#data management and visualization

[priyadarshinipanda]

Assignment 4: Creating Graphs for Your Data

PREVIOUS CONTENT

Assignment 1.

Assignment 2.

Assignment 3.

Link to download the dataset here.

Link to download the codebook here.

WHAT TO SUBMIT:

Once you have written a successful program that creates univariate and bivariate graphs, create a blog entry where you post your program and the graphs that you have created. Write a few sentences describing what your graphs reveal in terms of your individual variables and the relationship between them.

Download the graph program here.

In the last assignment (3), I had made the data management that I thought necessary. Now is time to create the graphics that represent this data.

I did that in two ways, in the first one I made the Quantitative->Quantitave method generating a scatterplot and the second one was a Qualitative->Quantitative method that creates a bar graph. Before I present the result of the relationship between the two variables in the graph, let’s see the histogram and the metrics extracted in each attribute separated.

Univariate graphs

Incidence of breast cancer

The first attribute was the incidence of breast cancer in 100,000 female residents during the 2002 year. As it is a quantitative attribute, was generated the histogram of the data. #Univariate histogram of the incidence of breast cancer in 100,000 female residents during the 2002 year.seaborn.distplot(sub1["breastCancer100th"].dropna(), kde=False);plt.xlabel('Incidence of breast cancer in 100,000 female residents during the 2002 year.')plt.ylabel('Number of counties.')plt.title('Histogram of the Incidence of Breast Cancer.')plt.show()

We can observe in the histogram that most of the countries have an incidence of cancer around 30 and 40 cases per 100,000 females. The extracted metrics of this attribute were: desc1 = sub1["breastCancer100th"].describe()print(desc1) count    129.000000mean      37.987597std       24.323873min        3.90000025%       20.60000050%       29.70000075%       50.300000max      101.100000Name: breastCancer100th, dtype: float64

With this, we can see that 75% of the countries have an incidence of breast cancer under 50.30 per 100,000 females.

Sugar consumption

The second attribute is the sugar consumption. For this attribute, I have made two graphs: one that shows the histogram of the original data and the other one that shows the bar graph of this attribute relocated into categories.

Histogram

#Univariate histogram of the Mean of the sugar consumption (grams per person and day) between 1961 and 2002.seaborn.distplot(sub1["meanSugarPerson"].dropna(), kde=False);plt.xlabel('Mean of the sugar consumption (grams per person and day) between 1961 and 2002.')plt.ylabel('Number of counties.')plt.title('Histogram of the Sugar Consumption.')plt.show()

This histogram is almost evenly distributed, we can see that the countries that have the most sugar consumption are in the 20 and the 110 grams per person. desc2 = sub1["meanSugarPerson"].describe()print(desc2) count    129.000000mean      76.238394std       42.488004min        6.13238125%       42.20642950%       79.71452475%      110.307619max      163.861429Name: meanSugarPerson, dtype: float64

The mean of sugar consumption is 76.24 and we can see that 75% of the countries have a consumption of sugar under 110.31 grams per day.

Bar graph

#Univariate bar graph of the Mean of the sugar consumption (grams per person and day) between 1961 and 2002.seaborn.countplot(x="sugar_consumption", data=sub1)plt.xlabel('Mean of the sugar consumption (grams per person and day) between 1961 and 2002.')plt.ylabel('Number of counties.')plt.title('Histogram of the Sugar Consumption.')plt.show()

Where the consumption is:

(0) Desirable between 0 and 30 g.

(1) Raised between 30 and 60 g.

(2) Borderline high between 60 and 90 g.

(3) High between 90 and 120 g.

(4) Very high under 120g.

The bar graph behaved very similarly to the histogram.

Bivariate graphs

The two bivariate graphics are presented below: #Bivariate Scatterplot Q->Q - Incidence of breast cancer versus sugar consumptionscat1 = seaborn.regplot(x="meanSugarPerson", y="breastCancer100th", fit_reg=True, data=sub1)plt.xlabel('Mean of the sugar consumption between 1961 and 2002.')plt.ylabel('Incidence of breast cancer in 100,000 female residents during the 2002 year.')plt.title('Scatterplot for the association between the incidence of breast cancer and the sugar consumption.')plt.show() #Bivariate bar graph C->Q - Incidence of breast cancer versus sugar consumptionseaborn.factorplot(x='sugar_consumption', y='breastCancer100th', data=sub1, kind="bar", ci=None)plt.xlabel('Mean of the sugar consumption between 1961 and 2002.')plt.ylabel('Incidence of breast cancer in 100,000 female residents during the 2002 year.')plt.title('Bar graph for the Association between the incidence of breast cancer and the sugar consumption.')plt.show()

In both graphics, we can see that there is a relation with the incidence of breast cancer and the consumption of sugar. While sugar consumption is increased the incidence of new breast cancer cases is increased too.

Review criteria

Your assessment will be based on the evidence you provide that you have completed all the steps. When relevant, gradients in the scoring will be available to reward clarity (for example, you will get one point for submitting graphs that do not accurately represent your data, but two points if the data is accurately represented). In all cases, consider that the peer assessing your work is likely not an expert in the field you are analyzing. You will be assessed equally in your description of your frequency distributions.

Specific rubric items, and their point values, are as follows:

Was a univariate graph created for each of the selected variables? (2 points)

Was a bivariate graph created for the selected variables? (2 points)

Did the summary describe what the graphs revealed in terms of the individual variables and the relationship between them? (2 points)

[mayaelsawy]

My Research Project Has Started

I have chosen to work with data from the World Bank Enterprise Surveys (WBES), which collect detailed information from firms across various countries about their business environment, performance, and practices. After reviewing the available datasets, I am particularly interested in exploring the relationship between green logistics practices and operating costs.

The motivation for this topic stems from growing interest in sustainable business practices, especially within supply chains, and how environmental initiatives such as green transportation, reduced packaging, or energy-efficient warehousing can impact financial performance. This issue is directly aligned with SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action).

Main Research Question:

Is the adoption of green logistics practices associated with reduced operating costs?

Secondary Research Questions:

Do firms with environmental policies targeting logistics report lower transportation or utility costs?

Does the size or sector of a business moderate the relationship between green logistics adoption and cost outcomes?

Are firms that implement green practices more likely to report innovation or investment in sustainability?

Variables and Codes:

1. Green Logistics Practices

a. Environmental Management System (EMS)

Variable Definition: Whether the business has a certified environmental management system (e.g., ISO 14001).

Potential Codes:

1: Yes

0: No

b. Packaging Reduction

Variable Definition: Whether the firm reports taking action to reduce packaging waste for environmental reasons.

Potential Codes:

1: Yes

0: No

c. Energy Efficiency in Logistics

Variable Definition: Measures adoption of energy-saving strategies in logistics or warehouse operations.

Potential Codes:

1: Yes

0: No

2. Operating Costs

a. Overall Operating Costs

Variable Definition: Total operational costs as a percentage of revenue.

Potential Codes: Continuous variable (0–100)

b. Transportation Costs

Variable Definition: Proportion of costs spent on logistics and transportation.

Potential Codes: Continuous variable (0–100)

c. Utility Costs

Variable Definition: Proportion of business costs related to electricity, water, and fuel.

Potential Codes: Continuous variable (0–100)

3. Control Variables (Firm Characteristics)

a. Firm Size

Variable Definition: Number of employees.

Potential Codes:

1: Small (<20 employees)

2: Medium (20–99 employees)

3: Large (100+ employees)

b. Industry Sector

Variable Definition: Sector of business operations.

Potential Codes:

1: Manufacturing

2: Retail

3: Services

4: Other

c. Country or Region

Variable Definition: Geographic location of the firm.

Potential Codes: Country names or regions (e.g., Latin America, East Asia)

Literature Review Summary:

There is growing evidence linking sustainability practices with operational efficiency. A few key studies include:

Rao & Holt (2005) : Found that environmentally conscious supply chains can enhance performance by reducing waste and cutting costs.

Chiarini & Vagnoni (2017) : Demonstrated that firms with ISO 14001 certification observed long-term savings in energy and logistics costs.

Ahi & Searcy (2015) : Emphasized the dual benefit of environmental and financial performance from green logistics adoption.

Zhu, Geng & Sarkis (2013) : Found that firms in developing economies that implemented green supply chain practices reported improved cost control.

These studies support the idea that green practices are not just environmentally beneficial, but can also enhance cost efficiency and resilience in the supply chain.

Hypothesis:

Firms that adopt green logistics practices will report lower operating costs, particularly in transportation and utility expenses, compared to firms that do not implement such practices.

This relationship may vary by firm size or industry sector but is expected to hold generally across contexts.

[youngtigercheesecake]

Week 4

Creating graphs for your data code

import pandas import numpy import pandas as pd import seaborn import matplotlib.pyplot as plt

data = pd.read_csv('gapminder_pds.csv', low_memory=False)

bindata = data.copy()

convert variables to numeric format using convert_objects function

data['internetuserate'] = pd.to_numeric(data['internetuserate'], errors='coerce') bindata['internetuserate'] = pd.cut(data.internetuserate, 10)

data['incomeperperson'] = pd.to_numeric(data['incomeperperson'], errors='coerce') bindata['incomeperperson'] = pd.cut(data.incomeperperson, 10)

data['employrate'] = pd.to_numeric(data['employrate'], errors='coerce') bindata['employrate'] = pd.cut(data.employrate, 10)

data['femaleemployrate'] = pd.to_numeric(data['femaleemployrate'], errors='coerce') bindata['femaleemployrate'] = pd.cut(data.femaleemployrate, 10)

data['polityscore'] = pd.to_numeric(data['polityscore'], errors='coerce') bindata['polityscore'] = data['polityscore'] sub2 = bindata.copy()

Scatterplot for the Association Between Employment rate and lifeexpectancy

scat1 = seaborn.regplot(x="internetuserate", y="incomeperperson", fit_reg=False, data=data) plt.xlabel('Internet use rate') plt.ylabel('Income per person') plt.title('Scatterplot for the Association Between Internet use rate and Income per person')

This scatterplot show the relationship and seems to be exponential.

Univariate histogram for quantitative variable:

seaborn.distplot(data["incomeperperson"].dropna(), kde=False); plt.xlabel('Income per person')

The graph is highly right skewed. Incomes are small for most of the world and the wealthy tail is quite long.

Univariate histogram for quantitative variable:

seaborn.distplot(data["employrate"].dropna(), kde=False); plt.xlabel('Employ rate')

Summary

It looks like there are associations between Internet use rate and income per person going up with internet use rate and going up an an accelerating rate.

[staybabblingbaby]

HAPPY BIRTHDAY OMG!!! HAVE THE LOVELIEST DAY EVER ‼️‼️‼️ and yummy cake!! and lots of presents!!

Thank you bby love 🫶🫶🫶🫶 I've been making charts all day lol 😅😅😅 decently fun, I suppose ❤️

Big forehead kisses 4 u ❤️ (ɔˆ ³(ˆ⌣ˆc)

[khushidubeyblog]

[chriscardenas]

Studying Mars Craters: What They Can Tell Us

I always been fascinated by the studies of space and about the stories that planets can tell us. That's why I choose to work with the data of Mars Craters Study. These craters aren't just random holes in the ground; they're like fingerprints that can reveal secrets about geological processes or atmospheric conditions over millions of years. These data can help us to uncover patterns about how impact processes work.

Research Questions

For this data analysis project, I've chosen to explore the relation between Crater Depth and Diameter Relationship, so the first research question will be:

Is there an association between crater depth and crater diameter?

Intuitively, you might think bigger craters would be deeper, but this is something that I would to confirm with the data, because reality can be way more complex due to factors like the angle of impact, composition of both the impactor and the Martian surface, erosion over time, etc.

A second topic that I would like to explore is a relation between Ejecta Complexity and Crater Depth. This second topic can tell us some information about how impacts of celestial bodies can create complex structures. I'm curious whether deeper craters, which presumably involved more energetic impacts, tend to produce more complex ejecta patterns. So, the second question will be:

Is there an association between the number of ejecta layers and crater depth?

Previous Researches Review

Using the Claude AI (Anthropic, 2023), I made a literature review about my first research question.

The relationship between crater depth and diameter has been extensively studied on Mars, researchers had found a power-law relationship that varies depending on the crater characteristics and preservation state. Tornabene et al. (2017) conducted a study using Mars Orbiter Laser Altimeter (MOLA) data on 224 pitted material craters ranging from ~1 to 150 km in diameter, finding that impact craters with pitted floor deposits are among the deepest on Mars.

Robbins and Hynek (2012) analyzed a global database of 384,343 Martian craters and found that simple craters in their database have a depth/diameter relationship of 8.9 ± 1.9%. Antoher work by Cintala and Head (1976) found depth/diameter ratios for 87 craters ranging from 12 to 100 km in diameter and 0.4 to 3.3 km in depth.

Hypothesis

Based on this small research, a hypothesis could be: There will be a significant power-law relationship between crater depth (DEPTH_RIMFLOOR_TOPOG) and crater diameter (DIAM_CIRCLE_IMAGE). Form of this relationship could be Depth = a × Diameter^b.

Bibliography

Tornabene, L. L., Osinski, G. R., McEwen, A. S., Boyce, J. M., Bray, V. J., Caudill, C. M., ... & Wray, J. J. (2017). A depth versus diameter scaling relationship for the best-preserved melt-bearing complex craters on Mars. Icarus, 299, 68-83. https://www.sciencedirect.com/science/article/abs/pii/S0019103516308363

Robbins, S. J., & Hynek, B. M. (2012). A new global database of Mars impact craters ≥1 km: 2. Global crater properties and regional variations of the simple‐to‐complex transition diameter. Journal of Geophysical Research: Planets, 117(E6). https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011JE003967

Cintala, M. J., Head, J. W., & Mutch, T. A. (1976). Martian crater depth/diameter relationships: Comparison with the Moon and Mercury. Proceedings, 7th Lunar and Planetary Science Conference, 3575-3587. https://ui.adsabs.harvard.edu/abs/1976LPSC….7.3575C/abstract

Anthropic. (2023). Claude (Sonnet 4 version) [Large language model]. https://www.anthropic.com/

[ppnsolutions]

How Power BI Managed Services Help the Healthcare Sector

The healthcare industry has undergone a significant digital transformation in recent years. Amid this evolution, data has become one of the most critical assets for healthcare providers. From patient records and diagnostic information to hospital management and operational data, every touchpoint generates vast amounts of data that must be properly managed, visualized, and analyzed. This is where Power BI Managed Services come into play—offering a game-changing way to streamline data management and enhance decision-making in the healthcare sector.

1. Streamlining Data Management

Healthcare organizations typically deal with numerous disparate systems—Electronic Health Records (EHR), billing systems, lab results, radiology reports, and more. Power BI Managed Services integrate all this data into a centralized dashboard, providing healthcare administrators with a unified view of their operations. Through data visualization services, these complex data sets are transformed into intuitive dashboards, helping stakeholders make informed decisions quickly.

2. Enhancing Patient Care with Real-Time Insights

Power BI Managed Services provide real-time analytics that can improve patient care significantly. For example, hospitals can monitor emergency room wait times, track patient flow, and even predict readmissions. These insights allow staff to respond quickly to emerging issues and adjust resources accordingly. With data visualization services, clinical data becomes more actionable, enabling physicians to detect patterns and intervene earlier.

3. Compliance and Regulatory Reporting

The healthcare sector is highly regulated. Organizations must meet compliance standards like HIPAA in the US or GDPR in Europe. Power BI Managed Services help automate and standardize compliance reporting by collecting data across departments and converting it into easy-to-understand compliance dashboards. These services minimize human error and ensure timely reporting, reducing regulatory risks.

4. Optimizing Operational Efficiency

Operational inefficiencies can lead to increased costs and reduced patient satisfaction. With Power BI Managed Services, administrators can track performance metrics like staff utilization, equipment usage, and departmental costs. The insights gained through data visualization services enable hospitals to identify inefficiencies and take corrective actions, thus optimizing overall performance.

5. Predictive Analytics for Proactive Healthcare

One of the biggest benefits of Power BI Managed Services in healthcare is the ability to leverage predictive analytics. By analyzing historical and real-time data, healthcare providers can predict disease outbreaks, patient readmissions, or even the likelihood of treatment success. These insights empower medical teams to take proactive steps, ultimately saving lives and reducing costs.

6. Financial Planning and Budgeting

With tight budgets and increasing costs, financial planning is a critical function for healthcare organizations. Power BI Managed Services allow financial managers to monitor revenues, expenditures, insurance claims, and payment cycles. Combined with powerful data visualization services, these tools make financial trends more transparent and easier to interpret.

7. Improved Collaboration Across Departments

Hospitals often suffer from departmental silos. Power BI Managed Services break down these barriers by integrating data from across the organization into a single platform. The result is improved collaboration between clinical, operational, and administrative teams, all working from the same data source and visual dashboards.

Conclusion

The healthcare sector’s complexity and the critical nature of its services make data management more important than ever. Power BI Managed Services not only simplify data reporting and regulatory compliance but also elevate patient care and operational efficiency. When paired with robust data visualization services, they transform raw data into actionable insights—empowering healthcare professionals to make faster, smarter decisions. For any healthcare provider looking to embrace the future of data-driven care, adopting Power BI Managed Services is not just an option—it’s a necessity.

[technologyequality]

Make Smarter Moves, Not Just Faster Ones: The AI Decision Matrix You Didn’t Know You Needed

Make Smarter Moves, Not Just Faster Ones The AI Decision Matrix You Didn’t Know You Needed Ever felt like you were making business decisions with one eye closed, spinning the Wheel of Fortune, and hoping for the best? Yeah, me too. Let’s be honest: most entrepreneurs spend more time guessing than assessing. But here’s the plot twist, guesswork doesn’t scale. That’s where the AI-powered…

[priyadarshinipanda]

Assignment 3: Making Data Management Decisions

WHAT TO SUBMIT:

Once you have written a successful program that manages your data, create a blog entry where you post your program and the results/output that displays at least 3 of your data managed variables as frequency distributions. Write a few sentences describing these frequency distributions in terms of the values the variables take, how often they take them, the presence of missing data, etc.

Download the program here and the dataset here;

In the last assignment, I had already made the data management that I thought necessary, but I made it in the excel with formulas.

Now, I remade the data management directly in python, and the program output can be seen down in the post.

The results were still the same. The sample used was the incidence of new breast cancer cases in 129 different countries. After running the program, it was possible to observe that the consumption of sugar is considered desirable only in 20.9% of the countries of the dataset. Taking into account that this metric is based on the average of the desirable sugar ingest in grams per day of the woman (25g) and the man (36g) [1] and [2].

To the food consumption data, I made the average of all countries consumption and compared each country consumption to this mean. 55% of the countries stay under the average.

At last, to range the total cholesterol in the blood of the countries I used as a base the metric of Mayo Clinic [3]. In the dataset, none of the values exceeded to a high level of total cholesterol and almost 73% of the countries presented to be in the desirable level.

Reference

[1] Life by Daily Burn Are You Exceeding Your Daily Sugar Intake in Just One Meal INFOGRAPHIC. Visited 05 Jul 2016. URL: http://dailyburn.com/life/health/daily-sugar-intake-infographic/.

[2] MD-Health How Many Grams of Sugar Per Day. Visited 06/07/2016. URL: http://www.md-health.com/How-Many-Grams-Of-Sugar-Per-Day.html.

[3] Cholesterol Test - Procedure details. Visited 05 Jul 2016. URL: http://www.mayoclinic.org/tests-procedures/cholesterol-test/details/results/rsc-20169555.

Output of the program: Importing the packages and the data set (csv)

import pandasimport numpyimport statistics # Import the data set to memorydata = pandas.read_csv("separatedData.csv", low_memory = False) # Change data type among variables to numericdata["breastCancer100th"] = data["breastCancer100th"].convert_objects(convert_numeric=True)data["meanSugarPerson"]   = data["meanSugarPerson"].convert_objects(convert_numeric=True)data["meanFoodPerson"]    = data["meanFoodPerson"].convert_objects(convert_numeric=True)data["meanCholesterol"]   = data["meanCholesterol"].convert_objects(convert_numeric=True) # Create a subData with only the variables breastCancer100th, meanSugarPerson, meanFoodPerson, meanCholesterolsub1=data[['breastCancer100th','meanSugarPerson', 'meanFoodPerson', 'meanCholesterol']]

Making the new variable sugar_consumption

# Create the conditions to a new variable named sugar_consumption that will categorize the meanSugarPerson answersdef sugar_consumption (row):   if 0 < row['meanSugarPerson'] <= 30 : return 0    # Desirable between 0 and 30 g.   if 30 < row['meanSugarPerson'] <= 60 : return 1   # Raised between 30 and 60 g.   if 60 < row['meanSugarPerson'] <= 90 : return 2   # Borderline high between 60 and 90 g.   if 90 < row['meanSugarPerson'] <= 120 : return 3  # High between 90 and 120 g.   if row['meanSugarPerson'] > 120 : return 4        # Very high under 120g. # Add the new variable sugar_consumption to subDatasub1['sugar_consumption'] = sub1.apply (lambda row: sugar_consumption (row),axis=1) # Count of sugar_consumptionprint("Count of sugar_consumption - Range of sugar consumption based on the mean of the quantity (grams per person and day) of sugar and sweeters between 1961 and 2002")c1 = sub1["sugar_consumption"].value_counts(sort=False)print(c1) # Percentage of sugar_consumptionprint("Percentage of sugar_consumption - Range of sugar consumption based on the mean of the quantity (grams per person and day) of sugar and sweeters between 1961 and 2002")p1 = sub1["sugar_consumption"].value_counts(sort=False,normalize=True)print(p1)

1.1.1.2                                                              Count and Percentage of the new variable sugar_consumption

Count of sugar_consumption - Range of sugar consumption based on the mean of the quantity (grams per person and day) of sugar and sweeters between 1961 and 20020    271    192    313    314    21Name: sugar_consumption, dtype: int64 Percentage of sugar_consumption - Range of sugar consumption based on the mean of the quantity (grams per person and day) of sugar and sweeters between 1961 and 20020    0.2093021    0.1472872    0.2403103    0.2403104    0.162791Name: sugar_consumption, dtype: float64

Making the new variable food_consumption

#Make the average of meanFoodPerson values.food_mean = statistics.mean(data["meanFoodPerson"]) # Create the conditions to a new variable named food_consumption that will categorize the meanFoodPerson answersdef food_consumption (row):   if row['meanFoodPerson'] <= food_mean : return 0  # Food consumption below the world average.   if row['meanFoodPerson'] > food_mean : return 1   # Food consumption under the world average. # Add the new variable food_consumption to subDatasub1['food_consumption'] = sub1.apply (lambda row: food_consumption (row),axis=1) # Count of food_consumptionprint("Count of food_consumption - Mean of the food consumption of countries based on the mean  of the total supply of food (kilocalories / person & day) between 1961 and 2002")c2 = sub1["food_consumption"].value_counts(sort=False)print(c2) # Percentage of food_consumptionprint("Percentage of food_consumption - Mean of the food consumption of countries based on the mean of the total supply of food (kilocalories / person & day) between 1961 and 2002")p2 = sub1["food_consumption"].value_counts(sort=False, normalize=True)print(p2)

Count and Percentage of the new variable food_consumption

Count of food_consumption - Mean of the food consumption of countries based on the mean  of the total supply of food (kilocalories / person & day) between 1961 and 20020    711    58Name: food_consumption, dtype: int64 Percentage of food_consumption - Mean of the food consumption of countries based on the mean of the total supply of food (kilocalories / person & day) between 1961 and 20020    0.5503881    0.449612Name: food_consumption, dtype: float64

Making the new variable cholesterol_blood

# Create the conditions to a new variable named cholesterol_blood that will categorize the meanCholesterol answersdef cholesterol_blood (row):   if row['meanCholesterol'] <= 5.2 : return 0         # Desirable below 5.2 mmol/L   if 5.2 < row['meanCholesterol'] <= 6.2 : return 1   # Borerline high between 5.2 and 6.2 mmol/L   if row['meanCholesterol'] > 6.2 : return 2          # High above 6.2 mmol/L # Add the new variable cholesterol_blood to subDatasub1['cholesterol_blood'] = sub1.apply (lambda row: cholesterol_blood (row),axis=1) # Count of cholesterol_bloodprint("Count of cholesterol_blood - Range of the average of the mean TC (Total Cholesterol) of the female population counted in mmol per L between 1980 and 2002")c3 = sub1["cholesterol_blood"].value_counts(sort=False)print(c3) # Percentage of cholesterol_bloodprint("Percentage of cholesterol_blood - Range of the average of the mean TC (Total Cholesterol) of the female population counted in mmol per L between 1980 and 2002")p3 = sub1["cholesterol_blood"].value_counts(sort=False, normalize=True)print(p3)

Count and Percentage of the new variable cholesterol_blood

Count of cholesterol_blood - Range of the average of the mean TC (Total Cholesterol) of the female population counted in mmol per L between 1980 and 20020    941    35Name: cholesterol_blood, dtype: int64 Percentage of cholesterol_blood - Range of the average of the mean TC (Total Cholesterol) of the female population counted in mmol per L between 1980 and 20020    0.7286821    0.271318Name: cholesterol_blood, dtype: float64

Review criteria

Your assessment will be based on the evidence you provide that you have completed all the steps. When relevant, gradients in the scoring will be available to reward clarity (for example, you will get one point for submitting output that is not understandable, but two points if it is understandable). In all cases, consider that the peer assessing your work is likely not an expert in the field you are analyzing. You will be assessed equally in your description of your frequency distributions.

Specific rubric items, and their point values, are as follows:

Was the program output interpretable (i.e., organized and labelled)? (1 point)

Does the program output display three data managed variables as frequency tables? (1 point)

Did the summary describe the frequency distributions in terms of the values the variables take, how often they take them, the presence of missing data, etc.? (2 points)

[igmpi]

Explore IGMPI’s Big Data Analytics program, designed for professionals seeking expertise in data-driven decision-making. Learn advanced analytics techniques, data mining, machine learning, and business intelligence tools to excel in the fast-evolving world of big data.

[infoveave]

Retailers face inventory mismanagement, demand fluctuations, and poor customer insights, impacting sales and profitability. Infoveave Pvt. Ltd. delivers retail analytics solutions, utilizing AI to track consumer behavior, forecast demand, and optimize pricing. With real-time data, retailers enhance supply chain efficiency, boost customer engagement, and maximize revenue through data-driven strategies.

[mitsdedistance]

SQL & Power BI Certification | MITSDE

Enhance your career with MITSDE’s Online Power BI Certification Course! This comprehensive program equips you with essential skills in data visualization, analytics, and business intelligence, helping you make data-driven decisions. Learn to create interactive dashboards, generate insightful reports, and analyze business trends effectively. Designed for professionals and beginners alike, this course offers hands-on training and expert guidance to boost your expertise. Stay ahead in the competitive job market—enroll today and transform your data analysis skills with Power BI!

[specbee-c-s]

Exploring the Drupal Views module

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[newfangled-vady]

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[munaeem]

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