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Data Visualization & Measures of central tendency
Data Visualization & Measures of Central Tendency - CFA level 1 Eaxm note by Professor James Forjan, PhD, CFA
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PART 1: DATA VISULIZATIONOrganizing and Visualizing Data: The Key to Making
Informed Decisions
Data is everywhere. It's in our phones, our computers, and even our
watches. But what good is data if we can't make sense of it? That's where
organizing and visualizing data comes in. By arranging data in a clear and
concise way, and then creating visual representations of that data, we can
gain insights and make informed decisions.
One way to organize data is by using tables. Tables allow us to display
data in a structured way, making it easy to read and understand. For
example, in the video "Organizing and Visualizing Data," the instructor
uses a table to display the results of a survey on people's favorite ice
cream flavors. The table is organized by flavor and frequency, making it
easy to see which flavors are the most popular.
Another way to organize data is by using graphs. Graphs allow us to
visualize data, making it easy to identify trends and patterns. For instance,
in the same video, the instructor creates a bar graph to show the
distribution of ages of the survey participants. The graph makes it easy to
see that the majority of participants are between 25 and 34 years old.
Once data is organized, we can then visualize it using a variety of
techniques. One common method is through the use of charts, such as pie
charts, line graphs, and scatter plots. These charts allow us to represent
data in a way that is easy to interpret and understand.
For example, in the video "Data Visualization with Python," the instructor
creates a pie chart to show the distribution of operating systems used by
survey participants. The chart makes it clear that the majority of
participants use Windows, followed by Mac OS and Linux.
Another way to visualize data is through the use of maps. Maps allow us
to represent data in a geographical context, making it easy to see patterns
and trends based on location. In the video "Geographic Data Visualization
with Python," the instructor uses a map to show the distribution of crime
rates in a city. The map makes it clear that crime rates are higher in
certain areas than others.
But visualizing data is not just about creating charts and maps. It's also
about telling a story. As the instructor in the video "The Power of Visual
Storytelling" says, "Data visualization is not just about numbers and charts.
It's about telling a story that resonates with people."
,To tell a story with data, we need to think about our audience and what
message we want to convey. We also need to consider the type of data
we are working with and the best way to represent it. For example, in the
video "Visualizing Data with R," the instructor uses a line graph to show
the trend of COVID-19 cases over time. The graph tells a story of the
pandemic's impact and makes it easy to see the trend of increasing cases.
In addition to charts and maps, we can also use other visualization
techniques such as network diagrams, heatmaps, and treemaps. These
techniques allow us to represent data in new and innovative ways, making
it easier to identify patterns and trends.
For instance, in the video "Network Visualization with Python," the
instructor uses a network diagram to show the connections between
different characters in a book. The diagram makes it clear which
characters have the most connections and how they are related.
In conclusion, organizing and visualizing data is a crucial step in making
informed decisions. By arranging data in a clear and concise way, and
then creating visual representations of that data, we can gain insights and
make informed decisions. Whether we are using tables, graphs, charts, or
maps, the key is to tell a story with the data and make it easy to
understand.
Code samples:
To create a table in Python, you can use the pandas library:
import pandas as pd
# Create a dictionary of data
data = {'Flavor': ['Vanilla', 'Chocolate', 'Strawberry', 'Mint
Chocolate Chip'],
'Frequency': [35, 30, 20, 15]}
# Create a DataFrame from the dictionary
df = pd.DataFrame(data)
# Display the DataFrame
print(df)
Output:
Flavor Frequency
0 Vanilla 35
1 Chocolate 30
2 Strawberry 20
3 Mint Chocolate Chip 15
To create a bar graph in Python, you can use the matplotlib library:
, import matplotlib.pyplot as plt
# Create a list of ages
ages = [22, 25, 28, 31, 34, 37, 40]
# Create a bar graph of ages
plt.bar(range(len(ages)), ages)
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.show()
To create a pie chart in Python, you can use the matplotlib library:
import matplotlib.pyplot as plt
# Create a dictionary of operating systems
os_data = {'Windows': 50, 'Mac OS': 30, 'Linux': 20}
# Create a pie chart of operating systems
plt.pie(os_data.values(), labels=os_data.keys())
plt.axis('equal')
plt.show()
To create a map in Python, you can use the folium library:
import folium
# Create a map of crime rates in a city
crime_data = {'North': 10, 'East': 8, 'South': 6, 'West': 4}
# Create a map
m = folium.Map(location=[40.7128, -74.0060], zoom_start=10)
# Add markers to the map
for location, crime in crime_data.items():
folium.CircleMarker(location=[40.7128,
Types of Data: Numerical, Categorical, and Ordinal" for you.
We start off by discussing numerical data, which is data that can be
measured and put into numbers. There are two types of numerical data:
discrete and continuous. Discrete numerical data can only take certain
values, usually whole numbers, while continuous numerical data can take
any value within a range.
For example, consider a video about students' heights in a class. The
heights of the students are measurements, so they are numerical data.
Since students' heights can take any value (e.g., 60.2 inches, 61.5 inches),
they are continuous numerical data.
Next, we move on to categorical data, which is data that falls into
categories or groups. There are two types of categorical data: nominal
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