Lecture 1 – Data 100, Fall 2024¶

Data 100, Fall 2024

Acknowledgments Page

Software Packages¶

We will be using a wide range of different Python software packages. To install and manage these packages we will be using the Conda environment manager. The following is a list of packages we will routinely use in lectures and homeworks:

In [1]:
# Linear algebra, probability
import numpy as np

# Data manipulation
import pandas as pd

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Interactive visualization library
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.figure_factory as ff
import plotly.express as px

We will learn how to use all of the technologies used in this demo.

For now, just sit back and think critically about the data and our guided analysis.

1. Starting with a Question: Who are you (the students of Data 100)?¶

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This is a pretty vague question but let's start with the goal of learning something about the students in the class.

Here are some "simple" questions:

  1. How many students do we have?
  2. What are your majors?
  3. What year are you?
  4. How did your major enrollment trend change over time?

2. Data Acquisition and Cleaning¶

In Data 100 we will study various methods to collect data.

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To answer this question, I downloaded the course roster and extracted everyone's names and majors.

In [2]:
# pd stands for pandas, which we will learn starting in the next lecture.
# Some pandas syntax shared with data8's datascience package.
majors = pd.read_csv("data/majors.csv")
names = pd.read_csv("data/names.csv")

3. Exploratory Data Analysis¶

In Data 100 we will study exploratory data analysis and practice analyzing new datasets.

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I didn't tell you the details of the data! Let's check out the data and infer its structure. Then we can start answering the simple questions we posed.

Peeking at the Data¶

In [3]:
# Let's peek at the first 20 rows of the majors dataframe.
majors.head(20)
Out[3]:
Majors Terms in Attendance
0 Letters & Sci Undeclared UG (Subplan: Applied ... 3.0
1 Comparative Literature BA, Data Science BA (Su... 7.0
2 Chemistry BS 7.0
3 Letters & Sci Undeclared UG 5.0
4 Materials Science & Eng BS 5.0
5 Civil Engineering BS, Minor: Data Science UG 6.0
6 Mol Sci & Software Engin MMSSE (Subplan: Full-... G
7 Letters & Sci Undeclared UG 5.0
8 Data Science BA (Subplan: Business/Industrial ... 2.0
9 Economics BA, Computer Science BA 5.0
10 Civil Engineering BS 8.0
11 Economics BA, Minor: Data Science UG 7.0
12 Letters & Sci Undeclared UG 5.0
13 Data Science BA (Subplan: Economics) 8.0
14 Chemical Engineering PhD G
15 Data Science BA (Subplan: Business/Industrial ... 5.0
16 Economics BA, Data Science BA (Subplan: Econom... 8.0
17 Economics BA 5.0
18 Civil Engineering BS, Minor: Data Science UG 5.0
19 Letters & Sci Undeclared UG 3.0
In [4]:
# Let's peek at the first 5 rows (default) of the names dataframe.
names.head()
Out[4]:
Name Role
0 Ethan Student
1 Rachel Student
2 Ethan Student
3 JAMES Student
4 Rachel Student

What is one potential issue we may need to address in this data?¶

Answer: Some names appear capitalized.

In the above sample we notice that some of the names are capitalized and some are not. This will be an issue in our later analysis so let's convert all names to lower case.

In [5]:
names['Name'] = names['Name'].str.lower()
In [6]:
names.head()
Out[6]:
Name Role
0 ethan Student
1 rachel Student
2 ethan Student
3 james Student
4 rachel Student

Exploratory Data Analysis on names dataset¶

What is the most common first letter in names? What is its distribution?¶

In [7]:
# Below are the most common, in descending frequency.
first_letter = names['Name'].str[0].value_counts()
first_letter.head()
Out[7]:
Name
a    200
j    118
s    114
m     85
c     68
Name: count, dtype: int64
In [8]:
# Let's visualize this first letter distribution. 

plt.bar(first_letter.index, first_letter.values)
plt.xlabel('First Letter')
plt.ylabel('Frequency')
plt.title('First Letter Frequency Distribution')
plt.show()
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In the United States, "J" and "A" names are the most popular first initials. Seems like our visualization also reflects this!

The average length of names in the United States is also around 6 letters!

How many records do we have?¶

In [9]:
print(len(names))
print(len(majors))

1201
1201

Based on what we know of our class, each record is most likely a student.

Understanding the structure of data¶

It is important that we understand the meaning of each field and how the data is organized.

In [10]:
names["Role"].value_counts()
Out[10]:
Role
Student    1200
#REF!         1
Name: count, dtype: int64

It appears that one student has an erroneous role given as "#REF!". What else can we learn about this student? Let's see their name.

In [11]:
# Boolean index to find rows where Role is #REF!
names[names['Name'] == "#ref!"]
Out[11]:
Name Role
1133 #ref! #REF!

Though this single bad record won't have much of an impact on our analysis, we can clean our data by removing this record.

In [12]:
names = names[names['Name'] != "#ref!"]

Double check: Let's double check that our record removal only removed the single bad record.

In [13]:
names['Role'].value_counts().to_frame()  # Again, counts of unique Roles.
Out[13]:
count
Role
Student 1200

Most Frequent Names¶

Let's see the distribution of names in our class.

In [14]:
names['Name'].value_counts().to_frame()   # Counting the frequency of each unique name.
Out[14]:
count
Name
ethan 10
ryan 10
daniel 10
alex 9
rachel 9
... ...
eugenia 1
archita 1
jun 1
audri 1
tianyuan 1

862 rows × 1 columns

Remember we loaded in two files. Let's explore the fields of majors and check for bad records:

Exploratory Data Analysis on majors dataset¶

In [15]:
majors.columns   # Get column names
Out[15]:
Index(['Majors', 'Terms in Attendance'], dtype='object')
In [16]:
majors['Terms in Attendance'].value_counts().to_frame()
Out[16]:
count
Terms in Attendance
5.0 479
3.0 264
7.0 255
G 112
8.0 59
6.0 22
4.0 7
2.0 1
#REF! 1
1.0 1

It looks like numbers represent semesters, G represents graduate students. But we do still have a bad record:

In [17]:
majors[majors['Terms in Attendance'] == "#REF!"]
Out[17]:
Majors Terms in Attendance
671 #REF! #REF!
In [18]:
majors = majors[majors['Terms in Attendance'] != "#REF!"]
majors['Terms in Attendance'].value_counts().to_frame()
Out[18]:
count
Terms in Attendance
5.0 479
3.0 264
7.0 255
G 112
8.0 59
6.0 22
4.0 7
2.0 1
1.0 1

Detail: The deleted majors record number is different from the record number of the bad names record. So while the number of records in each table matches, the row indices don't match, so we'll have to keep these tables separate in order to do our analysis.

Summarizing the Data¶

We will often want to numerically or visually summarize the data. The describe() method provides a brief high level description of our data frame.

In [19]:
names.describe()
Out[19]:
Name Role
count 1200 1200
unique 862 1
top ethan Student
freq 10 1200

Q: What do you think top and freq represent?

Answer: top: most frequent entry, freq: the frequency of that entry

In [20]:
majors.describe()
Out[20]:
Majors Terms in Attendance
count 1200 1200
unique 260 9
top Letters & Sci Undeclared UG 5.0
freq 267 479

4. Analysis: Understanding the World¶

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What are your majors?¶

What are the top majors:

In [21]:
majors_count = (       # Method chaining in pandas
    majors['Majors']
    .value_counts()
    .sort_values(ascending=False) # Highest first
    .to_frame()
    .head(20)          # Get the top 20
)

majors_count
Out[21]:
count
Majors
Letters & Sci Undeclared UG 267
Computer Science BA 78
Electrical Eng & Comp Sci BS 47
Letters & Sci Undeclared UG (Subplan: Applied HD Data Science) 40
Cognitive Science BA 37
Economics BA 36
Data Science BA (Subplan: Business/Industrial Analytics) 28
Mol Sci & Software Engin MMSSE (Subplan: Full-Time) 25
Electrical Eng & Comp Sci MEng 24
Civil Engineering BS 22
Statistics BA 21
Data Science BA (Subplan: Applied Mathematics & Modeling) 19
Data Science BA (Subplan: Cognition) 19
Applied Mathematics BA (Subplan: Data Science) 15
Economics BA, Minor: Data Science UG 15
Data Science BA (Subplan: Economics) 14
Environ Econ & Policy BS 13
Data Science BA 13
Industrial Eng & Ops Rsch BS 12
Letters & Sci Undeclared UG (Subplan: Applied HD Computer Science) 11

We will often use visualizations to make sense of data¶

In Data 100, we will deal with many different kinds of data (not just numbers) and we will study techniques to describe types of data.

How can we summarize the Majors field? A good starting point might be to use a bar plot:

In [22]:
# Interactive using plotly
fig = px.bar(majors_count.loc[::-1], orientation='h')
fig.update_layout(showlegend=False, 
                  xaxis_title='Count', 
                  yaxis_title='Major',
                  autosize=False, 
                  width=800, 
                  height=500)

What year are you?¶

In [23]:
fig = px.histogram(majors['Terms in Attendance'].sort_values(),
                   histnorm='probability')

fig.update_layout(showlegend=False,
                  xaxis_title="Term",
                  yaxis_title="Fraction of Class",
                  autosize=False, 
                  width=800, 
                  height=250)
In [24]:
# Replacing terms in attendance data with the degree objective
majors.loc[majors.loc[:, 'Terms in Attendance'] != 'G', 'Terms in Attendance'] = 'Undergraduate'
majors.loc[majors.loc[:, 'Terms in Attendance'] == 'G', 'Terms in Attendance'] = 'Graduate'

majors.rename(columns={'Terms in Attendance': 'Ungrad Grad'}, inplace=True)

majors.describe()
Out[24]:
Majors Ungrad Grad
count 1200 1200
unique 260 2
top Letters & Sci Undeclared UG Undergraduate
freq 267 1088

1. New Questions¶

  • What is the ratio between graduate and undergraduate students in Data 100, and how does it compare with campus distribution?

  • What is the proportion of different majors in Data 100, and how does it compare with historical campus trends?

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We often ask this question because we want to improve the data science program here in Berkeley, especially since it has now grown into a new college—College of Computing, Data Science, and Society—Berkeley's first new college in 50 years.

How could we answer this question?¶

In [25]:
print(majors.columns)
print(names.columns)

Index(['Majors', 'Ungrad Grad'], dtype='object')
Index(['Name', 'Role'], dtype='object')

We don't have the data.¶

Where can we get the data?

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UC Berkeley Student Headcount by Major and Demographics¶

Data comes from UC Berkeley's data warehouse reporting system, Cal Answers. Headcount figures are based on the Fall and Spring census snapshots, which occur at the end of the 5th week of the term.

2. Acquire data programmatically¶

Note 1: In the following, we download the data programmatically to ensure that the process is reproducible.

Note 2: We also load the data directly into Python.

In Data 100 we will think a bit more about how we can be efficient in our data analysis to support processing large datasets.

In [26]:
url = "https://docs.google.com/spreadsheets/d/1J7tz3GQLs3M6hFseJCE9KhjVhe4vKga8Q2ezu0oG5sQ/gviz/tq?tqx=out:csv"

university_majors = pd.read_csv(url, 
                                usecols = ['Academic Yr', 'Semester', 'Ungrad Grad', 
                                           'Entry Status', 'Major Short Nm', 'Student Headcount'])

3. Exploratory Data Analysis on Campus Data¶

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In [27]:
# Examining the data
university_majors
Out[27]:
Academic Yr Semester Ungrad Grad Entry Status Major Short Nm Student Headcount
0 2014-15 Fall Graduate Graduate Education 335
1 2014-15 Fall Graduate Graduate Educational Leadership Jnt Pgm 1
2 2014-15 Fall Graduate Graduate Special Education 18
3 2014-15 Fall Graduate Graduate Science & Math Education 15
4 2014-15 Fall Graduate Graduate Chemical Engineering 136
... ... ... ... ... ... ...
7199 2023-24 Spring Undergraduate Transfer Entrant Nut Sci-Physio & Metabol 13
7200 2023-24 Spring Undergraduate Transfer Entrant Nutritional Sci-Dietetics 1
7201 2023-24 Spring Undergraduate Transfer Entrant Nutritional Sci-Toxicology 2
7202 2023-24 Spring Undergraduate Transfer Entrant Genetics & Plant Biology 11
7203 2023-24 Spring Undergraduate Transfer Entrant Microbial Biology 39

7204 rows × 6 columns

The data is reported on a semester basis. We will aggregate data across different semesters in a year by taking average of Fall and Spring semester enrollment information.

In [28]:
# Reporting student data based on academic year
university_majors = (university_majors.groupby(
    ['Academic Yr', 'Ungrad Grad', 'Entry Status', 'Major Short Nm'], as_index = False)[["Student Headcount"]]
                     .mean()
                    )

university_majors
Out[28]:
Academic Yr Ungrad Grad Entry Status Major Short Nm Student Headcount
0 2014-15 Graduate Graduate African American Studies 30.0
1 2014-15 Graduate Graduate Ag & Resource Economics 73.5
2 2014-15 Graduate Graduate Anc Hist & Medit Archae 14.0
3 2014-15 Graduate Graduate Anthropology 76.5
4 2014-15 Graduate Graduate Applied Mathematics 18.5
... ... ... ... ... ...
3697 2023-24 Undergraduate Transfer Entrant Spanish and Portuguese 16.5
3698 2023-24 Undergraduate Transfer Entrant Statistics 46.0
3699 2023-24 Undergraduate Transfer Entrant Sustainable Environ Dsgn 4.0
3700 2023-24 Undergraduate Transfer Entrant Theater & Perf Studies 44.0
3701 2023-24 Undergraduate Transfer Entrant Urban Studies 15.5

3702 rows × 5 columns

What is the historical distribution of graduate and undergraduate students at Berkeley?¶

In [29]:
university_grad_vs_ungrd = (university_majors.groupby(
    ['Academic Yr', 'Ungrad Grad'], as_index = False)[["Student Headcount"]]
                            .sum()
                           )

proportions = university_grad_vs_ungrd.pivot(index='Academic Yr', columns='Ungrad Grad', values='Student Headcount')
proportions['Total'] = proportions['Undergraduate'] + proportions['Graduate']
proportions['Undergrad Proportion'] = proportions['Undergraduate'] / proportions['Total']
proportions['Grad Proportion'] = proportions['Graduate'] / proportions['Total']


fig = px.bar(proportions.reset_index(), 
             x='Academic Yr', 
             y=['Undergraduate', 'Graduate'],
             title='Number of Grad vs. Undergrad Students',
             labels={'value': 'Number of Students'},
             color_discrete_map={'Undergraduate': 'blue', 'Graduate': 'orange'})

fig.update_layout(barmode='relative', autosize=False, width=800, height=600)
fig.show()

4.1. Ratio between graduate and undergraduate students in Data 100, and its comparison with campus distribution¶

In [30]:
data100_grad = majors['Ungrad Grad'].loc[majors['Ungrad Grad'] == 'Graduate'].count()

data100_undergrad = majors['Ungrad Grad'].loc[majors['Ungrad Grad'] == 'Undergraduate'].count()

print("Number of graduate students in Data 100: ", data100_grad)
print("Number of undergraduate students in Data 100: ", data100_undergrad)

Number of graduate students in Data 100:  112
Number of undergraduate students in Data 100:  1088
In [31]:
data100_row = {'Graduate':[data100_grad], 
               'Undergraduate':[data100_undergrad], 
               'Total':[data100_grad + data100_undergrad], 
               'Undergrad Proportion':[data100_undergrad / (data100_grad + data100_undergrad)],
               'Grad Proportion':[data100_grad / (data100_grad + data100_undergrad)], 
               }

new_row_df = pd.DataFrame(data100_row)

proportions.loc['Data 100'] = new_row_df.iloc[0]


fig = px.bar(proportions.reset_index(), 
             x='Academic Yr', 
             y=['Undergrad Proportion', 'Grad Proportion'],
             title='Proportions of Grad vs. Undergrad Students',
             labels={'value': 'Proportion'},
             color_discrete_map={'Undergrad Proportion': 'blue', 'Grad Proportion': 'orange'})


fig.update_layout(barmode='relative', autosize=False, width=800, height=600)
fig.show()

4.2. Proportion of different majors in Data 100, and their historical emrollment trends¶

In [32]:
data100_top_20_majors = (       # Method chaining in pandas
    majors['Majors']
    .value_counts()
    .sort_values(ascending=False) # Highest first
    .to_frame()
    .head(20)          # Get the top 20
)

major_trends = university_majors.groupby(['Academic Yr', 'Major Short Nm'], 
                                         as_index = False)[["Student Headcount"]].sum()


print("Top 20 majors at Berkeley in 2022-23")
major_trends[major_trends.loc[:, 'Academic Yr'] == '2022-23'].sort_values('Student Headcount', ascending=False).head(20)

Top 20 majors at Berkeley in 2022-23
Out[32]:
Academic Yr Major Short Nm Student Headcount
1790 2022-23 Letters & Sci Undeclared 10651.0
1692 2022-23 CDSS Computer Science 2102.5
1731 2022-23 Electrical Eng & Comp Sci 2093.0
1691 2022-23 Business Administration 1645.5
1727 2022-23 Economics 1579.5
1717 2022-23 Data Science Undergrad Studies 1325.5
1817 2022-23 Molecular & Cell Biology 1225.5
1808 2022-23 Mechanical Engineering 1208.0
1789 2022-23 Law (JD) 1023.0
1772 2022-23 Info & Data Science-MIDS 1021.5
1839 2022-23 Political Science 1005.0
1747 2022-23 Evening & Weekend MBA 919.0
1840 2022-23 Psychology 760.0
1699 2022-23 Chemistry 691.0
1854 2022-23 Sociology 663.0
1678 2022-23 Architecture 604.5
1698 2022-23 Chemical Engineering 595.0
1686 2022-23 Bioengineering 576.0
1710 2022-23 Cognitive Science 505.5
1738 2022-23 English 497.0
In [33]:
print("Top 20 majors at Berkeley since 2013")
major_trends.groupby(['Major Short Nm'], as_index = False)[['Student Headcount']].sum().sort_values('Student Headcount', ascending=False).head(20)
     

Top 20 majors at Berkeley since 2013
Out[33]:
Major Short Nm Student Headcount
152 Letters & Sci Undeclared 102315.5
81 Electrical Eng & Comp Sci 18979.5
33 CDSS Computer Science 16345.5
32 Business Administration 14680.5
76 Economics 14268.5
178 Mechanical Engineering 10436.5
219 Political Science 10343.5
151 Law (JD) 9820.0
98 Evening & Weekend MBA 8138.0
236 Sociology 6847.0
220 Psychology 6543.5
189 Molecular & Cell Biology 6422.5
61 Data Science Undergrad Studies 6222.5
129 Info & Data Science-MIDS 6121.0
41 Chemistry 6106.5
40 Chemical Engineering 6088.5
13 Architecture 5684.5
89 English 5398.5
11 Applied Mathematics 5316.5
25 Bioengineering 5179.0
In [34]:
data100_top_20_majors.index = data100_top_20_majors.index.str.rsplit(' ', n=1).str[0]
print("Top 20 majors at Berkeley in Data 100")
print(data100_top_20_majors)

Top 20 majors at Berkeley in Data 100
                                                    count
Majors                                                   
Letters & Sci Undeclared                              267
Computer Science                                       78
Electrical Eng & Comp Sci                              47
Letters & Sci Undeclared UG (Subplan: Applied H...     40
Cognitive Science                                      37
Economics                                              36
Data Science BA (Subplan: Business/Industrial          28
Mol Sci & Software Engin MMSSE (Subplan:               25
Electrical Eng & Comp Sci                              24
Civil Engineering                                      22
Statistics                                             21
Data Science BA (Subplan: Applied Mathematics &        19
Data Science BA (Subplan:                              19
Applied Mathematics BA (Subplan: Data                  15
Economics BA, Minor: Data Science                      15
Data Science BA (Subplan:                              14
Environ Econ & Policy                                  13
Data Science                                           13
Industrial Eng & Ops Rsch                              12
Letters & Sci Undeclared UG (Subplan: Applied H...     11
In [35]:
fig = px.line(major_trends[major_trends["Major Short Nm"].isin(data100_top_20_majors.index)], 
                        x = "Academic Yr", y = "Student Headcount", color = "Major Short Nm")

fig.update_layout(autosize=False, width=800, height=600)
fig.show()
In [36]:
data100_top_19_majors = data100_top_20_majors.iloc[1:,:]

fig = px.line(major_trends[major_trends["Major Short Nm"].isin(data100_top_19_majors.index)], 
                        x = "Academic Yr", y = "Student Headcount", color = "Major Short Nm")

fig.update_layout(autosize=False, width=800, height=600)
fig.show()