Resources

Exam Resources

Study Guides

• The midterm study guide will be distributed with the midterm exam.
• The final study guide will be distributed with the final exam, as will the midterm study guide.

Review Videos

• Fall 2017 Midterm Review Problems by Wilton Wu and Howard Ki
• General reviews: day 1.

Content Specific Videos:

• Tables and Programming Review by Carlos Ortega and Amir Shahatit
• Histograms Review by Suraj Rampure
• Probability and Sampling Review by Vasilis Oikonomou and Tanay Nathan

Practice Exams

Midterm:

Please note that Hypothesis Testing WILL NOT be on the Fall 2017 midterm. Some parts of the past midterms go over questions on hypothesis testing.

• Spring 2017 midterm, solution, and video walkthrough by Suraj Rampure.
• Spring 2017 practice midterm, solution, and video walkthrough by Erik Cheng. The practice midterm is the Spring 2016 midterm, but modified to only include topics covered in Spring 2017.
• Summer 2017 midterm, solution, and video walkthrough by Suraj Rampure
• Fall 2016 midterm and video walkthrough by Suraj Rampure. This exam only includes questions on topics covered in Spring 2017.

Finals:

• Spring 2017 final, exam solution and video walkthrough by Suraj Rampure.
• Spring 2017 practice final, solution, and video walkthrough by Suraj Rampure. The practice final is the Spring 2016 final, but with modified solutions that use an updated version of the datascience module.
• Fall 2016 final and video walkthrough by Emma Jaeger.

GSI-created Materials

Slides

• Discussion slides by Erik Cheng
• Discussion slides by Scott Lee
• Discussion slides by Suraj Rampure
• Discussion slides by Vinitra Swamy
• Discussion slides by Sahil Upadhyay
• Discussion slides by Emma Jaeger

Other Guides

• Additional histogram practice and solutions by Supreme Overlord Professor Fahad Kamran
• Total Variation Distance discussion problem walkthrough by Suraj Rampure
• Final exam comprehensive statistics review by Francie McQuarrie

Tutoring Worksheets

• Worksheets from tutoring sections are available for review.

Table Functions and Methods

Name	Chapter	Description
Table	5	Create an empty table, usually to extend with data
Table.read_table	5	Create a table from a data file
with_columns	5	Create a copy of a table with more columns
column	5	Create an array containing the elements of a column
num_rows	5	Compute the number of rows in a table
num_columns	5	Compute the number of columns in a table
labels	5	Lists the column labels in a table
select	5	Create a copy of a table with only some of the columns
drop	5	Create a copy of a table without some of the columns
relabel	5	Modifies the existing table in place, changing the column heading in the first argument to the second
relabeled	5	Returns a new table with the column heading in the first argument changed to the second
sort	5.1	Create a copy of a table sorted by the values in a column. Defaults to ascending order unless "descending = True" is included
where	5.2	Create a copy of a table with only the rows that match some predicate
take	5.2	Create a copy of the table with only the rows whose indices are in the given array
scatter	6	Draw a scatter plot consisting of one point for each row of the table.
plot	6	Draw a line graph consisting of one point for each row of the table.
barh	6.1	Draws a bar chart of the frequencies of a categorical distribution
hist	6.2	Draws a histogram of a numerical distribution
apply	7.1	Returns an array of values resulting from applying a function to some column in a table
group	7.2	Create a copy of the table with all rows with the same values in a certain column aggregated into one row in the new table
groups	7.3	Create a copy of the table with all rows with the same value in a certain array of columns aggregated into one row in the new table
pivot	7.3	Create a copy of the table with a column for each element in the first argument and a row for each element in the second argument and aggregates values
join	7.4	Create a copy of the table that is the result of joining the columns of two tables, with a row for each shared value in the two tables
sample	9	Draws some number of rows at random from a table. By default, with replacement.
sample_from_distribution	10.1	Returns a new table with an additional column whose values correspond to a random sample (of specified size) based on proportions in a specified column.

Array Functions and Methods

Name	Chapter	Description
max	3.3	Returns the maximum value of an array
min	3.3	Returns the minimum value of an array
sum	3.3	Returns the sum of the values in an array
len	3.3	Returns the length (number of elements) of an array
make_array	4.4	Makes a numpy array with the values passed in
np.average	4.4	Returns the mean value of an array
np.diff	4.4	Returns a new array of size len(arr)-1 with elements equal to the difference between adjacent elements
np.sqrt	4.4	Returns an array with the square root of each element
np.arange	4.5	Returns an array of an end-exclusive range of variable step size
arr.item	4.6	Returns the i-th item in an array (remember Python indices start at 0!)
np.random.choice	8	Picks one (by default) or some number 'n' of items from an array at random. By default, with replacement.
np.count_nonzero	8	Returns the number of non-zero (or True) elements in an array.
np.append	8.2	Returns a copy of the input array with some item (must be the same type as the other entries in the array) appended to the end.
percentile	11.1	Returns the corresponding percentile of an array.