Enterprise Powershell Scripting Bootcamp
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Enterprise PowerShell Scripting Bootcamp
Author: Brenton J.W. Blawat
language: en
Publisher: Packt Publishing Ltd
Release Date: 2017-05-18
The quick start guide for an advanced enterprise PowerShell framework About This Book Introduces industry-proven techniques that improve script efficiency and reliability Example-rich guide based on real-world scenarios Facilitates building a script that can fully scan a Windows server and identify components Who This Book Is For This book is for IT professionals and Windows administrators who would like to gain intensive, hands-on knowledge and skills on PowerShell without spending hours and hours in learning. If you have been struggling to find the time to gain proficiency and confidence with PowerShell and everyday scripting tasks What You Will Learn Create an advanced PowerShell scripting template that provides repeatable code to jumpstart all of your scripting projects Learn how to securely encrypt and store usernames, passwords, and other sensitive data in PowerShell scripts and answer files Understand how to optimize the performance of scripts to help process large datasets quickly and avoid time-consuming mistakes Develop a script to scan for non-standard Windows Server configurations and identify service accounts used on Windows Servers Gather a large list of data from a Windows server without locally or remotely logging in interactively In Detail Enterprise PowerShell Scripting Bootcamp explains how to create your own repeatable PowerShell scripting framework. This framework contains script logging methodologies, answer file interactions, and string encryption and decryption strategies. This book focuses on evaluating individual components to identify the system's function, role, and unique characteristics. To do this, you will leverage built-in CMDlets and Windows Management Instrumentation (WMI) to explore Windows services, Windows processes, Windows features, scheduled tasks, and disk statistics. You will also create custom functions to perform a deep search for specific strings in files and evaluate installed software through executable properties. We will then discuss different scripting techniques to improve the efficiency of scripts. By leveraging several small changes to your code, you can increase the execution performance by over 130%. By the end of this book, you will be able to tie all of the concepts together in a PowerShell-based Windows server scanning script. This discovery script will be able to scan a Windows server to identify a multitude of components. Style and approach This book is all about fast and intensive learning. This means, we don't waste time in helping readers get started. The new content is about leveraging highly-effective examples to build new things, help solving problems in newer and unseen ways, and providing an enterprise-ready platform to create PowerShell Scripts.
Enterprise Mac Administrators Guide
Charles Edge, Zack Smith, and Beau Hunter provide detailed explanations of the technology required for large-scale Mac OS X deployments and show you how to integrate it with other operating systems and applications. Enterprise Mac Administrator's Guide addresses the growing size and spread of Mac OS X deployments in corporations and institutions worldwide. In some cases, this is due to the growth of traditional Mac environments, but for the most part it has to do with "switcher" campaigns, where Windows and/or Linux environments are migrating to Mac OS X. However, there is a steep culture shock with these types of migrations. The products that are used are different, the nomenclature is different, and most importantly the best practices for dealing with the operating system are different. Apple provides a number of tools to help automate and guide IT toward managing a large number of Mac OS X computers—it has since before Mac OS X was initially released. However, if you want to put together all of the pieces to tell a compelling story about how to run an IT department or a deployment of Macs, you need to compile information from a number of different sources. This book will provide explanations of the technology required. Provides complete solutions for the large- and medium-scale integration of directory services, imaging, and security Complete guide for integrating Macs and Mac OS X into mixed environments with confidence and no down time One-stop volume for IT professionals who need the technical details to get their job done as efficiently and effectively as possible
Data Science Bookcamp
Author: Leonard Apeltsin
language: en
Publisher: Simon and Schuster
Release Date: 2021-12-07
Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution