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. a historian traveling to italy to study ancient manuscripts to uncover historical insights about the roman empire b. a mathematician solving complex equations to model physical phenomena c. a biologist analyzing a large dataset of genetic sequences to gain insights about the genetic basis of diseases d. a chemist syn... | openstax_principles-of-data-science-web | [
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and danceability ( y - axis ) of the spotify dataset ( https : / / openstax. org / r / filed ) using : a. python matplotlib b. a spreadsheet program such as ms excel or google sheets ( hint : search “ scatterplot ” on help. ) 7. regenerate the scatterplot of the spotify dataset ( https : / / openstax. org / r / filed )... | openstax_principles-of-data-science-web | [
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/ openstax. org / r / spreadsheet4 ), calculate the average bpm of the songs released in 2023 using : a. python pandas b. a spreadsheet program such as ms excel or google sheets ( hint : the formula average ( ) computes the average across the cells specified in the parentheses. for example, within excel, typing in the ... | openstax_principles-of-data-science-web | [
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##achi drive farming efficiency with sustainable digital solutions. hitachi social innovation. https : / / social - innovation. hitachi / en - us / case _ studies / digital - solutions - in - agriculture - drive - sustainability - in - farming / iabac. ( 2023, september 20 ). fraud detection through data analytics : id... | openstax_principles-of-data-science-web | [
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2. 5 handling large datasets introduction data collection and preparation are the first steps in the data science cycle. they involve systematically gathering the necessary data to meet a project's objectives and ensuring its readiness for further analysis. well - executed data collection and preparation serve as a sol... | openstax_principles-of-data-science-web | [
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2. 1 overview of data collection methods learning outcomes by the end of this section, you should be able to : • 2. 1. 1 define data collection and its role in data science. • 2. 1. 2 describe different data collection methods commonly used in data science, such as surveys and experiments. • 2. 1. 3 recognize scenarios... | openstax_principles-of-data-science-web | [
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. a survey may be conducted online, via phone, or in person, while experimental research requires a controlled environment to ensure data validity and reliability. types of data observational and transactional data play important roles in data analysis and related decision - making, each offering unique insights into d... | openstax_principles-of-data-science-web | [
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include financial data, but it also includes data related to customer purchases, website clicks, user interactions, or any other type of activity that is recorded and tracked. transactional data can be used to understand patterns and trends, make predictions and recommendations, and identify potential opportunities or ... | openstax_principles-of-data-science-web | [
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2. 1 • overview of data collection methods 61 solution ashley wants to ensure that there is enough food for her local birds while she is away for three days. to do this, she will carefully observe the feeder daily for two consecutive weeks. she will record the total amount of feed eaten each day and make sure to refill... | openstax_principles-of-data-science-web | [
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health records system. this data may include patients'appointment schedules, visit durations, and wait times. this information can help identify patterns and trends in appointment scheduling and wait times. 2. appointment booking system : samantha can gather data from the hospital's appointment booking system. this dat... | openstax_principles-of-data-science-web | [
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measured and documented every week for four consecutive weeks. the main research objective was to determine the growth rate of plants exposed to eight hours of sunlight compared to those with only four hours. a total of 20 identical potted plants were used, with one group allocated to the " sunlight " condition and the... | openstax_principles-of-data-science-web | [
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2. 2 survey design and implementation learning outcomes by the end of this section, you should be able to : • 2. 2. 1 describe the elements of survey design and identify the steps data scientists take to ensure the reliability of survey results. • 2. 2. 2 describe methods for avoiding bias in survey questions. • 2. 2. ... | openstax_principles-of-data-science-web | [
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2. 2 • survey design and implementation 63 open - ended questions allow for more in - depth responses and provide the opportunity for unexpected insights. they also allow respondents to elaborate on their thoughts and provide detailed and personal responses. closed - ended questions have predetermined answer choices an... | openstax_principles-of-data-science-web | [
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such as age or location. sampling is necessary in a wide range of data science projects to make data collection more manageable and cost - effective while still drawing meaningful conclusions. a variety of techniques can be employed to determine a subset of data from a larger population to perform research or construct... | openstax_principles-of-data-science-web | [
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in engineering, this technique is used to select random samples of materials or components 64 2 • collecting and preparing data access for free at openstax. org for quality control testing. in the political arena, simple random sampling is commonly used to select randomly registered voters for polls or surveys, ensurin... | openstax_principles-of-data-science-web | [
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sampling applies to selecting people or items for the sample based on their availability and convenience to the data science research. for example, a researcher may choose to survey students in their classroom or manipulate data from social media users. convenience sampling is effortless to achieve, and it is useful fo... | openstax_principles-of-data-science-web | [
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which means every student with a number ending in 4 ( 14, 24, 34, etc. ) will be included in the sample. this way, the researcher will have a representative sample of 100 students from the high school, which is 10 % of the population. the sample will consist of students from different grades, genders, and backgrounds, ... | openstax_principles-of-data-science-web | [
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2. 2 • survey design and implementation 65 6. purposive sampling. with purposive sampling, one or more specific criteria are used to select participants who are likely to provide the most relevant and useful information for the research study. this can involve selecting participants based on their expertise, characteri... | openstax_principles-of-data-science-web | [
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, as it allows for a fast and cost - effective way to gather data from a diverse range of individuals. however, it is important to note that the results of quota sampling may not accurately represent the entire population, as the sample is not randomly selected and may be biased toward certain characteristics. therefor... | openstax_principles-of-data-science-web | [
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ensure that each member of the population has an equal chance of being included in 66 2 • collecting and preparing data access for free at openstax. org the sample. additionally, careful consideration of the sampling frame should ideally encompass all members of the target population and provide a clear and accessible ... | openstax_principles-of-data-science-web | [
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: 1. sampling bias. one potential source of bias in this study is self - selection bias. as the participants are all college students, they may not be representative of the larger population, as college students tend to have more access and motivation to exercise compared to the general population. this could limit the... | openstax_principles-of-data-science-web | [
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2. 2 • survey design and implementation 67 researchers need to be aware of these potential errors and take steps to minimize them when designing and conducting their studies. example 2. 3 problem mark is a data scientist who works for a marketing research company. he has been tasked to lead a study to understand consum... | openstax_principles-of-data-science-web | [
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2. 3 web scraping and social media data collection learning outcomes : by the end of this section, you should be able to : • 2. 3. 1 discuss the uses of web scraping for collecting and preparing data for analysis. • 2. 3. 2 apply regular expressions for data manipulation and pattern matching. • 2. 3. 3 write python cod... | openstax_principles-of-data-science-web | [
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to navigate to other pages and collect data from them • useful for scraping data from multiple pages of a website xpath • powerful query language • navigates through the elements in an html document • often used in combination with html parsing to select specific elements to scrape regular expressions • search for and ... | openstax_principles-of-data-science-web | [
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can reveal influential users and communities. it involves identifying and analyzing influential individuals or groups as well as understanding patterns and trends within the network. image and video analysis can provide insights into visual trends and user behavior. | openstax_principles-of-data-science-web | [
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2. 3 • web scraping and social media data collection 69 an example of social media data collection is conducting a twitter survey on customer satisfaction for a food delivery company. data scientists can use twitter's api to collect tweets containing specific hashtags related to the company and analyze them to understa... | openstax_principles-of-data-science-web | [
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4 ) from the list of dataframe objects returned by the read _ html ( ) function. the index here represents the table order in the web page. 4. store the data in a dataframe. the result of the read _ html ( ) function is a list of dataframe objects, and each dataframe represents a table from the web page. we can store t... | openstax_principles-of-data-science-web | [
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same web page. this enables efficient data processing and analysis, making the 70 2 • collecting and preparing data access for free at openstax. org task more manageable and streamlined. example 2. 4 problem extract data table " current population survey : household data : ( table a - 13 ). employed and unemployed pers... | openstax_principles-of-data-science-web | [
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2. 3 • web scraping and social media data collection 71 in python, there are several libraries and methods that can be used for parsing and extracting data from text. these include the following : 1. regular expressions ( regex or re ) ( https : / / openstax. org / r / docpython ). this is a built - in library in pytho... | openstax_principles-of-data-science-web | [
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the specific task and type of data being analyzed. it is important to research and determine the best approach for a given project. regular expressions in python regular expressions, also known as regex, are a set of symbols used to define a search pattern in text data. in python, these expressions are supported by the... | openstax_principles-of-data-science-web | [
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a? b " would match either " ab " or " b ". • the + character is known as the " plus sign " and is used to match one or more occurrences of the preceding character or group. for example, the regular expression " a + b " would match one or more " a " s followed by a " b ", such as " ab ", " aab ", " aaab ", etc. if there... | openstax_principles-of-data-science-web | [
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2. 3 • web scraping and social media data collection 73 # use the " findall " to search for matching patterns in the data string words = re. findall ( pattern, story ) # print the number of repeated python print ( " the word'python'is repeated ", len ( words ), " times in the story. " ) # print the matched patterns pri... | openstax_principles-of-data-science-web | [
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, deleting, or inserting new content into specific positions within the string. parsing and extracting data involves the analysis of a given dataset or string to extract specific pieces of information. this is accomplished using various techniques and functions, such as splitting and slicing strings, which allow for th... | openstax_principles-of-data-science-web | [
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the words " model : " and captures anything that comes after it until it reaches a comma. the result is saved in a variable named " model ". finally, the third pattern searches for the words " price : " and captures any sequence of numbers or symbols that follows it until the end of the string. the result is saved in a... | openstax_principles-of-data-science-web | [
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) # print the extracted data print ( " product : " + product ) print ( " model : " + model ) print ( " price : " + price ) the resulting output will look like this : product : iphone 12 model : a2172 price : $ 799 processing and storing data once the data is collected, it should be processed and stored in a suitable fo... | openstax_principles-of-data-science-web | [
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2. 3 • web scraping and social media data collection 75 cleaning process includes identifying and correcting any errors, inconsistencies, and missing values in a dataset and is essential for ensuring that the data is accurate, reliable, and usable for analysis or other purposes. python is often utilized for data proces... | openstax_principles-of-data-science-web | [
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3 " : [ " thomas jefferson ", " virginia " ], " 4 " : [ " james madison ", " virginia " ], " 5 " : [ " james monroe ", " virginia " ], " 6 " : [ " john quincy adams ", " massachusetts " ], " 7 " : [ " andrew jackson ", " south carolina " ], " 8 " : [ " martin van buren ", " new york " ], " 9 " : [ " william henry harri... | openstax_principles-of-data-science-web | [
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" theodore roosevelt ", " new york " ], " 27 " : [ " william howard taft ", " ohio " ], " 28 " : [ " woodrow wilson ", " virginia " ], " 29 " : [ " warren g. harding ", " ohio " ], " 30 " : [ " calvin coolidge ", " vermont " ], " 31 " : [ " herbert hoover ", " iowa " ], " 32 " : [ " franklin d. roosevelt ", " new york ... | openstax_principles-of-data-science-web | [
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", " state of birth " ] # create a writer object writer = csv. dictwriter ( csv _ file, fieldnames = fieldnames ) # write the header row writer. writeheader ( ) # loop through the presidents dictionary for key, value in presidents. items ( ) : # write the data for each president to the csv file writer. writerow ( { " n... | openstax_principles-of-data-science-web | [
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2. 4 data cleaning and preprocessing learning outcomes by the end of this section, you should be able to : • 2. 4. 1 apply methods to deal with missing data and outliers. • 2. 4. 2 explain data standardization techniques, such as normalization, transformation, and aggregation. • 2. 4. 3 identify sources of noise in dat... | openstax_principles-of-data-science-web | [
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still maintaining the overall characteristics of the original dataset. the goal of data cleaning and preprocessing is to guarantee that the data used for analysis is accurate, consistent, and relevant. it helps to improve the quality of the results and increase the efficiency of the analysis process. a well - prepared ... | openstax_principles-of-data-science-web | [
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can be done through visualization techniques, such as scatterplots, box plots, iqr ( interquartile range ), or histograms, or through statistical methods, such as calculating the mean, median, and standard deviation ( see measures of center and measures of variation as well as encoding univariate data ). it is importan... | openstax_principles-of-data-science-web | [
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helpful to understand the reasons behind the missing data and outliers. some expected reasons for missing data include measurement errors, human error, or data not being collected for a particular variable. similarly, outliers can be caused by incorrect data entry, measurement errors, or genuine extreme values in the d... | openstax_principles-of-data-science-web | [
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2. 4 • data cleaning and preprocessing 79 example 2. 6 problem starting in 1939, the united states bureau of labor statistics tracked employment on a monthly basis. the number of employers in the construction field between 1939 and 2019 is presented in figure 2. 2. a. determine if there is any outlier in the dataset th... | openstax_principles-of-data-science-web | [
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1990 9523 7 / 1 / 1990 5580 8 / 1 / 1990 5581 9 / 1 / 1990 5487 10 / 1 / 1990 5379 11 / 1 / 1990 5208 12 / 1 / 1990 4963 table 2. 3 monthly employment over 1990 the median employment level from may 1, 1990, to july 1, 1990, is 5, 289, as a replacement value for the outlier on may 28, 1990. the adjusted data is presente... | openstax_principles-of-data-science-web | [
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2. 4 • data cleaning and preprocessing 81 duplicates as well as converting data from various sources into a unified format, often termed a normal form ( defined in the next section ). data transformation involves modifying the data to make it more suitable for the analysis that is planned. data validation ensures that ... | openstax_principles-of-data-science-web | [
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free from certain types of data irregularities, such as redundancy and inconsistency. the most commonly used normal forms are 1nf, 2nf, 3nf ( first, second, and third normal form ), and bcnf ( boyce - codd normal form ). normalization is the process of applying these rules to a database. the data must be organized and ... | openstax_principles-of-data-science-web | [
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and in - store purchases, they might choose one or the other to avoid duplication. the marketing team would ensure that the data is properly structured and organized. this could involve creating a data table with domains for each data element, such as a customer id, product code, and purchase amount. by normalizing the... | openstax_principles-of-data-science-web | [
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women ’ s boutique apparel 40000 20000 branch 8 designer & fashion sunglasses 55000 27500 table 2. 5 retail company sales and profits 2. 4 • data cleaning and preprocessing 83 solution using the normalization formula, the maximum sale is $ 55, 000, and the minimum sale is $ 15, 000, as shown in table 2. 6. branch produ... | openstax_principles-of-data-science-web | [
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1. 00 table 2. 6 data of retail company sales on normalization scale overall, the retail company ’ s top - selling products generate the most profits for the company, with “ designer & fashion sunglasses ” being the highest in the normalization scale. the company can use this information to focus on promoting and resto... | openstax_principles-of-data-science-web | [
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of the data values. whichever operation is used, data transformation can be a useful tool for data analysts to address issues with data distribution and improve the accuracy of their analyses. dealing with noisy data noisy data refers to data that retains errors, outliers, or irrelevant information that can conceal tru... | openstax_principles-of-data-science-web | [
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known medical conditions and past treatments. binning a researcher is studying the age demographics of a population in a city. instead of looking at individual ages, the researcher decides to bin the data into age groups of 10 years ( e. g., 0 – 10, 10 – 20, 20 – 30, etc. ). this allows for a more comprehensive and eas... | openstax_principles-of-data-science-web | [
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performed | openstax_principles-of-data-science-web | [
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2. 4 • data cleaning and preprocessing 85 through a variety of techniques, including manual checks, automated scripts, and statistical analysis. some common inspections in data validation include checking for duplicates, checking for mislaid values, and verifying data against external sources or references. before coll... | openstax_principles-of-data-science-web | [
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it is always a good idea to involve multiple team members or experts in the validation process to catch any errors or inconsistencies that may have been overlooked by a single person. documentation of the validation process, including the steps taken and any issues identified, is important in future data audits or for ... | openstax_principles-of-data-science-web | [
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statistical aggregation, attribute aggregation, and temporal aggregation. this methodology is commonly operated in marketing, finance, health care, and research to analyze large sets of data. data aggregation is used in various industries to combine and analyze large sets of data. examples include calculating total sal... | openstax_principles-of-data-science-web | [
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out commonly occurring words that do not add meaning or context consider : “ the sun was shining bright in the sky and the birds were chirping. it was a lovely day in the park and people were enjoying the beautiful weather. ” after removing stopwords, the paragraph would be transformed into : “ sun shining bright sky b... | openstax_principles-of-data-science-web | [
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2. 4 • data cleaning and preprocessing 87 preprocessing technique explanation example part - of - speech tagging identifying the grammatical components of each word where each word in a sentence is assigned a specific part - of - speech tag ( e. g., noun, verb, or adjective ) in the sentence “ i went to the market to b... | openstax_principles-of-data-science-web | [
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. table 2. 8 summary of text preprocessing techniques 88 2 • collecting and preparing data access for free at openstax. org preprocessing technique explanation example spell - checking and correction correcting spelling errors to improve accuracy suppose we have a text like : “ the writting was very inpprecise and had ... | openstax_principles-of-data-science-web | [
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, 0, 0, 0, 0 ] ; food : [ 0, 0, 0, 1, 0, 0, 0 ] ; delicious : [ 0, 0, 0, 0, 1, 0, 0 ] ; service : [ 0, 0, 0, 0, 0, 1, 0 ] ; top - notch : [ 0, 0, 0, 0, 0, 0, 1 ]. these one - hot encoded vectors can now be used as input features for machine learning models. removing special characters and punctuation simplifying the te... | openstax_principles-of-data-science-web | [
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for machine learning algorithms. for example, when analyzing customer reviews, it may be beneficial to normalize text data so that variations of the same word ( e. g., “ colour ” and “ color ” ) are treated as the same, providing a more accurate understanding of sentiment toward a product or service. | openstax_principles-of-data-science-web | [
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2. 5 handling large datasets learning outcomes by the end of this section, you should be able to : • 2. 5. 1 recognize the challenges associated with large data, including storage, processing, and analysis limitations. • 2. 5. 2 implement techniques for efficient storage and retrieval of large datasets, including compr... | openstax_principles-of-data-science-web | [
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data that is stored in a structured format — and cloud computing. data compression data compression is a method of reducing file size while retaining essential information ; it can be applied to many types of databases. data compression is classified into two categories, lossy and lossless : • lossy compression reduces... | openstax_principles-of-data-science-web | [
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of storage solution will depend on data volume, variety, velocity, performance requirements, scalability needs, budget constraints, and existing infrastructure. organizations often deploy a combination of storage technologies to address different uses and requirements within their big data environments. some common typ... | openstax_principles-of-data-science-web | [
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each node contains a range of data values and pointers to child nodes, allowing for efficient searching within a specific range of values. hashes indexing involves using a hash function to map data to a specific index in a table. this allows for direct access to the data based on its hashed value, making retrieval fast... | openstax_principles-of-data-science-web | [
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2. 5 • handling large datasets 91 figure 2. 5 b - tree indexing data chunking data chunking, also known as data segmentation or data partitioning, is a technique used to break down large datasets into smaller, more manageable chunks and make them easier to manage, process, analyze, and store. chunking is particularly u... | openstax_principles-of-data-science-web | [
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##ing allows for the transfer and processing of only the required chunks rather than the entire dataset, providing flexibility in managing large datasets. database management systems database management is a crucial aspect of data science projects as it involves organizing, storing, retrieving, and managing large volum... | openstax_principles-of-data-science-web | [
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for the hospital. this showcases the significance of incorporating advanced data management techniques in health care systems. through accurate and efficient management and analysis of patient data, hospitals and health care providers are able to make informed decisions, eventually resulting in a more efficient and eff... | openstax_principles-of-data-science-web | [
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2. 5 • handling large datasets 93 and increasing efficiency. however, this decision would require careful consideration of security measures and potentially training for employees to adapt to the new system. 2. implementing a data archiving strategy : instead of immediately migrating to the cloud or investing in new te... | openstax_principles-of-data-science-web | [
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9 problem what is the descending order of storage capacity in example 2. 8, starting from highest to lowest, while maintaining the same cost and the same level of security? solution the available storage options in example 2. 8, ranked from highest to lowest capacity at the same cost and security level, are : 1. cloud ... | openstax_principles-of-data-science-web | [
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consolidating data and processes : by streamlining processes and eliminating redundancies, the company can potentially reduce the need for excessive storage capacity. however, this would also depend on the company's current setup and level of optimization. 8. regular audits of data usage and storage : this option may n... | openstax_principles-of-data-science-web | [
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2. 5 • handling large datasets 95 key terms application programming interface ( api ) set of protocols, tools, and definitions for building software applications that allow different software systems to communicate and interact with each other and enable developers to access data and services from other applications, o... | openstax_principles-of-data-science-web | [
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unified view data processing the process of collecting, organizing, and manipulating data to produce meaningful information involving the transformation of raw data into a more useful and understandable format data standardization the process of transforming data into a common scale or range to help remove any unit dif... | openstax_principles-of-data-science-web | [
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data can be accounted for and included in the analysis through statistical techniques missing completely at random ( mcar ) data type of missing data that is not related to any other variables, with no underlying cause for its absence missing not at random ( mnar ) data type of data missing in a way that depends on uno... | openstax_principles-of-data-science-web | [
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structured or unstructured format relational databases databases that organize data in tables and use structured query language ( sql ) for data retrieval and management ; often used with financial data sampling the process of selecting a subset of a larger population to represent and gather information about that popu... | openstax_principles-of-data-science-web | [
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called tokens, which can be words, phrases, symbols, or individual characters transactional data a type of information that records financial transactions, including purchases, sales, and payments. it includes details such as the date, time, and parties involved in the transaction. web scraping a method of collecting d... | openstax_principles-of-data-science-web | [
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project a required data and example 4. collaborative data cleaning. students will work together to clean the collected data, identifying and addressing any missing or duplicate information. they will also ensure that the data is standardized, allowing for accurate analysis and comparison. 5. data storage determination.... | openstax_principles-of-data-science-web | [
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to maintain the same surrounding conditions. this includes using the same tools, such as a thermometer, and staying in the same location throughout the time period. any uncontrolled changes or errors should be noted and recorded to ensure accuracy in the data. repeat this process for seven days to gather a sufficient a... | openstax_principles-of-data-science-web | [
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school is conducting a survey to understand the opinions of the school ’ s students toward the food options available in the cafeteria. they have divided the student population into four groups based on grade level ( 9th, 10th, 11th, 12th ). a random sample of 60 students is selected from each group and asked to rate t... | openstax_principles-of-data-science-web | [
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6. 1 table 2. 11 high school survey what sampling technique was used to collect this data? 4. a research study is being conducted to understand the opinions of people on the new city budget. the population consists of residents from different neighborhoods in the city with varying income levels. the following table pro... | openstax_principles-of-data-science-web | [
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##5 10 / 19 / 2021 tuesday 4849 10 / 20 / 2021 wednesday 3940 10 / 21 / 2021 thursday 4821 10 / 22 / 2021 friday 4357 10 / 23 / 2021 saturday 0 10 / 24 / 2021 sunday 0 10 / 25 / 2021 monday 8572 10 / 26 / 2021 tuesday 4463 10 / 27 / 2021 wednesday 5323 10 / 28 / 2021 thursday 5012 10 / 29 / 2021 friday 4710 10 / 30 / 2... | openstax_principles-of-data-science-web | [
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there was noise ( error ) present in the data. he wanted to investigate the main source of the error in the data so that he could take corrective actions and thus minimize the impact of the error ( s ) on the experiment results. what is the most likely source of error that paul might encounter in this experiment : huma... | openstax_principles-of-data-science-web | [
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figure 2. 6 data collection based on text processing 12. how can a data analyst for a large airline company efficiently collect and organize a significant amount of data pertaining to flight routes, passenger demographics, and operational metrics in a manner that ensures reliable and up - to - date records, complies wi... | openstax_principles-of-data-science-web | [
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/ pmc / articles / pmc5325924 / 2. lusinchi, d. ( 2012 ). president landon and the 1936 literary digest poll : were automobile and telephone owners to blame? social science history, 36 ( 1 ), 23 – 54. https : / / doi. org / 10. 1017 / s014555320001035x 2 • references 103 104 2 • references access for free at openstax. ... | openstax_principles-of-data-science-web | [
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3. 5 discrete and continuous probability distributions introduction statistical analysis is the science of collecting, organizing, and interpreting data to make decisions. statistical analysis lies at the core of data science, with applications ranging from consumer analysis ( e. g., credit scores, retirement planning,... | openstax_principles-of-data-science-web | [
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human resources administrator might also want to derive estimates and predictions about the average salary in the company five years into the future, taking into account inflation effects, or they might want to create a model to predict an individual employee ’ s salary based on the employee's years of experience in th... | openstax_principles-of-data-science-web | [
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3. 1 measures of center learning outcomes by the end of this section, you should be able to : • 3. 1. 1 define and calculate mean, trimmed mean, median, and mode for a dataset. • 3. 1. 2 determine the effect of outliers on the mean and the median. • 3. 1. 3 use python to calculate measures of center for a dataset. meas... | openstax_principles-of-data-science-web | [
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you will encounter two types of datasets : sample data and population data. population data represents all the outcomes or measurements that are of interest. sample data represents outcomes or measurements collected from a subset, or part, of the population of interest. of course in many applications, collecting data f... | openstax_principles-of-data-science-web | [
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this sum by the number of data values, which is 10. the result is : | openstax_principles-of-data-science-web | [
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3. 1 • measures of center 107 example 3. 2 problem a college professor records the ages of 25 students in a data science class as shown : student age number of students ( frequency ) 19 3 20 4 21 8 22 6 23 2 27 1 31 1 total 25 calculate the mean age for this sample of students. solution substitute the values from the t... | openstax_principles-of-data-science-web | [
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values. then calculate the mean for the resulting dataset. any outliers would tend to be deleted as part of the trimmed mean calculation, and thus the trimmed mean would then be a more representative measure of the center of the data for datasets containing outliers. example 3. 3 problem a real estate agent collects da... | openstax_principles-of-data-science-web | [
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. the sum of these 9 data values is 4, 880, 200. divide this sum by the number of data values ( 9 ). the result is : notice how the mean calculated in part ( a ) is significantly larger as compared to the trimmed mean calculated in part ( b ). the reason is the presence of several large outlier home prices. once these ... | openstax_principles-of-data-science-web | [
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3. 1 • measures of center 109 to determine the median of a dataset, first order the data from smallest to largest, and then find the middle value in the ordered dataset. for example, to find the median value of 50 exam scores, find the score that splits the data into two equal parts. the exam scores for 25 students wil... | openstax_principles-of-data-science-web | [
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that the median is located in the 13th data position. as another example, let ’ s say a dataset has 100 data values. since is even, to identify the position of the median, calculate, which is, which is 50, and also calculate, which is, which is 51. this indicates that the median is calculated as the mean of the 50th an... | openstax_principles-of-data-science-web | [
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influence of outliers on measures of center as mentioned earlier, when outliers are present in a dataset, the mean may not represent the center of the dataset, and the median will provide a better measure of center. the reason is that the median focuses on the middle value of the ordered dataset. thus, any outliers at ... | openstax_principles-of-data-science-web | [
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how the dataframe. describe ( ) method is used to | openstax_principles-of-data-science-web | [
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3. 1 • measures of center 111 summarize data. recall that the method describe ( ) is defined for a dataframe type object so should be called upon a dataframe type variable ( e. g. given a dataframe d, use d. describe ( ) ). figure 3. 2 shows the output of dataframe. describe ( ) on the “ movie profit ” dataset we used ... | openstax_principles-of-data-science-web | [
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... |
3. 2 measures of variation learning outcomes by the end of this section, you should be able to : • 3. 2. 1 define and calculate the range, the variance, and the standard deviation for a dataset. • 3. 2. 2 use python to calculate measures of variation for a dataset. providing some measure of the spread, or variation, in... | openstax_principles-of-data-science-web | [
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for a dataset that is calculated by subtracting the minimum from the maximum of the dataset : range is a straightforward calculation but makes use of only two of the data values in a dataset. the range can also be affected by outliers. example 3. 6 problem calculate the range for dataset a and dataset b : dataset a : e... | openstax_principles-of-data-science-web | [
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