Q1) Employ information technology to identify market trends and opportunities.

Q2) Analyze the ethical use of information technology to develop competitive intelligence.

Q3) Evaluate the benefits of business analytics in measuring enterprise performance.

Q4) Supporting Activity – Special Discussion – Improving the Course

Explanation & Answer length: 4 Questions 400 Words Each One reason lambda functions are called anonymous functions is that unlike functions declared with the def keyword, the function object itself is never given an explicit name attribute. Mathematical and Statistical Methods A set of mathematical functions that compute statistics. We can use aggregations (often called reductions) like sum, mean, and std (standard deviation) either by calling the list instance method or using the top-level NumPy function as we will learn later. From your reading of Chapter 3 of our textbook’s, math and statistics calculations, share with the class examples of each the following basic statistical methods for detecting and filtering data. Organize your information so it is easy to follow and understand (use headings). Make sure all information is provided: Sum: Sum of all the elements in the array or along an axis; zero-length arrays have sum 0 Mean: Arithmetic mean; zero-length arrays have NaN mean std, var: Standard deviation and variance, respectively, with optional degrees of freedom adjustment (default denominator n) min, max: Minimum and maximum argmin, argmax: Indices of minimum and maximum elements, respectively cumsum: Cumulative sum of elements starting from 0 cumprod: Cumulative product of elements starting from 1 1 Descriptive Data Methods Student Name Department/Faculty Name, Institution Name Course Code: Course Title Professor Date 2 Descriptive Data Methods Sum For sum of all the elements along an axis, the following function returns the sum of array elements over the specified axis: Sum(arr, axis, dtype, out). The parameters for the function include: Arr as the input array Axis along which the sum value will be computed. Otherwise, arr will be regarded as flattened, meaning that all the axis will be considered. Where axis = 0, it means along the column. When axis = 1, the function will work along the row. Out stands for different array where the result will be placed. For this, array must be allocated the same dimensions as the anticipated output. Below is an example of sum function: # 1D array arr = [20, 2, .2, 10, 4] print(“\nSum of arr : “, np.sum(arr)) print(“Sum of arr(uint8) : “, np.sum(arr, dtype = np.uint8)) print(“Sum of arr(float32) : “, np.sum(arr, dtype = np.float32)) print (“\nIs np.sum(arr).dtype == np.uint : “, np.sum(arr).dtype == np.uint) print (“Is np.sum(arr).dtype == np.float : “, np.sum(arr).dtype == np.float) Output Sum of arr : 36.2 Sum of arr(uint8) : 36 Sum of arr(float32) : 36.2 3 Is np.sum(arr).dtype == np.uint : False Is np.sum(arr).dtype == np.uint : True Arithmetic Mean Function Arithmetic mean function is suitable for computing the average of a list of numbers. The function returns the mean data set, given as parameters. The average is given by dividing the sum of the numbers with the count of the numbers in the list. Set of numbers: [n10, n20, n30, n40, n50] Sum of data-set = (n10 + n20 + n30 + n40 + n50) Number of data generated = 5 Average or arithmetic mean = (n10 + n20 + n30 + n40 + n50) / 5 data1 = [1, 3, 4, 5, 7, 9, 2] x = statistics.mean(data1) # Printing the mean print(“Mean is :”, x) Output Mean is: 4.428571428571429 Std, var Standard Deviation, Variance Standard deviation indicates the measure of spread and variation of a data set. The function is given by stdev( [data-set], xbar ) sample = [1, 2, 3, 4, 5] # Prints standard deviation # xbar is set to default value of 1 print(“Standard Deviation of sample is % s ” % (statistics.stdev(sample))) Output: Standard Deviation of the sample is 1.5811388300841898 4 Min, Max Minimum, and Maximum Min, max Minimum and maximum function calculates the maximum and minimum of the values passed in the argument. For the maximum, the function is max(j,k,l,..,key,default) # Python code to illustrate the functioning of # max() # printing the maximum of 8,24,40,36,98 print(“Maximum of 8,24,40,36 and 98 is : “,end=””) print (max(8,24,40,36,98 ) ) Output: Maximum of 8,24,40,36 and 98 is 98. For the minimum, the function is expressed as min(j,k,l,..,key,default) # printing the minimum of 8,24,40,36,98 print(“Minimum of 8,24,40,36 and 98 is : “,end=””) print (min(8,24,40,36,98 ) ) Output: Maximum of 8,24,40,36 and 98 is 8. Argmin and Argmax Argmin, argmax returns indices of the minimum and maximum elements of an array in a given axis, expressed as argmax(array, axis = None, out = None) array = geek.arrange(12).reshape(3, 4) print(“INPUT ARRAY : \n”, array) # No axis mentioned, so works on entire array print(“\nMax element : “, geek.argmax(array)) # returning Indices of the max element 5 # as per the indices print(“\nIndices of Max element : “, geek.argmax(array, axis=0)) print(“\nIndices of Max element : “, geek.argmax(array, axis=1)) Output: Input array: [[ 10 11 12 13] [ 14 15 16 17] [ 18 19 20 21]] Minimum element: 10 Max element: 21 Indices of Max element : [2 2 2 2] Indices of Max element : [3 3 3] Cumsum Functions Cumsum function is applied when calculating cumulative sum of array elements over a specific axis, expressed as cumsum(arr, axis=None, dtype=None, out=None) in_arr = geek.array([[2, 4, 6], [1, 3, 5]]) print (“Input array : “, in_arr) out_sum = geek.cumsum(in_arr) print (“cumulative sum of array elements: “, out_sum) Output: Input array : [[2 4 6] [1 3 5]] Cumulative sum of array elements: [ 2 6 12 13 16 21] 6 References Deitel, P., & Deitel, H. (2020). Introduction to data science: Measures of central tendency – Mean, median and mode. In Intro to python for computer science and data science. Pearson Education. Sayantan, J. (2018). Cumsum () in Python

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