing a data warehouse for a business and the implementation steps and best practices required. After completing this course, you will have a foundational knowledge of implementing a data warehousing solution for your business.

Azure Databricks is a data analytics platform optimized to work with Microsoft Azure cloud services and is an example of a cloud platform designed to serve business analytics needs. Use this course to explore the architecture, features, advantages, and disadvantages of Azure Databricks - a leading cloud-based tool used for data engineering, and Snowflake - a data warehouse-as-a-service. Examine different types of data pipelines and their components and advantages. You will also compare various data pipeline tools and learn more about building a data pipeline through a case study. Upon finishing this course, you will be able to recognize the capabilities of different data warehouses and the steps required for building data pipelines.

Final Exam: Emerging New Age Architectures will test your knowledge and application of the topics presented throughout the Emerging New Age Architectures track of the Skillsoft Aspire Data for Leaders and Decision Makers Journey.

As companies transition to the digital age, it is increasingly essential for decision-makers to utilize the vast amount of data in their systems properly. Proper governance and a working knowledge of data management systems ensure a significant competitive advantage, allowing companies to have more insight into their work and utilize their resources more efficiently. Use this course to familiarize yourself with the various strategies for handling and transacting data. Examine how data management systems work, study domain-wise data handling, and outline strategies to develop data management systems. Study how to integrate data management into different domains and identify and prioritize domains in various fields of data technologies and data architectures. When you're done with this course, you'll have a solid foundational comprehension of how to establish appropriate data management solutions in an organizational setting.

Data governance is important in data management, as it focuses on the availability, consistency, usability, and security of data sources. Utilizing data governance is important for creating consistent pipelines for data management solutions. Use this course as an introduction to data governance, exploring how it relates to master data management and is implemented into a business program. Then, examine how to create consistent and transparent governance models across multiple domains in data management. Investigate data stewardship, integrity, and security, studying how data governance interacts with information technology in a business enterprise context. Identify the benefits of establishing multi-domain data governance. Lastly, list various ways different data management systems interact to maintain data integrity and enhance data security. Upon completing this course, you'll know how to implement a data governance model correctly for your data management systems.

Since low-quality data can provide poor insights and be detrimental to an organization, data quality improvement is essential in data management and governance. Use this course to learn how to improve the quality of your data. Learn how to distinguish between high and low-quality data. Then, examine the entire cycle for developing high-quality data from data acquisition, advanced data process implementation, and effective distribution. Recognize the importance of managerial oversight in information processing, data compliance, and governance implementations in developing high-quality data. As you advance, learn how to create an integrated system of good data quality management processes. Upon completing this course, you'll know the best techniques and cloud-based data management solutions to ensure the data used in decision-making is always of the highest quality.

Final Exam: Data Governance and Management will test your knowledge and application of the topics presented throughout the Data Governance and Management track of the Skillsoft Aspire Data for Leaders and Decision Makers Journey.

In today's fast-paced business landscape, data is the driving force behind strategic decision-making. Navigating the latest trends in data has never been more important or offered greater potential benefits for savvy leaders. In this course, you will be introduced to adaptive governance, data governance and agility, and unlocking the value of data through monetization. Then you will explore the role of artificial intelligence (AI)-driven storytelling, decision-making with AI insights, augmented data management, and integrating AI and automation into data management. Next, you will discover democratizing real-time data, tools and technologies for real-time insights, and ethical considerations for data practices. You will learn about data privacy and trust and the role of leadership in data adoption, and dive into data innovation case studies. Finally, you will examine continuous learning in the data landscape and emerging data trends.

The modern leader needs to gain insights into cutting-edge technologies and strategies. With those insights, the opportunities are practically limitless. In this course, you will be introduced to the power of practical data fabric, beginning with an exploration of data fabric, the benefits of data fabric, data mesh, and the relationship between data fabric and data mesh. Then you will discover how data fabric addresses data silos and complexity. Next, you will investigate data fabric architecture, the advantages of a data fabric architecture, and the role of data fabric in analytics. Finally, you will learn about operationalizing data fabric, the challenges and limitations of data fabric, and data fabric use cases.

Data is the driving force behind strategic decision-making, and the most successful organizations are the ones that recognize and champion understanding the health and performance of their data systems. They recognize the need for knowledge and skills in order to harness the full potential of data in our rapidly evolving digital world. In this course, you will unlock data observability, beginning with an introduction to data observability, benefits and challenges of data observability and the data observability framework. Then you will dive a little deeper and compare data observability to data quality. Next, you will explore data observability tools, different types of data observability, and data observability best practices. Finally, you will discover data observability use cases and the importance of having a data observability strategy.

Converged infrastructure is represented by systems containing preconfigured software and hardware in a single integrated software-defined architecture. Composability is a system design principle that focuses on the interrelationships of components. In this course, you will delve into converged and composable systems, beginning with an introduction to converged and composable systems, the benefits of converged and composable systems, and the elements of a converged system. Then you will focus on the modularity and interoperability in composable systems. You will learn about integrating diverse technologies into unified systems, business considerations for system strategies, composability building blocks, and adaptability in modern business. Finally, you will explore convergence and composability in cloud computing, challenges in converged and composable systems, composable systems and leadership, and use cases for converged systems.

Modern organizations must embrace data governance methodologies to remain competitive and compliant; of late, artificial intelligence (AI) has become a must-have for organizations. AI TRiSM (trust, risk, and security management) is the key concept that ensures AI models are governed and trustworthy. In this course, you will explore the role of AI TRiSM in risk management. Then you will focus on the pillars of the AI TRiSM framework, advantages of AI TRiSM, and how AI can be leveraged for informed decision-making. Next, you will discover how organizations can take advantage of AI TRiSM and examine the fundamental factors that make AI TRiSM successful. Finally, you will delve into how to achieve success with AI TRiSM and implement an AI strategy in your organization.

Final Exam: Emerging Data Trends will test your knowledge and application of the topics presented throughout the Emerging Data Trends track.