Creating prototype simulations on workstations is one thing. But to transform an organization, you need to build simulations, deploy simulations, and share insights on an enterprise scale. In this Learning Path, you'll learn essential techniques for building and validating prescriptive-model simulations. You might want to take a look at other Learning Paths by Instructor Jerry Overton once you complete this course.
If you're building relational, time series, IoT, or real-time architectures using Hadoop, you should be familiar with Apache Kudu. Kudu enables fast analytics on fast data by completing Hadoop’s storage layers, providing a new option to achieve fast scans and fast random access from a single API. This Learning Path introduces you to Apache Kudu, exploring what it is, when to use it, and how to use it in conjunction with popular Hadoop ecosystem tools. You’ll apply this knowledge by building and running a real-time analytical application using Kudu, Impala, Kafka, and Spark.
In this Learning Path, you’ll discover how to apply a 5-step process for evaluating business models you create through prescriptive model simulation. You’ll learn how to find the most viable innovations, and to spot and eliminate risk. Through simulation, you can stop trying to predict the future and, instead, generate possible futures, choose the one you want, and identify the conditions that are required to move it from simulation to reality. You might want to take a look at other Learning Paths by Instructor Jerry Overton once you complete this course.
If you need to understand how to query relational databases, this Learning Path will get you up and running quickly. Commands, joins, and views are the most fundamental elements of SQL (Structured Query Language) and with them you can make powerful insights into your data. In the just an hour and a half, you will learn how to use SQL to manage data that is held in relational databases. This Learning Path is designed for beginners who have no previous experience with SQL of any kind. Working files are included, allowing you to follow along with the instructor.
If you want to develop a modern enterprise data strategy in your organization this learning path is for you. In this series of videos, the team at Silicon Valley Data Science will explain how you can solve real business challenges with data. From determining the business imperatives that underpin the goals, to defining the roadmap that leads to tactical decision-making, they’ll walk you through the complete process for creating a data strategy that will help your company experiment in such a way that you fail well, create value quickly, and become innovative and data-driven.
SpaCy, a fast, user-friendly library for teaching computers to understand text, simplifies NLP techniques, such as speech tagging and syntactic dependencies, so you can easily extract information, attributes, and objects from massive amounts of text to then document, measure, and analyze. This Learning Path is a hands-on introduction to using SpaCy to discover insights through natural language processing. While end-to-end natural language processing solutions can be complex, you’ll learn the linguistics, algorithms, and machine learning skills to get the job done.
Whether you’re a programmer with little to no knowledge of Python, or an experienced data scientist or engineer, this Learning Path will walk you through natural language processing, using both Python and Scala, and show you how to implement a range of popular tools including Spark, scikit-learn, SpaCy, NLTK, and gensim for text mining.
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Enterprise companies are big and profitable for a reason—at some point, they found a formula that worked and executed on it. However, what worked in the past won’t work forever. You and your team already know what needs to change in your organization, but change is hard, and—like playing an instrument—you need to practice every day. In this Learning Path, Cindy walks you through common problems that plague enterprise change and shares tactics that will help your team stop doubting and start doing.