This course will familiarize students with all aspects of Windows forensics.By the end of this course students will be able to perform live analysis, capture volatile data, make images of media, analyze filesystems, analyze network traffic, analyze files, perform memory analysis, and analyze malware for a Windows subject on a Linux system with readily available free and open source tools. Students will also gain an in-depth understanding of how Windows works under the covers.
Reactive programming is shaping the future of how we model data. With reactive, not only can you concisely wrangle and analyze static data, you can effectively work with data as a real-time infinite feed. Reactive Extensions (Rx) first gained traction in 2009 and has been ported to over a dozen major languages and platforms.
Learn to perform efficient data analysis using Haskell
Refine your data science skills with the heavy armory of tools provided by Julia
The course is designed for engineers and data scientists who have some familiarity with Scala, Apache Spark, and machine learning who need to process large natural language text in a distributed fashion.We will use sample of posts from the subreddit /r/WritingPrompts, which contains short stories and comments about the short stories.The course has four parts1. Building a natural language processing and entity extraction pipeline on Scala & Spark2.
With this Learning Path, you'll master three of the most important technologies for a Windows system or network administrator to possess: Microsoft Exchange Server, Windows PowerShell, and SharePoint Server. Get field-tested tips, real-world examples, and candid advice culled from a wide range of business and technical scenarios as you learn how to install, configure, integrate, and support each of these crucial technologies.
If you have some Python experience, and you want to take it to the next level, this practical, hands-on Learning Path will be a helpful resource. Video tutorials in this Learning Path will show you how to use Python for distributed task processing, perform large-scale data processing in Spark using the PySpark API, and tackle machine learning tasks with Python.