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- R Programming Hands-on Specialization for Data Science (Lv1) An in-depth course with hands-on real-world Data Science use-case examples to supercharge your data analysis skills.(More…)

- Luckily, R can provide a framework to easily import and process data in order to implement statistical analysis and models.(More…)

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**KEY TOPICS**

** R Programming Hands-on Specialization for Data Science (Lv1) An in-depth course with hands-on real-world Data Science use-case examples to supercharge your data analysis skills.** [1] Learn more about reporting your results in the online course: R for Data Science Solutions. [1] For a more complete education, read R for Data Science by Hadley Wickham and Garrett Grolemund. [2] They then show the best ways to do data science with a suite of R packages that have become known as the “tidyverse.” [2] I am post-graduate student in the field of Business Informatics, Shiny App developer with a strong interest in statistical programming and data science. [1]

Who this book is written for: This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. [3] Who this book is written for: This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. [3]

Step-by-Step Machine Learning with Python (Video) Yuxi (Hayden) Liu ISBN 13: 9781788622370 Packt Publishing Course Length: 4 hours 56 minutes (September 2017) Video Overview: Put your Python skills to the test and enter the big world of data science to learn the most effective machine learning tools and techniques with this interesting guide. [3] Getting Started with NLP and Deep Learning with Python Giuseppe Bonaccorso ISBN 13: 9781789138894 Course Length: 1 hour and 41 minutes (February 2018) Video Overview: Build a strong foundation to enter the world of Machine Learning and data science with the help of this comprehensive guide As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. [3]

Python Machine Learning – Part 1 (Video) Jason Wolosonovich ISBN 13: 9781786461636 Packt Publishing 3 hours 22 minutes (January 2017) Video Overview: This video gives you access to the world of predictive analytics and demonstrates why Python is one of the world?s leading data science languages. [3] Data Science and Machine Learning with Python – Hands On! By Frank Kane ISBN 13: 9781787127081 Packt Publishing Video length 8 hour 50 minutes (September 2016) Video Overview: This video will help you take your first steps in the world of data science, and empower you to conduct data analysis and perform efficient machine learning using Python. [3]

Edureka’s Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. [4] Practical advice on using Git, Github and Markdown for data science projects, with a focus on R. [5] Edureka offers you a complimentary self-paced course, i.e. “R Essentials” when you enroll in Data Science Training. [4] Data Science Training employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science from the sub-domains of machine learning, classification, cluster analysis, data mining, databases, and visualization. [4] Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. [4]

Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this video to be very useful. [3] Understand the common programming patterns and algorithms used in Python data science. [3] The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. [3] Who this book is written for: This book is for anyone interested in entering the data science stream with machine learning. [3] If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science courseis invaluable. [3] The Data Science Certification Course enables you to gain knowledge of the entire life cycle of Data Science, analyse and visualise different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes. [4] Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. [4] System Analyst at Sonata Software It was a great experience to undergo and get certified in the Data Science course from Edureka. [4] Team Lead, Marketing Applications at Robert Half The course was really helpful to me to understand as a beginner to Data Science. [4] It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. [3] Data Science Certification Training is designed by industry experts to make you a Certified Data Scientist. [4] Edureka’s Data Science Training includes real-time industry-based projects, which will hone your skills as per current industry standards and prepare you for the upcoming Data Scientist roles. [4] Data Science Training will help you become a Data Science Expert. [4] Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. [4] There is no specific pre-requisite for Data Science Training. [4] Win-Vector LLC: providing expert data science consulting and training. [6] You will be ready to start using pandas for different data science tasks with confidence. [3] This makes machine learning well-suited to the present-day era of Big Data and Data Science. [3] I intend to continue pursuing this area of Data Science and Machine learning algorithms. [4] From Analytics to Data Integration to Visualization, Data Science Central provides a community experience that includes a robust editorial platform, social interaction, forum-based technical support, the latest in technology, tools and trends and industry job opportunities. [7] This tutorial illustrates how to simply and quickly spin up a Ubuntu-based Azure Data Science Virtual Machine (DSVM) and to configure a Keras and CNTK environment. [5] An Azure DSVM is a curated virtual machine image coming with an extensive collection of pre-installed open source data science tools. [5] Live project based on any of the selected use cases, involving the implementation of Data Science. [4] Hello, you need to enable JavaScript to use Data Science Central. [7] Data Science Central is the industry’s online resource for big data practitioners. [7]

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization ( ggplot2, Boxplots, maps, animation ), programming ( RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping ) statistics ( regression, PCA, time series, trading ) and more. [8] Data Science and Machine Learning is now being described as the “Fourth Paradigm” for scientific discovery providing important insights from data and helping in making hypothesis leading to a new scientific theory. [9]

**POSSIBLY USEFUL**

** Luckily, R can provide a framework to easily import and process data in order to implement statistical analysis and models.** [10] This book will teach you how to use the most modern parts of R to import, tidy, transform, visualize, and model data, as well as how to communicate findings with R Markdown. [2] The online video course with O’Reilly on How to Manipulate, Visualize, and Model Data with the R Language http://shop.oreilly.com/product/0636920034834.do. [2] One of reasons to use R is to exclude using Excel for complex data processing. [10] Cleaning data in R which shows you how to make use of tidyr efficiently. [2] INTRODUCTION This R package is aimed at accessing the openaq API. OpenAQ is a community of scientists, software developers, and lovers of open environmental data who are building an open, real-time database that provides programmatic and historical access to air quality data. [10] INTRODUCTION The openair package is specifically designed to plot air pollution data. [10]

In this set of exercises, we will use the openair package to work and analyze air quality and meteorological data. [10]

This 30 page guide will show you how to install R, load data, run analyses, make graphs, and more. [2] You can automatically update an R Markdown document whenever your data or R code changes, which creates one of the most reproducible and efficient workflows possible. [2] Felipe Ardilac 28 February 2018 at 16:42 on R For Hydrologists Loading and Plotting Data Part 1: Exercises Hi Fabien, thank you for reporting the error, I fix already the link, please let me know if you are. [1] Fabien Cremona 28 February 2018 at 14:41 on R For Hydrologists Loading and Plotting Data Part 1: Exercises I would like to do the exercises, unfortunately, the PAICOL.csv file is not downloadable as I am returned a 404. [1]

LOADING AND PLOTTING THE DATA (Part 1) Working with hydro-meteorological data can be very time consuming and exhausting. [10]

You can use R Markdown to create attractive, fully customizable, HTML, PDF, and MS Word documents as well as Beamer slides. [2] In combination with R Markdown, you can create a nice PDF document with your table inside. [1]

Hadley explains in clear prose how R works from a computer science perspective. [2]

With the knowledge you gain from this book, you will quickly learn pandas and how it can empower you in the exciting world of data manipulation, analysis and science. [3] Anyone who has taken a high school science class will be able to follow along as the book introduces the basics of programming, then goes on to show readers how to work with databases, download data from the web automatically, build graphical interfaces, and most importantly, how to think like a professional programmer. [3]

In the first half of the book you?ll learn basic programming concepts, from installing Python to testing code, including how to use a text editor, write clean and readable code, store data in lists and dictionaries, create classes that simulate real-life objects, and write loops to perform actions on your data. [3] As you progress through the book, you?ll learn about function parameters and how to use control statements with the loop. You?ll further learn how to create modules and packages, storing of data as well as handling errors. [3]

With this data you can use any function from base R or any R package. (Several common R packages are provided in the environment, or you can upload and install other packages directly, or use the checkpoint package to install everything you need.) [5] There’s a 500Mb limit for the data passed to R, but the basic idea is that you perform the main data munging tasks in U-SQL, and then pass the prepared data to R for analysis. [5] You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more. [3] Learn the steps, tips and tricks to get your data ready for analysis effortlessly and save hundreds of work-hours. [11] This advanced course, will make you ready to start doing Predictive Analysis on your data by showing you how to build Machine Learning models with scikit-learn and pandas. [3] The course will also teach you how to analyze and model your data, and organize the results of your analysis in the form of plots or other visualization means. [3]

Learning Pandas (Video) Harish Garg ISBN 13: 9781787287891 Packt Publishing Course Length: 1 hour and 14 minutes (June 2017) Video Overview: This video will show you how you can get the most out of Pandas for data analysis.The course starts with teaching you the absolute basics such as installing and setting up of the pandas library. [3] Learning Data Mining with Python – Second Edition Robert Layton ISBN 13: 9781787126787 Packt Publishing 358 pages (April 2017) Book Overview: This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. [3] Artificial Intelligence with Python Prateek Joshi ISBN 13: 9781786464392 Packt Publishing 446 pages (January 2017) Book Overview: Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. [3]

Learning pandas – Second Edition Michael Heydt ISBN 13: 9781787123137 Packt Publishing 446 pages (June 2017) Book Overview: With this book you will learn how to use pandas to perform data analysis in Python. [3] Python Data Analysis – Second Edition Armando Fandango ISBN 13: 9781787127487 Packt Publishing 330 pages (March 2017) Book Overview: With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. [3]

Python: End-to-end Data Analysis Phuong Vothihong, Martin Czygan, Ivan Idris, Magnus Vilhelm Persson & Luiz Felipe Martins ISBN 13: 9781788394697 Packt Publishing 931 pages (May 2017) Book Overview: This book will get you started with Python data analysis by introducing the basics of data analysis and supported Python libraries such as matplotlib, NumPy, and pandas. [3] Python: Data Analytics and Visualization Phuong Vo.T.H, Martin Czygan, Ashish Kumar, Kirthi Raman ISBN 13: 9781788290098 Packt Publishing 866 pages (March 2017) Book Overview: Data analysis is the process of applying logical and analytical reasoning in order to study each data component. [3] Bayesian Analysis with Python Osvaldo Martin ISBN 13: 9781785883804 Packt Publishing 284 pages (November 2016) Book Overview: The purpose of this book is to teach the main concepts of Bayesian data analysis. [3] Practical Data Analysis Cookbook By Tomasz Drabas ISBN 13: 9781783551668 Packt Publishing 384 pages (April 2016) Book overview: This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. [3] Pandas Cookbook Theodore Petrou ISBN 13: 9781784393878 Packt 538 Pages (October 2017) Book Overview: Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. [3] Pandas for Predictive Analysis using scikit-learn (Video) Harish Garg ISBN 13: 9781788391429 Packt Course Length: 1 hours 12 minutes (August 2017) Video Overview: Learn how to use Pandas for Predictive Analysis by employing scikit-learn In this course you learn that stand alone data analysis is fine but what most companies these days are looking for is to do Predictive analysis using their data. [3] Supervised and Unsupervised Learning with Python (Video) Prateek Joshi ISBN 13: 9781788990325 Packt Course Length: 2 hours 08 minutes (November 2017) Video Overview: Hop on the wonderful journey of machine learning and data analysis Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. [3]

Who this video is for: If you want to find out how to use Python to start answering critical questions using your data, this video is ideal. [3] To interface with Azure Data Lake, you’ll use U-SQL, a SQL-like language extensible using C#. [5] You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. [3] Learn how to identify and act on your big data opportunities and challenges with EMC Global Services. [11] Learning Objectives – In this module, you should learn about Time Series data, different component of Time Series data, Time Series modeling – Exponential Smoothing models and ARIMA model for Time Series Forecasting. [4] Learning Objectives – Learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data. [4] Learning Objectives – Discuss the different sources available to extract data, arrange the data in structured form, analyze the data, and represent the data in a graphical format. [4]

Marwick, Boettiger and Mullen: Packaging data analytical work reproducibly using R (and friends). [5] This makes it possible to run your R function on the SQL Server itself, which makes the power of that server available for R computations, and also eliminates the time required to move data to and from the server. [5] The R Extensions for U-SQL allow you to reference an R script from a U-SQL statement, and pass data from Data Lake into the R Script. [5] Mapview: Interactive Viewing of Spatial Data in R. https://CRAN.R-project.org/packagemapview. [12]

Geoprocessing is the science of reading, analyzing, and presenting geospatial data programmatically. [3] Geoprocessing with Python Chris Garrard Manning Publications ISBN: 9781617292149, 400 pages, MEAP Began May 2014 Geospatial data is hard to ignore. [3] Along the way, you’ll survey the current Python development landscape, including Pythonic best practices, data extraction and cleaning, database access, and web frameworks. [3] Knowing more about how Python works under the covers, including the relationship between data objects and memory management, will make you a much more effective Python programmer coming out of the gate. [3] A working knowledge of Python (and a strong interest in playing with your data) is recommended. [3]

Who this book is written for: This hands-on recipe guide is divided into three sections that tackle and overcome real-world data modeling problems faced by data analysts/scientist in their everyday work. [3] This book contains all the basic ingredients you need to become an expert data analyst. [3] The book also covers how to manipulate and analyze raster data, such as aerial photographs, satellite images, and digital elevation models. [3] The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. [3]

Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. [3] Explore a variety of techniques to classify and cluster outbound marketing campaign calls data of a bank using Pandas, mlpy, NumPy, and Statsmodels. [3] Be trained in analyzing and visualizing different kinds of data using Pandas to gain real world insights. [3] Understand your data and explore the relationships between variables using Pandas and D3.js. [3]

We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. [3] You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. [3] You’ll also learn how to create geospatial data, rather than just consuming it. [3] Description: The Use-Case scenario is to create recommendations for customers of a grocery store based upon historical transaction data, which could recommend preferable articles. [4]

Jr, Paulo J. Ribeiro, and Peter J. Diggle. 2016. geoR: Analysis of Geostatistical Data. https://CRAN.R-project.org/packagegeoR. [12] You will then implement the basic functionalities of the pandas library such as working with different kinds of data, indexing, and handling missing data. [3] Get the hang of taking out transformed data out of Pandas dataframes and into the formats your application expects. [3] Read, clean, transform, and store your data usng Pandas and OpenRefine. [3]

This course will provide you with a solid foundation from which to analyze data with varying complexity. [3] Definitely a course to undergo if you are a Big Data enthusiast. [4] Big Data has moved past its due date of just being a buzz, and we may not realize but we are already part of the movement. [11] Together they illustrate how much of a game-changer Big Data is. [11] Here are 25 eye-opening facts about the phenomenon we call Big Data. [11] Here are the big data and data analytics certifications that will give your career an edge. [11]

Description: The goal of this Use-case is to make predictions using Real Estate market data. [4] Description: This Use-Case is about analyzing the data and applying time series model to forecast the number of bookings an Airline firm can expect each month. [4] You will also look into mixture models and clustering data, and you will finish with advanced topics like non-parametrics models and Gaussian processes. [3]

As Julia notes it’s not perfect, but you’re still 95% of the way there to gathering data from a page intended for human rather than computer consumption. [5] You?ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. [3] You’ll master core practices like handling multiple vector file formats, editing and manipulating geometries, applying spatial and attribute filters, working with projections, and performing basic analyses on vector data. [3] It will hone your skills by helping you to understand and analyze actual phenomena with data and provide the required hands-on experience for solving real-time industry-based projects. [4] This is followed by exponential smoothing to produce meaningful insights from noisy time series data. [3] The LSTM layer basically captures patterns and long-term dependencies in the historical time series data of solar power readings, to predict the maximum value of total power generation on a specific day. [5]

We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. [3] Create visualizations by choosing color maps, different shapes, sizes, and palettes then delve into statistical data analysis using distribution algorithms and correlations. [3] Gain value from your data using the various data mining and data analysis techniques in Python, and develop efficient predictive models to predict future results. [3] With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems. [3] Moving ahead, you will learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. [3] Who this book is written for: This course is for developers, analysts, and data scientists who want to learn data analysis from scratch. [3] Who this book is written for: This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner. [3] Who this book is written for: If you are a Python programmer who wants to get started with data mining, then this book is for you. [3] If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. [3]

Python Data Structures and Algorithms Benjamin Baka ISBN 13: 9781786467355 Packt Publishing 310 pages (May 2017) Book Overview: In this book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. [3] Fundamentals of Python: Data Structures Ken Lambert ISBN 13: 978-1-285-75200-6 (2014, 496 pages) An introduction to Python with data structures (CS2). [3]

The textbook is organized into 24 “lectures” that cover all the basics of programming (sequence, selection, iteration, functions etc), plus all the major data structures supplied by Python. [3] After exploring Python’s syntax, control flow, and basic data structures, the book shows how to create and deploy full applications and larger code libraries. [3] Packed with tutorials and examples this title features everything from data structures, writing reusable code, testing, paradigms, and how Python can be adapted. [3]

On using the tidyverse tomake data analysis in R as smooth as possible. [5] Applied Spatial Data Analysis with R. 2nd ed. 2013 edition. [12]

Learning Objectives – In this module, you will learn about different statistical techniques and terminologies used in data analysis. [4] This learning path will start with basic data analysis and you will then will gradually move on to increasingly complex problems; your journey will end with representing analyzed data in meaningful visualizations. [3] Covers algorithmic problem solving, basic data structures, functional design, and object-oriented design. [3] Grasp the fundamental concepts of coding, along with the basics of data structures and control flow. [3]

You will be introduced to fundamental data structures in pandas and the different data types, indexing, and more. [3] You will start with an overview of data analysis and iteratively progress from modeling data, to accessing data from remote sources, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and applying pandas to finance. [3] Know how to read different kinds of data into Pandas Dataframes for data analysis. [3]

It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. [3] Who this video is for: If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this video is for you. [3] Who this video is for: The video is for web developers, programmers, enterprise programmers, engineers, big data scientist, and so on. [3]

While the articles provide great advice for any data scientist, the content does heavily feature the use of R, so it’s particularly useful to R users. [5]

Description: The Use-Case focuses on making predictions based on the patient?s characteristic data set, the dataset contains attributes such as “glucose level”, “blood pressure”, “age” etc. At last, the goal is to make a high accuracy machine learning model to predict, whether a patient is Diabetic or not. [4] Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. [4]

The exercises in chapters will help cement your learning so you can put that knowledge into practice in the second half, with three substantial projects: a Space-Invaders-inspired arcade game, data visualization with Python?s super-handy libraries, and a simple Web app you can deploy online. [3] We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples. [3]

You will be able to create complex data structures such as graphs, stacks and queues. [3]

The British Ecological Society has published a new volume in their Guides to Better Science series : A Guide to Reproducible Code in Ecology and Evolution (pdf). [5] While written for an ecology audience (and also including some gorgeous photography of animals), this guide would be useful for anyone in the science looking to implement a reproducible workflow. [5]

Computers are used in every part of science from ecology to particle physics. [3]

Python First: Introduction to Computing with Python Atanas Radenski The ‘Python First’ digital pack provides a gentle introduction to computer science. [3] This introduction to computer science continually reinforces those ties by using real-world science problems as examples. [3] The authors carefully build upon the many important computer science concepts and problem solving techniques throughout the text and offer relevant, real-world examples and exercises to reinforce key material. [3]

As you learn to think computationally, you’ll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science. [3] This is a textbook for an object-oriented introduction to computer science course (CS1) using Python. [3]

**RANKED SELECTED SOURCES**(12 source documents arranged by frequency of occurrence in the above report)

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2. (26) Data Science Course | Data Science Certification Training | Edureka

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6. (6) R-exercises – Train like you fight, fight like you train

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10. (1) John Mount Win-Vector Blog

11. (1) Using R/exams for Short Exams during a Statistics Course | R-bloggers