Basic statistical data description Data visualization Proximity measures Boxplot
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A: Big data is distinguished by two primary characteristics: its speed and its diversity? 1. Speed: Big…
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Data Mining
1.What is the method name to gain insight into the data that involves central tendency, dispersion, and graphical displays of data?
a.Basic statistical data description
b.Data visualization
c.Proximity measures
d.Boxplot
Step by step
Solved in 2 steps
- Explain a problem and asking the right questions exploring the data Data Sciensce Steps: 1- Asking the right questions exploring the data 2- Modeling the data using various algorithms 3- Finally communicating and visualizing results.which statement best describes data visualization? a. data visualization was created to help accessibility issues b. data visualization is one way we share the story c. data visualization helps users understand a UML diagram d. data visualization is the way we input data into tablesData Analysis is a process of? a. All of the mentioned points b. Transforming data c. Cleaning data d. Inspecting data
- B. Please complete the following question. Make sure you are responding to all parts of the question and show all your work. As a tip let the marks assigned be a guide as to how much information is required to respond. The attached datafile awards_data.csv contains two variables: the type of program [prog] in which the student was enrolled (i.e., general, academic, or vocational) and the score on their final exam in math [math]. Use α = .05 for all analyses. 1. Use the information about the variables to develop a research question for a one-way ANOVA and conduct the analysis. Are the assumptions met? Please include the appropriate statistics or information to support your answer. What do you conclude? Present your answer in APA.Which of the following is the process of analyzing data before using it with a model? * a) Extra data addition b) Exploratory data analysis (EDA) c) Building a subset d) Missing data imputationLoad & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column.Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with…
- Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column.Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with…Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column. Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add them to your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student number for the seed. Build Classification ModelsSupport vector machine classifier with…Load & check the data:1. Load the data into a pandas dataframe named data_firstname where first name is you name.2. Carryout some initial investigations:a. Check the names and types of columns.b. Check the missing values.c. Check the statistics of the numeric fields (mean, min, max, median, count..etc.)d. In you written response write a paragraph explaining your findings about each column.Pre-process and visualize the data3. Replace the ‘?’ mark in the ‘bare’ column by np.nan and change the type to ‘float’4. Fill any missing data with the median of the column.5. Drop the ID column6. Using Pandas, Matplotlib, seaborn (you can use any or a mix) generate 3-5 plots and add themto your written response explaining what are the key insights and findings from the plots.7. Separate the features from the class.8. Split your data into train 80% train and 20% test, use the last two digits of your student numberfor the seed.Build Classification ModelsSupport vector machine classifier with…
- Which characteristic of data refers to capturing data consistently? Multiple Choice Truthfulness Strictness Elusiveness Abstractness VerifiabilityData transformation may be as basic as re-presenting existing data in a different manner, or as sophisticated as combining data from many sources. Do you have any thoughts?Is data visualization just beneficial when dealing with large amounts of information? Extend and explain.