Tabla de Contenidos: “…Viewing columns of data -- Viewing summary statistics -- Collecting the data -- Using take -- Using tail -- Using head -- Counting the number of rows of data -- Converting a PySpark DataFrame to a Pandas DataFrame -- How to manipulate data on rows and columns -- Selecting columns -- Creating columns -- Dropping columns -- Updating columns -- Renaming columns -- Finding unique values in a column -- Changing the case of a column -- Filtering a DataFrame -- Logical operators in a DataFrame -- Using isin() -- Datatype conversions -- Dropping null values from a DataFrame -- Dropping duplicates from a DataFrame -- Using aggregates in a DataFrame -- Summary -- Sample question -- Answer -- Chapter 5: Advanced Operations and Optimizations in Spark -- Grouping data in Spark and different Spark joins -- Using groupBy in a DataFrame -- A complex groupBy statement -- Joining DataFrames in Spark -- Reading and writing data -- Reading and writing CSV files -- Reading and writing Parquet files -- Reading and writing ORC files -- Reading and writing Delta files -- Using SQL in Spark -- UDFs in Apache Spark -- What are UDFs? …”

Tabla de Contenidos: “…-- Summarizing data and EDA -- Primer on descriptive statistics -- Percentiles, quantiles, and distributions -- Measures of central tendency -- Measures of variability -- Dealing with common data issues -- Bill Gates walks into a bar -- Missing values and data imputation -- Digging deeper into variable transformations -- Data standardization or scaling -- Power transformations -- Summary -- Further reading -- Chapter 3: Design Principles and Presenting Results with Streamlit -- Technical requirements -- Types of dashboards and their design -- Understanding the design concepts of a dashboard -- Thinking about how to best present data -- Thinking a bit about processing information -- Generating effective filters, dimensions, and metrics -- Filters -- Dimensions -- Metrics -- Getting your data into Streamlit and generating a basic dashboard -- Starting out with Streamlit -- Creating a marketing data dashboard with Streamlit -- Summary…”

“…It incorporates leading-edge research, alongside up-to-date examples and current statistics tracking global developments in this field. …”

Tabla de Contenidos:

Tabla de Contenidos: “…-- Customer Relationship Management Activities -- Campaign Management -- Product Development -- Web Log Analyses -- Pricing -- Human Resource Development -- Corporate Performance Management -- Finance -- Inventory Management -- Supply Chain Management -- Lean -- A Catalogue of Ideas with Key Performance Indicators for the Company's Different Functions -- Summary -- Chapter 4 Business Analytics at the Analytical Level -- Data, Information, and Knowledge -- Analyst's Role in the Business Analytics Model -- Three Requirements the Analyst Must Meet -- Business Competencies -- Tool Kit Must Be in Order (Method Competencies) -- Technical Understanding (Data Competencies) -- Required Competencies for the Analyst -- Analytical Methods (Information Domains) -- How to Select the Analytical Method -- The Three Imperatives -- Descriptive Statistical Methods, Lists, and Reports -- Hypothesis-Driven Methods -- Tests with Several Input Variables -- Data Mining with Target Variables -- Data Mining Algorithms -- Explorative Methods -- Data Reduction -- Cluster Analysis -- Cross-Sell Models -- Up-Sell Models -- Business Requirements -- Definition of the Overall Problem -- Definition of Delivery -- Definition of Content -- Summary -- Chapter 5 Business Analytics at the Data Warehouse Level -- Why a Data Warehouse? …”

Tabla de Contenidos: “….] -- EXPOSICIÓN A LA VIOLENCIA Y VIOLENCIA FILIOPARENTAL EN MENORES INFRACTORES Y RECLUSOS / [EXPOSURE TO VIOLENCE AND CHILD-TO-PARENT VIOLENCE IN JUVENILE OFFENDERS AND PRISONERS] -- ESTRATEGIAS MOTIVACIONALES EN INTERVENCIÓN CON AGRESORES DE PAREJA: EL PLAN MOTIVACIONAL INDIVIDUALIZADO / [MOTIVATIONAL STRATEGIES IN INTIMATE PARTNER VIOLENCE OFFENDER INTERVENTION: INDIVIDUALIZED MOTIVATIONAL PLANS] -- SÍNTOMAS PREFRONTALES EN DELINCUENTES Y NO DELINCUENTES CON UN MISMO NIVEL EDUCATIVO / [PREFRONTAL SYMPTOMS IN OFFENDERS AND NON-OFFENDERS WITH THE SAME EDUCATION LEVEL] -- VECINDARIOS Y PROBLEMAS SOCIALES: UN ACERCAMIENTO DESDE LA ESTADÍSTICA ESPACIAL / [NEIGHBORHOODS AND SOCIAL PROBLEMS: A SPATIAL STATISTICS APPROACH] -- ¿DIFIERE LA CAPACIDAD DE CLASIFICACIÓN DE RESPUESTAS SIMULADAS DE LA ESCALA DE F DEL MMPI-2 CUANDO SE CONTRASTA CON UN GRUPO CONTROL Y CON LA POBLACIÓN NORMATIVA? …”

Tabla de Contenidos: “…Introduction to Renewable Energy Systems / An Overview of Renewable Energy Systems -- Climate Change -- Renewable Energy Trend -- Types of Renewable Energy Systems -- Introduction -- Wind Power -- Solar Energy -- Geothermal Power -- Hydro Power -- Bioenergy -- Ocean Power -- Economic Aspects of Renewable Energy Systems -- Introduction -- Affecting Factors -- Life-Cycle Costs -- Economic Trend of Renewable Energy Systems -- Opportunities and Challenges of Renewable Energy Systems Integration -- Renewable Energy Systems Integration Opportunities -- Renewable Energy Systems Integration Challenges and Barriers -- Alleviating the Challenges and Barriers -- Current Trends and Future Prospects -- The Need for Optimization Tools to Solve Problems Related to Renewable Energy Systems -- Conclusions -- Acknowledgments -- References -- Introduction to Optimization / Introduction -- Formulation of Optimization Problem -- General Models of Optimization Problems -- Convex Optimization -- Basic Concepts and Models of Linear Optimization -- Solution Techniques for Unconstrained Optimization -- Nelder -- Mead Method -- Golden Section Search -- Fibonacci Search -- Hooke and Jeeves' Method -- Gradient Descent Method -- Coordinate Descent Method -- Solution Techniques for Constrained Optimization and Relevant Applications to Renewable Energy Systems -- Genetic Algorithm -- Particle Swarm Optimization -- Simulating Annealing -- Ant Colony Optimization -- Tabu Search -- Firefly Algorithm -- Artificial Bee Colony Algorithm -- Artificial Immune System -- Game Theory -- Simplex Technique Specifically for Mixed Integer Linear Programming Concept -- Optimal Control Technique -- Conclusion and Discussion -- References -- Optimal Procurement of Contingency and Load Following Reserves by Demand Side Resources Under Wind-Power Generation Uncertainty / Introduction -- Mathematical Model -- Overview and Modeling Assumptions -- Objective Function -- Constraints -- Case Studies -- Wind-Power Production Scenario Generation -- Illustrative Example -- Application on a 24-Bus System -- Computational Statistics -- Conclusions -- Appendix A: 24-Bus System Test System Data -- Nomenclature -- Sets and Indices -- Parameters -- Variables -- References -- Optimum Bidding of Renewable Energy System Owners in Electricity Markets / Renewable Energy System Owners in Electricity Markets -- Electricity Market Overview -- Importance of Integrating Renewables into the Electricity Market -- Current Market Rules for Renewable Energy System Owners in North America -- Price-Taker Models -- Multistage Stochastic Programming Approach -- Multistage Stochastic Programming Approach -- Scenario Generation and Reduction Method -- Risk Management -- Bidding Strategy Models for Price-Taker Renewable Energy Owner -- Mitigating the Trading Risk by Purchasing Additional Power from Conventional Energy Owner -- Price-Maker Models -- MPEC Approach -- Why Consider Wind Power Energy Owners as Price-Makers? …”

Tabla de Contenidos: “…Model fitting and evaluation -- Statistical significance of regression outputs -- Generalize estimating equations -- Mixed effects models -- Time series data -- Generalized linear models -- Applying regularization to linear models -- Tree methods -- Decision trees -- Random forest -- Scaling out with PySpark - predicting year of song release -- Summary -- Chapter 5: Putting Data in its Place - Classification Methods and Analysis -- Logistic regression -- Multiclass logistic classifiers: multinomial regression -- Formatting a dataset for classification problems -- Learning pointwise updates with stochastic gradient descent -- Jointly optimizing all parameters with second-order methods -- Fitting the model -- Evaluating classification models -- Strategies for improving classification models -- Separating Nonlinear boundaries with Support vector machines -- Fitting and SVM to the census data -- Boosting: combining small models to improve accuracy -- Gradient boosted decision trees -- Comparing classification methods -- Case study: fitting classifier models in pyspark -- Summary -- Chapter 6: Words and Pixels - Working with Unstructured Data -- Working with textual data -- Cleaning textual data -- Extracting features from textual data -- Using dimensionality reduction to simplify datasets -- Principal component analysis -- Latent Dirichlet Allocation -- Using dimensionality reduction in predictive modeling -- Images -- Cleaning image data -- Thresholding images to highlight objects -- Dimensionality reduction for image analysis -- Case Study: Training a Recommender System in PySpark -- Summary -- Chapter 7: Learning from the Bottom Up - Deep Networks and Unsupervised Features -- Learning patterns with neural networks -- A network of one - the perceptron -- Combining perceptrons - a single-layer neural network -- Parameter fitting with back-propagation…”

Tabla de Contenidos: “…-- Missing data -- Locating missing data -- Zero values -- Fixing missing data -- Ignore the problem rows -- Fix the problem manually -- Use a fabricated value -- Use a central measure -- Use Last Observation Carried Forward -- Use a similar value -- Use the most likely value -- Data errors -- Truncated fields -- Data type and character set errors -- Logic or semantic errors -- Outliers -- Visual mining for outliers -- Statistical detection of outliers -- Summary -- Index…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: A Gentle Introduction to Machine Learning -- Introduction - classic and adaptive machines -- Only learning matters -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Beyond machine learning - deep learning and bio-inspired adaptive systems -- Machine learning and big data -- Further reading -- Summary -- Chapter 2: Important Elements in Machine Learning -- Data formats -- Multiclass strategies -- One-vs-all -- One-vs-one -- Learnability -- Underfitting and overfitting -- Error measures -- PAC learning -- Statistical learning approaches -- MAP learning -- Maximum-likelihood learning -- Elements of information theory -- References -- Summary -- Chapter 3: Feature Selection and Feature Engineering -- scikit-learn toy datasets -- Creating training and test sets -- Managing categorical data -- Managing missing features -- Data scaling and normalization -- Feature selection and filtering -- Principal component analysis -- Non-negative matrix factorization -- Sparse PCA -- Kernel PCA -- Atom extraction and dictionary learning -- References -- Summary -- Chapter 4: Linear Regression -- Linear models -- A bidimensional example -- Linear regression with scikit-learn and higher dimensionality -- Regressor analytic expression -- Ridge, Lasso, and ElasticNet -- Robust regression with random sample consensus -- Polynomial regression -- Isotonic regression -- References -- Summary -- Chapter 5: Logistic Regression -- Linear classification -- Logistic regression -- Implementation and optimizations -- Stochastic gradient descent algorithms -- Finding the optimal hyperparameters through grid search -- Classification metrics -- ROC curve -- Summary -- Chapter 6: Naive Bayes…”

Tabla de Contenidos: “…The Big Overview -- Statistical Techniques and Methods -- Regression -- A/B Testing -- Data Visualization -- Heat Maps -- Time Series Analysis -- Automation -- Machine Learning and Intelligence -- Sensors and Nanotechnology -- RFID and NFC -- Semantics -- Natural Language Processing -- Text Analytics -- Sentiment Analysis -- Big Data and the Gang of Four -- Predictive Analytics -- Two Key Laws of Big Data -- Collaborative Filtering -- Limitations of Big Data -- Summary -- Notes -- Chapter 4 Big Data Solutions -- Projects, Applications, and Platforms -- Hadoop -- Other Data Storage Solutions -- NoSQL Databases -- NewSQL -- Columnar Databases -- Google: Following the Amazon Model? …”

Tabla de Contenidos: “…Multicriteria Decision Making 3.1 Pugh Concept Selection Method 3.2 Analytic Hierarchy Process (AHP) 3.3 Multiattribute Utility Theory (MAUT) 3.4 Conjoint Analysis 3.5 Value of a Statistical Life 3.6 Compensation 3.7 The Impact of Changing Weights Exercises References 4. …”

Tabla de Contenidos: “…Gray models -- 1.4. Statistical models -- 1.5. Artificial intelligence models -- 1.5.1. …”

Tabla de Contenidos:

Tabla de Contenidos: “…Easy Cost Planning via WBS: Planning Costs Against a WBS via Unit Costing, but with the Use of Cost Models to Make Data Entry Simpler -- Plan Versions: Distinct Repositories for Keeping Your Planned Costs Separate -- Statistical Key Figures: Objects Used for Storing and Planning Invisible Costs or Consumption -- Overheads (CO): Additional Costs Calculated via Costing Sheets -- Value Categories: Giving Meaning to Values -- Budgeting -- Budgeting in Projects: When the Budget Is Controlled from Within a Project -- Budgeting in Investment Programs: When the Budget for All Projects Is Controlled by a Capital Investment Program -- Availability Control: When You Need to Control Expenditure -- Time and Capacity Planning -- Dates in WBS: Managing Basic and Forecast Dates -- Scheduling in Networks (Part 1): Managing Start/Finish Rules and Executing Scheduling -- Scheduling in Networks (Part 2): Managing Start/Finish Rules and Executing Scheduling -- Work Centers and Activity Types: Planning Capacities and the Cost of Utilizing the Resource -- Workforce Planning: Planning Who Does What, and When -- Revenue Planning -- Structure Revenue Planning: Direct Entry of Revenue Against a WBS Structure, Without Regard to Revenue Elements -- Detailed Revenue Planning: Planning Revenue Against a WBS by Revenue Element -- Sales Orders (SD): Revenue Planning via Sales Order (or Quotation) -- Execution -- Status Management: Setting Project Restrictions (Business Processes) by Status -- Actuals: Posting Actual Costs, Revenues, and Dates to a Project -- Simulations: Simulating What You Want to Do Before You Really Do It, Without Committing -- Project Versions: Snapshots of a Project for Comparison Purposes -- Period-End (and Regular Processes) -- Settlement: Transferring Actual Values to the "Owner" -- Period-End: Executing Regular, Cyclical Processes…”

Tabla de Contenidos: “…Introducing example-based machine translation -- Introducing statistical machine translation -- Modeling the translation problem -- Creating the models -- Introducing sequence-to-sequence learning -- Deciphering the encoder/decoder architecture -- Understanding long short-term memory units -- Putting seq2seq in action -- Measuring translation performance -- Summary -- Chapter 7: Summarizing Wikipedia Articles -- Technical requirements -- Understanding text summarization -- Introducing web scraping -- Scraping popular quotes -- Scraping book reviews -- Scraping Wikipedia articles -- Performing extractive summarization -- Performing abstractive summarization -- Introducing the attention mechanism -- Introducing transformers -- Putting the transformer into action -- Measuring summarization performance -- Summary -- Chapter 8: Detecting Hateful and Offensive Language -- Technical requirements -- Introducing social networks -- Understanding BERT -- Pre-training phase -- Fine-tuning phase -- Putting BERT into action -- Introducing boosting algorithms -- Understanding AdaBoost -- Understanding gradient boosting -- Understanding XGBoost -- Creating validation sets -- Learning the myth of Icarus -- Extracting the datasets -- Treating imbalanced datasets -- Classifying with BERT -- Training the classifier -- Applying early stopping -- Understanding CNN -- Adding pooling layers -- Including CNN layers -- Summary -- Chapter 9: Generating Text in Chatbots -- Technical requirements -- Understanding text generation -- Creating a retrieval-based chatbot -- Understanding language modeling -- Understanding perplexity -- Building a language model -- Creating a generative chatbot -- Using a pre-trained model -- Creating the GUI -- Creating the web chatbot -- Fine-tuning a pre-trained model -- Summary -- Chapter 10: Clustering Speech-to-Text Transcriptions…”

Tabla de Contenidos: “…The civil invalidity disability system -- Civil invalidity pensions -- Attendance allowance -- In-kind benefits -- 3.1.3. Descriptive statistics -- Civil invalidity claims are key to the disability pension system in Italy -- Regional differences uncover large financial incentives to claim disability benefits -- 3.2. …”

Tabla de Contenidos: “…1.9 Maximum wave height in a stationary random sea 1.10 Long-term statistics of irregular seaway; 1.11 Wave data from observations; 1.12 Wave climate; 1.13 Freak waves; 1.14 Oceanography; 1.14.1 Distribution of water on earth; 1.14.2 Properties of water; 1.14.2.1 Chlorophyll; 1.14.2.2 Circulation; 1.14.2.3 Compressibility; 1.14.2.4 Conductivity; 1.14.2.5 Density; 1.14.2.6 Depth; 1.14.2.7 Dissolved gases; 1.14.2.8 Fresh water; 1.14.2.9 Ionic concentration; 1.14.2.10 Light and other electro-magnetic transmissions through water; 1.14.2.11 Pressure; 1.14.2.12 Salt water and salinity…”

Tabla de Contenidos: “…Machine generated contents note: Preface Acknowledgments Chapter 1: Discount Rates and Returns Estimating Returns Geometric and Arithmetic Averages Caveats to Return Extrapolation Discounting Present Values of Cash Flow Streams Internal Rate of Return and Yield to Maturity Real and Nominal Returns Summary Chapter 2: Fixed Income Securities Coupon Bearing Bonds Infinite Cash Flow Streams (Perpetuities) General Pricing Formulas for Finite Cash Flow Streams Interest Rate Risk Analysis of Duration Interest Rate Risk Dynamics Immunization and Duration Applications - Liability Discounting and Cash Matching Pension Logic Risky Coupons Inflation Risk and TIPS A Bond Portfolio Strategy (Optional) Summary Appendix 2.1: Solving Infinite and Finite Power Series References Chapter 3: Term Structure Discounting Using Spot Rates Forward Rates NPV revisited Short Rates The Bootstrap Method Duration Redux Summary Chapter 4: Equity The Determination of Stock Prices Discount Rates Redux Price and Dividend Multiples Extrapolating Multiples to Forecast Returns Pitfalls of Trend Analysis The Gordon Growth Model Sources of Return Summary References Chapter 5: Portfolio Construction Stochastic Returns and Risk Diversification The Efficient Frontier Markowitz Portfolio Selection Criteria Capital Market Line and the CAPM Performance Evaluation Summary Appendix 5.1: Statistical Review Appendix 5.2: Risk Adjusted Performance References Chapter 6: Optimal Portfolios Portfolio 1: Minimum Variance Portfolio (Fully Invested) Portfolio 2: Minimum Variance Portfolios with Targeted Return Portfolio 3: Minimum Variance Portfolios with No Short Sales Portfolio 4: Minimum Variance Portfolios with Capped Allocations Portfolio 5: Maximum Risk-Adjusted Return Performance Attribution The Efficient Frontier (Again) Summary Appendix 6.1: Matrix Operations Chapter 7: Data and Applications Analyzing Returns on a Ten Asset Portfolio Performance Attribution Changing the Investment Horizon Benchmarking to the Market Portfolio The Cost of Constraints A Bond Strategy Summary Chapter 8: Anomalies Deviations from the CAPM Behavioral Finance Summary References Chapter 9: Factor Models Arbitrage Pricing Theory (APT) Factor Selection Model Estimation Principal Components Applications and Examples Summary References Chapter 10: Active Portfolio Management Active Portfolio Construction and Attribution Analysis Performance Attribution Summary Appendix 10.1: Active Space Chapter 11: Risk The Failure of VaR Taxonomy of Risk Visualizing Risk Estimating Volatilities Maximum Likelihood Estimation (Optional) Credit Risk Adjusting for Leverage Adjusting for Illiquidity Other Risks Summary References Chapter 12: Monte Carlo Methods Example 1: Generating Random Numbers - Estimating pi Example 2: Confirming the Central Limit Theorem Example 3: Credit Default Risk Non-Normal Distributions The Gaussian Copula Summary References Chapter 13: Systemic Risk Extreme Value Theory Estimating the Hazards of Downside Risks A Systemic Risk Indicator Summary References Chapter 14: Incorporating Subjective Views Methodological Concepts An Example using Black-Litterman Active Space Risk Attribution Summary References Chapter 15: Futures, Forwards, and Swaps Institutional Detail and Futures Mechanics The Relationship between Spot Prices and Forward (Futures) Prices Hedging Basis Risk Hedging Portfolio Risk Futures Pricing Swaps Summary References Chapter 16: Introduction to Options Option Payoffs and Put-Call Parity Pricing European Call Options Pricing European Put Options Option Strategies Real Options Summary References Chapter 17: Models of Stock Price Dynamics Stock Price Dynamics Ito Processes Lognormal Stock Prices Deriving the Parameters of the Binomial Lattice Black-Scholes-Merton Model The Greek Letters Monte Carlo Methods Summary Appendix 17.1: Derivation of Ito's Lemma Chapter 18: Hedging Portfolio Risk Simple Hedging Strategies S&P 500 Index Puts Selling Volatility VIX Calls Liability Driven Investment Summary References Chapter 19: Private Equity The Private Equity Model Return and Risk Methodology Summary Appendix 19.1: CAPM References Chapter 20: Structured Credit Securitization Credit Enhancement Basics of Pricing Interest Rate Derivatives Interest Rate Dynamics CDO Valuation The Crash of the Housing Bubble Summary References Chapter 21: Optimal Rebalancing Trigger Strategies and No-trade Regions An Optimal Control Problem Implications Optimal Rebalancing in a Static Optimization Model The Comparative Statics of Transactions Costs References Chapter 22: Data Problems* Covariance Estimation An Example Empirical Results Overlapping Observations Conclusions Appendix 22.1: Covariance Matrix Estimation Removing the effects of smoothing References About the Author Index…”

Tabla de Contenidos: “…Water Jet Machining -- Abrasive Water-Jet Machining -- Abrasive Jet Machining -- Plasma ARC Cutting -- Multiple Choice Questions -- Answers -- Short Answer Questions -- True/False Statements -- Answers -- Review Questions -- Chapter 12: Powder Metallurgy -- Multiple Choice Questions -- Answers -- Short Answer Questions -- True/False Statements -- Answers -- Review Questions -- Chapter 13: Forming Processes -- Rolling -- Drawing -- Forging -- Extrusion -- Sheet Metal Operations -- Deep Drawing -- Other Sheet Metal Operations -- Multiple Choice Questions -- Answers -- Short Answer Questions -- True/False Statements -- Answers -- Numerical Problems -- Review Questions -- Chapter 14: Metrology -- Multiple Choice Questions -- Answers -- Short Answer Questions -- True/False Statements -- Answers -- Review Questions -- Chapter 15: Elements of Modern Manufacturing -- Inventory and Inventory Control -- Material Requirements Planning (MRP) -- Manufacturing Resource Planning (MRP II) -- Enterprise Resource Planning (ERP) -- Just-in-Time (JIT) -- Supply Chain Management -- Quality Circle -- Kaizen -- Total Quality Control (TQC) -- Statistical Quality Control -- Total Quality Management -- Electronic Data Interchange -- Computer-Aided Design -- Computer-Aided Manufacturing -- Computer Integrated Manufacturing -- Flexible Manufacturing System -- Numerical Control and Computer Numerical Control -- Group Technology -- Cellular Manufacturing -- Concurrent Engineering -- Multiple Choice Questions -- Answers -- Short Answer Questions -- True/False Statements -- Answers -- Review Questions -- References -- Chapter Index -- Subject Index…”