# 程序代写代做代考 python SQL data science Java javascript Data Science Self-Assessment

Data Science Self-Assessment

Data Science Self-Assessment

Galvanize Inc.

Contents

How to Use This Document 1

Spot the Di�erences 1

For Loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

For Loops in Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Make a function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

More Advanced Python Challenges 4

Challenge 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Challenge 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Challenge 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Challenge 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Challenge 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Getting Ready for the SQL Assessment 8

Our Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Simple Queries on a Single Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Build Queries with Aggregates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Build Complex Queries on Multiple Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Practice some probability and statistics 10

Counting: permutations, combinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Probability distributions Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Inferential Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Relationship between two quantitative variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1

How to Use This Document

This document is designed to give you an idea of the baseline of Python, SQL and probability/statistics

knowledge required to apply for the Data Science Immersive program. If understanding any of the scripts

included in this PDF is challenging, we encourage you to take the time to study Python and/or SQL

and/or probability/statistics before beginning the application process. For a list of free Python, SQL and

probability/statistics resources, please refer to the DSI Study Resources PDF.

This document starts with some simple python statements which you should be able to evaluate without

actually executing. We then proceed to more advanced challenges that will require a solid understanding of

strings, lists, sets, dictionaries, file I/O, and functions. We then continue the self assessment with a variety of

SQL statements you should be comfortable with. We end the document with probability/statistics exercises

that cover counting (permutations, combinations), probability (conditional probability, Bayes’ Theorem),

probability distribution for discrete and continuous random variables, descriptive and inferential statistics as

well as basic linear regression.

Spot the Di�erences

Without running the scripts, can you tell what the output will be? If you have some Python or programming

background, this section should take very little time.

For Loops

1 # Script 1

2 list_num = [1,2,3]

3 for num in list_num:

4 total = 0

5 total += num

6 print total

1 # Script 2

2 list_num = [1,2,3]

3 total = 0

4 for num in list_num:

5 total += num

6 print total

1 # Script 3

2 list_num = [1,2,3]

3 total = 0

4 for num in list_num:

5 total += num

6 print total

For Loops in Functions

1 # Script 1

2 def my_function1(my_list):

3 output = []

4 for item in my_list:

5 output.append(item)

6 return item

7

8 print my_function1([�cat�, �bad�, �dad�])

1 # Script 2

2 def my_function2(my_list):

3 output = []

4 for item in my_list:

5 output.append(item)

6 return output

7

8 print my_function2([�cat�, �bad�, �dad�])

2

1 # Script 3

2 def my_function3(my_list):

3 output = []

4 for item in my_list:

5 output.append(item)

6 return item

7

8 print my_function3([�cat�, �bad�, �dad�])

1 # Script 4

2 def my_function4(my_list):

3 for item in my_list:

4 output = []

5 output.append(item)

6 return output

7

8 print my_function4([�cat�, �bad�, �dad�])

1 # Script 5

2 def my_function5(my_list):

3 output = []

4 for item in my_list:

5 output.append(item)

6 return output

7

8 print my_function5([�cat�, �bad�, �dad�])

9 print my_function5([�cat�, �bad�, �dad�])

1 # Script 6

2 output = []

3 def my_function6(my_list):

4 for item in my_list:

5 output.append(item)

6 return output

7

8 print my_function6([�cat�, �bad�, �dad�])

9 print my_function6([�cat�, �bad�, �dad�])

Make a function

Functions, blocks of reusable code, keep your code modular, well organized and easily maintainable. You

should try to keep your code organized in functions. Take a look at each of the following snippets of code

and organize them into functions.

1. We want a function that takes a list of numbers and returns that list where 10 was added to each

number.

1 list_num = [1,2,3]

2 list_add_10 = []

3 for num in list_num:

4 list_add_10.append(num + 10)

5 print list_add_10

2. We want a function that takes in a list of strings and returns the list with the length of the words.

1 list_words = [�great�, �job�, �so�, �far�]

2 list_length_words = []

3 for word in list_words:

4 list_length_words.append(len(word))

5 print list_length_words

3

More Advanced Python Challenges

Practice, practice, practice: we encourage you to work through these challenges.

Challenge 1

Write a function that looks at the number of times given letters appear in a document. The output should be

in a dictionary.

1 def letter_counter(path_to_file, letters_to_count):

2 ��� Returns the number of times specified letters appear in a file

3

4 Parameters

5 ———–

6 path_to_file: str

7 Relative or absolute path to file of interest

8 letters_to_count: str

9 String containing the letters to count in the text

10

11 Returns

12 ——–

13 letter_dict: dict

14 – key: letter

15 – value: the count of that letter in the file

16 The counting is case insensitive

17

18 Example

19 ——–

20 ���file.txt

21 This is the file of interest. Count my vowels!

22 ���

23 >>> letter_counter(�file.txt�, �aeiou�)

24 {�i�: 4, �e�:4, �o�:2, �u�:1}

25 ���

26 pass

4

Challenge 2

Write a function that removes one occurrence of a given item from a list. Do not use methods .pop() or

.remove()! If the item is not present in the list, output should be ‘The item is not in the list’.

1 def remove_item(list_items, item_to_remove):

2 ��� Remove first occurrence of item from list

3

4 Parameters

5 ———-_

6 list_items: list

7 item_to_remove: object

8 The object to be removed form list_items

9

10 Returns

11 ——–

12 – if the item is in the list: list

13 list with first occurrence of item removed

14 – if the item is not in the list: str

15 �The item is not in the list�

16

17 Example

18 ——–

19 >>>list_items = [1,3,7,8,0]

20 >>>remove_item(list_items, 7)

21 [1,3,8,0]

22 ���

23 pass

5

Challenge 3

The simple substitution cipher basically consists of substituting every plaintext character for a di�erent

ciphertext character. The following is an example of one possible cipher from http://practicalcryptography.

com/ciphers/simple-substitution-cipher/:

• Plain alphabet : abcdefghijklmnopqrstuvwxyz

• cipher alphabet: phqgiumeaylnofdxjkrcvstzwb

1 def cipher(text, cipher_alphabet, option=�encipher�):

2 ��� Run text through a particular cipher alphabet

3

4 Parameters

5 ———–

6 text: str

7 Either the plain text to encipher, or the cipher text to decrypt

8 cipher_alphabet: dict

9 Dictionary specifying {�original_letter�: �cipher_letter�}

10 option: str (default �encipher�)

11 �encipher� (accept plain text and output cipher text)

12 �decipher� (accept cipher text and output plain text)

13

14 Returns

15 ——–

16 cipher text by default,

17 plain text if option is set to decipher

18

19 >>> d = dict(zip(�abcdefghijklmnopqrstuvwxyz�,

20 �phqgiumeaylnofdxjkrcvstzwb�))

21 >>> cipher(�defend the east wall of the castle�,

22 d)

23 �giuifg cei iprc tpnn du cei qprcni�

24 >>> cipher(�giuifg cei iprc tpnn du cei qprcni�,

25 d,

26 option=�decopher�)

27 �defend the east wall of the castle�

28 ���

29 pass

6

http://practicalcryptography.com/ciphers/simple-substitution-cipher/

http://practicalcryptography.com/ciphers/simple-substitution-cipher/

Challenge 4

Implement a function that counts the number of isograms in a list of strings.

• An isogram is a word that has no repeating letters, consecutive or non-consecutive.

• Assume the empty string is an isogram and that the function should be case insensitive.

1 def count_isograms(list_of_words):

2 ��� Count the number of strings without repeating characters in a list

3

4 Parameters

5 ———–

6 list_of_words: list of strings

7

8 Returns

9 ——-

10 count of isograms (as integer)

11

12 >>>count_isograms([�conduct�, letter�, �contract�, �hours�, �interview�])

13 1

14 ���

15 pass

Challenge 5

Write a function that returns a list of matching items. Items are defined by a tuple with a letter and a

number and we consider item 1 to match item 2 if:

1. Both their letters are vowels (aeiou), or both are consonnants and,

2. The sum of their numbers is a multiple of 3

(1,2) contains the same information as (2,1), the output list should only contain one of them.

1 def matching_pairs(data_list):

2 ���

3 Parameters

4 ———-

5 data_list: as list of tuples (letter, number)

6

7 Returns

8 ——-

9 A list of the matching pair referenced by their index (index_A, index_B).

10 Each pair should appear only once. (A,B) is the same as (B,A)

11

12 >>> data = [(�a�, 4), (�b�, 5), (�c�, 1), (�d�, 3), (�e�, 2), (�f�,6)]

13 >>> matching_pairs(data)

14 [(0,4), (1,2), (3,4)]

15 ���

16 pass

7

Getting Ready for the SQL Assessment

You should be able to write the SQL queries that use SELECT, FROM, WHERE, CASE clauses, aggregates, and

JOINs . To check your work, you can run your queries on w3school’s site (http://bit.ly/1foSkgu).

Our Data

We will be querying the following tables.

Table 1: flags

name country w_prop l_prop adoption_date

“Tricolour” “France” 2 3 1830

“Union Jack” “United Kingdom” 1 2 1801

“The Star-Strangled Banner” “USA” 10 19 1960

“Hinomaru” “Japan” 2 3 1999

“NA” “Brazil” 7 10 1992

“Jalur Gemilang” “Malaysia” 1 2 1963

where w_prop is the width proportion and l_prop is the length proportion

Table 2: countries

country capital contient

“France” “Paris” “Europe”

“Malaysia” “Kuala Lumpur” “Asia”

“Brazil” “Brasilia” “South America”

“United Kingdom” “London” “Europe”

“Japan” “Tokyo” “Asia”

“USA” “Washington DC” “North America”

“Germany” “Berlin” “Europe”

Simple Queries on a Single Table

1. Use the WHERE clause to show the countries with a flag ratio of 2:3 (i.e. w_prop = 2 and l_prop = 3).

2. Use IN to check if an item is in a list and show the countries on a continent that is either Europe or

North America.

3. Use BETWEEN xxx AND xxx to show names of flags and countries that have width proportion higher

than 1 but lower than 8.

4. Use LIKE �X%� to show countries that have an name that starts with ‘U’.

5. Use CASE to show countries, their capital and a column to indicate whether the continent is ‘Eurasia’

(i.e. Europe or Asia) or ‘Americas’ (North or South America). Add a filter to select countries with

capitals that are at least 7 character long.

Build Queries with Aggregates

Aggregates include commands such as DISTINCT, COUNT, SUM, GROUP BY, HAVING, and ORDER BY. Try using

these commands on the following questions!

8

http://www.w3schools.com/sql/trysql.asp?filename=trysql_select_all

1. Use DISTINCT to list the continents in the countries table – each continent should appear only once.

2. Use COUNT to see how many countries are in Europe.

3. Use GROUP BY to count how many countries are in each continent, with continents alphabetically ordered

(hint: use ORDER BY).

4. Use HAVING to determine which continents are represented at least twice in the countries table.

Build Complex Queries on Multiple Tables

1. Use JOIN to display the capital, the country, and the flag name.

2. Use JOIN and WHERE to display the continents associated to the flags in the flags table when the flag

has a name (i.e. not ‘NA’).

3. Use JOIN and HAVING to display continents that have at least 2 countries represented as well as the

average adoption date of the flag (as avg_date).

9

Practice some probability and statistics

Here is a small selection of exercises to make sure you know how to apply your knowledge in statistics,

probability and simple regression. If you want to practice some more, or to practice on exercises with a

solution, checkout the links in each section. They come from the recommended resources (Khan Academy,

Udacity and the probability review).

Table of content – Counting: permutations, combinations

• Probability: Probability of an event, Probability of 2 or more events (Conditional probability, Indepen-

dent and dependent events, Mutually exclusive events, Bayes’ Theorem)

• Probability distribution (Binomial, Geometric and Poisson distributions for discrete random variables,

Uniform, Normal and Exponential distributions for continuous random variables)

• Descriptive Statistics: mean, variance, standard deviation, range, IQR

• Inferential Statistics: confidence intervals, hypothesis testing, inference for proportions and means

• Linear regression: model performance, interpretation of coe�cients, underfitting/overfitting

NOTE: Some exercises are labeled as Extra Credit, and as such are not mandatory.

Counting: permutations, combinations

1. Permutations

1. How many ways can you arrange the numbers 1, 2, 3, 4 and 5?

2. How many ways can you arrange 1, 1, 2, 3, 4?

3. How many ways can you arrange two 3s and three 5s?

Some links: http://bit.ly/2iGgrir, http://bit.ly/2jXtFIt

2. Combinations

1. How many di�erent poker hands (5 cards) can you have? A deck holds 52 cards.

2. There are five flavors of ice cream: Stracciatella, Mint chocolate chip, Cookies and Cream, Butter

Pecan, Pistachio and Pralines and cream. How many three scoop ice-creams can you make if all the

scoops must be di�erent flavors?

Extra Credit: what happens if you can take several scoops of the same flavor?

Some links: http://bit.ly/2iNIXSF, http://bit.ly/2jXlDiI

Probability

1. Probability of an event

1. In a deck of cards (52 cards), what’s the probability of picking a queen? A heart? Of picking a card

that’s not a queen nor a heart?

2. If I do not replace the cards, what is the probability of picking 2 kings? 4 diamonds? How do these

probabilities evolve if I replace the cards after each draw?

Some links: http://bit.ly/2iNCwyS, http://bit.ly/OtSNH2, http://bit.ly/2j7R4qF

10

https://www.khanacademy.org/math/statistics-probability

https://www.udacity.com/course/intro-to-statistics–st101

http://bit.ly/2iGgrir

http://bit.ly/2jXtFIt

http://bit.ly/2iNIXSF

http://bit.ly/2jXlDiI

http://bit.ly/2iNCwyS

http://bit.ly/OtSNH2

http://bit.ly/2j7R4qF

2. Probability of 2 or more events

Conditional probability

1. What is the probability that the total of two dice is less than four, knowing that the first die is a 2?

2. 90% of candidates to a Web developer position can code both in Javascript and HTML. 70% of these

candidates can code in Javascript and 50% can code in HTML. What is the probability that a candidate

can code in HTML knowing that he can code in Javascript?

Some links: http://bit.ly/2iGktHi

Independent and dependent events

1. Number of kids dressed as pumpkins or ghosts on Halloween night and the amount of candy they

received:

Amount of Candy less than 10 10 – 20 20 – 30 greater than 30

Pumpkins 5 10 60 25

Ghosts 15 40 80 15

• What is the probability that a kid dressed as a pumpkin gets 20 or more pieces of candy? How

about if he dresses as a ghost?

• What is the probability that a kid obtains less than 10 pieces of candy?

• What is the probability that two siblings, one dressed as a ghost and one dressed as a pumpkin,

each receive 20 to 30 pieces of candy?

2. You toss a fair die twice. What is the probability of getting less than 3 on the first toss and an even

number on the second?

Some links: http://bit.ly/2jmalpl

Mutually exclusive events

Let’s consider a population from which we draw a sample of 40 individuals. The probability of your sample

having no-one with glasses is 26%. The probability of having only one individual wearing glasses is 32%.

What is the probability of

(a) Obtaining not more than one individual wearing glasses in a sample?

(b) Obtaining more than one individual wearing glasses in a sample?

Some links: http://bit.ly/2jmjyxO

Bayes’ Theorem

1. To detect a medical condition, patients are given two tests. 25% of the patients receive positive results

on both tests and 42% of the patients receive positive results on the first test. What percent of those

who have positive results on the first test passed also had positive result on the second test?

2. Extra Credit: A jar contains red and blue marbles. You draw two marbles one after the other without

replacing the first marble in the jar. You know that:

• The probability of selecting a blue marble and then a red marble is 30%.

• The probability of selecting a red marble on the first draw is 50%.

You first draw a red marble. What is the probability of selecting a blue marble on the second draw?

Some links: http://bit.ly/2jmjHRS

11

http://bit.ly/2iGktHi

http://bit.ly/2jmalpl

http://bit.ly/2jmjyxO

http://bit.ly/2jmjyxO

Probability distributions Problems

Common problems relying on discrete (Binomial, Geometric, Poisson) or continuous (Uniform, Normal,

Exponential) probability distributions.

Here are some exercises (http://bit.ly/2j7GK25) with their solutions as video.

Binomial distribution

1. Fair coin: Imagine you were to flip a fair coin 10 times. What would be the probability of getting 5

heads?

2. Unfair coin: You have a coin with which you are 2 times more likely to get heads than tails. You flip

the coin 100 times. What is the probability of getting 20 tails? What is the probability of getting at

least one heads?

Geometric distributions

Suppose you have an unfair coin, with a 68% chance of getting tails. What is the probability that the first

head will be on the 3rd trial?

Poisson distribution

On average 20 taxis drive past your o�ce every 30 minutes. What is the probability that 30 taxis will drive

by in 1 hour?

Exponential distribution

Let X, the number of years a computer works, be a random variable that follows an exponential distribution

with a lambda of 3 years. You just bought a computer, what is the probability that the computer will work

in 8 years?

Extra Credit: Let X be a random variable that now follows an exponential distribution with a half-life of

6 years. Find the parameter of the exponential distribution. What is the probability P (X > 10) and the

conditional probability P (X > 20|X > 10)?

Uniform distribution

Let the random variable X be the angle of a slice of pizza. The angle X has a uniform distribution on the

interval [0, 90]. What is the probability that your slice of pizza will have an angle between 30 and 40°?

Extra Credit: X is uniform on the interval [a, b], can you derive the expected value E(X)? The variance

V (X)?

Normal distribution

1. Suppose X has a standard normal distribution. Compute P (X > 9), P (1 < X < 3) and P (X > ≠3).

2. The weight in pounds of individuals in a population of interest has a normal distribution, with a mean

of 150 and a standard deviation of 40. What is the expected range of values that describe the weight of

68% of the population (Hint: use the empirical rule)? Of the people who weigh more than 170 pounds,

what percent weigh more than 200 pounds (Hint: this is conditional probability)?

12

http://bit.ly/2j7GK25

Descriptive Statistics

3 Measures of Average

Give the mean, median and mode of the following data:

(20, 45, 68, 900, 57, 45, 33, 35, 45, 22)

Do you think the mean is a good summary statistic? Why or why not?

Variance, Range, IQR

Give the mean, the variance, the standard deviation, the range and the interquartile of range of the following

data:

(20, 45, 68, 900, 57, 45, 33, 35, 45, 22)

Discrete random variables

Give the expression of the mean and the variance for a discrete random variable X.

Continuous random variables

Give the expression of the mean and the variance for a continuous random variable X.

Inferential Statistics

1. Confidence intervals

1. We are polling to get the approval rate of the president. Out of a population of 4 million, 6014 were

surveyed and 3485 expressed their approval. Construct a 95% confidence interval for the approval rate

of the president.

2. The weight of a random sample of 100 individuals from a population of interest was surveyed and

yielded a sample average weight of 150 pounds and sample standard deviation of 20 pounds. Construct

a 95% confidence interval for the average weight of the population.

2. General hypothesis testing

1. What is the definition of a significance level? Of a p-value?

2. Would you use a one tailed or two tailed tests in the following cases:

• Investigating if women are paid less than men.

• Comparing the click-through rate of website when the ‘subscribe’ button is green vs. when it is

blue.

3. A man goes to trial. In a hypothesis testing framework, let’s define the null hypothesis as Not Guilty

and the alternative hypothesis as Guilty.

• What type of error is made when the man is actually not guilty but verdict returned is guilty?

• What type of error is made when the man is actually guilty but verdict returned is not guilty?

13

3. One sample hypothesis testing

1. We want the test the hypothesis that at least 68% of the Canadian population (aged 18+) went to

the movies at least once in the past 12 months with a significance level of 5%. We surveyed 4,000

respondents and found 3,012 did go at least once to the movies in the past 12 months. How would your

conclusion compare if you only had 40 respondents, 30 of which went to the movies at least once in the

past 12 months

Some links: http://bit.ly/2jIM1h3

2. We want to test the hypothesis that the average weight in North America is at least 175 pounds. The

mean of weights of the 100 individuals sampled is 178 pounds, with a sample standard deviation of 8

pounds. What are you conclusions?

Some links: http://bit.ly/2jmht5d

3. We want to investigate the claim that on average, sea turtles lay 110 eggs in a nest. Volunteers have

gone out and counted the number of eggs in 20 nest. What do you conclude?

• Data: 101, 120, 154, 89, 97, 132, 126, 105, 94, 111, 98, 90, 88, 115, 99, 85, 131,

127, 116

Some links: http://bit.ly/2j7KpN2

3. Two sample hypothesis testing

1. Is there a meaningful di�erence between the proportion of teenagers vs that of adults that go to the

movies at least once per month?

• Data:

– 1000 teenagers are surveyed, 780 answer positively.

– 1000 adults are surveyed, 620 answer positively.

Some links: http://bit.ly/2j7GUXg

2. Is there a meaningful di�erence between the average wingspan of bald eagles vs that of crowned eagles?

• Data for bald eagles (in ft):

[7.4, 7.7, 6.0, 6.7, 8.3, 6.5, 6.9, 7.7, 7.8, 7.3, 6.9, 6.5, 6.3, 4.8, 8.0, 6.8,

5.8, 6.9, 6.3, 6.3, 6.4, 5.1, 6.9, 7.6, 5.6, 6.5, 6.7, 7.8, 6.6, 6.9, 7.0, 6.4, 7.4,

6.0, 7.0, 5.3, 5.8, 6.4, 7.1, 5.5, 7.0, 6.7, 5.8, 6.1, 7.1, 7.9, 7.7, 6.2, 5.3, 6.4,

6.9, 5.9, 7.8, 5.6, 5.0, 5.5, 6.4, 7.1, 8.6, 9.3, 6.8, 7.6, 7.2, 7.1, 5.8, 5.9, 5.1,

6.6, 6.8, 5.7, 6.3, 7.3, 6.3, 7.2, 7.7, 6.0, 7.2, 5.9, 7.2, 7.0, 7.4, 6.5, 7.8, 5.9,

6.3, 6.3, 8.3, 5.9, 6.9, 7.8]

• Data for crowned eagles (in ft):

[5.3, 5.6, 5.8, 5.3, 5.6, 4.9, 5.7, 5.4, 5.8, 5.4, 6.0, 5.4, 5.1, 5.4, 5.2, 5.7,

4.8, 5.8, 5.7, 5.1, 5.3, 5.4, 5.7, 6.6, 5.0, 5.4, 5.3, 5.5, 5.2, 5.6, 5.2, 5.9, 5.7,

5.8, 5.5, 5.2, 4.0, 5.8, 5.2, 6.2, 5.4, 4.6, 5.3, 5.8, 6.3, 4.8, 5.6, 5.4, 5.2, 5.4,

5.1, 6.0, 6.1, 5.4, 5.4, 5.3, 5.0, 6.0, 5.0, 5.8, 5.1, 5.3, 4.8, 5.6, 5.7, 6.1, 5.0,

6.4, 5.1, 4.6, 5.3, 6.0, 4.8, 5.4, 4.3, 5.4, 5.1, 4.7, 6.0, 5.5, 5.4, 5.6, 5.2, 5.8,

5.3, 4.9, 5.3, 5.5, 5.7, 4.7, 6.0, 5.6, 4.9, 5.4, 4.3, 5.5, 4.9, 5.3, 5.6, 6.0]

Some links: http://bit.ly/2jva7OY

14

http://bit.ly/2jIM1h3

http://bit.ly/2jmht5d

http://bit.ly/2j7KpN2

http://bit.ly/2j7GUXg

http://bit.ly/2jva7OY

Relationship between two quantitative variables

1. Dataset

x 0 1 2 3 5

y 1 2.1 3.2 4 6.1

(a) Plot corresponding the scatter plot.

(b) Find the least square regression line y = ax + b. Add it to your plot.

(c) Estimate the value of y when x = 4.

Extra Credit: Can you do these steps in Python?

2. Dataset

x 0 1 2 3 4 7 9 11 30

y 2. 4.9 8. 10.8 13.9 23.1 29. 35. 92.1

(a) Find the least square regression line for the given data points.

(b) Plot the given points and the regression line on the same graph.

3. We have the following (x,y) points:

[(0, 42.0), (1, -101.0), (2, 21.0), (3, -38.0), (4, 5.0), (7, 20.0), (9, 293.0),

(11, 266.0), (15, 625.0), (20, 1266.0), (25, 1757.0), (30, 2844.0)]

(a) Plot the data.

(b) How do you think a linear model would perform? How about a 100 degree polynomial model?

How would you figure out which of these models was preferable?

(c) How would you model the relationship between these features?

4. We have a dataset that gives the height and age of a sample of people. The range of age spans from 1

to 60 years. We decide to compute the correlation coe�cient to model to understand the relationship

between these features.

(a) Do you expect the correlation coe�cient to be positive or negative?

(b) What are some of the limitation of this approach?

Some links: http://bit.ly/2jXyDF6, http://bit.ly/2jqXuRp, http://bit.ly/2jxlCFA

15

http://bit.ly/2jXyDF6

http://bit.ly/2jqXuRp

http://bit.ly/2jxlCFA

How to Use This Document

Spot the Differences

For Loops

For Loops in Functions

Make a function

More Advanced Python Challenges

Challenge 1

Challenge 2

Challenge 3

Challenge 4

Challenge 5

Getting Ready for the SQL Assessment

Our Data

Simple Queries on a Single Table

Build Queries with Aggregates

Build Complex Queries on Multiple Tables

Practice some probability and statistics

Counting: permutations, combinations

Probability

Probability distributions Problems

Descriptive Statistics

Inferential Statistics

Relationship between two quantitative variables