Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality. This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science. Key Features: Applies a modern, computational approach to working with data Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues Teaches the fundamentals of some of the most important tools in the Python data-science stack Provides a basic, but rigorous, introduction to Probability and its application to Statistics Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material

The 1960s saw the beginning of computer science as an academic field of study. The programming languages, compilers, and operating systems, as well as the mathematical theory that underpinned these fields, were the primary focuses of this course. Finite automata, regular expressions, context-free languages, and computability were some of the topics that were addressed in theoretical computer science courses. In the 1970s, the study of algorithms became an essential component of theory when it had previously been neglected. The goal was to find practical applications for computers. At this time, a significant shift is taking place, and more attention is being paid to the diverse range of applications. This shift came about for a variety of different causes. The convergence of computer and communication technologies has been a significant contributor to this change. Our current conception of data and how best to work with it in a contemporary environment has to be revised in light of recent advances in the capacity to monitor, collect, and store data in a variety of domains, including the natural sciences, business, and other areas. The rise of the internet and social networks as fundamental components of everyday life carries with it a wealth of theoretical possibilities as well as difficulties. Traditional subfields of computer science continue to hold a significant amount of weight in the field as a whole, but researchers of the future will focus more on how to use computers to comprehend and extract usable information from massive amounts of data arising from applications rather than how to make computers useful for solving particular problems in a well-defined manner. With this in mind, we have prepared this book to cover the theory that we anticipate will be important in the next 40 years, in the same way that a grasp of automata theory, algorithms, and other similar areas provided students an advantage in the previous 40 years. An increased focus on probability, statistical approaches, and numerical methods is one of the key shifts that has taken place. The book's early draughts have been assigned reading at a variety of academic levels, from undergraduate to graduate. The appendix contains the necessary background information for a course taken at the 1 | P a ge undergraduate level. Because of this, the appendix contains problems for your homework.

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.

The academic field of computer science did not develop as a separate subject of study until the 1960s after it had been in existence since the 1950s. The mathematical theory that underpinned the fields of computer programming, compilers, and operating systems was one of the primary focuses of this class. Other important topics were the various programming languages and operating systems. Context-free languages, finite automata, regular expressions, and computability were a few of the topics that were discussed in theoretical computer science lectures. The area of study known as algorithmic analysis became an essential component of theory in the 1970s, after having been mostly overlooked for the majority of its existence up to that point in time. The purpose of this initiative was to investigate and identify practical applications for computer technology. At the time, a significant change is taking place, and a greater amount of attention is being paid to the vast number of different applications that may be utilized. This shift is the cumulative effect of several separate variables coming together at the same time. The convergence of computing and communication technology has been a major motivator, and as a result, this change may be primarily attributed to that convergence. Our current knowledge of data and the most effective approach to work with it in the modern world has to be revised in light of recent advancements in the capability to monitor, collect, and store data in a variety of fields, including the natural sciences, business, and other fields. This is necessary because of the recent breakthroughs in these capabilities. This is as a result of recent advancements that have been made in these capacities. The widespread adoption of the internet and other forms of social networking as indispensable components of people's lives brings with it a variety of opportunities for theoretical development as well as difficulties in actual use. Traditional subfields of computer science continue to hold a significant amount of weight in the field as a whole; however, researchers of the future will focus more on how to use computers to comprehend and extract usable information from massive amounts of data arising from applications rather than how to make computers useful for solving particular problems in a well-defined manner. This shift in emphasis is due to the fact that researchers of 1 | P a ge the future will be more concerned with how to use computers to comprehend and extract usable information from massive amounts of data arising from applications. This shift in emphasis is because researchers of the future will be more concerned with how to use the information they find. As a result of this, we felt it necessary to compile this book, which discusses a theory that would, according to our projections, play an important role within the next 40 years. We think that having a grasp of this issue will provide students with an advantage in the next 40 years, in the same way that having an understanding of automata theory, algorithms, and other topics of a similar sort provided students an advantage in the 40 years prior to this one, and in the 40 years after this one. A movement toward placing a larger emphasis on probabilities, statistical approaches, and numerical processes is one of the most significant shifts that has taken place as a result of the developments that have taken place. Early drafts of the book have been assigned reading at a broad variety of academic levels, ranging all the way from the undergraduate level to the graduate level. The information that is expected to have been learned before for a class that is taken at the undergraduate level may be found in the appendix. As a result of this, the appendix will provide you with some activities to do as a component of your project.

Dive into data analysis with Python, starting from the basics to advanced techniques. This course covers Python programming, data manipulation with Pandas, data visualization, exploratory data analysis, and machine learning. Key Features From Python basics to advanced data analysis techniques. Apply your skills to practical scenarios through real-world case studies. Detailed projects and quizzes to help gain the necessary skills. Book DescriptionEmbark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently. Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling. To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.What you will learn Develop a strong foundation in Python for data analysis. Manipulate and analyze data using NumPy and Pandas. Create insightful data visualizations with Matplotlib and Seaborn. Understand and apply probability theory and hypothesis testing. Implement supervised and unsupervised machine learning algorithms. Execute real-world data analysis projects with confidence. Who this book is for This course adopts a hands-on approach, seamlessly blending theoretical lessons with practical exercises and real-world case studies. Practical exercises are designed to apply theoretical knowledge, providing learners with the opportunity to experiment and learn through doing. Real-world applications and examples are integrated throughout the course to contextualize concepts, making the learning process engaging, relevant, and effective. By the end of the course, students will have a thorough understanding of the subject matter and the ability to apply their knowledge in practical scenarios.

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesús Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods. The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms. This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.

Data Science and Machine Learning are two interconnected fields that play a pivotal role in modern technological advancements. Data science involves extracting insights and knowledge from vast amounts of data using various tools and techniques. This includes data collection, cleaning, analysis, and interpretation to uncover valuable patterns and trends. On the other hand, machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms and models capable of learning from data to make predictions and decisions. Machine learning algorithms can automatically improve their performance over time by learning from new data, making them crucial for tasks such as image recognition, natural language processing, and predictive analytics. Together, data science and machine learning empower businesses and researchers to leverage data-driven insights for informed decision-making and innovation across diverse domains. This book is intended for the first course in Data Science and Machine Learning and covers the required topics in sufficient depth to meet the requirements of the readers.

Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.