Inhalt: Many organizations are turning to NoSQL databases to store large volumes of complex data, sparking an increased need for data scientists and analysts to understand non-relational data stores. If you''re a data scientist or business analyst who needs to work with NoSQL, then this course is for you. Learn about the differences between relational and NoSQL databases, review types of NoSQL databases, and see how to perform common data science tasks, including data preparation, exploration, and building and applying models. The course begins with an introduction to NoSQL, and then delves into the specifics of document, wide-column, and graph databases. Learn key details for performing data preparation, exploration, and extraction for each type of NoSQL database. Review case studies that show how to use various NoSQL databases with popular data science tools, including the document database MongoDB, the wide-column database Cassandra, and the graph database Neo4j. Umfang: 01:56:26.00
Inhalt: Many applications require a relational database. But poorly designed data architecture and poorly written SQL can result in subpar performance, unreliable services, and difficulty scaling. This course includes hands-on examples and lessons that show how to build scalable and resilient databases to support any application. Learn how to write optimized SQL for transaction processing, use indexes to reduce read latency, partition data to improve scalability, and use established design patterns. Instructor Dan Sullivan also explores object relational mapping and shows how to respond to database errors such as query timeouts and refused connections. After completing this course, you will be able to design robust database applications that can scale to meet increasingly demanding workloads. Umfang: 02:07:21.00
Inhalt: Time series data is data gathered over time: performance metrics, user interactions, and information collected by sensors. Since different time series data have different measures and different intervals, these data present a unique challenge for data scientists. However, SQL has some features designed to help. This course teaches you how to standardize and model time series data with them. Instructor Dan Sullivan discusses windowing and the difference between sliding and tumbling window calculations. Then learn how SQL constructs such as OVER and PARTITION BY help to simplify analysis, and how denormalization can be used to augment data while avoiding joins. Plus, discover optimization techniques such as indexing. Dan also introduces time series analysis techniques such as previous time period comparisons, moving averages, exponential smoothing, and linear regression. Umfang: 01:18:52.00
Inhalt: Many data scientists know how to work with SQL-the industry-standard language for data analysis. But as data sizes grow, you need to know how to do more than simply read and write from a database. This course provides a more sophisticated approach to designing data models and optimizing queries in SQL. Instructor Dan Sullivan begins with the logical and physical design of tables-with particular focus on very large databases-and then presents a deep dive review of indexes, including specialized indexes and when to use them. The next section introduces query optimization and shows how to optimize basic, multi-join, and more complex queries. The course also covers SQL extensions, including user-defined functions and specialized data types. The techniques taught here enable more efficient analysis of large data sets using SQL, statistics, and custom business logic. Umfang: 02:30:38
Inhalt: There is an increasing need for data scientists and analysts to understand relational data stores. Organizations have long used SQL databases to store transactional data as well as business intelligence related data. If you need to work with SQL databases, this course is designed to help you learn how to perform common data science tasks, including finding, exploration, and extraction within relational databases. The course begins with a brief overview of SQL. Then the five major topics a data scientist should understand when working with relational databases: basic statistics in SQL, data preparation in SQL, advanced filtering and data aggregation, window functions, and preparing data for use with analytics tools. Umfang: 01:24:09.00
Inhalt: SQL queries can be fast and highly efficient, but they can also be slow and demand excessive CPU and memory resources. For many SQL programmers, occasional bouts with long-running queries and poor performance are simply par for the course. But by gaining a better understanding of how databases translate SQL queries into execution plans, you can take steps to avoid these issues. In this course, Dan Sullivan shows developers how to analyze query execution plans and use data modeling strategies to boost query performance. Dan describes how SQL queries are executed; highlights different types of indexes and how they factor in query tuning; covers several methods for performing joins; and discusses how to use partitioning and materialized views to improve performance. Umfang: 01:44:39.00
Inhalt: Apache Cassandra is a NoSQL database capable of handling large amounts of data that change rapidly. In this course, learn about the architecture of this popular database, and discover how to design Cassandra data models that support scalable applications. Dan Sullivan highlights the differences between Cassandra and relational databases, discusses the Cassandra Query Language (CQL), and shows techniques for modeling based on application query requirements. He also dives into Cassandra implementation details that impact data modeling choices, to help you reason through other design decisions while taking into account the database's architecture and limitations. Umfang: 01:38:14.00
Inhalt: Machine learning models often run in complex production environments that can adapt to the ebb and flow of big data. The tools and practices that help data scientists rapidly build machine learning models are not sufficient to deploy those models at scale. To deliver scalable solutions, you need a whole new toolset. This course provides data scientists and DevOps engineers with an overview of common design patterns for scalable machine learning architectures, as well as tools for deploying and maintaining machine learning models in production. Instructor Dan Sullivan reviews three technologies that enable scalable machine learning: services that expose models through APIs, containers for deploying models, and orchestration tools like Kubernetes that help manage containers and clusters. Plus, get tips for monitoring the performance of your services in production environments. Umfang: 01:43:10.00
Inhalt: Online activities, mobile devices, and Internet of Things (IoT) sensors are generating immense volumes of data. Much of that data requires relational database capabilities, such as consistent reads/writes and complex transaction processing. In this course, get a holistic overview of the essential elements of designing and implementing highly scalable and available relational databases. Instructor Dan Sullivan helps developers and data modelers grasp essential architecture concepts and design patterns to ensure their databases can scale to the needs of their business. Dan goes over key requirements related to both specific functions and nonfunctional requirements, such as availability. He shows how to use your requirements to create data architectures and data models. Plus, he examines the problems of data ingestion at scale, describes design patterns to support a variety of ingestion patterns, discusses how to design for scalable querying, and more. Umfang: 02:39:03
Inhalt: Data scientists create data models that need to run in production environments. Many DevOps practices are relevant to production-oriented data science applications, but these practices are often overlooked in data science training. In addition, data science and machine learning have distinct requirements, such as the need to revise models while in use. This course was designed for data scientists who need to support their models in production, as well as for DevOps professionals who are tasked with supporting data science and machine learning applications. Learn about key data science development practices, including the testing and validation of data science models. This course also covers how to use the Predictive Model Markup Language (PMML), monitor models in production, work with Docker containers, and more. Umfang: 00:32:16.00
Inhalt: There is an increasing need for data scientists and analysts to understand relational data stores. Organizations have long used SQL databases to store transactional data as well as business intelligence related data. This course was designed for data scientists who need to work with SQL databases. Specifically, it was designed to help these professionals learn how to perform common data science tasks, including exploration and extraction of data within relational databases. Instructor Dan Sullivan kicks off the course with a brief overview of SQL data manipulation and data definition commands. He then focuses on how to use SQL queries to prepare data for analysis; leverage statistical functions to better understand that data; and work with aggregates, window operations, and more. Umfang: 02:38:49
Inhalt: Explore DataFrames, a widely used data structure in Apache Spark. DataFrames allow Spark developers to perform common data operations, such as filtering and aggregation, as well as advanced data analysis on large collections of distributed data. With the addition of Spark SQL, developers have access to an even more popular and powerful query language than the built-in DataFrames API. In this course, instructor Dan Sullivan shows how to perform basic operations-loading, filtering, and aggregating data in DataFrames-with the API and SQL, as well as more advanced techniques that are easily performed in SQL. In this section of the course, Dan explains how to join data, eliminate duplicates, and deal with null or NA values. The lessons conclude with three in-depth examples of using DataFrames for data science: exploratory data analysis, time series analysis, and machine learning. Umfang: 01:53:25.00
Inhalt: Discover how to leverage Scala-the popular language that combines object-oriented design with functional programming-in your data science work. In this course, learn about the Scala features most useful to data scientists, including custom functions, parallel processing, and programming Spark with Scala. Dan Sullivan kicks off the course with an introduction for non-Scala programmers. Next, he describes how to use SQL from Scala-a particularly useful concept for data scientists, since they often have to extract data from relational databases. He then covers parallel processing constructs in Scala, sharing techniques that are useful for medium-sized data sets that can be analyzed on a single server with multiple cores. Dan also focuses on using Scala with Spark, a distributed processing platform. He first describes how to work with Resilient Distributed Datasets (RDDs)-a fundamental Spark data structure-and then explains how to use Scala with Spark DataFrames, a new class of data structure specially designed for analytic processing. He wraps up the course by providing a summary of advantages of using Scala for data science. Umfang: 01:51:32.00
Inhalt: Apache Spark is one of the most widely used and supported open-source tools for machine learning and big data. In this course, discover how to work with this powerful platform for machine learning. Instructor Dan Sullivan discusses MLlib-the Spark machine learning library-which provides tools for data scientists and analysts who would rather find solutions to business problems than code, test, and maintain their own machine learning libraries. He shows how to use DataFrames to organize data structure, and he covers data preparation and the most commonly used types of machine learning algorithms: clustering, classification, regression, and recommendations. By the end of the course, you will have experience loading data into Spark, preprocessing data as needed to apply MLlib algorithms, and applying those algorithms to a variety of machine learning problems. Umfang: 01:51:14.00
Inhalt: Learn how to use SQL to understand the characteristics of data sets destined for data science and machine learning. The course begins with an introduction to exploratory data analysis and how it differs from hypothesis-driven statistical analysis. Instructor Dan Sullivan explains how SQL queries and statistical calculations, and visualization tools like Excel and R, can help you verify data quality and avoid incorrect assumptions. Next, find out how to perform data-quality checks, reveal and recover missing values, and check business logic. Discover how to use box plots to understand non-normal distribution of data and use histograms to understand the frequency of data values in particular attributes. Dan also explains how to use the chi square test to understand dependencies and measure correlations between attributes. The course concludes with a collection of tips and best practices for exploratory data analysis. Umfang: 00:44:07.00
Inhalt: Descriptive statistics help us understand the overall structure of data, and SQL is the most widely used language for manipulating it. Together, they can help data analysts derive better insights and make far-reaching predictions. This course provides an overview of basic descriptive statistics and the SQL commands you need to know to summarize data sets, find averages, and calculate variance and standard deviation. Instructor Dan Sullivan also introduces more detailed analysis techniques using discreet and continuous percentiles to help segment data, and correlations between variables to identify relationships. He concludes with an introduction to linear regression, a widely used predictive analytics technique. Umfang: 00:49:14.00
Inhalt: Traditional marketing puts content in front of a passive, potentially uninterested audience. Search engine optimization (SEO) helps you target a different kind of crowd, the millions of people actively searching-typing on their phones and laptops or speaking to their Amazon Echo or Siri-for exactly what you''re selling. LinkedIn influencer Danny Sullivan has been in on SEO since its early days, and educates businesses about the power of SEO as the chief content officer at Third Door Media and cofounder of Search Engine Land. In this course, he explains what SEO is, touches on the history and evolution of the practice, and provides technical, content, and search strategies that help you bolster your online presence. Danny also explains how to connect SEO to other business activities, identifies the skills that matter in SEO, and looks at upcoming trends, including mobile, machine learning, and search bots. Umfang: 00:47:07.00
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