Kevin P. Valliappa Lakshmanan. Jill Lepore. Deep Learning: A Visual Approach. Andrew Glassner. But how are they the same and how exactly do they differ? Thinking Clearly with Data threads a needle between two advanced subjects by clearly laying out a theory of both. About the authors Follow authors to get new release updates, plus improved recommendations.
Ethan Bueno de Mesquita. Brief content visible, double tap to read full content. Full content visible, double tap to read brief content. See more on the author's page. Anthony Fowler. Customer reviews. How are ratings calculated? Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzes reviews to verify trustworthiness.
Images in this review. Reviews with images. See all customer images. Top reviews Most recent Top reviews. Top reviews from the United States. There was a problem filtering reviews right now. Please try again later. Verified Purchase. Readers would have hard time figuring out what fomulas mean.
It does not even differentiate between 2 and squared symbol! Trying to "interpret" the above formulas for standard deviation a snapshot of the book free sample as seen in the Kindle PC app becomes a sort of math puzzle for people who already know exactly what the formula is supposed to look like, but it would be hopelessly confusing to somebody who is actually trying to figure it our from the book.
I had Fowler as a professor and he is genuinely manipulative and cruel to his students. Please do not support him by purchasing this book. To any students about to take the class which he wrote the book for to increase profits , I highly recommend withdrawing immediately.
See all reviews. Your recently viewed items and featured recommendations. Back to top. Get to Know Us. Make Money with Us. Amazon Payment Products. Product Information An engaging introduction to data science that emphasizes critical thinking over statistical techniques An introduction to data science or statistics shouldn't involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize.
In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives. Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn't influence decision-making; and how to make better decisions by using moral values as well as data.
Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel. Above all else, Thinking Clearly with Data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking. An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields Introduces the basic toolkit of data analysis--including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity Uses real-world examples and data from a wide variety of subjects Includes practice questions and data exercises.
Product Identifiers Publisher. Product Key Features Book Title. Publication Year. Number of Pages. Dimensions Item Length. Item Height. Item Width.
Other quantitative data types and examples include cross-tabulation and trend analysis. Below are descriptions and typical steps involved in two popular quantitative data analysis techniques: regression analysis and hypothesis analysis. Regression analysis is a type of statistical analysis method that determines the relationships between independent and dependent variables.
In finance, regression is defined as a method to help investment and financial managers value assets and determine variable relationships in commodity prices and stocks. Through experiments that involve manipulating the values of independent variables, a quantitative data analyst can assess the impact of the changes on the dependent variable. The process can be thought of in terms of cause and effect.
For example, an independent variable can be the amount an individual invests in the stock market with the dependent variable the total amount of money an individual will have when they retire. A simple linear regression analysis formula includes a dependent variable and an independent variable.
The mathematical representation of the dependent variable is typically Y, while X represents the independent variable. By ranking customer satisfaction levels on a scale of 1 to 10, the market researcher can place numerical values on the data collected. Using these quantitative data, they can perform a regression analysis to determine a linear relationship between a product independent variable and customer satisfaction dependent variable. Multiple linear regression analysis also includes a dependent variable.
The main difference is that it contains various independent variables, resulting in a potentially complex formula for performing a regression analysis. However, tools such as Microsoft Excel and statistics software such as SPSS can simplify the task of multiple linear regression analysis.
Hypothesis analysis is a data analysis technique that uses sample data to test a hypothesis. In this approach, an analyst develops two hypotheses — only one of them can be true. Two foundational components of hypothesis analysis are the null hypothesis and the alternative hypothesis.
The first hypothesis is the null hypothesis. Null means no difference between two groups represented in the data. For example, a null hypothesis would claim that no difference in school achievement exists between students from high-income communities group 1 and those from low-income areas group 2.
In performing a hypothesis analysis, the aim of the researcher or analyst is to demonstrate that a difference does exist between the groups in the study, therefore rejecting the validity of the null hypothesis.
The alternative hypothesis is typically the opposite of the null hypothesis. The aim of a hypothesis analysis is to determine if the null hypothesis is not true. At the end of the test, a data analyst can draw a conclusion based on the results. Qualitative data analysis techniques are built on two main qualitative data approaches: deductive and inductive. Two main qualitative data analysis techniques used by data analysts are content analysis and discourse analysis.
Below are descriptions and typical steps involved in content analysis and discourse analysis. Researchers and data analysts can use content analysis to identify patterns in various forms of communication. Content analysis can reveal patterns in recorded communication that indicate the purpose, messages, and effect of the content.
Content analysis can also help determine the intent of the content producers and the impact on target audiences. For example, content analysis of political messages can provide qualitative insights about employment policy amid the COVID pandemic. The first step in the content analysis process is to select the type of content to be analyzed. Sources can range from text found in written form from books, newspapers, and social media posts to visual form found in photographs and video.
This step involves determining what will make a particular text relevant to the study. Questions to assess data criteria can include: Does the text mention a specific topic or connote an event related to the issue? Does it fall within a specified date range or geographic location? Since qualitative data is not numerical, it needs to be codified in preparation for measurement. This requires developing a set or system of codes to categorize the data. Once the coding system is developed, relevant codes can be applied to specific texts.
All the work in the previous steps leads to the data examination process. Data analysts look for patterns and correlations in the data to interpret results and make conclusions. They can incorporate statistical techniques for data analysis to draw insights from the data further. When communications, whether verbal or written, have an indirect or underlying message, it can be interpreted one way by one group and in an entirely different way by another, potentially leading to a breakdown in civil discourse.
Discourse analysis helps provide an understanding of the social and cultural context of verbal and written communication throughout conversations. Discourse analysis aims to investigate the social context of communication and how people use language to achieve their aims, such as evoking an emotion, sowing doubt, or building trust. Discourse analysis analyzes verbal and nonverbal cues. Discourse analysis helps interpret the true meaning and intent of communication and clarifies misunderstandings.
For example, an analysis of transcripts of conversations between a physician and a patient can reveal whether the patient truly understood a diagnosis. An analyst can distinguish subtle subtext in communication through discourse analysis to differentiate whether the content is fact, fiction, or propaganda.
Defining the research question determines the aim of the investigation and provides a clear purpose. The research question will guide the analysis. Materials used for investigation can include social media text, speeches, messaging in marketing brochures, press releases, and more. The content collected for the analysis typically focuses on a subject delivering the message such as a political leader or company and its targeted audience citizens and customers, for example.
Businesses can use quantitative and qualitative data analysis techniques to improve decision-making and forecasting, enhance business performance and competitiveness, maximize sales and marketing effectiveness, streamline operational processes, create better customer experiences, drive business agility, lower costs and reduce waste, and raise quality standards.
Statistical techniques use mathematical approaches to provide insights, observations, and conclusions. The processes encompass testing hypotheses and making estimates and predictions of unknown data or quantities. Statistical techniques for data analysis can help decision-makers in various ways, such as determining the risk of different business scenarios or forecasting sales in changing market conditions. Quantitative data is numerical, therefore, it can be analyzed using statistical analysis techniques to find patterns or meaning.
Qualitative data can also be analyzed using statistical analysis techniques. But since qualitative data is typically nonnumerical, it must first be classified and grouped into meaningful categories. Statistical techniques used in both qualitative and quantitative data analysis include grounded theory and cross-tabulation. This systematic inductive approach gathers, synthesizes, analyzes, and conceptualizes qualitative and quantitative data. Analysts using a grounded theory approach observe the data and identify patterns before developing a theory.
This type of approach is typical in qualitative research. Quantitative methods are typically structured the opposite way; first, a theory is developed and then the data is observed for patterns. Grounded theory research methods are useful when data about a particular topic is scarce.
This data analysis technique provides information about the relationship between different variables in a table format. It allows researchers to observe two or more variables simultaneously. The data is classified according to at least two categorical variables, represented as rows and columns. Therefore, each variable must be classified under at least two categories. For example, cross-tabulation can be useful in marketing and for reviewing customer feedback.
A column can provide values indicating whether a customer was satisfied or dissatisfied with their experience. A row can present variables identifying the type of customer online or in store, for example. A statistical analysis of the data can reveal insights from tables populated with a lot of data. For example, the Chi-square is a statistical hypothesis technique that allows analysts to observe values and draw conclusions across more than one category, providing valuable business insight.
Businesses have a treasure trove of data within reach thanks to digital music, movies, television, and games, and the digitization of business processes. Trading Business 2ed Harris D. Quantitative Chemical Analysis 9ed Oriakhi C. Chemistry in Quantitative Language Quantitative Chemical Analysis 10ed Educational Research: Quantitative , Quantitative Epidemiology by Xinguang Chen.
Quantitative Analysis and Modeling of Earth Data Khattar D. Quantitative Aptitude for Competitive Exam. Quantitative Evaluation of Systems: 14th International Conferenc. Guerard J. Quantitative Corporate Finance 2ed Quantitative -Aptitude-RS-Aggarwal. Handbook of Quantitative Finance and Risk Management. Quantitative Methods in Reservoir Engineering 2nd ed. Ibps,ssc Quantitative aptitude session by Rohith agarwal. Quantitative Chemical Analysis 8th ed with Solutions Manual.
Harris - Quantitative Chemical Analysis - 8th edition. Quantitative Assessment of Securitisation Deals. Computer Architecture. A Quantitative Approach. Guidelines for Quantitative Risk Assessment. Quantitative Chemical Analysis Vogel, 5th Ed. Quantitative Process Control Theory. A First Course in Quantitative Finance x. Quantitative Analysis for Management, Global 12th Edition x. Udemy - Introduction to Quantitative Research Methods x.
Udemy - Backtest Quantitative Trading strategies from Scratch x. Udemy - Backtest Quantitative Trading strategies from Scratch kickass. Algorithmic Trading and Quantitative Strategies thepiratebay Ebooks Financial Mathematics and Quantitative Finance thepiratebay Trading Business 2ed thepiratebay A First Course in Quantitative Finance thepiratebay Quantitative Chemical Analysis 9ed kickass.
Quantitative Chemical Analysis 10ed thepiratebay Quantitative Analysis for Management, Global 12th Edition kickass. Quantitative Epidemiology by Xinguang Chen kickass.