Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, you’ll learn:
Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.
This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.
By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.
What you will learn
Data scientists are changing the way big data is used in different institutions. Big data is everywhere, but without the right person to interpret it, it means nothing. So where do business find these people to help change their business?
You could be that person!
It has become a universal truth that businesses are full of data. With the use of big data, the US healthcare could reduce their healthcare spending by $300 billion to $450 billion. It can easily be seen that the value of big data lies in the analysis and processing of that data, and that's where data science comes in.
Grab your copy today and learn:
When data science can reduce spending costs by billions of dollars in the healthcare industry, why wait to jump in?
If you want to get started in a new, ever-growing career, don't wait any longer. Download your copy now!
This is a book regarding data science with Python. A python is an excellent tool for several analyzers because of its libraries for manipulating, storing and gaining insight from data. Python code is ideal for tackling day-to-day problems like- visualizing different types of data; manipulating, transforming, and cleaning data; using data to build machine learning or statistical models. It is terribly merely that it should have a reference for scientific computing in Python. It is meant to assist Python users by learning the way to use Python’s data science stack libraries like- IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools—to effectively store, manipulate, and gain insight from data.
What you will learn:
• Jupyter and IPython – In several, Python using data scientists work with these packages and provide the computational environment.
• NumPy -This library provides the array object for economical storage and manipulation of dense data arrays in Python.
• Pandas- For efficient storage and manipulation of labeled/columnar data in Python the Data Frame object is provided by the library.
• Matplotlib- Capabilities of a versatile range of data visualizations in Python is provided by the library.
• Scikit-Learn- This library provides economical and clean Python with implementations of the foremost necessary and established machine learning algorithms.
This book will help you in getting familiar with data science using Python 3.5; Save time (and effort) with all the essential tools explained and additionally produce effective data science projects which can avoid common problems with the help of hints prescribed by experiences and examples. Get trendy vision into the core of Python data which incorporates the latest versions of NumPy, Jupyter notebooks, sci-kit-learn, and pandas. This book gives the complete overview of all the visualization and deployment instruments which makes it an easier option to present your results to an audience of both business users and data science experts; principal of machine learning algorithms and graph analysis techniques.
What you will learn:
• By using a Python scientific environment on Windows, Mac, and Linux how you can set up your data science toolbox
• Get data prepared for your data science project
• Explore, Manipulate and fix data so as to resolve data science problems
• For testing your data science hypotheses how you can set up an experimental pipeline
• For your data science tasks how to select the foremost effective and scalable learning algorithm
• Forgetting the best performance of how to optimize your machine learning models
• Taking advantage of interconnections and links in your data by exploring and the cluster of graphs.
In this book, Master data science uses Python and its libraries in many ways. This comprehensive guide helps you to move beyond the enhancement and transform the theory which provides a hands-on and advanced study of data science using python, and also easy-to-follow. Data science is comparatively a new cognitive content which is employed by various organizations to produce data-driven decisions. Informing high-end visualizations in Python matplot library is used and also uncovers the basics of machine learning. All the topics covered in this book can be used in real-world circumstances.
What You Will Learn:
•Perform linear algebra and manage data in Python; evaluate and apply linear and logistic regression techniques in various application techniques for estimating the relationships among variables.
•Derive assumptions from the analysis by mining data to reveal hidden patterns and trends and performing inferential statistics
• Resolve data science issues in Python
•With the help of various collaborative filtering algorithms, how you can build recommendation engines
• Apply the ensemble ways to boost your predictions
• For handling data at large scale how we work with large data technologies
•Produce mine for patterns and data visualizations.
•The four basics of Data Science with Python having advanced techniques like- data mining, machine learning, data visualization, and data analysis
•Perform clustering together with an analysis of unstructured data with completely different text mining techniques and to invest the power of Python in big data analytics.
The book begins with setting up the environment for Anaconda platform so as to make it accessible for tools and frameworks like- Jupyter, pandas, matplotlib, Python, R, Julia, and more. Anaconda is an open source platform which brings along the simplest tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages.
Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how to perform data science operations in the real world. It is ideal for data analysts and data science professionals who want to boost the efficiency of their data science applications by using the best libraries in multiple languages. Basic programming knowledge with R or Python and introductory knowledge of linear algebra is expected.
What you will learn:
· Perform cleaning, sorting, classification, clustering, regression, prediction, and building machine learning models and optimizing them and dataset modeling using Anaconda
· Use the package manager conda and discover, install, and use functionally efficient and scalable packages
· Get comfortable with heterogeneous data exploration using multiple languages within a project
· Discover and share packages, notebooks, and environments, and use shared project drives on Anaconda Cloud
· Tackle advanced data prediction issues
· Explore all the necessities information of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, and many more.
· Find out how to visualize data using the packages available for Julia, Python, and R. Analyze your data efficiently with the foremost powerful data science stack.
Practical Data Science with R shows helpful statistical techniques for everyday business situations and ways for using the R programming language. Without plenty of academic theory or advanced mathematics, this R language is associated with tools which give simple ways to tackle with day-to-day data science tasks .
In this book, you will learn the statistical analysis techniques to explain examples which are based mostly on decision support, business intelligence, and marketing. This is the book for you if you are a data scientist, want to be a data scientist, or want to work with data scientists.
This is a good “what next” book for analysts and programmers wanting to know more about machine learning and data wrangling. Concept of this book is to present data science from a pragmatic, practice-oriented viewpoint.
What you will learn
• How to work as a data scientist. Learn how important listening, collaboration, honest presentation, and iteration are to and what we do.
• The key significance of the book is loading data, collecting requirements, validating models, examining data, deploying models to production, building models and documenting.
• This provides over 10 significant examples of datasets and demonstrates the concepts which are discussed with fully worked exercises using standard R methods.
• It will demonstrate all the preparatory steps necessary for any real-world project. Every result and almost every graph in the book is given as a fully worked example.
• It is scrupulously correct on statistics, but presents topics in the context and order a practitioner worries about them.
This book focuses primarily on R, but also uses several other domain-specific languages (DSLs) and even touches on languages such as the UNIX shell and C, also illustrate the process by which programmers approach a problem and implement the solution in different ways. This book has 3 parts, with each part having a general theme.
Part I contains case studies that involve reading and transforming raw data, manipulating and visualizing them, and then using statistical techniques to try to solve a problem or understand relationships between variables.
Part II focuses on using simulation to understand stochastic processes for their own sake and also explore how to use simulation to model interesting situations.
Part III explores different data technologies. These include databases, visualization with KML, and scraping data from Web pages with HTTP requests and text processing.
The scope of this book is wide, covering three main topics:
• Applications of R to specific disciplines
• For the study of topics of the statistical methodology by Using R
• The development of R also including building packages, programming, and graphics
What you will learn
• Non-standard data formats (robot logs, email messages)
• Text processing and regular expressions
• Newer/less-traditional technologies (Web scraping, Web services, JSON, XML, HTML, KML and Google Earth™)
• Statistical methods (classification trees, k-nearest neighbors, naïve Bayes)
• Visualization and exploratory data analysis; • relational databases and SQL
• Implementing algorithms
• Large data and efficiency
• Software design, development, and testing
• Using and interfacing to other languages such as the UNIX shell, C, and Python.
This book aims to teach you how to begin performing the data science tasks by taking advantage of R's powerful ecosystem of packages. R is the most widely used programming language and when used with data science, it can be a great combination to solve the problems involved with varied data sets in the real world. For statistical simulation to the users, it will provide a methodological and computational framework. This book is for them who want to learn about the computer-intense Monte-Carlo methods, the advanced features of R and computational tools for statistical simulation. Good knowledge of R programming is assumed/required.
You will learn five different simulation techniques in-depth using real-world case studies which are as follows-
1. Monte Carlo; 2. Discrete Event Simulation;
3. System Dynamics; 4 .Agent-Based Modeling;
It teaches the essential and fundamental concepts in statistical modeling and simulation. For explaining the statistical computing methods, it takes a practical and hands-on approach and gives advice on the usage of these methods. It provides computational tools to help you in solving common problems in statistical simulation and computer-intense methods. This book helps in uncovering the large-scale patterns of complex systems where interdependencies and variation are critical.
What You Will Learn
· Advanced R features to extract insights from your data and to simulate data
· How simulation project can be plan and structure to aid in the presentation of results and also in the decision-making process.
· To simulate distributions, data sets, and populations is done by seeing random number simulation
· For solving scientific and real-world problems by using design statistical solutions with R
· High-performance computing and advanced data manipulation
· Comprehensive coverage of several R statistical packages like simPop, boot, VIM, and many more.