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    R: Data Analysis and Visualization. Click here to enter text

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi V. Mittal, Bater Makhabel, Edina Berlinger (EURO), Ferenc Illés, Ádám Banai, Gergely Daróczi, Barbara Dömötör, Gergely Gabler, Dániel Havran, Péter Juhász, Margitai István, Ágnes Tuza, Milán Badics, Kata Váradi, István Margitai, Péter Medvegyev, Agnes Vidovics-Dancs, Julia Molnár, Balázs Árpád Sz?+-cs, Balázs Márkus, Tamás Vadász
    R: Data Analysis and Visualization. Click here to enter text Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi V. Mittal, Bater Makhabel, Edina Berlinger (EURO), Ferenc Illés, Ádám Banai, Gergely Daróczi, Barbara Dömötör, Gergely Gabler, Dániel Havran, Péter Juhász, Margitai István, Ágnes Tuza, Milán Badics, Kata Váradi, István Margitai, Péter Medvegyev, Agnes Vidovics-Dancs, Julia Molnár, Balázs Árpád Sz?+-cs, Balázs Márkus, Tamás Vadász - okladka książki

    R: Data Analysis and Visualization. Click here to enter text Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi V. Mittal, Bater Makhabel, Edina Berlinger (EURO), Ferenc Illés, Ádám Banai, Gergely Daróczi, Barbara Dömötör, Gergely Gabler, Dániel Havran, Péter Juhász, Margitai István, Ágnes Tuza, Milán Badics, Kata Váradi, István Margitai, Péter Medvegyev, Agnes Vidovics-Dancs, Julia Molnár, Balázs Árpád Sz?+-cs, Balázs Márkus, Tamás Vadász - okladka książki

    R: Data Analysis and Visualization. Click here to enter text Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi V. Mittal, Bater Makhabel, Edina Berlinger (EURO), Ferenc Illés, Ádám Banai, Gergely Daróczi, Barbara Dömötör, Gergely Gabler, Dániel Havran, Péter Juhász, Margitai István, Ágnes Tuza, Milán Badics, Kata Váradi, István Margitai, Péter Medvegyev, Agnes Vidovics-Dancs, Julia Molnár, Balázs Árpád Sz?+-cs, Balázs Márkus, Tamás Vadász - audiobook MP3

    R: Data Analysis and Visualization. Click here to enter text Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi V. Mittal, Bater Makhabel, Edina Berlinger (EURO), Ferenc Illés, Ádám Banai, Gergely Daróczi, Barbara Dömötör, Gergely Gabler, Dániel Havran, Péter Juhász, Margitai István, Ágnes Tuza, Milán Badics, Kata Váradi, István Margitai, Péter Medvegyev, Agnes Vidovics-Dancs, Julia Molnár, Balázs Árpád Sz?+-cs, Balázs Márkus, Tamás Vadász - audiobook CD

    Wydawnictwo:
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    Do przechowalni

    The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module!

    This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility.

    The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework.

    With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs.

    The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions.

    Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on.

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    O autorach książki

    Tony Fischetti is a data scientist at College Factual, where he gets to use R everyday to build personalized rankings and recommender systems. He graduated in cognitive science from Rensselaer Polytechnic Institute, and his thesis was strongly focused on using statistics to study visual short-term memory.



    Tony enjoys writing and contributing to open source software, blogging at onthelambda, writing about himself in third person, and sharing his knowledge using simple, approachable language and engaging examples.

    The more traditionally exciting of his daily activities include listening to records, playing the guitar and bass (poorly), weight training, and helping others.

    Brett Lantz korzysta z innowacyjnych metod analizy danych, aby lepiej zrozumieć ludzkie zachowanie. Jest z wykształcenia socjologiem i instruktorem DataCamp, prowadzi warsztaty uczenia maszynowego na całym świecie. Interesuje się między innymi zastosowaniami data science w sporcie, grach wideo, pojazdach autonomicznych i nauce języków obcych.

    Jaynal Abedin currently holds the position of Statistician at the Centre for Communicable Diseases (CCD) at icddr,b ( www.icddrb.org). He attained his Bachelor's and Master's degrees in Statistics from the University of Rajshahi, Rajshahi, Bangladesh. He has vast experience in R programming and Stata and has efficient leadership qualities. He is currently leading a team of statisticians. He has hands-on experience in developing training material and facilitating training in R programming and Stata along with statistical aspects in public health research. His primary area of interest in research includes causal inference and machine learning. He is currently involved in several ongoing public health research projects and is a co-author of several work-in-progress manuscripts. In the useR! Conference 2013, he presented a poster—edeR: Email Data Extraction using R, available at https://www.edii.uclm.es/~useR-2013/abstracts/files/34_edeR_Email_Data_Extraction_using_R.pdf—and obtained the best application poster award. He is also involved in reviewing scientific manuscripts for the Journal of Applied Statistics (JAS) and the Journal of Health Population and Nutrition (JHPN). He is also a successful freelance statistician on online platforms and has an excellent reputation through his high-quality work, especially in R programming. He can be contacted at joystatru@gmail.com, https://bd.linkedin.com/in/jaynal; his Twitter handle is @jaynal83.
    Hrishi V. Mittal has been working with R for a few years in different capacities. He was introduced to the exciting world of data analysis with R when he was working as a senior air quality scientist at King's College, London, where he used R extensively to analyze large amounts of air pollution and traffic data for London's Mayor's Air Quality Strategy. He has experience in various other programming languages but prefers R for data analysis and visualization. He is also actively involved in various R mailing lists, forums, and the development of some R packages.
    Bater Makhabel (LinkedIn: BATERMJ and GitHub: BATERMJ) is a system architect who lives across Beijing, Shanghai, and Urumqi in China. He received his master's and bachelor's degrees in computer science and technology from Tsinghua University between the years 1995 and 2002. He has extensive experience in machine learning, data mining, natural language processing (NLP), distributed systems, embedded systems, the web, mobile, algorithms, and applied mathematics and statistics. He has worked for clients such as CA Technologies, META4ALL, and EDA (a subcompany of DFR). He also has experience in setting up start-ups in China._x000D_ Bater has been balancing a life of creativity between the edge of computer sciences and human cultures. For the past 12 years, he has gained experience in various culture creations by applying various cutting-edge computer technologies, one being a human-machine interface that is used to communicate with computer systems in the Kazakh language. He has previously collaborated with other writers in his fields too, but Learning Data Mining with R is his first official effort.
    Edina Berlinger has a PhD in economics from the Corvinus University of Budapest. She is an associate professor, teaching corporate finance, investments, and financial risk management. She is the head of the Finance department of the university, and is also the chair of the finance subcommittee of the Hungarian Academy of Sciences. Her expertise covers loan systems, risk management, and more recently, network analysis. She has led several research projects in student loan design, liquidity management, heterogeneous agent models, and systemic risk.
    Gergely Gabler is the head of the Business Model Analysis department at the banking supervisory division of National Bank of Hungary (MNB) since 2014. Before this, he used to lead the Macroeconomic Research department at Erste Bank Hungary after being an equity analyst since 2008. He graduated from the Corvinus University of Budapest in 2009 with an MSc degree in financial mathematics. He has been a guest lecturer at Corvinus University of Budapest since 2010, and he also gives lectures in MCC College for advanced studies. He is about to finish the CFA program in 2015 to become a charterholder.

    Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi V. Mittal, Bater Makhabel, Edina Berlinger (EURO), Ferenc Illés, Ádám Banai, Gergely Daróczi, Barbara Dömötör, Gergely Gabler, Dániel Havran, Péter Juhász, Margitai István, Ágnes Tuza, Milán Badics, Kata Váradi, István Margitai, Péter Medvegyev, Agnes Vidovics-Dancs, Julia Molnár, Balázs Árpád Sz?+-cs, Balázs Márkus, Tamás Vadász - pozostałe książki

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