Data Science for Decision Makers: Enhance Your Leadership Skills with Data Science and AI Expertise
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing, Limited
2024
|
| Ausgabe: | 1st ed |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (270 Seiten) |
| ISBN: | 9781837638345 |
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| 505 | 8 | |a Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Understanding Data Science and Its Foundations -- Introducing Data Science -- Data science, AI, and ML - what's the difference? -- The mathematical and statistical underpinnings of data science -- Statistics and data science -- What is statistics? -- Descriptive and inferential statistics -- Sampling strategies -- Probability -- Probability distribution -- Conditional probability -- Describing our samples -- Measures of central tendency -- Measures of dispersion -- Degrees of freedom -- Correlation, causation, and covariance -- The shape of data -- Probability distributions -- Discrete probability distributions -- Continuous probability distributions -- Summary -- Characterizing and Collecting Data -- What are the key criteria to consider when evaluating datasets? -- Data quantity -- Data velocity -- Data variety -- Data quality -- First-, second-, and third-party data -- First-party data - the treasure trove within -- Second-party data - building bridges through collaboration -- Third-party data - broadening horizons with external expertise -- Structured, unstructured, and semi-structured data -- Structured data -- Unstructured data -- Semi-structured data -- Methods for collecting data -- Storing and processing data -- Cloud, on-premises, and hybrid solutions - navigating the data storage and analysis landscape -- Cloud computing - scalable services in the cloud -- On-premises - maintaining control within your walls -- Hybrid - the best of both worlds? -- Data processing -- Summary -- Exploratory Data Analysis -- Getting started with Google Colab -- What is Google Colab? -- A step-by-step guide to setting up Google Colab -- Understanding the data you have -- EDA techniques and tools -- Descriptive statistics -- Data visualization | |
| 505 | 8 | |a Histograms -- Density curves -- Boxplots -- Heatmaps -- Dimensionality reduction -- Correlation analysis -- Outlier detection -- Summary -- The Significance of Significance -- The idea of testing hypotheses -- What is a hypothesis? -- How does hypothesis testing work? -- Formulating null and alternative hypotheses -- Determining the significance level -- Understanding errors -- Getting to grips with p-values -- Significance tests for a population proportion - making informed decisions about proportions -- The z-test - comparing a sample proportion to a population proportion -- Z-test example made easy -- Significance tests for a population average (mean) -- Writing hypotheses for a significance test about a mean -- Conditions for a t-test about a mean -- When to use z or t statistics in significance tests -- Example - calculating the t-statistic for a test about a mean -- Using a table to estimate the p-value from the t-statistic -- Comparing the p-value from the t-statistic to the significance level -- One-tailed and two-tailed tests -- Walking through a case study -- Summary -- Understanding Regression -- How can I benefit from understanding regression? -- Introduction to trend lines -- Fitting a trend line to data -- Estimating the line of best fit -- Calculating the equations of the lines of best fit -- Interpreting the slope of a regression line -- Interpreting the intercept of a regression line -- Understanding residuals -- Evaluating the goodness of fit in least-squares regression -- Summary -- Part 2: Machine Learning - Concepts, Applications, and Pitfalls -- Introducing Machine Learning -- From statistics to machine learning -- What is machine learning? -- How does machine learning relate to statistics? -- Why is machine learning important? -- Customer personalization and segmentation -- Fraud detection and security | |
| 505 | 8 | |a Supply chain and inventory optimization -- Predictive maintenance -- Healthcare diagnostics and treatment -- The different types of machine learning -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement learning -- Transfer learning -- Popular machine learning algorithms -- Linear regression -- Logistic regression -- Decision trees -- Random forests -- Support vector machines -- k-nearest neighbors -- Neural networks -- The machine learning process -- Training a supervised machine learning model -- Validation of a supervised machine learning model -- Testing a supervised machine learning model -- Evaluating machine learning models -- Risks and limitations of machine learning -- Overfitting and underfitting -- Bias and variance -- Balanced dataset -- Models are approximations of reality -- Machine learning on unstructured data -- Natural language processing (NLP) -- Computer vision -- Deep learning and artificial intelligence -- Artificial intelligence -- Deep learning -- Summary -- Supervised Machine Learning -- Defining supervised learning -- Applications of supervised learning -- The two types of supervised learning -- Key factors in supervised learning -- Steps within supervised learning -- Data preparation - laying the foundation -- Algorithm selection - choosing the right tool -- Model training - learning from data -- Model evaluation - assessing performance -- Prediction and deployment - putting the model to work -- Characteristics of regression and classification algorithms -- Regression algorithms -- Classification algorithms -- Key considerations in supervised learning -- Evaluation metrics -- Applications of supervised learning -- Consumer goods -- Retail -- Manufacturing -- Summary -- Unsupervised Machine Learning -- Defining UL -- Practical examples of UL -- Steps in UL -- Step 1 - Data collection | |
| 505 | 8 | |a Step 2 - Data preprocessing -- Step 3 - Choosing the right model -- Step 4 - Training the model -- Step 5 - Interpretation and evaluation -- In summary -- Clustering - unveiling hidden patterns in your data -- What is clustering? -- How does clustering work? -- k-means clustering -- Practical applications of clustering -- Evaluation metrics for clustering -- In summary -- Association rule learning -- What is association rule learning? -- The Apriori algorithm - a practical example -- Evaluation metrics -- In summary -- Applications of UL -- Market segmentation -- Anomaly detection -- Feature extraction -- Summary -- Interpreting and Evaluating Machine Learning Models -- How do I know whether this model will be accurate? -- Evaluating on test (holdout) data -- Understanding evaluation metrics -- Evaluating regression models -- R-squared -- Root mean squared error -- Mean absolute error -- When and how to use each metric -- Practical evaluation strategies -- Summarizing the evaluation of regression models -- Evaluating classification models -- Classification model evaluation metrics -- Precision, recall, and F1-Score -- Recall -- F1-score -- Methods for explaining machine learning models -- Making sense of regression models - the power of coefficients -- Decoding classification models - unveiling feature importance -- Beyond specific models - universal insights using SHAP values -- Summary -- Common Pitfalls in Machine Learning -- Understanding the complexity -- Dirty data, damaged models - how data quantity and quality impact ML -- The importance of adequate training data -- Dealing with poor data quality -- Conclusion -- Overcoming overfitting and underfitting -- Navigating training-serving skew and model drift -- Ensuring fairness -- Mastering overfitting and underfitting for optimal model performance | |
| 505 | 8 | |a Overfitting - when your model is too specific -- Underfitting - when your model is too simplistic -- Spotting the problem -- Conclusion -- Training-serving skew and model drift -- Training-serving skew -- Model drift -- Key takeaways -- Bias and fairness -- Understanding bias -- Understanding fairness -- Mitigating bias and ensuring fairness -- Key takeaways -- Summary -- Part 3: Leading Successful Data Science Projects and Teams -- The Structure of a Data Science Project -- The various types of data science projects -- Data products -- Reports and analytics -- Research and methodology -- The stages of a data product -- Identifying use cases -- Evaluating use cases -- Planning the data product -- Developing a data product -- Data preparation and exploratory analysis -- Model design and development -- Evaluation and testing -- Deploying and monitoring a data product -- General best practices for data product development -- Evaluating impact -- Predictive maintenance in manufacturing -- Fraud detection in banking -- Customer churn prediction in telecom -- Demand forecasting in retail -- Personalized recommendations in e-commerce -- Predictive maintenance in energy -- Workforce optimization in quick service restaurants -- Chatbot-assisted customer support -- Summary -- The Data Science Team -- Assembling your data science team - key roles and considerations -- Data scientists -- Machine learning engineers -- Data engineers -- MLOps engineers -- Analytics engineers -- Software engineers (full stack, frontend, backend) -- Product managers -- Business analysts -- Data storytellers/visualization experts -- Considerations when assembling your team -- Data science teams within larger organizations -- The hub and spoke model -- What is the hub and spoke model? -- Practical applications of the hub and spoke model -- Building a hub and spoke model | |
| 505 | 8 | |a The art of recruitment | |
| 650 | 4 | |a Decision making-Data processing | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Howells, Jon |t Data Science for Decision Makers |d Birmingham : Packt Publishing, Limited,c2024 |z 9781837637294 |
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Datensatz im Suchindex
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| author | Howells, Jon |
| author_facet | Howells, Jon |
| author_role | aut |
| author_sort | Howells, Jon |
| author_variant | j h jh |
| building | Verbundindex |
| bvnumber | BV049876780 |
| collection | ZDB-30-PQE |
| contents | Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Understanding Data Science and Its Foundations -- Introducing Data Science -- Data science, AI, and ML - what's the difference? -- The mathematical and statistical underpinnings of data science -- Statistics and data science -- What is statistics? -- Descriptive and inferential statistics -- Sampling strategies -- Probability -- Probability distribution -- Conditional probability -- Describing our samples -- Measures of central tendency -- Measures of dispersion -- Degrees of freedom -- Correlation, causation, and covariance -- The shape of data -- Probability distributions -- Discrete probability distributions -- Continuous probability distributions -- Summary -- Characterizing and Collecting Data -- What are the key criteria to consider when evaluating datasets? -- Data quantity -- Data velocity -- Data variety -- Data quality -- First-, second-, and third-party data -- First-party data - the treasure trove within -- Second-party data - building bridges through collaboration -- Third-party data - broadening horizons with external expertise -- Structured, unstructured, and semi-structured data -- Structured data -- Unstructured data -- Semi-structured data -- Methods for collecting data -- Storing and processing data -- Cloud, on-premises, and hybrid solutions - navigating the data storage and analysis landscape -- Cloud computing - scalable services in the cloud -- On-premises - maintaining control within your walls -- Hybrid - the best of both worlds? -- Data processing -- Summary -- Exploratory Data Analysis -- Getting started with Google Colab -- What is Google Colab? -- A step-by-step guide to setting up Google Colab -- Understanding the data you have -- EDA techniques and tools -- Descriptive statistics -- Data visualization Histograms -- Density curves -- Boxplots -- Heatmaps -- Dimensionality reduction -- Correlation analysis -- Outlier detection -- Summary -- The Significance of Significance -- The idea of testing hypotheses -- What is a hypothesis? -- How does hypothesis testing work? -- Formulating null and alternative hypotheses -- Determining the significance level -- Understanding errors -- Getting to grips with p-values -- Significance tests for a population proportion - making informed decisions about proportions -- The z-test - comparing a sample proportion to a population proportion -- Z-test example made easy -- Significance tests for a population average (mean) -- Writing hypotheses for a significance test about a mean -- Conditions for a t-test about a mean -- When to use z or t statistics in significance tests -- Example - calculating the t-statistic for a test about a mean -- Using a table to estimate the p-value from the t-statistic -- Comparing the p-value from the t-statistic to the significance level -- One-tailed and two-tailed tests -- Walking through a case study -- Summary -- Understanding Regression -- How can I benefit from understanding regression? -- Introduction to trend lines -- Fitting a trend line to data -- Estimating the line of best fit -- Calculating the equations of the lines of best fit -- Interpreting the slope of a regression line -- Interpreting the intercept of a regression line -- Understanding residuals -- Evaluating the goodness of fit in least-squares regression -- Summary -- Part 2: Machine Learning - Concepts, Applications, and Pitfalls -- Introducing Machine Learning -- From statistics to machine learning -- What is machine learning? -- How does machine learning relate to statistics? -- Why is machine learning important? -- Customer personalization and segmentation -- Fraud detection and security Supply chain and inventory optimization -- Predictive maintenance -- Healthcare diagnostics and treatment -- The different types of machine learning -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement learning -- Transfer learning -- Popular machine learning algorithms -- Linear regression -- Logistic regression -- Decision trees -- Random forests -- Support vector machines -- k-nearest neighbors -- Neural networks -- The machine learning process -- Training a supervised machine learning model -- Validation of a supervised machine learning model -- Testing a supervised machine learning model -- Evaluating machine learning models -- Risks and limitations of machine learning -- Overfitting and underfitting -- Bias and variance -- Balanced dataset -- Models are approximations of reality -- Machine learning on unstructured data -- Natural language processing (NLP) -- Computer vision -- Deep learning and artificial intelligence -- Artificial intelligence -- Deep learning -- Summary -- Supervised Machine Learning -- Defining supervised learning -- Applications of supervised learning -- The two types of supervised learning -- Key factors in supervised learning -- Steps within supervised learning -- Data preparation - laying the foundation -- Algorithm selection - choosing the right tool -- Model training - learning from data -- Model evaluation - assessing performance -- Prediction and deployment - putting the model to work -- Characteristics of regression and classification algorithms -- Regression algorithms -- Classification algorithms -- Key considerations in supervised learning -- Evaluation metrics -- Applications of supervised learning -- Consumer goods -- Retail -- Manufacturing -- Summary -- Unsupervised Machine Learning -- Defining UL -- Practical examples of UL -- Steps in UL -- Step 1 - Data collection Step 2 - Data preprocessing -- Step 3 - Choosing the right model -- Step 4 - Training the model -- Step 5 - Interpretation and evaluation -- In summary -- Clustering - unveiling hidden patterns in your data -- What is clustering? -- How does clustering work? -- k-means clustering -- Practical applications of clustering -- Evaluation metrics for clustering -- In summary -- Association rule learning -- What is association rule learning? -- The Apriori algorithm - a practical example -- Evaluation metrics -- In summary -- Applications of UL -- Market segmentation -- Anomaly detection -- Feature extraction -- Summary -- Interpreting and Evaluating Machine Learning Models -- How do I know whether this model will be accurate? -- Evaluating on test (holdout) data -- Understanding evaluation metrics -- Evaluating regression models -- R-squared -- Root mean squared error -- Mean absolute error -- When and how to use each metric -- Practical evaluation strategies -- Summarizing the evaluation of regression models -- Evaluating classification models -- Classification model evaluation metrics -- Precision, recall, and F1-Score -- Recall -- F1-score -- Methods for explaining machine learning models -- Making sense of regression models - the power of coefficients -- Decoding classification models - unveiling feature importance -- Beyond specific models - universal insights using SHAP values -- Summary -- Common Pitfalls in Machine Learning -- Understanding the complexity -- Dirty data, damaged models - how data quantity and quality impact ML -- The importance of adequate training data -- Dealing with poor data quality -- Conclusion -- Overcoming overfitting and underfitting -- Navigating training-serving skew and model drift -- Ensuring fairness -- Mastering overfitting and underfitting for optimal model performance Overfitting - when your model is too specific -- Underfitting - when your model is too simplistic -- Spotting the problem -- Conclusion -- Training-serving skew and model drift -- Training-serving skew -- Model drift -- Key takeaways -- Bias and fairness -- Understanding bias -- Understanding fairness -- Mitigating bias and ensuring fairness -- Key takeaways -- Summary -- Part 3: Leading Successful Data Science Projects and Teams -- The Structure of a Data Science Project -- The various types of data science projects -- Data products -- Reports and analytics -- Research and methodology -- The stages of a data product -- Identifying use cases -- Evaluating use cases -- Planning the data product -- Developing a data product -- Data preparation and exploratory analysis -- Model design and development -- Evaluation and testing -- Deploying and monitoring a data product -- General best practices for data product development -- Evaluating impact -- Predictive maintenance in manufacturing -- Fraud detection in banking -- Customer churn prediction in telecom -- Demand forecasting in retail -- Personalized recommendations in e-commerce -- Predictive maintenance in energy -- Workforce optimization in quick service restaurants -- Chatbot-assisted customer support -- Summary -- The Data Science Team -- Assembling your data science team - key roles and considerations -- Data scientists -- Machine learning engineers -- Data engineers -- MLOps engineers -- Analytics engineers -- Software engineers (full stack, frontend, backend) -- Product managers -- Business analysts -- Data storytellers/visualization experts -- Considerations when assembling your team -- Data science teams within larger organizations -- The hub and spoke model -- What is the hub and spoke model? -- Practical applications of the hub and spoke model -- Building a hub and spoke model The art of recruitment |
| ctrlnum | (ZDB-30-PQE)EBC31497686 (ZDB-30-PAD)EBC31497686 (ZDB-89-EBL)EBL31497686 (OCoLC)1441716909 (DE-599)BVBBV049876780 |
| dewey-full | 658.403 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.403 |
| dewey-search | 658.403 |
| dewey-sort | 3658.403 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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-- Introduction to trend lines -- Fitting a trend line to data -- Estimating the line of best fit -- Calculating the equations of the lines of best fit -- Interpreting the slope of a regression line -- Interpreting the intercept of a regression line -- Understanding residuals -- Evaluating the goodness of fit in least-squares regression -- Summary -- Part 2: Machine Learning - Concepts, Applications, and Pitfalls -- Introducing Machine Learning -- From statistics to machine learning -- What is machine learning? -- How does machine learning relate to statistics? -- Why is machine learning important? -- Customer personalization and segmentation -- Fraud detection and security</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Supply chain and inventory optimization -- Predictive maintenance -- Healthcare diagnostics and treatment -- The different types of machine learning -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement learning -- Transfer learning -- Popular machine learning algorithms -- Linear regression -- Logistic regression -- Decision trees -- Random forests -- Support vector machines -- k-nearest neighbors -- Neural networks -- The machine learning process -- Training a supervised machine learning model -- Validation of a supervised machine learning model -- Testing a supervised machine learning model -- Evaluating machine learning models -- Risks and limitations of machine learning -- Overfitting and underfitting -- Bias and variance -- Balanced dataset -- Models are approximations of reality -- Machine learning on unstructured data -- Natural language processing (NLP) -- Computer vision -- Deep learning and artificial intelligence -- Artificial intelligence -- Deep learning -- Summary -- Supervised Machine Learning -- Defining supervised learning -- Applications of supervised learning -- The two types of supervised learning -- Key factors in supervised learning -- Steps within supervised learning -- Data preparation - laying the foundation -- Algorithm selection - choosing the right tool -- Model training - learning from data -- Model evaluation - assessing performance -- Prediction and deployment - putting the model to work -- Characteristics of regression and classification algorithms -- Regression algorithms -- Classification algorithms -- Key considerations in supervised learning -- Evaluation metrics -- Applications of supervised learning -- Consumer goods -- Retail -- Manufacturing -- Summary -- Unsupervised Machine Learning -- Defining UL -- Practical examples of UL -- Steps in UL -- Step 1 - Data collection</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Step 2 - Data preprocessing -- Step 3 - Choosing the right model -- Step 4 - Training the model -- Step 5 - Interpretation and evaluation -- In summary -- Clustering - unveiling hidden patterns in your data -- What is clustering? 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| id | DE-604.BV049876780 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-06T15:18:34Z |
| institution | BVB |
| isbn | 9781837638345 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035216230 |
| oclc_num | 1441716909 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (270 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Packt Publishing, Limited |
| record_format | marc |
| spelling | Howells, Jon Verfasser aut Data Science for Decision Makers Enhance Your Leadership Skills with Data Science and AI Expertise 1st ed Birmingham Packt Publishing, Limited 2024 ©2024 1 Online-Ressource (270 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Understanding Data Science and Its Foundations -- Introducing Data Science -- Data science, AI, and ML - what's the difference? -- The mathematical and statistical underpinnings of data science -- Statistics and data science -- What is statistics? -- Descriptive and inferential statistics -- Sampling strategies -- Probability -- Probability distribution -- Conditional probability -- Describing our samples -- Measures of central tendency -- Measures of dispersion -- Degrees of freedom -- Correlation, causation, and covariance -- The shape of data -- Probability distributions -- Discrete probability distributions -- Continuous probability distributions -- Summary -- Characterizing and Collecting Data -- What are the key criteria to consider when evaluating datasets? -- Data quantity -- Data velocity -- Data variety -- Data quality -- First-, second-, and third-party data -- First-party data - the treasure trove within -- Second-party data - building bridges through collaboration -- Third-party data - broadening horizons with external expertise -- Structured, unstructured, and semi-structured data -- Structured data -- Unstructured data -- Semi-structured data -- Methods for collecting data -- Storing and processing data -- Cloud, on-premises, and hybrid solutions - navigating the data storage and analysis landscape -- Cloud computing - scalable services in the cloud -- On-premises - maintaining control within your walls -- Hybrid - the best of both worlds? -- Data processing -- Summary -- Exploratory Data Analysis -- Getting started with Google Colab -- What is Google Colab? -- A step-by-step guide to setting up Google Colab -- Understanding the data you have -- EDA techniques and tools -- Descriptive statistics -- Data visualization Histograms -- Density curves -- Boxplots -- Heatmaps -- Dimensionality reduction -- Correlation analysis -- Outlier detection -- Summary -- The Significance of Significance -- The idea of testing hypotheses -- What is a hypothesis? -- How does hypothesis testing work? -- Formulating null and alternative hypotheses -- Determining the significance level -- Understanding errors -- Getting to grips with p-values -- Significance tests for a population proportion - making informed decisions about proportions -- The z-test - comparing a sample proportion to a population proportion -- Z-test example made easy -- Significance tests for a population average (mean) -- Writing hypotheses for a significance test about a mean -- Conditions for a t-test about a mean -- When to use z or t statistics in significance tests -- Example - calculating the t-statistic for a test about a mean -- Using a table to estimate the p-value from the t-statistic -- Comparing the p-value from the t-statistic to the significance level -- One-tailed and two-tailed tests -- Walking through a case study -- Summary -- Understanding Regression -- How can I benefit from understanding regression? -- Introduction to trend lines -- Fitting a trend line to data -- Estimating the line of best fit -- Calculating the equations of the lines of best fit -- Interpreting the slope of a regression line -- Interpreting the intercept of a regression line -- Understanding residuals -- Evaluating the goodness of fit in least-squares regression -- Summary -- Part 2: Machine Learning - Concepts, Applications, and Pitfalls -- Introducing Machine Learning -- From statistics to machine learning -- What is machine learning? -- How does machine learning relate to statistics? -- Why is machine learning important? -- Customer personalization and segmentation -- Fraud detection and security Supply chain and inventory optimization -- Predictive maintenance -- Healthcare diagnostics and treatment -- The different types of machine learning -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement learning -- Transfer learning -- Popular machine learning algorithms -- Linear regression -- Logistic regression -- Decision trees -- Random forests -- Support vector machines -- k-nearest neighbors -- Neural networks -- The machine learning process -- Training a supervised machine learning model -- Validation of a supervised machine learning model -- Testing a supervised machine learning model -- Evaluating machine learning models -- Risks and limitations of machine learning -- Overfitting and underfitting -- Bias and variance -- Balanced dataset -- Models are approximations of reality -- Machine learning on unstructured data -- Natural language processing (NLP) -- Computer vision -- Deep learning and artificial intelligence -- Artificial intelligence -- Deep learning -- Summary -- Supervised Machine Learning -- Defining supervised learning -- Applications of supervised learning -- The two types of supervised learning -- Key factors in supervised learning -- Steps within supervised learning -- Data preparation - laying the foundation -- Algorithm selection - choosing the right tool -- Model training - learning from data -- Model evaluation - assessing performance -- Prediction and deployment - putting the model to work -- Characteristics of regression and classification algorithms -- Regression algorithms -- Classification algorithms -- Key considerations in supervised learning -- Evaluation metrics -- Applications of supervised learning -- Consumer goods -- Retail -- Manufacturing -- Summary -- Unsupervised Machine Learning -- Defining UL -- Practical examples of UL -- Steps in UL -- Step 1 - Data collection Step 2 - Data preprocessing -- Step 3 - Choosing the right model -- Step 4 - Training the model -- Step 5 - Interpretation and evaluation -- In summary -- Clustering - unveiling hidden patterns in your data -- What is clustering? -- How does clustering work? -- k-means clustering -- Practical applications of clustering -- Evaluation metrics for clustering -- In summary -- Association rule learning -- What is association rule learning? -- The Apriori algorithm - a practical example -- Evaluation metrics -- In summary -- Applications of UL -- Market segmentation -- Anomaly detection -- Feature extraction -- Summary -- Interpreting and Evaluating Machine Learning Models -- How do I know whether this model will be accurate? -- Evaluating on test (holdout) data -- Understanding evaluation metrics -- Evaluating regression models -- R-squared -- Root mean squared error -- Mean absolute error -- When and how to use each metric -- Practical evaluation strategies -- Summarizing the evaluation of regression models -- Evaluating classification models -- Classification model evaluation metrics -- Precision, recall, and F1-Score -- Recall -- F1-score -- Methods for explaining machine learning models -- Making sense of regression models - the power of coefficients -- Decoding classification models - unveiling feature importance -- Beyond specific models - universal insights using SHAP values -- Summary -- Common Pitfalls in Machine Learning -- Understanding the complexity -- Dirty data, damaged models - how data quantity and quality impact ML -- The importance of adequate training data -- Dealing with poor data quality -- Conclusion -- Overcoming overfitting and underfitting -- Navigating training-serving skew and model drift -- Ensuring fairness -- Mastering overfitting and underfitting for optimal model performance Overfitting - when your model is too specific -- Underfitting - when your model is too simplistic -- Spotting the problem -- Conclusion -- Training-serving skew and model drift -- Training-serving skew -- Model drift -- Key takeaways -- Bias and fairness -- Understanding bias -- Understanding fairness -- Mitigating bias and ensuring fairness -- Key takeaways -- Summary -- Part 3: Leading Successful Data Science Projects and Teams -- The Structure of a Data Science Project -- The various types of data science projects -- Data products -- Reports and analytics -- Research and methodology -- The stages of a data product -- Identifying use cases -- Evaluating use cases -- Planning the data product -- Developing a data product -- Data preparation and exploratory analysis -- Model design and development -- Evaluation and testing -- Deploying and monitoring a data product -- General best practices for data product development -- Evaluating impact -- Predictive maintenance in manufacturing -- Fraud detection in banking -- Customer churn prediction in telecom -- Demand forecasting in retail -- Personalized recommendations in e-commerce -- Predictive maintenance in energy -- Workforce optimization in quick service restaurants -- Chatbot-assisted customer support -- Summary -- The Data Science Team -- Assembling your data science team - key roles and considerations -- Data scientists -- Machine learning engineers -- Data engineers -- MLOps engineers -- Analytics engineers -- Software engineers (full stack, frontend, backend) -- Product managers -- Business analysts -- Data storytellers/visualization experts -- Considerations when assembling your team -- Data science teams within larger organizations -- The hub and spoke model -- What is the hub and spoke model? -- Practical applications of the hub and spoke model -- Building a hub and spoke model The art of recruitment Decision making-Data processing Erscheint auch als Druck-Ausgabe Howells, Jon Data Science for Decision Makers Birmingham : Packt Publishing, Limited,c2024 9781837637294 |
| spellingShingle | Howells, Jon Data Science for Decision Makers Enhance Your Leadership Skills with Data Science and AI Expertise Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Understanding Data Science and Its Foundations -- Introducing Data Science -- Data science, AI, and ML - what's the difference? -- The mathematical and statistical underpinnings of data science -- Statistics and data science -- What is statistics? -- Descriptive and inferential statistics -- Sampling strategies -- Probability -- Probability distribution -- Conditional probability -- Describing our samples -- Measures of central tendency -- Measures of dispersion -- Degrees of freedom -- Correlation, causation, and covariance -- The shape of data -- Probability distributions -- Discrete probability distributions -- Continuous probability distributions -- Summary -- Characterizing and Collecting Data -- What are the key criteria to consider when evaluating datasets? -- Data quantity -- Data velocity -- Data variety -- Data quality -- First-, second-, and third-party data -- First-party data - the treasure trove within -- Second-party data - building bridges through collaboration -- Third-party data - broadening horizons with external expertise -- Structured, unstructured, and semi-structured data -- Structured data -- Unstructured data -- Semi-structured data -- Methods for collecting data -- Storing and processing data -- Cloud, on-premises, and hybrid solutions - navigating the data storage and analysis landscape -- Cloud computing - scalable services in the cloud -- On-premises - maintaining control within your walls -- Hybrid - the best of both worlds? -- Data processing -- Summary -- Exploratory Data Analysis -- Getting started with Google Colab -- What is Google Colab? -- A step-by-step guide to setting up Google Colab -- Understanding the data you have -- EDA techniques and tools -- Descriptive statistics -- Data visualization Histograms -- Density curves -- Boxplots -- Heatmaps -- Dimensionality reduction -- Correlation analysis -- Outlier detection -- Summary -- The Significance of Significance -- The idea of testing hypotheses -- What is a hypothesis? -- How does hypothesis testing work? -- Formulating null and alternative hypotheses -- Determining the significance level -- Understanding errors -- Getting to grips with p-values -- Significance tests for a population proportion - making informed decisions about proportions -- The z-test - comparing a sample proportion to a population proportion -- Z-test example made easy -- Significance tests for a population average (mean) -- Writing hypotheses for a significance test about a mean -- Conditions for a t-test about a mean -- When to use z or t statistics in significance tests -- Example - calculating the t-statistic for a test about a mean -- Using a table to estimate the p-value from the t-statistic -- Comparing the p-value from the t-statistic to the significance level -- One-tailed and two-tailed tests -- Walking through a case study -- Summary -- Understanding Regression -- How can I benefit from understanding regression? -- Introduction to trend lines -- Fitting a trend line to data -- Estimating the line of best fit -- Calculating the equations of the lines of best fit -- Interpreting the slope of a regression line -- Interpreting the intercept of a regression line -- Understanding residuals -- Evaluating the goodness of fit in least-squares regression -- Summary -- Part 2: Machine Learning - Concepts, Applications, and Pitfalls -- Introducing Machine Learning -- From statistics to machine learning -- What is machine learning? -- How does machine learning relate to statistics? -- Why is machine learning important? -- Customer personalization and segmentation -- Fraud detection and security Supply chain and inventory optimization -- Predictive maintenance -- Healthcare diagnostics and treatment -- The different types of machine learning -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement learning -- Transfer learning -- Popular machine learning algorithms -- Linear regression -- Logistic regression -- Decision trees -- Random forests -- Support vector machines -- k-nearest neighbors -- Neural networks -- The machine learning process -- Training a supervised machine learning model -- Validation of a supervised machine learning model -- Testing a supervised machine learning model -- Evaluating machine learning models -- Risks and limitations of machine learning -- Overfitting and underfitting -- Bias and variance -- Balanced dataset -- Models are approximations of reality -- Machine learning on unstructured data -- Natural language processing (NLP) -- Computer vision -- Deep learning and artificial intelligence -- Artificial intelligence -- Deep learning -- Summary -- Supervised Machine Learning -- Defining supervised learning -- Applications of supervised learning -- The two types of supervised learning -- Key factors in supervised learning -- Steps within supervised learning -- Data preparation - laying the foundation -- Algorithm selection - choosing the right tool -- Model training - learning from data -- Model evaluation - assessing performance -- Prediction and deployment - putting the model to work -- Characteristics of regression and classification algorithms -- Regression algorithms -- Classification algorithms -- Key considerations in supervised learning -- Evaluation metrics -- Applications of supervised learning -- Consumer goods -- Retail -- Manufacturing -- Summary -- Unsupervised Machine Learning -- Defining UL -- Practical examples of UL -- Steps in UL -- Step 1 - Data collection Step 2 - Data preprocessing -- Step 3 - Choosing the right model -- Step 4 - Training the model -- Step 5 - Interpretation and evaluation -- In summary -- Clustering - unveiling hidden patterns in your data -- What is clustering? -- How does clustering work? -- k-means clustering -- Practical applications of clustering -- Evaluation metrics for clustering -- In summary -- Association rule learning -- What is association rule learning? -- The Apriori algorithm - a practical example -- Evaluation metrics -- In summary -- Applications of UL -- Market segmentation -- Anomaly detection -- Feature extraction -- Summary -- Interpreting and Evaluating Machine Learning Models -- How do I know whether this model will be accurate? -- Evaluating on test (holdout) data -- Understanding evaluation metrics -- Evaluating regression models -- R-squared -- Root mean squared error -- Mean absolute error -- When and how to use each metric -- Practical evaluation strategies -- Summarizing the evaluation of regression models -- Evaluating classification models -- Classification model evaluation metrics -- Precision, recall, and F1-Score -- Recall -- F1-score -- Methods for explaining machine learning models -- Making sense of regression models - the power of coefficients -- Decoding classification models - unveiling feature importance -- Beyond specific models - universal insights using SHAP values -- Summary -- Common Pitfalls in Machine Learning -- Understanding the complexity -- Dirty data, damaged models - how data quantity and quality impact ML -- The importance of adequate training data -- Dealing with poor data quality -- Conclusion -- Overcoming overfitting and underfitting -- Navigating training-serving skew and model drift -- Ensuring fairness -- Mastering overfitting and underfitting for optimal model performance Overfitting - when your model is too specific -- Underfitting - when your model is too simplistic -- Spotting the problem -- Conclusion -- Training-serving skew and model drift -- Training-serving skew -- Model drift -- Key takeaways -- Bias and fairness -- Understanding bias -- Understanding fairness -- Mitigating bias and ensuring fairness -- Key takeaways -- Summary -- Part 3: Leading Successful Data Science Projects and Teams -- The Structure of a Data Science Project -- The various types of data science projects -- Data products -- Reports and analytics -- Research and methodology -- The stages of a data product -- Identifying use cases -- Evaluating use cases -- Planning the data product -- Developing a data product -- Data preparation and exploratory analysis -- Model design and development -- Evaluation and testing -- Deploying and monitoring a data product -- General best practices for data product development -- Evaluating impact -- Predictive maintenance in manufacturing -- Fraud detection in banking -- Customer churn prediction in telecom -- Demand forecasting in retail -- Personalized recommendations in e-commerce -- Predictive maintenance in energy -- Workforce optimization in quick service restaurants -- Chatbot-assisted customer support -- Summary -- The Data Science Team -- Assembling your data science team - key roles and considerations -- Data scientists -- Machine learning engineers -- Data engineers -- MLOps engineers -- Analytics engineers -- Software engineers (full stack, frontend, backend) -- Product managers -- Business analysts -- Data storytellers/visualization experts -- Considerations when assembling your team -- Data science teams within larger organizations -- The hub and spoke model -- What is the hub and spoke model? -- Practical applications of the hub and spoke model -- Building a hub and spoke model The art of recruitment Decision making-Data processing |
| title | Data Science for Decision Makers Enhance Your Leadership Skills with Data Science and AI Expertise |
| title_auth | Data Science for Decision Makers Enhance Your Leadership Skills with Data Science and AI Expertise |
| title_exact_search | Data Science for Decision Makers Enhance Your Leadership Skills with Data Science and AI Expertise |
| title_full | Data Science for Decision Makers Enhance Your Leadership Skills with Data Science and AI Expertise |
| title_fullStr | Data Science for Decision Makers Enhance Your Leadership Skills with Data Science and AI Expertise |
| title_full_unstemmed | Data Science for Decision Makers Enhance Your Leadership Skills with Data Science and AI Expertise |
| title_short | Data Science for Decision Makers |
| title_sort | data science for decision makers enhance your leadership skills with data science and ai expertise |
| title_sub | Enhance Your Leadership Skills with Data Science and AI Expertise |
| topic | Decision making-Data processing |
| topic_facet | Decision making-Data processing |
| work_keys_str_mv | AT howellsjon datasciencefordecisionmakersenhanceyourleadershipskillswithdatascienceandaiexpertise |