"Cracking the Data Science Interview" is your ultimate resource for preparing for roles in the competitive field of data science. With this book, you'll explore essential topics such as Python, SQL, statistics, and machine learning, as well as learn practical skills for building portfolios and acing interviews. Follow its guidance and you'll be equipped to stand out in any data science interview. What this Book will help me do Confidently explain complex statistical and machine learning concepts. Develop models and deploy them while ensuring version control and efficiency. Learn and apply scripting skills in shell and Bash for productivity. Master Git workflows to handle collaborative coding in projects. Perfectly tailor portfolios and resumes to land data science opportunities. Author(s) Leondra R. Gonzalez, with years of data science and mentorship experience, co-authors this book with None Stubberfield, a seasoned expert in technology and machine learning. Together, they integrate their expertise to provide practical advice for navigating the data science job market. Who is it for? If you're preparing for data science interviews, this book is for you. It's ideal for candidates with a foundational knowledge of Python, SQL, and statistics looking to refine and expand their technical and professional skills. Professionals transitioning into data science will also find it invaluable for building confidence and succeeding in this rewarding field.

"Hands-On Data Science with the Command Line" introduces the incredible power of command-line tools to simplify and automate data science tasks. Leveraging tools like AWK, Bash, and more, you'll learn not only to handle datasets effectively but also to create efficient data pipelines and visualize data directly from the command line. What this Book will help me do Learn to set up and optimize the command line interface for data science tasks. Master using AWK and similar tools for data processing. Discover strategies for scripting, automation, and managing files efficiently. Understand how to visualize data directly from the command line. Gain fluency in combining tools to create seamless data pipelines. Author(s) The authors, None Morris, None McCubbin, and None Page, are experienced data scientists and technical authors with a passion for teaching complex topics in approachable ways. Their extensive experience using command-line tools for data-related workflows equips them to guide readers step-by-step in mastering these powerful techniques. Who is it for? This book is ideal for data scientists and data analysts seeking to streamline and automate their workflows using command-line tools. If you have basic experience with data science and are curious about incorporating the efficiency of the command line into your work, this guide is perfect for you.

Apache Spark is an in-memory framework that allows data scientists to explore and interact with big data much more quickly than with Hadoop. Python users can work with Spark using an interactive shell called PySpark. Why is it important? PySpark makes the large-scale data processing capabilities of Apache Spark accessible to data scientists who are more familiar with Python than Scala or Java. This also allows for reuse of a wide variety of Python libraries for machine learning, data visualization, numerical analysis, etc. What you'll learn—and how you can apply it Compare the different components provided by Spark, and what use cases they fit. Learn how to use RDDs (resilient distributed datasets) with PySpark. Write Spark applications in Python and submit them to the cluster as Spark jobs. Get an introduction to the Spark computing framework. Apply this approach to a worked example to determine the most frequent airline delays in a specific month and year. This lesson is for you because… You're a data scientist, familiar with Python coding, who needs to get up and running with PySpark You're a Python developer who needs to leverage the distributed computing resources available on a Hadoop cluster, without learning Java or Scala first Prerequisites Familiarity with writing Python applications Some familiarity with bash command-line operations Basic understanding of how to use simple functional programming constructs in Python, such as closures, lambdas, maps, etc. Materials or downloads needed in advance Apache Spark This lesson is taken from by Jenny Kim and Benjamin Bengfort. Data Analytics with Hadoop

Elasticsearch in Action teaches you how to build scalable search applications using Elasticsearch. You'll ramp up fast, with an informative overview and an engaging introductory example. Within the first few chapters, you'll pick up the core concepts you need to implement basic searches and efficient indexing. With the fundamentals well in hand, you'll go on to gain an organized view of how to optimize your design. Perfect for developers and administrators building and managing search-oriented applications. About the Technology Modern search seems like magic'you type a few words and the search engine appears to know what you want. With the Elasticsearch real-time search and analytics engine, you can give your users this magical experience without having to do complex low-level programming or understand advanced data science algorithms. You just install it, tweak it, and get on with your work. About the Book Elasticsearch in Action teaches you how to write applications that deliver professional quality search. As you read, you'll learn to add basic search features to any application, enhance search results with predictive analysis and relevancy ranking, and use saved data from prior searches to give users a custom experience. This practical book focuses on Elasticsearch's REST API via HTTP. Code snippets are written mostly in bash using cURL, so they're easily translatable to other languages. What's Inside What is a great search application? Building scalable search solutions Using Elasticsearch with any language Configuration and tuning About the Reader This book is for developers and administrators building and managing search-oriented applications. About the Authors Radu Gheorghe is a search consultant and software engineer. Matthew Lee Hinman develops highly available, cloud-based systems. Roy Russo is a specialist in predictive analytics. Quotes To understand how a modern search infrastructure works is a daunting task. Radu, Matt, and Roy make it an engaging, hands-on experience. - Sen Xu, Twitter Inc. An indispensable guide to the challenges of search of semi-structured data. - Artur Nowak, Evidence Prime The best resource for a complex topic. Highly recommended. - Daniel Beck, juris GmbH Took me from confused to confident in a week. - Alan McCann, Givsum.com

Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, you’ll learn how to use freely available open source tools to extract meaning from large complex biological data sets. At no other point in human history has our ability to understand life’s complexities been so dependent on our skills to work with and analyze data. This intermediate-level book teaches the general computational and data skills you need to analyze biological data. If you have experience with a scripting language like Python, you’re ready to get started. Go from handling small problems with messy scripts to tackling large problems with clever methods and tools Process bioinformatics data with powerful Unix pipelines and data tools Learn how to use exploratory data analysis techniques in the R language Use efficient methods to work with genomic range data and range operations Work with common genomics data file formats like FASTA, FASTQ, SAM, and BAM Manage your bioinformatics project with the Git version control system Tackle tedious data processing tasks with with Bash scripts and Makefiles