This book is intended for business managers who work with data scientists as well as those who want to learn the field in order to support data science initiatives within their organizations. Additionally, you’ll learn how to think analytically about data and thoroughly comprehend how data science techniques may aid in commercial decision-making. This book will assist you in comprehending how data science fits into your business and how to use it to your benefit. You will pick up some fundamental ideas for really getting knowledge out of data. You’ll learn how to participate wisely in your company’s data science projects as well as how to enhance communication between business stakeholders and data scientists. This book also aids in your comprehension of the various data-mining methods now in use. Examples of actual business issues from the real world are used in this book to demonstrate these concepts. I hope you will find this book to be worthwhile and helpful.
Overview:
Data science is a well-known developing field and career area. Practically every industry is being transformed by data science, which is growing every day. The knowledge and training needed to succeed in data analytics can be acquired through a Data Science certification course. Businesses can monitor, manage, and gather performance metrics with the aid of data science to enhance decision-making throughout the firm. Trend research can help businesses decide how best to engage customers, perform better overall, and increase sales.
Data science models can replicate a wide range of tasks and use current data. As a result, employers might seek for applicants who have professional certificates and have taken the best data analytics courses. By fusing existing data with new data points to produce insightful insights, data science helps companies identify and refine target audiences. The present and future of company growth are in data science. The most effective resources and insights to build and maintain a successful business belong to data scientists. In order to grow their businesses and experience unparalleled success, multinational corporations are actively recruiting data scientists.
Topics covered by this book:
- Chapter 1 is about Introduction of Data-Analytic Thinking. What is Ubiquity of Data Opportunities. What is Data Processing and “Big Data”. What is Data-Analytic Thinking as well as Data Mining and Data Science.
- Chapter 2 is about Business Problems and Data Science Solutions. From Business Problems to Data Mining Tasks. The Data Mining Process. Implications for Managing the Data Science Team as well as Other Analytics Techniques and Technologies.
- Chapter 3 is about Introduction to Predictive Modeling. From Correlation to Supervised. What is Segmentation. Supervised Segmentation as well as Visualizing Segmentations
- Chapter 4 is about Fitting a Model to Data. What is Classification via Mathematical Functions. Linear Discriminant Functions as well as Optimizing an Objective Function. What is Regression via Mathematical Functions.
- Chapter 5 is about Overfitting and Its Avoidance. What is Generalization. From Holdout Evaluation to Cross-Validation. What is Overfitting Avoidance and Complexity Control.
- Chapter 6 is about Similarity, Neighbors, and Clusters. What is Similarity and Distance. How Many Neighbors and How Much Influence? What are the Issues with Nearest-Neighbor Methods. What are Technical Details Relating to Similarities and Neighbors.
- Chapter 7 is Decision Analytic Thinking. What Is a Good Model? What are Evaluating Classifiers. Generalizing Beyond Classification. What is Evaluation, Baseline Performance, and Implications for Investments in Data.
- Chapter 8 is about Visualizing Model Performance. Ranking Instead of Classifying. What are ROC Graphs and Curves. What is Performance Analytics for Churn Modeling.
Zain