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Instructions Assessment Task 1 Questions There are total 2 parts in the assessment task: Part 1 The first part will focus on the basic Python programming skills which includes the data types, the control flow, the function and Class, the modules and library from M02. Part 2 The second part is for those who are aiming to achieve ‘High Distinction’ (HD) for this assessment task, and it will focus on more advanced Python programming skills for data science. This part will require the knowledge covered in M02 and also M03, in particular, numpy and beyond. Task1.ipynb The completed notebook with all the run-able code on all requirements. In general, you need to complete, save the results of running, download and submit your notebook from Python platform such as Google Colab. You need to clearly list the answer for each question, and the expected format from your notebook will be like in Figure 1. Figure 1: Notebook Format Task1Part2.avi If you are aiming to achieve ‘High Distinction’ (HD) and choose to work on Part 2 of this assessment task, one important submission is a short video, in which You are required to orally present the solutions that you provide in your submitted notebook and to illustrate the running of code line by line. The length of video demonstration should be between 5 and 10 minutes, and the file format can be common ones, such as ‘MKV’, ‘WMV’, ‘MOV’ etc. Part I Python Programming There are 8 questions in this part for 80 marks, and each question is 10 marks. You are required to use Google Colab to finish all the coding in the code block cell, provide sufficient coding comments, and also save the result of running as well. Question 1.1 ages = [5,31,43,48,50,41,7,11,15,39,80,82,32,2,8,6,25,36,27,61,31] · find the median value of age (don’t use numpy); · find the age which is larger than 90% of other ages (don’t use numpy); Question 1.2 • define a function sum_test(), which is summation of a sequence numbers. It takes the argument n. For example, when n = 5, the result should be 1 + 2 + 3 + 4 + 5 = 15; • call the function and print out the results when the n = 12. Question 1.3 You are required to design the code (use while statement) could achieve the score grade mechanism (control flow) which could allow you to: · input a score to variable score · return “F” when score < 60="" ·="" return="" “p”="" when="" score="">= 60 and < 70="" ·="" return="" “c”="" when="" score="">= 70 and score < 80="" ·="" return="" “d”="" when="" score="">= 80 and score < 90="" ·="" return="" “hd”="" when="" score="">= 90 In the code, you also need to judge whether the input for score is int type or not. while True : try : score = int (input(” please_fill_your_score : ” )) #continuetowriteyourcodeinbelow# Question 1.4 You are required to use while statement to print out below * mark in 9 lines (must use while statement). * ** *** **** ***** **** *** ** * num = 1 while num>0: # print∗ marki fnotabove 5 print(”∗”∗num) #continuetowriteyourcodeinbelow# Question 1.5 Given a string variable test, for example, test = "aAsmr3idd4bgs7Dlsf9eAF", using code with for statement to find out all the digits (for example, here it is ”3479”) in the string variable test, and store those digits (here it is ”3479”) into another string variable result, then print result. You can implement it as a function. Question 1.6 Define a function find_all, which could find the first index of substring "hello" in the input string such as "helloworldhelloPythonhelloc++hellojava", the return value of the function is a list of the first index, such as [0,10,21,29]. Question 1.7 Define a class Person with two variables on name and age. In the class Person, there are two methods for you to implement: · one is Get_age(), · another one is Set_age(). So that your code can achieve below: daniel = Person ( ’ Daniel ’ ,50) print( daniel ) #Theresultofaboveprintshouldbe : name:Daniel ,age:50# daniel . Set_age (60) print( daniel . Get_age ()) #Theresultofaboveprintshouldbe : 60# print( daniel ) #Theresultofaboveprintshouldbe : name:Daniel ,age:60# Question 1.8 Given the array nums with integers, return all possible permutations of the array. You can return the answer in any order by defining the function permute. Details as below: Input: nums = [1,2,3] Output: [[1,2,3],[1,3,2],[2,1,3],[2,3,1],[3,1,2],[3,2,1]] Input: nums = [1] Output: [[1]] You code should also check and ensure that all the integers in nums are unique. Part II Python Foundations for Data Science This part is for students who are aiming to achieve ‘High Distinction’ (HD) for this assessment task. There are 2 versions of Question 2 in this part for 20 marks: 10 marks for coding, and 10 marks for video presentation Task1Part2.avi (as in ‘What to Submit’). You should only work on one version based on your own enrollment details as in below Section 1, and working on the wrong one will result in zero for this question. For your question, you are required to use Google Colab to finish all the coding in the code block cell, provide sufficient coding comments, and also save the result of running. 1Which version of Question 2 for you? The code of determining your Q2 version is provided: def sum_digits (n ): r = 0 while n: r , n = r + n % 10 , n // 10 returnr def check_studentid ( studentid ): x = sum_digits ( studentid ) i f x % 2 == 0: print ( ’ versionI ’) else : print ( ’ version II ’) check_studentid (9876543210) #replacethevalue by your student ID You need to copy this code to your notebook and run the function with your student ID. You will also need to print/save the result of the code running. Question 2 (Version-I) Find the area First import below libraries in Google Colab: import numpy as np import pandas as pd import matplotlib . pyplot as plt import seaborn as sns %matplotlibinline Then run below code: def f1 (x ): a = (−x∗∗2)+2∗x return a fig ,ax = plt . subplots () ax . set_title ( ’−x^2+2x ’ ) ax . plot (np. linspace (0 ,2 ,100) , f1 (np. linspace (0 ,2 ,100)) , label=’−x^2+2x ’ ) ax . fill_between (np. linspace (0 , ax . legend () plt . show() You will have a visualization as in Figure 2. 2 ,100) ,f1 (np . linspace (0 ,2 , 100))) Figure 2: −x2 + 2x function from x = 0 to x = 2 Interpretation of the visualization: · This is function −x2 + 2x that defines f1, · The x-axis is limited for 100 points from 0 to 2 · The y-axis is the output of f1(x) for each x value. · Therefore, you have the plot for f1(x) function. Question 2.1 · What is the maximum value of f1(x) function? · You are required to implement a function find_max(n) to find the max value of f1(x)? One possible solution you may try n = 100000 samples of the x value from -100 to 100 (use np.linspace(-100, 100, n)) and initialize the maximum of f1(x) with variable max_value, on each value from f1(x), you would like to compare the current f1(x) with max_value, if the current is larger, then you will update the max_value with current f1(x). You will run with all 100000 samples and round your result of max_value to integer. Question 2.2 The problem is how to calculate the area of the f1(x) when x is from 0 to 2? There is one method to calculate the area of given shape - Monte Carlo method as below: · You will need to obtain the max_value from the result of Question 2.1, · You will need to sample points within the rectangle r.In the rectangle, the first point on bottom left is [0,0], second point on bottom right is [2,0], the third point on top left is [0,max_value], the fourth point on top right is [2,max_value], · You need to find how many sampled points are within the area of f1(x) where x is from 0 to 2, • You need to use the area of r multiply the ratio of points in area of f1(x), · Then the area of f1(x) could be calculated. You are required to define the function find_area(sample_num, max_value) for this problem, and you will need to run the find_area(sample_num=100000,max_value) and print / save the results. Question 2 (Version-II) Hill climb on linear regression First import below libraries in Google Colab: import numpy as np import pandas as pd import matplotlib . pyplot as plt import seaborn as sns %matplotlibinline Then run below code: X = np . linspace (0 ,3 , 30) Y = 2.5 ∗ np . linspace (0 ,3 , 30) + 5 ∗ np . random . rand (30) fig ,ax = plt . subplots () ax . set_title ( ’ Regression line to fit ’) ax . set_ylabel ( ’Y’) ax . set_xlabel ( ’X’) ax . plot (X, Y, label =’ line to fit ’) ax . legend () plt . show() You will have a visualization as in Figure 3: Figure 3: Regression line to fit You will need to fit a linear regression to this line plot and the regression equation is like yfit = a∗X To fit the line, you will need to find the optimal a here, and you could follow hill climb as below steps: · defining the total rounds of run n, randomly giving value to a in first round, · calculating the error (yfit − Y ), · adjusting the a for little as aadjust, and get new yfit = aadjust ∗ X, · calculating the errornew (yfit − Y ), · if the errornew < error, then a = aadjust and error = errornew. · finishing all n rounds question 2.1 you will need to define function find_error(a,x,y) for root mean square error as in equation (1) (you can only use numpy to do this question) (1) where yfit = a ∗ x question 2.2 you are required to define the hill climb function fit_regression(n,x,y) to find optimal a. you will also need to run below code after you have found the optimal a: fig ,ax = plt . subplots () ax . set_title ( ’ regression line to fit ’) ax . set_ylabel ( ’y’) ax . set_xlabel ( ’x’) ax . plot (x, y, label =’ line to fit ’) ax . plot (x, a error,="" then="" a="aadjust" and="" error="errornew." ·="" finishing="" all="" n="" rounds="" question="" 2.1="" you="" will="" need="" to="" define="" function="" find_error(a,x,y)="" for="" root="" mean="" square="" error="" as="" in="" equation="" (1)="" (you="" can="" only="" use="" numpy="" to="" do="" this="" question)="" (1)="" where="" yfit="a" ∗="" x="" question="" 2.2="" you="" are="" required="" to="" define="" the="" hill="" climb="" function="" fit_regression(n,x,y)="" to="" find="" optimal="" a.="" you="" will="" also="" need="" to="" run="" below="" code="" after="" you="" have="" found="" the="" optimal="" a:="" fig="" ,="" ax="plt" .="" subplots="" ()="" ax="" .="" set_title="" (="" ’="" regression="" line="" to="" fit="" ’)="" ax="" .="" set_ylabel="" (="" ’y’)="" ax="" .="" set_xlabel="" (="" ’x’)="" ax="" .="" plot="" (x,="" y,="" label="’" line="" to="" fit="" ’)="" ax="" .="" plot="" (x,="">