Course schedule¶
Here is the current week-by-week schedule 📅 . We may adjust as we go along. To get started, we’re going to create the calendar of weeks for the course programmatically rather than manually!
The course will be offered in two distinct sessions, which will each follow the same schedule and have common deadlines for problem sets. To make things simpler:
3A/Section 01: Monday-Wednesday schedule
6A/Section 02: Monday-Thursday schedule; the Thursday content will correspond to Section 01’s Wednesday content
## import modules
import pandas as pd
import re
import numpy as np
## tell python to display output and print multiple objects
from IPython.display import display, HTML
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
## create range b/t start and end date
## of course
start_date = pd.to_datetime("2022-01-05")
end_date = pd.to_datetime("2022-03-08")
st_alldates = pd.date_range(start_date, end_date)
## subset to days in that range equal to Tuesday or Thursday
st_mw_3A = st_alldates[st_alldates.day_name().isin(['Monday', 'Wednesday'])]
st_mt_6A = st_alldates[st_alldates.day_name().isin(['Monday', 'Thursday'])]
## create data frame with that information
st_dates_3A = [re.sub("2022\\-", "", str(day.date())) for day in st_mw_3A]
st_dates_6A = [re.sub("2022\\-", "", str(day.date())) for day in st_mt_6A]
course_sched = pd.DataFrame({'dow_3A': st_mw_3A.day_name(),
'dow_6A': st_mt_6A.day_name(),
'date_3A': st_dates_3A,
'date_6A': st_dates_6A})
course_sched['Dates_3Asection'] = course_sched.dow_3A.astype(str) + " " + \
course_sched.date_3A.astype(str)
course_sched['Dates_6Asection'] = course_sched.dow_6A.astype(str) + " " + \
course_sched.date_6A.astype(str)
course_sched_display = course_sched[['Dates_3Asection',
'Dates_6Asection']].copy()
## display the resulting date sequence
display(course_sched_display)
## next block of code creates the
## actual content; can click "show"
## to see the underlying code
| Dates_3Asection | Dates_6Asection | |
|---|---|---|
| 0 | Wednesday 01-05 | Thursday 01-06 |
| 1 | Monday 01-10 | Monday 01-10 |
| 2 | Wednesday 01-12 | Thursday 01-13 |
| 3 | Monday 01-17 | Monday 01-17 |
| 4 | Wednesday 01-19 | Thursday 01-20 |
| 5 | Monday 01-24 | Monday 01-24 |
| 6 | Wednesday 01-26 | Thursday 01-27 |
| 7 | Monday 01-31 | Monday 01-31 |
| 8 | Wednesday 02-02 | Thursday 02-03 |
| 9 | Monday 02-07 | Monday 02-07 |
| 10 | Wednesday 02-09 | Thursday 02-10 |
| 11 | Monday 02-14 | Monday 02-14 |
| 12 | Wednesday 02-16 | Thursday 02-17 |
| 13 | Monday 02-21 | Monday 02-21 |
| 14 | Wednesday 02-23 | Thursday 02-24 |
| 15 | Monday 02-28 | Monday 02-28 |
| 16 | Wednesday 03-02 | Thursday 03-03 |
| 17 | Monday 03-07 | Monday 03-07 |
## create the actual content
### list of concepts
concepts = ["Course intro. and checking software setup",
"Python pandas: aggregation, joins, lambda and user-defined functions",
"Python pandas: aggregation, joins, lambda and user-defined functions (continued)",
"MLK day (no class)",
"Workflow basics: command line/jhub, Github workflow",
"Intro to merging",
"Regex",
"Probabilistic matching",
"Text as data: part one",
"Text as data: part two",
"Text as data: part two (continued)",
"APIs (part 1)",
"APIs (twitter)",
"Supervised machine learning",
"SQL",
"Interactive data viz. or web scraping (if time)",
"Final project work session",
"Final presentations"]
## check that concepts match number of weeks
assert len(course_sched_display.Dates_3Asection) == len(concepts)
assert len(course_sched_display.Dates_6Asection) == len(concepts)
## combine
course_sched_concepts = pd.DataFrame({'Week_3A': course_sched_display.Dates_3Asection,
'Week_6A': course_sched_display.Dates_6Asection,
'Concepts': concepts})
df = course_sched_concepts.copy()
print(df)
Week_3A Week_6A \
0 Wednesday 01-05 Thursday 01-06
1 Monday 01-10 Monday 01-10
2 Wednesday 01-12 Thursday 01-13
3 Monday 01-17 Monday 01-17
4 Wednesday 01-19 Thursday 01-20
5 Monday 01-24 Monday 01-24
6 Wednesday 01-26 Thursday 01-27
7 Monday 01-31 Monday 01-31
8 Wednesday 02-02 Thursday 02-03
9 Monday 02-07 Monday 02-07
10 Wednesday 02-09 Thursday 02-10
11 Monday 02-14 Monday 02-14
12 Wednesday 02-16 Thursday 02-17
13 Monday 02-21 Monday 02-21
14 Wednesday 02-23 Thursday 02-24
15 Monday 02-28 Monday 02-28
16 Wednesday 03-02 Thursday 03-03
17 Monday 03-07 Monday 03-07
Concepts
0 Course intro. and checking software setup
1 Python pandas: aggregation, joins, lambda and ...
2 Python pandas: aggregation, joins, lambda and ...
3 MLK day (no class)
4 Workflow basics: command line/jhub, Github wor...
5 Intro to merging
6 Regex
7 Probabilistic matching
8 Text as data: part one
9 Text as data: part two
10 Text as data: part two (continued)
11 APIs (part 1)
12 APIs (twitter)
13 Supervised machine learning
14 SQL
15 Interactive data viz. or web scraping (if time)
16 Final project work session
17 Final presentations
## add datacamp modules conditionally
col = "Concepts"
### older code on more exhaustive modules
# topics = [df[col] == "Python basic data wrangling: data structures (vectors; lists; dataframes; matrices), control flow, and loops",
# df[col] == "Python basic data wrangling: basic regular expressions and text mining",
# df[col] == "Python basic data wrangling: combining data (row binds, column binds, joins); aggregation",
# df[col] == "Review of visualization: ggplot; plotnine",
# df[col] == "Python: writing your own functions",
# df[col] == "Python: text data using nltk and gensim",
# df[col] == "SQL: reading data from a database and basic SQL (postgres) syntax",
# df[col] == "SQL: more advanced SQL syntax (subqueries; window functions)",
# df[col] == "Python: reading data from APIs and basic web scraping"]
# datacamp_modules = ["Python basics; python lists; Pandas: extracting and transforming data; Intermediate python for data science (loops)",
# "First three modules of regular expressions in Python",
# "Merging DataFrames with Pandas",
# "Introduction to Data Visualization with ggplot2",
# "Python data science toolbox (Part one): user-written functions, default args, lambda functions and error handling",
# "Natural language processing fundamentals in Python",
# "Introduction to databases in Python",
# "Intermediate SQL",
# "Importing JSON data and working with APIs; Importing data from the Internet"]
topics_trunc = [df[col] == "Python pandas: aggregation, joins, lambda and user-defined functions",
df[col] == "Intro to merging",
df[col] == "Probabilistic matching",
df[col] == "Supervised machine learning"]
datacamp_modules_trunc = ["Data manipulation with Pandas",
"Joining data with pandas",
"Regular expressions for pattern matching",
"Supervised Learning with scikit-learn"]
df["DataCamp module(s) (if any)"] = np.select(topics_trunc,
datacamp_modules_trunc,
default = "")
## add slides or tutorial link
# df['Link to slides or tutorial'] = np.select([df["Concepts"] == "Course intro. and checking software setup",
# df["Concepts"] == "Workflow basics: command line, Github workflow, basic LaTeX syntax, pre-analysis plans"],
# ["https://github.com/rebeccajohnson88/qss20_slides_activities/blob/main/slides/qss20_s21_class1.pdf",
# "https://github.com/rebeccajohnson88/qss20_slides_activities/blob/main/slides/qss20_s21_class2.pdf"],
# default = "")
# df['Link to slides or tutorial'] = np.where(df['Link to slides or tutorial'] != "",
# '<a target="_blank" href=' + df['Link to slides or tutorial'] + '><div>' + "Link" + '</div></a>',
# "")
# df['Link to activity (blank)'] = np.select([df["Concepts"] == "Workflow basics: command line, Github workflow, basic LaTeX syntax, pre-analysis plans"],
# ["https://github.com/rebeccajohnson88/qss20_slides_activities/blob/main/activities/00_latex_output_examples.ipynb"],
# default = "")
# df['Link to activity (blank)'] = np.where(df['Link to activity (blank)'] != "",
# '<a target="_blank" href=' + df['Link to activity (blank)'] + '><div>' + "Link" + '</div></a>',
# "")
date_col = "Week_3A"
due_dates = [df[date_col] == "Wednesday 01-12",
df[date_col] == "Wednesday 01-26",
df[date_col] == "Wednesday 02-09",
df[date_col] == "Wednesday 02-23",
df[date_col] == "Monday 03-07"]
assig = ["Problem set one (due Sunday 01-16)",
"Problem set two (due Friday 01-28)",
"Final project milestone 1 (due Wednesday 02-09);<br>Problem set three (due Friday 02-11)",
"Problem set four (due Friday 02-25);<br>Final project milestone 2 (due Sunday 02-27) ",
"Problem set five (due Friday 03-11);<br>Final project presentation (paper due on Monday 03-14)"]
df["Due (11:59 PM EST unless otherwise specified)"] = np.select(due_dates,
assig,
default = "")
HTML(df.to_html(index=False, escape = False))
| Week_3A | Week_6A | Concepts | DataCamp module(s) (if any) | Due (11:59 PM EST unless otherwise specified) |
|---|---|---|---|---|
| Wednesday 01-05 | Thursday 01-06 | Course intro. and checking software setup | ||
| Monday 01-10 | Monday 01-10 | Python pandas: aggregation, joins, lambda and user-defined functions | Data manipulation with Pandas | |
| Wednesday 01-12 | Thursday 01-13 | Python pandas: aggregation, joins, lambda and user-defined functions (continued) | Problem set one (due Sunday 01-16) | |
| Monday 01-17 | Monday 01-17 | MLK day (no class) | ||
| Wednesday 01-19 | Thursday 01-20 | Workflow basics: command line/jhub, Github workflow | ||
| Monday 01-24 | Monday 01-24 | Intro to merging | Joining data with pandas | |
| Wednesday 01-26 | Thursday 01-27 | Regex | Problem set two (due Friday 01-28) | |
| Monday 01-31 | Monday 01-31 | Probabilistic matching | Regular expressions for pattern matching | |
| Wednesday 02-02 | Thursday 02-03 | Text as data: part one | ||
| Monday 02-07 | Monday 02-07 | Text as data: part two | ||
| Wednesday 02-09 | Thursday 02-10 | Text as data: part two (continued) | Final project milestone 1 (due Wednesday 02-09); Problem set three (due Friday 02-11) |
|
| Monday 02-14 | Monday 02-14 | APIs (part 1) | ||
| Wednesday 02-16 | Thursday 02-17 | APIs (twitter) | ||
| Monday 02-21 | Monday 02-21 | Supervised machine learning | Supervised Learning with scikit-learn | |
| Wednesday 02-23 | Thursday 02-24 | SQL | Problem set four (due Friday 02-25); Final project milestone 2 (due Sunday 02-27) |
|
| Monday 02-28 | Monday 02-28 | Interactive data viz. or web scraping (if time) | ||
| Wednesday 03-02 | Thursday 03-03 | Final project work session | ||
| Monday 03-07 | Monday 03-07 | Final presentations | Problem set five (due Friday 03-11); Final project presentation (paper due on Monday 03-14) |