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9. 3 • graphing probability distributions 439 mu is the mean of the poisson distribution ( mu refers to the greek letter ). x is the number of successes in the specific interval of interest. example 9. 8 problem the port of miami is home to many cruise lines, and the port is typically heavily congested with arriving cr... | openstax_principles-of-data-science-web | [
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440 9 • visualizing data access for free at openstax. org plt. show ( ) the resulting output will look like this : from the graph, there are lower probabilities for and larger. so there is not much of a chance that 7 or more ships will arrive in a one - hour time period. the administrators for the port of miami should ... | openstax_principles-of-data-science-web | [
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9. 3 • graphing probability distributions 441 to generate a graph of the normal distribution, we can plot many points that fall on the normal curve according to the formal definition of the probability density function ( pdf ) of the normal distribution, as follows : of course, this is a job left to python, and the pyt... | openstax_principles-of-data-science-web | [
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. linspace ( mu - 3 * sigma, mu + 3 * sigma, 1000 ) # calculate values of the normal density function using norm. pdf function y = norm. pdf ( x, mu, sigma ) python code 442 9 • visualizing data access for free at openstax. org # plot the normal distribution plt. plot ( x, y ) # add labels and title plt. xlabel ('blood... | openstax_principles-of-data-science-web | [
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9. 4 geospatial and heatmap data visualization using python learning outcomes by the end of this section, you should be able to : • 9. 4. 1 describe the insights produced by spatial heatmaps based on geospatial data. • 9. 4. 2 discuss the common features of gis mapping. • 9. 4. 3 use a gis mapper to produce a visualiza... | openstax_principles-of-data-science-web | [
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9. 4 • geospatial and heatmap data visualization using python 443 geographic component, such as latitude and longitude coordinates. python offers several libraries and utilities for geospatial data visualization and analysis, including the pandas library discussed in what are data and data science? pandas is a python l... | openstax_principles-of-data-science-web | [
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. climate. gov / media / 15930, accessed june 23, 2024. ) spatial heatmaps are generated by condensing the spatial coordinates of individual data points into a grid or a set of bins covering the geographic area of interest. the density of data points within each grid cell or bin is then calculated, and these density re... | openstax_principles-of-data-science-web | [
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##otlib : a 2d graphics environment ", computing in science & engineering, vol. 9, no. 3, pp. 90 - 95, 2007. doi : https : / / zenodo. org / records / 13308876 ) exploring further interactive heatmaps various interactive heatmaps are available that allow the user to preselect variables of interest and then view the cor... | openstax_principles-of-data-science-web | [
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9. 4 • geospatial and heatmap data visualization using python 445 • heatmap ( ) function, which is part of the seaborn library. seaborn provides a high - level capability for statistical graphs. exploring further seaborn seaborn includes a wide variety of functions for visualizing statistical relationships. this user g... | openstax_principles-of-data-science-web | [
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##map function also shows the color bar to provide the viewer with the scale of color gradient used in the heatmap. import seaborn as sns import matplotlib. pyplot as plt # load the dataset from seaborn library called flights heatmap _ data = sns. load _ dataset ( " flights " ) # create an array with heatmap data from ... | openstax_principles-of-data-science-web | [
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of data according to geographic boundaries such as states, counties, etc. a choropleth graph is created by overlaying a grid on a geographic map and then typically coloring the map based on density or some other measurement for a specific grid location on the map. choropleth graphs are commonly used to plot data overla... | openstax_principles-of-data-science-web | [
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9. 4 • geospatial and heatmap data visualization using python 447 these graphs provide a very natural and intuitive way to visualize data for certain geographic regions and thus identify patterns, trends, and correlations. these types of maps also allow the user to identify clusters and outliers to assist in data inter... | openstax_principles-of-data-science-web | [
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python for geographic mapping as mentioned earlier, python provides a number of libraries and tools to facilitate the generation of 448 9 • visualizing data access for free at openstax. org geographic mapping. there are several steps involved in the process : 1. establish and collect geospatial data : the first step is... | openstax_principles-of-data-science-web | [
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9. 5 multivariate and network data visualization using python learning outcomes by the end of this section, you should be able to : • 9. 5. 1 produce labeled scatterplots and scatterplots with variable density points, different colors, etc. to indicate additional information. • 9. 5. 2 create and interpret correlation ... | openstax_principles-of-data-science-web | [
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reporting results ), or geospatial maps to show the geographic distribution of users and associated demographics. in this section, we explore more advanced visualization techniques such as scatterplots with variable density points, correlation heatmaps, and three - dimensional type analysis. scatterplots, revisited a s... | openstax_principles-of-data-science-web | [
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9. 5 • multivariate and network data visualization using python 449 determine if there is a correlation or dependency between the two quantities. scatterplots were discussed extensively in inferential statistics and regression analysis as a key element in correlation and regression analysis. recall that in a scatterplo... | openstax_principles-of-data-science-web | [
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##arators def format _ ticks ( value, tick _ number ) : return f'{ value :,. 0f }'# apply the custom formatter to the y - axis plt. gca ( ). yaxis. set _ major _ formatter ( ticker. funcformatter ( format _ ticks ) ) # show the plot plt. show ( ) the resulting output will look like this : python code 450 9 • visualizin... | openstax_principles-of-data-science-web | [
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color palette plt. scatter ( x, y, c = y, cmap ='coolwarm') python code | openstax_principles-of-data-science-web | [
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9. 5 • multivariate and network data visualization using python 451 # plot a color bar so the user can interpret the color scale cbar = plt. colorbar ( ) # define a function to format the color bar ticks with commas as thousands separators def format _ colorbar _ ticks ( value, tick _ number ) : return f'{ value :,. 0f... | openstax_principles-of-data-science-web | [
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##plot can be created based on data, where x is the square footage of the home, y is the price of the home, and z is the distance from center of the city, represented by the color of the point. # import the matplotlib library import matplotlib. pyplot as plt # define the x - data, which is square footage of a home x = ... | openstax_principles-of-data-science-web | [
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9. 5 • multivariate and network data visualization using python 453 # also plot a color bar so user can interpret the color scale plt. colorbar ( ) # add a title using the title function plt. title ( " home price versus square footage ( color = miles from center city ) " ) # add labels to the x and y axes by using xlab... | openstax_principles-of-data-science-web | [
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y tends to increase, and when x decreases, y tends to decrease ( positive correlation ). • a negative value of r means that when x increases, y tends to decrease, and when x decreases, y tends to increase ( negative correlation ). data scientists often collect data on more than two variables and then are interested in ... | openstax_principles-of-data-science-web | [
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5 fields and descriptions for the “ car _ crashes ” dataset from the seaborn library there are 50 records in the dataset ( one record per state ). the first five lines of the dataset appear in table 9. 6. total speeding alcohol not _ distracted no _ previous ins _ premium ins _ losses abbrev 18. 8 7. 332 5. 64 18. 048 ... | openstax_principles-of-data-science-web | [
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9. 5 • multivariate and network data visualization using python 455 the short python program that follows can be used to generate a correlation heatmap for the variables in the dataset ; it makes use of the heatmap function available in the seaborn library ( see also decision - making using machine learning basics ). i... | openstax_principles-of-data-science-web | [
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. let ’ s say an auto mechanic needs to repair a transmission on a car and attempts to read the written technical service manual to understand how to start the repair. compare this to an interactive 3d model of the car ’ s transmission where the mechanic can visualize the exact location for the repair and interact with... | openstax_principles-of-data-science-web | [
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9. 5 • multivariate and network data visualization using python 457 more. • vtk ( https : / / openstax. org / r / vtk ) is a library for 3d graphics and image processing ( vtk stands for visualization toolkit ). to illustrate an example of a 3d scatterplot in python using matplotlib, let ’ s generate python code to plo... | openstax_principles-of-data-science-web | [
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1. 8 virginica table 9. 7 “ iris ” dataset excerpt we will use the mpl _ toolkits. mplot3d ( ) function to generate the three - dimensional scatterplot. first, we will read in the “ iris ” dataset from the seaborn library ( https : / / openstax. org / r / seabornpydata ). next, the 3d scatterplot is created using the f... | openstax_principles-of-data-science-web | [
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# set labels and title ax. set _ xlabel ('sepal length ( cm )') ax. set _ ylabel ('sepal width ( cm )') ax. set _ zlabel ('petal length ( cm )') ax. set _ title ('3d scatterplot of iris dataset by species') # map numerical codes to actual species names species _ names = { 0 :'setosa ', 1 :'versicolor ', 2 :'virginica'}... | openstax_principles-of-data-science-web | [
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). cat. codes. unique ( ) ], title ='species') plt. show ( ) the resulting output will look like this : | openstax_principles-of-data-science-web | [
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9. 5 • multivariate and network data visualization using python 459 datasets note : the primary datasets referenced in the chapter code may also be downloaded here ( https : / / openstax. org / r / folders14a ). 460 9 • visualizing data access for free at openstax. org key terms 3d visualization a graph or display that... | openstax_principles-of-data-science-web | [
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##atial data data that describes the geographic location, shape, size, and other attributes relative to a location on the earth's surface geospatial information system ( gis ) mapping a tool for visualizing, analyzing, and interpreting spatial data that makes use of various types of geographical data, such as maps and ... | openstax_principles-of-data-science-web | [
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characteristic or attribute 9 • key terms 461 group project data visualization : scatterplot with color shading for your group, you will select at least 15 different models and types of vehicles and research the value of a used car for that model in your area. you can search used car websites such as autotrader. com ( ... | openstax_principles-of-data-science-web | [
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, symmetry, and skewness. 2. a. create a graph for the binomial distribution with and. b. analyze the graph and comment on symmetry and skewness. 3. a. create a graph for the poisson distribution with mean. b. analyze the graph and comment on symmetry and skewness. 4. a. create a graph of the normal distribution for we... | openstax_principles-of-data-science-web | [
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4. 9 table 9. 9 social media subscribers b. comment on any trends based on a review of the graph. 6. access and download any dataset from the seaborn repository at https : / / github. com / mwaskom / seaborn - data ( https : / / github. com / mwaskom / seaborn - data ). a. create a correlation heatmap for the selected ... | openstax_principles-of-data-science-web | [
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10. 3 effective executive summaries introduction data science is not just about analyzing datasets. communicating the findings effectively is an essential part of the process aimed at promoting understanding and retention of key information. the ability to convey complex information to various kinds of audiences in a c... | openstax_principles-of-data-science-web | [
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10. 1 writing an informative report learning outcomes by the end of this section, you should be able to : • 10. 1. 1 identify the audience for a technical report and tailor word choice and style appropriately. • 10. 1. 2 document and describe the methods used in a data science project. • 10. 1. 3 determine the most eff... | openstax_principles-of-data-science-web | [
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, along with strengths and weaknesses of the models, potential biases ( and how they were addressed ), and areas for further exploration • conclusion, containing a summary of key findings, final thoughts, and recommendations additional parts may be included in the report, including a table of contents ( toc ), introduc... | openstax_principles-of-data-science-web | [
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, discuss advanced techniques, or delve into the nuances of a particular issue. technicians technicians are practical users of information who may apply the knowledge in a hands - on manner. they typically have good familiarity with the subject but may not have the theoretical knowledge of experts. information presente... | openstax_principles-of-data-science-web | [
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dashboards include the ability to monitor sales performance through real - time sales data, revenue trends, and customer engagement metrics, which can enable teams to adjust strategies in a timely manner. data dashboards can also support operational efficiency by displaying key metrics such as production rates, invento... | openstax_principles-of-data-science-web | [
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10. 1 • writing an informative report 467 figure 10. 2 the executive dashboard is a visual representation of data to give executives and senior management a quick, real - time overview of an organization's key performance indicators ( kpis ). ( credit : modification of work " analyst analytics blur " by david stewart /... | openstax_principles-of-data-science-web | [
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your audience and should avoid nonessential jargon, cryptic acronyms, and excessive details ( unless the level of audience requires a high level of technicality and theory ). consider the human element when creating an informative narrative. in other words, take into account the perspectives, emotions, motivations, and... | openstax_principles-of-data-science-web | [
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learning and processing styles. additionally, providing summaries or abstracts at the beginning of each section can aid in comprehension for those who might struggle with large blocks of text or complex information. by adopting these strategies, report writers not only uphold the principles of inclusivity and accessibi... | openstax_principles-of-data-science-web | [
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such a report adheres to a recognized editorial style, such as one of these : • american medical association ( ama ) ( https : / / openstax. org / r / amastyle ) for medical and health fields • american psychological association ( apa ) ( https : / / openstax. org / r / apastyle ) for scientific and psychological field... | openstax_principles-of-data-science-web | [
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10. 1 • writing an informative report 469 some reports may require the development of a codebook or data dictionary to detail the variables, their units, values, and labels within the data or datasets used in the project. this codebook must align with the final report and the code used during the project to maintain co... | openstax_principles-of-data-science-web | [
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or presentation can lead to bias or misinterpretation. thus, each visual aid should be paired with succinct, clear explanations that clarify the purpose of the visual aid, its content, and possible limitations. the inclusion of alt text, or brief descriptions of images that accompany the image or may be embedded within... | openstax_principles-of-data-science-web | [
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whole in percentages or proportions scatterplots demonstrate correlations between data from two continuous variables histograms show the distribution frequencies of data from a single continuous variable table 10. 1 summary of visuals used in data science reports 470 10 • reporting results access for free at openstax. ... | openstax_principles-of-data-science-web | [
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markets for coronary heart disease and diabetes medications prepared for : [ pharmaceutical company name ] prepared by : [ data scientist's name ] date : [ report date ] • executive summary : this section provides a concise overview of the entire report, summarizing the key findings, methodologies, and recommendations.... | openstax_principles-of-data-science-web | [
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10. 1 • writing an informative report 471 data from the diabetes _ data. csv ( https : / / openstax. org / r / spreadsheetsd ) dataset. preliminary analysis indicates significant correlations between factors [ x, y, z ] and disease prevalence. recommendations include targeted marketing strategies in high - prevalence a... | openstax_principles-of-data-science-web | [
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##liers were addressed through data normalization techniques. • results and discussion : this section presents the findings of the analysis, interpreting the results and discussing their implications. it should relate the findings back to the research questions and objectives outlined in the introduction. strengths and... | openstax_principles-of-data-science-web | [
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10. 2 validating your model learning outcomes : by the end of this section, you should be able to : • 10. 2. 1 identify meaningful assumptions, state how they are used in the modeling process, and provide justifications for each assumption. • 10. 2. 2 discuss various error measures in the context of a modeling problem.... | openstax_principles-of-data-science-web | [
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- making process and shaping the solution's development. however, it is important to note that assumptions come with their own risks. if the assumptions turn out to be false or inaccurate, they can lead to bias, errors, delays, or outright failures in the project. therefore, documenting assumptions in a clear and expli... | openstax_principles-of-data-science-web | [
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10. 2 • validating your model 473 certain resource requirements or that must attain certain goals. using precise and unambiguous language and avoiding any vagueness or generalities helps in accurately stating the assumptions. assumptions should be verifiable statements, meaning they should be capable of being evaluated... | openstax_principles-of-data-science-web | [
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uniform performance, or are some capable of producing more of the product? if so, are additional employees required to work the higher - performing machines ( e. g., some machines may be larger and require more operators, producing more of the product as a result )? • what is the average uptime and downtime for each ma... | openstax_principles-of-data-science-web | [
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, the technical writer should offer a justification supported by evidence, data, or logical reasoning. for example, we might assume that a software product will be compatible with the microsoft windows 11 operating system because market research data shows that windows 11 is the most widely used operating system among ... | openstax_principles-of-data-science-web | [
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certain groups, leading to inequitable outcomes. therefore, it is necessary to examine and challenge assumptions, employ diverse and representative datasets, and implement techniques to detect and mitigate bias. often, the best approach is to allow a third party to vet the methodology, identifying assumptions that you ... | openstax_principles-of-data-science-web | [
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10. 2 • validating your model 475 interpreting measures of fit as we saw in time series and forecasting, what is machine learning?, and deep learning and ai basics, models need to be evaluated using appropriate measures of fit — that is, the metrics that assess how well a model's predictions align with the actual obser... | openstax_principles-of-data-science-web | [
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that is predictable from the independent variables. consider an example of a linear model in which the factors ( feature variables ) are square footage of the house, number of bathrooms, proximity to a city center, local school district ranking, etc., while the response ( dependent variable ) is the market value of the... | openstax_principles-of-data-science-web | [
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in the model, implying that a quadratic model might be more appropriate than a linear one, for example. moreover, the features might explain some of the remaining variability in the response ; however, this estimate may be impacted by the sample size, presence of outliers, and the extent to which model assumptions are ... | openstax_principles-of-data-science-web | [
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load data. the code for your dataset will be dependent upon the file path you # set up df = pd. read _ csv ('diabetes _ data. csv ', index _ col ='patient number') when preparing data to run various analyses or models, it is recommended to examine the data characteristics to ensure that the data is properly formatted a... | openstax_principles-of-data-science-web | [
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10. 2 • validating your model 477 data columns ( total 13 columns ) : # column non - null count dtype - - - - - - - - - - - - - - - - - - - - - - - - - - - - 0 cholesterol 390 non - null int64 1 glucose 390 non - null int64 2 hdl chol 390 non - null int64 3 age 390 non - null int64 4 gender 390 non - null object 5 heig... | openstax_principles-of-data-science-web | [
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. 7 + kb to use the sklearn ( https : / / openstax. org / r / scikitlearn ) library, all data must be numeric. however, if you have columns with string data, there are several methods to convert them to numeric values. for “ true ” / “ false ” values, simply use. astype ('int'). for other strings, you can employ the pa... | openstax_principles-of-data-science-web | [
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( x _ train, y _ train ) print ( dt. score ( x _ train, y _ train ) ) print ( dt. score ( x _ test, y _ test ) ) the resulting output will look like : 1. 0 0. 826530612244898 the accuracy observed on the training set is clean, registering at 100 %, whereas the accuracy on the test set is significantly lower, recorded a... | openstax_principles-of-data-science-web | [
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10. 2 • validating your model 479 seeing how deep the tree got and the number of samples in the leaf nodes illustrates that the model is likely overfitting on the data. we can restrict the number of levels ( depth ) with max _ depth. you can try a few values below and see how it changes. here, we settled on two since t... | openstax_principles-of-data-science-web | [
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0. 9081632653061225 # create a decision tree with a max depth of 2 f = plt. figure ( figsize = ( 8, 8 ) ) _ = plot _ tree ( dt, fontsize = 10, feature _ names = features. columns. tolist ( ), filled = true ) the resulting output will look like : python code python code 480 10 • reporting results access for free at open... | openstax_principles-of-data-science-web | [
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real - world applications, as they allow for model selection and fine - tuning before deployment, ultimately leading to more reliable and effective predictive analytics. cross - validation, a variation on validation, divides the training set into multiple subsets and iteratively trains and evaluates the model on differ... | openstax_principles-of-data-science-web | [
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10. 2 • validating your model 481 in regression analysis ). bootstrapping is a powerful statistical technique used to estimate the distribution of a sample by repeatedly resampling with replacement. in the context of model evaluation, the bootstrap method involves creating multiple new datasets, or bootstrap samples, b... | openstax_principles-of-data-science-web | [
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, which is used as the validation set, and this process is repeated for each data point in the dataset. this method provides an exhaustive validation mechanism, ensuring that every single data point is used for testing exactly once, thus offering an unbiased evaluation of the model's performance. however, loocv can be ... | openstax_principles-of-data-science-web | [
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in what is machine learning? that precision measures the proportion of correctly identified positive instances ( true positives ) among all instances that the model predicts as positive — that is,. on the other hand, recall measures the proportion of true positives among all positives in dataset — that is,. finally, th... | openstax_principles-of-data-science-web | [
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, mean absolute percentage error ( mape ), and root mean square error ( rmse ), in addition to statistical measures of fit such as r - squared or the pearson correlation coefficient ( r ), which are detailed in statistical inference and confidence intervals. adding one more measure to this list, consider the mean perce... | openstax_principles-of-data-science-web | [
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on the number of parameters compared to aic and so will select simpler models overall. during the model selection process, various candidate models are fitted to the data. aic and bic values are computed for each model, and the model with the lowest criterion value is selected : | openstax_principles-of-data-science-web | [
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10. 2 • validating your model 483 # cross validation with bayesian information criterion from sklearn. model _ selection import cross _ val _ score, gridsearchcv import numpy as np # import numpy # define the decision tree classifier dt = decisiontreeclassifier ( ) # perform cross - validation with 5 folds scores = cro... | openstax_principles-of-data-science-web | [
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best score : 0. 72 bic score : 2165. 56 the goal of bic is to balance the fit of the model with its complexity, helping to avoid overfitting. a lower bic score indicates a better model, as it suggests that the model has a good fit to the data while being relatively simple. for instance, the bic score of 2, 165. 56 woul... | openstax_principles-of-data-science-web | [
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of a model. it is useful for identifying the impact of individual variables on the result, allowing decision - makers to understand which single factors are most critical. multi - way sensitivity analysis explores the effects of simultaneous changes in multiple input parameters on the outcome. this approach helps in un... | openstax_principles-of-data-science-web | [
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10. 2 • validating your model 485 # define the number of simulations num _ simulations = 1000 # initialize a list to store the results results = [ ] # loop through the number of simulations for i in range ( num _ simulations ) : # randomly split the data into training and testing sets x _ train, x _ test, y _ train, y ... | openstax_principles-of-data-science-web | [
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accurate real estate pricing decisions. 486 10 • reporting results access for free at openstax. org sensitivity analysis supports decision - making processes across various domains to build an understanding of how different inputs influence the outcomes of their models or projects. by carefully changing important facto... | openstax_principles-of-data-science-web | [
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as a strength of the model. weaknesses no one likes to admit weaknesses ; however, in the context of a data science project, listing real or potential weaknesses is an essential part of writing an effective and informative report. weakness statements should be as specific as possible and should indicate steps taken to ... | openstax_principles-of-data-science-web | [
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s performance and include a comparison with alternative models to provide a comprehensive analysis. ” | openstax_principles-of-data-science-web | [
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10. 2 • validating your model 487 for those who are providing feedback, it is best to use constructive criticism, acknowledging limitations while proposing potential solutions or future research directions. instead of outlining weaknesses, emphasize opportunities for improvement, suggesting alternative modeling approac... | openstax_principles-of-data-science-web | [
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the limitations of the dataset and logistic regression technique. • present the results of the model with a data visual such as a confusion matrix. • invite opportunity for the marketing director to provide suggestions for additional data to be used. • provide suggestions on other data science techniques available base... | openstax_principles-of-data-science-web | [
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10. 3 effective executive summaries learning outcomes : by the end of this section, you should be able to : • 10. 3. 1 define executive summaries, noting their important features. • 10. 3. 2 determine which details of the modeling process would be most important to include in an effectively written executive summary. •... | openstax_principles-of-data-science-web | [
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first element that readers encounter, and it plays a critical part in setting the tone for the entire document. the structure of the executive summary is designed to guide the reader through a logical progression of ideas. it typically begins with a clear statement of the problem or research question, setting the conte... | openstax_principles-of-data-science-web | [
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desired outcomes. consider the order of the suggestions based on their perceived impact and feasibility within the organizational landscape ( e. g., culture, politics, priorities ). for example, suppose that you have made a model for home prices. your analysis may suggest the following actionable advice : “ homes with ... | openstax_principles-of-data-science-web | [
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10. 3 • effective executive summaries 489 executive summary example below is an example of an executive summary report in python.! pip install fpdf from fpdf import fpdf # define a custom pdf class that inherits from fpdf class pdf ( fpdf ) : def _ _ init _ _ ( self, * args, * * kwargs ) : super ( ). _ _ init _ _ ( * a... | openstax_principles-of-data-science-web | [
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_ page ( ) # add a page pdf. chapter _ title ( title ) # add the main title for section _ title, section _ body in sections. items ( ) : # loop through sections pdf. chapter _ title ( section _ title ) # add section title pdf. chapter _ body ( section _ body ) # add section body text pdf. output ('executive _ summary. ... | openstax_principles-of-data-science-web | [
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practice, alongside public health initiatives, to manage obesity and glucose levels for diabetes prevention. ','recommendation':'based on the findings of the study, we recommend integrating the classification decision tree model into clinical practice to enhance early detection and diagnosis of diabetes. by utilizing t... | openstax_principles-of-data-science-web | [
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10. 3 • effective executive summaries 491 exploring further additional resources many useful articles and books have been written about technical writing and communicating data science findings. a brief list is included here, and we encourage readers to continue their learning about this important phase of the data sci... | openstax_principles-of-data-science-web | [
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. com / 2023 / 05 / 04 / delivering - facts ( https : / / openstax. org / r / theactuary ) marino, v., & dragan, d. ( 2018, july ). science communication with diverse audiences. adult development & 492 10 • reporting results access for free at openstax. org aging news. https : / / www. apadivisions. org / division - 20... | openstax_principles-of-data-science-web | [
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10. 3 • effective executive summaries 493 key terms actionable advice recommendations or guidance in a report, particularly in an executive summary, providing practical ways to apply the report's findings or insights alt text brief descriptions of images that accompany the image or may be embedded within the image data... | openstax_principles-of-data-science-web | [
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- fold cross - validation cross - validation strategy that works by dividing the dataset into equally sized subsets or folds, where the model is trained on of the folds and tested on the remaining fold, a process that is repeated times with each fold serving as the test set once key performance indicators ( kpis ) quan... | openstax_principles-of-data-science-web | [
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hands - on manner validation evaluation of multiple predictive models and / or hyperparameter tuning version control system a software tool that helps keep track of changes to files, code, or any type of digital content over time group project you are working in a business analyst role on a team that provides analytica... | openstax_principles-of-data-science-web | [
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detailed b. focused on practical application c. concise with a focus on summaries and outcomes d. broad and nontechnical for general understanding 2. what should a technical report for nonspecialists avoid? a. complex statistical data b. technical jargon c. visual aids like graphs and charts d. descriptions of practica... | openstax_principles-of-data-science-web | [
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is based on personal opinion. b. it is specific and verifiable. c. it is vague and general. d. it is always proven to be true. 10. in the context of measuring house prices, what does an r - squared value of 0. 75 indicate? a. the model predicts housing prices with 75 % accuracy. b. seventy - five percent of the varianc... | openstax_principles-of-data-science-web | [
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. every step in the methodological process d. the technical language used in the methodologies 16. how should the executive summary in a data science report conclude? a. with a persuasive call to action b. by summarizing the data sources used c. with a detailed technical explanation d. by introducing new topics critica... | openstax_principles-of-data-science-web | [
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? 13. in what ways can the actionable advice in an executive summary be tailored to different audience types or departments to maximize the report's utility and impact? references willems, j. p., saunders, j. t., hunt, d. e., & schorling, j. b. ( 1997 ). prevalence of coronary heart disease risk factors among rural bla... | openstax_principles-of-data-science-web | [
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with rows and columns. we call each rectangle, defined by the combination of row number and column name ( e. g., a1, b3 ), a cell. we can use a mouse pointer to select a specific cell and then edit the data inside. once a cell is selected, namebox shows the identifier of the selected cell figure a3. formula bar is used... | openstax_principles-of-data-science-web | [
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0.018131954595446587,
0.05133063346147537,
0.062133513391017914,
0.015729334205389023,
-0.025059834122657776,
0.0... |
##s14 ), write a formula that counts the number of outfielders. solution the formula is = countif ( c2 : c13, " outfielder " ) ” this will count the rows whose position, located at column c, is “ outfielder. ” note that double quotation marks ( i. e., “ ” ) are used to indicate “ outfielder ” is a string. the result is... | openstax_principles-of-data-science-web | [
0.00802899431437254,
-0.0019504412775859237,
0.03814638406038284,
0.005584214814007282,
0.0031650338787585497,
-0.013742325827479362,
0.03916749730706215,
0.006464216858148575,
0.029541688039898872,
0.07331361621618271,
0.044085849076509476,
0.023433944210410118,
-0.02228092961013317,
0.05... |
the range. for example, the following formula on ch1 - socr - small. csv ( https : / / openstax. org / r / folders14 ) will return 72 since the cell located at the fifth row and second column within the range of c8 : e13 will refer to the row 12 and column d — so the cell d12. the value at d12 is 72. 502 a • appendix a... | openstax_principles-of-data-science-web | [
0.02556178718805313,
0.025199685245752335,
0.059787292033433914,
0.011148910969495773,
-0.02787035144865513,
-0.019978484138846397,
0.002313927048817277,
-0.01388314738869667,
0.021909961476922035,
0.08105411380529404,
0.05689988285303116,
0.025034401565790176,
-0.062238868325948715,
0.022... |
returns the very first instance. for example, this formula will simply return 2 ( i. e., the second row within d2 : d13, which is d3 ) even though there are three occurrences of 72 at rows 3, 5, and 12. = match ( 72, d2 : d13, 0 ) example a. 3 problem using match, find the absolute row number of a first player whose we... | openstax_principles-of-data-science-web | [
0.014106079936027527,
0.023022273555397987,
0.046001285314559937,
0.016247889026999474,
-0.045175909996032715,
-0.022740667685866356,
-0.023511765524744987,
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0.035596974194049835,
0.050326790660619736,
0.05349419638514519,
-0.017610181123018265,
-0.03426892310380936,
... |
., not the “ partial ” match ) for vlookup. = vlookup ( “ pitcher ”, c2 : e13, 3, false ) then, the formula reads a specific cell value in the same row. the column number is indicated as the third argument in the formula, called the relative position. it refers to how many columns the search key is away to the right fr... | openstax_principles-of-data-science-web | [
0.023150209337472916,
0.008506429381668568,
0.03722294792532921,
0.03955472260713577,
0.008455568924546242,
-0.01656990498304367,
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0.06831938028335571,
0.031442295759916306,
0.0006023175665177405,
-0.023801669478416443,
0.02... |
visualization, can be done easily with excel. let ’ s draw a bar chart as an example. you can start by simply clicking insert > bar chart > clustered column icon ( figure a10 ). figure a10 ms excel ’ s bar chart icon ( used with permission from microsoft ) once the icon is clicked, you will see an arbitrary bar chart. ... | openstax_principles-of-data-science-web | [
-0.009119105525314808,
-0.004291682969778776,
0.039444390684366226,
-0.03499629721045494,
0.00868979562073946,
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0.030106885358691216,
0.0116588668897748,
0.01783597469329834,
0.06189953535795212,
0.037396322935819626,
0.01809011399745941,
0.013824459165334702,
0.01676... |
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