Data Essentials: Data intermediate
Technology:
Expertise:
- 2 Courses | 1h 13m 44s
- 3 Books | 9h 42m
- 3 Courses | 2h 37m 12s
- 4 Courses | 3h 42m 8s
- 3 Courses | 2h 51m 39s
- 3 Books | 9h 42m
- 1 Course | 1h 13m 39s
- 1 Course | 45m 26s
- 5 Courses | 6h 7m 20s
- 16 Courses | 26h 43m 54s
These days, it is essential for businesses to work with large amounts of data on a daily basis. In this channel, you will explore the basics of data in data-driven organizations.
GETTING STARTED
Traditional Data Architectures: Relational Databases
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1m 44s
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3m 10s
GETTING STARTED
Setting up the Data Infrastructure in an Organization
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2m 12s
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7m 19s
GETTING STARTED
Emerging Data Trends: Navigating the Latest Trends in Data for Leaders
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1m 5s
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7m 48s
COURSES INCLUDED
Traditional Data Architectures: Relational Databases
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.
12 videos |
34m
Assessment
Badge
Traditional Data Architectures: Data Warehousing and ETL Systems
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.
12 videos |
38m
Assessment
Badge
COURSES INCLUDED
Setting up the Data Infrastructure in an Organization
In this course, you will look into the data mesh architecture and the process of selecting data platforms which best fulfill the needs of a large, data-driven organization. Begin by delving into two approaches to managing the data in an organization: a centralized data team and a data mesh architecture, which is a more federated approach. Explore how a data mesh allows individual domain teams in an organization to manage their own data as long as it is made available to other teams and adheres to certain standards. Next, discover the various considerations for selecting data-related tools in an organization. You will get a glimpse into Apache Kafka and RabbitMQ, two widely used messaging tools, and will see use cases where each of them excel. Finally, you will look into two use cases for data stores: one for a web or mobile app and another for a team performing data analysis. Here, you will look into the use of Apache Cassandra and the Snowflake platform.
7 videos |
45m
Assessment
Badge
New Age Data Infrastructures: Factors Driving Data Infrastructures
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.
12 videos |
37m
Assessment
Badge
Data Infrastructure: Databases in FSD Development
In this course, learners discover the role played by databases in the FSD (full stack development) process. The 14-video course explores differences between relational and non-relational databases and the advantages associated with each type; how to install and configure the MySQL, PostgresSQL, and MongoDB database systems; and how these systems are used in both the test and live environments of FSD development. Learn how to recognize best practices associated with the design of database systems in the FSD development process. You will then examine how to download, install, and configure the MySQL relational database system for use in FSD development. Then move on to the installation and configuration of the PostgreSQL, MongoDB NoSQL, and SQL Server relational database systems for use in FSD development. Learners will examine components required in both a test and live environment for FSD development, and the requirements of the FSD test environment and specific challenges. Finally, you will learn about the requirements of the FSD production environment and specific challenges.
14 videos |
1h 13m
Assessment
Badge
COURSES INCLUDED
Data Nuts & Bolts: Fundamentals of Data
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.
10 videos |
29m
Assessment
Badge
Modern Data Management: Data Management Systems
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.
12 videos |
1h 2m
Assessment
Badge
Modern Data Management: Data Governance
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.
12 videos |
1h 6m
Assessment
Badge
Modern Data Management: Data Quality Management
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.
12 videos |
1h 3m
Assessment
Badge
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FREE ACCESS
COURSES INCLUDED
Data Architecture Getting Started
In this 12-video course, learners explore how to define data, its lifecycle, the importance of privacy, and SQL and NoSQL database solutions and key data management concepts as they relate to big data. First, look at the relationship between data, information, and analysis. Learn to recognize personally identifiable information (PII), protected health information (PHI), and common data privacy regulations. Then, study the data lifecycle's six phases. Compare and contrast SQL and NoSQL database solutions and look at using Visual Paradigm to create a relational database ERD (entity-relationship diagram). To implement an SQL solution, Microsoft SQL Server is deployed in the Amazon Web Services (AWS) cloud, and a NoSQL solution by deploying DynamoDB in the AWS cloud. Explore definitions of big data and governance. Learners will examine various types of data architecture, including TOGAF (The Open Group Architecture Framework) enterprise architecture. Finally, learners study data analytics and reporting, how organizations can derive value from data they have. The concluding exercise looks at implementing effective data management solutions.
13 videos |
1h 2m
Assessment
Badge
Cloud Data Architecture: Cloud Architecture & Containerization
In this course, learners discover how to implement cloud architecture for large- scale data science applications, serverless computing, adequate storage, and analytical platforms using DevOps tools and cloud resources. Key concepts covered here include the impact of implementing containerization on cloud hosting environments; the benefits of container implementation, such as lower overhead, increased portability, operational consistency, greater efficiency and better application development; and the role of cloud container services. You will study the concept of serverless computing and its benefits; the approaches of implementing DevOps in the cloud; and how to implement OpsWorks on AWS by using Puppet which provides the ability to define which software and configuration a system requires. See demonstrations of how to classify storage from the perspective of capacity and data access technologies; the benefits of implementing machine learning, deep learning, and artificial intelligence in the cloud; and the impact of cloud technology on BI analytics. Finally, learners encounter container and cloud storage types, container and serverless computing benefits, and advantages of implementing cloud-based BI analytics.
10 videos |
44m
Assessment
Badge
Cloud Data Architecture: Data Management & Adoption Frameworks
Explore how to implement containers and data management on popular cloud platforms like Amazon Web Services (AWS) and Google Cloud Platform (GCP) for data science. Planning big data solutions, disaster recovery, and backup and restore in the cloud are also covered in this course. Key concepts covered here include cloud migration models from the perspective of architectural preferences; prominent big data solutions that can be implemented in the cloud; and the impact of implementing Kubernetes and Docker in the cloud, and how to implement Kubernetes on AWS. Next, learn how to implement data management on AWS, GCP, and DBaaS; how to implement big data solutions using AWS; how to build backup and restore mechanisms in the cloud; and how to implement disaster recovery planning for cloud applications. Learners will see prominent cloud adoption frameworks and their associated capabilities, and hear benefits of and how to implement blockchain technologies or solutions in the cloud. Finally, learn how to implement Kubernetes on AWS, build backup and restore mechanisms on GCP, and implement big data solutions in the cloud.
13 videos |
1h 4m
Assessment
Badge
COURSES INCLUDED
Data Lakes
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.
12 videos |
1h 13m
Assessment
Badge
COURSES INCLUDED
Data Engineering Getting Started
Data engineering is the area of data science that focuses on practical applications of data collection and analysis. This 12-video course helps learners explore distributed systems, batch versus in-memory processing, NoSQL uses, and the various tools available for data management/big data and the ETL (extract, transform, and load) process. Begin with an overview of distributed systems from a data perspective. Then look at differences between batch and in-memory processing. Learn about NoSQL stores and their use, and tools available for data management. Explore ETL-what it is, the process, and the different tools available. Learn to use Talend Open Studio to showcase the ETL concept. Next, examine data modeling and creating a data model in Talend Open Studio. Explore the hierarchy of needs when working with AI and machine learning. In another tutorial, learn how to create a data partition. Then move on to data engineering and best practices, with a look at approaches to building and using data reporting tools. Conclude with an exercise designed to create a data model.
13 videos |
45m
Assessment
Badge
COURSES INCLUDED
Emerging Data Trends: Navigating the Latest Trends in Data for Leaders
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.
18 videos |
1h 45m
Assessment
Badge
Emerging Data Trends: Unveiling the Power of Practical Data Fabric
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.
13 videos |
1h 11m
Assessment
Badge
Emerging Data Trends: Unlocking Data Observability
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.
11 videos |
57m
Assessment
Badge
Emerging Data Trends: Converged & Composable Systems
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.
14 videos |
1h 14m
Assessment
Badge
Emerging Data Trends: AI TRiSM Unleashed
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.
11 videos |
58m
Assessment
Badge
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FREE ACCESS
COURSES INCLUDED
CompTIA Data+: Understanding Databases
Databases are the backbone of modern life, powering everything from online shopping to social media to memberships and countless other activities. They enable us to store, manage, and retrieve vast amounts of information quickly and efficiently. Understanding databases is the very first step in mastering data analytics. In this course, you will explore databases, beginning with the basic concepts of data analytics, databases, including relational and non-relational databases, and common roles in the field of data science. Then you will examine structured query language (SQL) including examples of SQL operations. Finally, you will investigate the purpose of databases in applications, database management systems (DBMS), how databases are implemented in everyday business environments, and common database tasks. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
15 videos |
1h 40m
Assessment
Badge
CompTIA Data+: Database Concepts
Databases are used for creating and storing virtually any type of data. Data drives business in the twenty-first century, and IT professionals interested in mastering data analytics must understand the key concepts surrounding databases and their uses in almost every facet of business. In this course, you will discover database concepts, beginning with challenges associated with databases, self-driving databases, data warehouses, data marts, and data lakes and lakehouses. Then you will explore the concepts of Online Transactional Processing (OLTP) and Online Analytical Processing (OLAP). You will learn about database schemas and look closely at star and snowflake schemas, which are common in data warehouses. Finally, you will explore slowly changing dimensions that shape the methods analysts use to keep historical and current data. This course can be used to prepare for the DA0-001: CompTIA Data+ exam.
13 videos |
1h 14m
Assessment
Badge
CompTIA Data+: Understanding Data
Databases cannot perform at all without data - it is that simple. Data is the lifeblood of databases, and once a database is populated with data, the things a data analyst can do with it are truly remarkable. By harnessing the power of data, modern life has become more efficient in virtually every way. In this course, you will explore the basics of data, beginning with an introduction to data types, structured data, defined rows and columns, and key-value pairs. You will then proceed to explore unstructured data, undefined fields, machine data, and discrete and continuous data. Next, you will dig into categorical data, numerical data, text data, multimedia data. Finally, you will examine text files, HTML files, XML files, and JSON files. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
19 videos |
1h 59m
Assessment
Badge
CompTIA Data+: Data Analytics Tools
Data that lives in a database is only part of the equation when considering data analytics. Data needs to be accessed and processed in order to be useful. The importance of data in the modern world can easily be observed by considering the sheer number of data analytics tools. Without these tools, data loses some of its usefulness. In this course, you will explore popular data analytics tools, beginning with Structured Query Language (SQL), and Python. Next, you will dig into data science styling recommendations in Python, data science reporting best practices, Microsoft Excel, and the R programming language. Then you will discover tools like RapidMiner, IBM Cognos, IBM SPSS Modeler, SPSS, SAS, Tableau, and Power BI. Finally, you will focus on the purposes and roles of tools such as Qlik, MicroStrategy, BusinessObjects, APEX, Amazon QuickSight, Stata, and Minitab. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
21 videos |
2h 13m
Assessment
Badge
CompTIA Data+: Data Acquisition & Cleansing
Data, when coming in from a source en masse, is rarely structured the way that data analysts would like it to be. When you consider the multitude of sources that data comes from, it would be highly unrealistic to assume that you could take a tranche of data and begin working with it without some sort of processing to make it more useful. In this course, you will explore data acquisition and cleansing, beginning with data integration and data integration tools, focusing on the roles and characteristics of the extract, transform, load (ETL) and extract, load, transform (ELT) processes. Then you will examine tools and methods such as delta load and data acquisition application programming interfaces (APIs). Next, you will learn how to clean datasets and investigate common data issues, including data redundancy, missing values, non-parametric data, and outliers. Finally, you will take a look at key characteristics of data type validation. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
20 videos |
2h 35m
Assessment
Badge
CompTIA Data+: Understanding Data Manipulation
Data is rarely received in perfect form and often requires some sort of manipulation to make it sing. That is why the world needs data analysts. They can squeeze every bit of usefulness from datasets, and they also know how to prep datasets to extract meaning from them. In this course, you will explore key concepts of data manipulation, beginning with data manipulation tools. Then you will learn organization techniques like filtering and sorting data to make it easier to interpret. Next, you will focus on date functions, logical functions, aggregate functions, and system functions. Finally, you will investigate the best practices associated with data manipulation. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
11 videos |
55m
Assessment
Badge
CompTIA Data+: Data Manipulation Techniques
Data rarely comes in perfect form and often requires manipulation in order to make it truly meaningful. Without the ability to manipulate data, you often cannot extract full meaning from it. In this course, you will explore techniques for manipulating data, beginning with recoding data, derived variables, and data merging and blending. Then you will perform a data merge and a data blend and dig into concatenation and appending. Next, you will focus on imputation, reduction, and aggregation. Finally, you will learn how and why to transpose datasets, how to achieve standardized data formats using data normalization, and how to parse and manipulate strings. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
14 videos |
1h 29m
Assessment
Badge
CompTIA Data+: Query Optimization
You have received a large dataset, spent the time and requisite care cleansing and manipulating it to make it the best possible dataset so that you can extract useful information from it. Now what? Well, now you can begin querying the data. But queries can get complicated fast, especially when dealing with massive amounts of data. That's where query optimization comes in. Queries can be optimized to make your results faster and more meaningful. In this course, you will explore the key concepts of query optimization, beginning with query optimization and typical tools used in query optimization. Then you will conduct parameterized queries, perform index scans, use temporary tables, and use record subsets. Finally, you will dig into execution plans and common best practices for query optimization. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
10 videos |
1h 7m
Assessment
Badge
CompTIA Data+: Descriptive Statistical Methods
Descriptive statistics are used to describe the characteristics of datasets. They are leveraged by data analysts to find answers or characteristics of data that aren't immediately or directly answered by analyzing the data alone. In other words, descriptive statistics are used to summarize characteristics of data that are not actually contained or explicitly described by the data. In this course, you will explore descriptive statistical methods, beginning with the purpose and role of descriptive statistics. Then you will dig into measures of central tendency, measures of dispersion, and frequency distribution. Finally, you'll examine percent change, percent difference, and confidence intervals. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
15 videos |
1h 21m
Assessment
Badge
CompTIA Data+: Inferential Statistical Methods
Unlike descriptive statistics, which describe characteristics of datasets, inferential statistics are used to make inferences or predictions about data. Information not readily evident by analyzing datasets can be gleaned by talented data analysts in order to form conclusions, and while it may sound like educated guessing, descriptive statistics are far more sophisticated than simply guessing. In this course, you will explore inferential statistical methods, identifying the purpose of inferential statistics and comparing them to descriptive statistics. Then you will investigate and perform inferential statistical methods such as t-tests, z-score, p-values, and chi-square. Finally, you will focus on hypothesis testing, simple linear regression, and correlation testing. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
18 videos |
1h 31m
Assessment
Badge
CompTIA Data+: Data Analysis Types & Techniques
Databases are places where information resides. What that information looks like is up to the creators of a database, but one thing is for certain: analyzing data is one of the main points of collecting and storing data. Analysis types and techniques are plentiful, and how a data analyst chooses to analyze data really depends on a number of factors. In this course, you will explore data analysis types and techniques, beginning with the data analysis process. Next, you will review and refine business questions and determine data needs and sources. Then you will discover scoping and gap analysis, analysis types, trend analysis, and performance analysis. Finally, you will examine exploratory data analysis and link analysis. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
11 videos |
1h 39m
Assessment
Badge
CompTIA Data+: Data Visualization Reports
Data is meaningful only when information is extracted from it. That information can tell a story, and the best data analysts are magnificent storytellers. But no matter how accomplished a data analyst, a story can't be told compellingly without visualizing what the data says and a key part of a data analyst's role is in reporting on what the data is saying. In this course, you will explore data visualization reports, beginning with data visualization tools and best practices. Then you will focus on examples of data visualization translating requirements for reports, key report components, report best practices, corporate standardization, and style guides. Next, you will discover how to create a report and examine the differences between static and dynamic reports, ad-hoc and self-service reports, and recurring vs. tactical reports. Finally, you will learn how to implement various design and documentation elements in reports, including using charts and graphs to enhance your report. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
23 videos |
3h 1m
Assessment
Badge
CompTIA Data+: Data Visualization Dashboards
Data visualization prepares data for presentation to make it easier for non-data analysts to understand what the data is saying. Often, non-data analysts can't see relationships in data without visual aids, but effective data presentation allows them to change parameters and understand and view the data in a similar manner to a data scientist. In this course, explore the purpose and key considerations for data visualization dashboards in data analytics, dashboard utilization best practices, and elements of dashboard development. Next, learn about dashboard delivery and how to create a dashboard. Finally, discover how to utilize saved searches, implement filters, optimize dashboards, and use access permissions. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
12 videos |
1h 11m
Assessment
Badge
CompTIA Data+: Creating Charts & Graphs
Some accomplished data scientists may be able to look at raw data and easily gather some findings from it. Everyone else may need to see visuals such as charts and graphs to help them understand the relationships expressed by data. In this course, explore how to create charts and graphs for data analytics, beginning with line charts, pie charts, bubble charts, and scatter plots. Next, discover how to use datasets and chart generation software to construct bar charts, histograms, waterfalls, heatmaps, and geographic maps. Finally, learn how to make treemaps, stacked charts, infographics, and word clouds to visualize data. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
15 videos |
1h 20m
Assessment
Badge
CompTIA Data+: Data Governance
Data collection and how that data is used is highly regulated and for good reason. The world operates on the circulation of data, whether that's personally identifying information, health information, financial information, or even something as seemingly innocuous as someone's web browsing or purchasing habits. The regulations that exist are there to protect users, and in some cases, the people collecting the data. In this course, you will explore basic data governance concepts, beginning with access requirements, and role-based access control (RBAC). Then, you will delve into users and groups, data use, and the release process. Next, you will focus on data protection, data de-identification and masking, and storage environment requirements. Finally, you will learn about use requirements, entity relationship requirements, data classification, regulatory requirements, and data breach reporting. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
16 videos |
1h 45m
Assessment
Badge
CompTIA Data+: Data Quality & Master Data Management
Ask any data analyst and they will tell you in no uncertain terms how important it is to work with quality data. Quality must be paramount to properly analyze data and glean important information from it. Likewise, master data management (MDM) is a key activity required of data analysts to ensure that information and business work in concert to ensure the completeness and proper care of important data. In this course, you will explore the importance of data quality, including common issues, best practices, and data quality tools. Then you will dig into examples of data quality, data validation, and types of data validation. Next, you will focus on data quality dimensions, data quality metrics, and validation methods. Finally, you'll learn about Master Data Management (MDM), MDM processes, and the importance of MDM. This course can be used to prepare for CompTIA Data+ (DA0-001) exam.
16 videos |
1h 36m
Assessment
Badge
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EARN A DIGITAL BADGE WHEN YOU COMPLETE THESE COURSES
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Digital badges are yours to keep, forever.BOOKS INCLUDED
Book
Modern Big Data Architectures: A Multi-Agent Systems PerspectiveWith practical examples and detailed solutions suitable for a wide variety of applications, this unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets.
3h 24m
By Dominik Ryżko
Book
Scalable Big Data Architecture: A Practitioner's Guide to Choosing Relevant Big Data ArchitectureCovering real-world, concrete industry use cases, this book is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a big data project and which tools to integrate into that pattern.
1h 51m
By Bahaaldine Azarmi
Book
Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data VaultDrawing upon years of practical experience and using numerous examples and an easy to understand framework, this timely guide defines the importance of data architecture and how it can be used effectively to harness big data within existing systems.
4h 27m
By Daniel Linstedt, W.H. Inmon
BOOKS INCLUDED
Book
Modern Big Data Architectures: A Multi-Agent Systems PerspectiveWith practical examples and detailed solutions suitable for a wide variety of applications, this unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets.
3h 24m
By Dominik Ryżko
Book
Scalable Big Data Architecture: A Practitioner's Guide to Choosing Relevant Big Data ArchitectureCovering real-world, concrete industry use cases, this book is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a big data project and which tools to integrate into that pattern.
1h 51m
By Bahaaldine Azarmi
Book
Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data VaultDrawing upon years of practical experience and using numerous examples and an easy to understand framework, this timely guide defines the importance of data architecture and how it can be used effectively to harness big data within existing systems.
4h 27m
By Daniel Linstedt, W.H. Inmon
SKILL BENCHMARKS INCLUDED
Data Literacy (Beginner Level)
The data literacy benchmark will measure your ability to speak the language of data. You will be evaluated on your ability to recognize key topics such as; data science concepts, analytics, database types, predictive analytics, data visualization, data stewardship, data compliance, and data governance. A learner who scores high on this benchmark demonstrates that you have the skills to interpret data and incorporate it into your daily life.
37m 30s
| 25 questions
Data for Leaders Awareness
The Data for Leaders Awareness benchmark will measure your ability to recall and relate to basic data concepts. You will be evaluated on your ability to recognize the foundational concepts of data such as data formats, sources, various data operations, terminologies, and processing methods. A learner who scores high on this benchmark demonstrates that they have a basic level of awareness of data concepts.
20m
| 20 questions
Data for Leaders Competency (Intermediate Level)
The Data for Leaders Competency benchmark will measure whether a learner has had exposure to data concepts and terminology. You will be evaluated on your ability to recognize key concepts of data such as big data, data governance and management, and emerging new age architecture. A learner who scores high on this benchmark demonstrates that they have the basic data skills to understand and grasp various data related technologies, tools, and frameworks.
20m
| 20 questions
Data for Leaders Proficiency (Advanced Level)
The Data for Leaders Proficiency benchmark will measure whether a learner has had significant exposure and experience with data technologies. You will be evaluated on your ability to recognize the concepts of data such as big data analytics, data architecture, data processing, data governance and management, and emerging new age architecture. A learner who scores high on this benchmark demonstrates independent knowledge across a variety of data technologies and platforms.
25m
| 25 questions
Data Management and Governance Literacy (Beginner Level)
The Data Management and Governance Literacy (Beginner Level) benchmark will measure your ability to recall and relate the foundational concepts of data management, domains, data sources, and governance. A learner who scores high on this benchmark demonstrates that they have a basic understanding of data management and integration concepts.
11m
| 11 questions
Data Management and Governance Competency (Intermediate Level)
The Data Management and Governance Competency (Intermediate Level) benchmark will measure your ability to recall and relate the concepts of data management, quality, compliance, and governance. A learner who scores high on this benchmark demonstrates that they have a good understanding of data management, compliance, quality, and governance elements.
15m
| 15 questions
Data Essentials
The Data Essentials benchmark will measure your ability to recall and relate the underlying concepts of data. You will be evaluated on the value of good quality data, ethical data practices, mitigating bias in data decision-making, how to use evidence-based decision-making, techniques for data analysis, and data protection best practices. A learner who scores high on this benchmark demonstrates that they have the essential data skills and can understand and grasp the underlying data concepts and practices.
15m
| 15 questions
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SKILL BENCHMARKS INCLUDED
Emerging Data Trends Competency (Intermediate Level)
The Emerging Data Trends Competency (Intermediate Level) benchmark measures your knowledge of modern data governance key concepts. You will be evaluated on your recognition of practical data fabric, data observability benefits, challenges, tools, best practices, and use cases, as well as your knowledge of converged and composable systems and AI TRiSM. A learner who scores high on this benchmark demonstrates that they have the necessary skills and knowledge to be a data-savvy visionary. They can drive innovation, operational excellence, and competitive advantage through the strategic use of data and emerging trends, ensuring their organizations thrive in an increasingly data-centric world.
27m
| 27 questions
SKILL BENCHMARKS INCLUDED
CompTIA Data+ Literacy (Beginner Level)
The CompTIA Data+ Literacy (Beginner Level) benchmark measures your knowledge of key data analytics concepts, common roles in data science, and the basics of working with databases. You will be evaluated on your recognition of key concepts surrounding databases and their use in the modern world, various data types, and the relationship of data with modern database systems. A learner who scores high on this benchmark demonstrates that they have a good understanding of the basic data concepts required for the CompTIA Data+ certification Data Concepts and Environments domain.
19m
| 19 questions
CompTIA Data+ Competency (Intermediate Level)
The CompTIA Data+ Competency (Intermediate Level) benchmark measures your knowledge of the relationship of data with modern database systems and data analytics tools and their various uses. You will be evaluated on your skills in recognizing the need for and methods of data acquisition, as well as cleansing and key data manipulation concepts. A learner who scores high on this benchmark demonstrates that they have a good understanding of data mining and the level of experience required for the CompTIA Data+ certification.
23m
| 23 questions
CompTIA Data+ Proficiency (Advanced Level)
The CompTIA Data+ Proficiency (Advanced Level) benchmark measures your knowledge of key techniques used in data manipulation, common techniques for query optimization, and the importance of and methods for using descriptive statistics. You will be evaluated on your ability to define the importance and methods of inferential statistics, identify data analysis types and techniques, describe key activities and elements for data analysis reporting, recognize key activities and elements for data analysis dashboards, and identify methods for creating popular charts and graphs used in visualization data. A learner who scores high on this benchmark demonstrates that they have a good level of experience in data analysis and visualization that's required for the CompTIA Data+ certification.
37m
| 37 questions
CompTIA Data+ Mastery (Expert Level)
The CompTIA Data+ Mastery (Expert Level) benchmark measures your knowledge of data concepts and environments and applying data mining tasks and the appropriate descriptive statistical methods. You will be evaluated on your ability to summarize types of analysis and critical analysis techniques, create the appropriate visualization in a report or dashboard with proper design components, summarize important data governance concepts, and apply data quality control concepts. A learner who scores high on this benchmark demonstrates that they have expertise in data mining, analysis, and visualization and have the necessary skills to facilitate data-driven business decisions.
40m
| 40 questions
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