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Python Book
The Python Book Discover the power of one of the fastest growing programming languages in the world with this insightful new resource The Python Book delivers an essential introductory guide to learning Python for anyone who works with data but does not have experience in programming. The author, an experienced data scientist and Python programmer, shows readers how to use Python for data analysis, exploration, cleaning, and wrangling. Readers will learn what in the Python language is important for data analysis, and why. The Python Book offers readers a thorough and comprehensive introduction to Python that is both simple enough to be ideal for a novice programmer, yet robust to be useful for those more experienced in the language. The book assists budding programmers to gradually increase their skills as they move through the book, always with an understanding of what they are covering and why it is useful. Used by major companies like Google, Facebook, Instagram, Spotify, and more, Python promises to remain central to the programming landscape for years to come. Containing a thorough discussion of Python programming topics like variables, equalities and comparisons, tuple and dictionary data types, while and for loops, and if statements, readers will also learn: How to use highly useful Python programming libraries, including Pandas and MatplotlibHow to write Python functions and classesHow to write and use Python scriptsTo deal with different data types within Python Perfect for statisticians, computer scientists, software programmers, and practitioners working in private industry and medicine, The Python Book will also be of interest to students in any of the aforementioned fields. As it assumes no programming experience or knowledge, the book is ideal for those who work with data and want to learn to use Python to enhance their work.
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Data Science Fundamentals with R, Python, and Open Data
Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start data science projects Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out data science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate. This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks) of all examples. Exam-style questions are implemented and multiple choice questions to support the readers' active learning. Each chapter presents one or more case studies. Written by a highly qualified academic, Data Science Fundamentals with R, Python, and Open Data discuss sample topics such as: Data organization and operations on data frames, covering reading CSV dataset and common errors, and slicing, creating, and deleting columns in RLogical conditions and row selection, covering selection of rows with logical condition and operations on dates, strings, and missing valuesPivoting operations and wide form-long form transformations, indexing by groups with multiple variables, and indexing by group and aggregationsConditional statements and iterations, multicolumn functions and operations, data frame joins, and handling data in list/dictionary format Data Science Fundamentals with R, Python, and Open Data is a highly accessible learning resource for students from heterogeneous disciplines where Data Science and quantitative, computational methods are gaining popularity, along with hard sciences not closely related to computer science, and medical fields using stochastic and quantitative models.
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Computer Program Synthesis Methodologies
powerful operations on them. An early step in this direction was the development of APl, and more recent examples have been SETl which enables a user to code in terms of mathematical enti ties such as sets and BDl which allows a user, presumably a businessman, to specify a computation in terms of a series of tabular forms and a series of processing paths through which data flows. The design and implementation of such languages are examined in chapters by P. GOLDBERG. Another extension to traditional methods is made possible by systems designed to automatically handle low level flow-of control decisions. All the above higher level languages do this implicitly with their built in operators. PROLOG is a language which does this with a theorem proving mechanism employing primarily unification and backtracking. The programmer specifies the problem to be solved with a set of formal logic statements including a theorem to be proved. The theorem proving system finds a way to combine the axioms to prove the theorem, and in the process, it completes the desired calculation. H. GAllAIRE has contributed a chapter describing PROLOG giving many examples of its usage.
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Theoretical Foundations of Programming Methodology
Long ago~ the welfare of a sooiety used to depend heavily on the skill and dedioation of its oraftsmen - the miller~ the blaok- smith~ the oobbler and the tailor. These oraftsmen aoquired their skill by a long and poorly paid apprentioeship to some master of their oraft. They learned by imitation and experienoe~ and by tri- al and error. They did not read books or study soienoe~ they knew nothing of the theory of their subjeot~ the geometry of their ru- dimentary drawings~ nor the mathematios underlying their primitive oaloulations. They oould not explain how or why they used their methods; yet they worked effeotively by themselves or in small teams to oomplete their tasks at a predioted oost~ to a fairly well predioted timesoale~ and usually to the satisfaotion of their olients. The programmer of today shares many of these attributes of a oraftsman. He learns his oraft by apprentioeship in an existing team of programmers - but his apprentiaeship is highZy paid and usually very short. He develops his skill by trial; but mostly by error. He does not study theory~ or even read books on Computer Soience.He knows nothing of the logical and mathematical founda- tions of his profession; and he hates to explain or justifY~ or even to dooument what he has done.
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