Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key Features Use powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial data Explore unique recipes for financial data analysis and processing with Python Estimate popular financial models such as CAPM and GARCH using a problem-solution approach Book DescriptionPython is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you'll have learned how to effectively analyze financial data using a recipe-based approach. What you will learn Download and preprocess financial data from different sources Backtest the performance of automatic trading strategies in a real-world setting Estimate financial econometrics models in Python and interpret their results Use Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessment Improve the performance of financial models with the latest Python libraries Apply machine learning and deep learning techniques to solve different financial problems Understand the different approaches used to model financial time series data Who this book is forThis book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.

Leverage the full potential of SAS to get unique, actionable insights from your data Key Features Build enterprise-class data solutions using SAS and become well-versed in SAS programming Work with different data structures, and run SQL queries to manipulate your data Explore essential concepts and techniques with practical examples to confidently pass the SAS certification exam Book DescriptionSAS is one of the leading enterprise tools in the world today when it comes to data management and analysis. It enables the fast and easy processing of data and helps you gain valuable business insights for effective decision-making. This book will serve as a comprehensive guide that will prepare you for the SAS certification exam. After a quick overview of the SAS architecture and components, the book will take you through the different approaches to importing and reading data from different sources using SAS. You will then cover SAS Base and 4GL, understanding data management and analysis, along with exploring SAS functions for data manipulation and transformation. Next, you'll discover SQL procedures and get up to speed on creating and validating queries. In the concluding chapters, you'll learn all about data visualization, right from creating bar charts and sample geographic maps through to assigning patterns and formats. In addition to this, the book will focus on macro programming and its advanced aspects. By the end of this book, you will be well versed in SAS programming and have the skills you need to easily handle and manage your data-related problems in SAS. What you will learn Explore a variety of SAS modules and packages for efficient data analysis Use SAS 4GL functions to manipulate, merge, sort, and transform data Gain useful insights into advanced PROC SQL options in SAS to interact with data Get to grips with SAS Macro and define your own macros to share data Discover the different graphical libraries to shape and visualize data with Apply the SAS Output Delivery System to prepare detailed reports Who this book is forBudding or experienced data professionals who want to get started with SAS will benefit from this book. Those looking to prepare for the SAS certification exam will also find this book to be a useful resource. Some understanding of basic data management concepts will help you get the most out of this book.

Understand how machine learning works and get hands-on experience of using R to build algorithms that can solve various real-world problems Key Features Gain a comprehensive overview of different machine learning techniques Explore various methods for selecting a particular algorithm Implement a machine learning project from problem definition through to the final model Book DescriptionWith huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you'll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you'll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it. What you will learn Define a problem that can be solved by training a machine learning model Obtain, verify and clean data before transforming it into the correct format for use Perform exploratory analysis and extract features from data Build models for neural net, linear and non-linear regression, classification, and clustering Evaluate the performance of a model with the right metrics Implement a classification problem using the neural net package Employ a decision tree using the random forest library Who this book is forIf you are a data analyst, data scientist, or a business analyst who wants to understand the process of machine learning and apply it to a real dataset using R, this book is just what you need. Data scientists who use Python and want to implement their machine learning solutions using R will also find this book very useful. The book will also enable novice programmers to start their journey in data science. Basic knowledge of any programming language is all you need to get started.

Discover the story of your data using the essential elements of associations and correlations Key Features Get a comprehensive introduction to associations and correlations Explore multivariate analysis, understand its limitations, and discover the assumptions on which it's based Gain insights into the various ways of preparing your data for analysis and visualization Book DescriptionAssociations and correlations are ways of describing how a pair of variables change together as a result of their connection. By knowing the various available techniques, you can easily and accurately discover and visualize the relationships in your data. This book begins by showing you how to classify your data into the four distinct types that you are likely to have in your dataset. Then, with easy-to-understand examples, you'll learn when to use the various univariate and multivariate statistical tests. You'll also discover what to do when your univariate and multivariate results do not match. As the book progresses, it describes why univariate and multivariate techniques should be used as a tag team, and also introduces you to the techniques of visualizing the story of your data. By the end of the book, you'll know exactly how to select the most appropriate univariate and multivariate tests, and be able to use a single strategic framework to discover the true story of your data. What you will learn Identify a dataset that's fit for analysis using its basic features Understand the importance of associations and correlations Use multivariate and univariate statistical tests to confirm relationships Classify data as qualitative or quantitative and then into the four subtypes Build a visual representation of all the relationships in the dataset Automate associations and correlations with CorrelViz Who this book is forThis is a book for beginners - if you're a novice data analyst or data scientist, then this is a great place to start. Experienced data analysts might also find value in this title, as it will recap the basics and strengthen your understanding of key concepts. This book focuses on introducing the essential elements of association and correlation analysis.

Build efficient forecasting models using traditional time series models and machine learning algorithms. Key Features Perform time series analysis and forecasting using R packages such as Forecast and h2o Develop models and find patterns to create visualizations using the TSstudio and plotly packages Master statistics and implement time-series methods using examples mentioned Book DescriptionTime series analysis is the art of extracting meaningful insights from, and revealing patterns in, time series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models. You will learn how to preprocess raw time series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the book delves into traditional forecasting models such as time series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also cover advanced time series regression models with machine learning algorithms such as Random Forest and Gradient Boosting Machine using the h2o package. By the end of this book, you will have the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods. What you will learn Visualize time series data and derive better insights Explore auto-correlation and master statistical techniques Use time series analysis tools from the stats, TSstudio, and forecast packages Explore and identify seasonal and correlation patterns Work with different time series formats in R Explore time series models such as ARIMA, Holt-Winters, and more Evaluate high-performance forecasting solutions Who this book is forHands-On Time Series Analysis with R is ideal for data analysts, data scientists, and all R developers who are looking to perform time series analysis to predict outcomes effectively. A basic knowledge of statistics is required; some knowledge in R is expected, but not mandatory.