Surveys

In [1]:

import pandas as pd

best_practices = pd.read_csv("Best practices assessment.csv")
best_practices

import pandas as pd best_practices = pd.read_csv("Best practices assessment.csv") best_practices

Out[1]:

	How many years of software engineering experience do you have?	How many years of data science experience do you have?	Write reusable scripts for data cleaning and merging	Make datasets available on shared infrastructure	Use versioning for data, model, configurations and training scripts	Continuously monitor the behaviour of deployed models	Log production predictions with the model’s version and input data	Store models in a single format for ease of use	Equip with web interface, package image, provide REST API	Provide simple API for serving batch and real-time requests	Integration with existing data infrastructure	Querying, visualising and understanding metrics and event logging	Allow experimentation with the inference code	Keep the model’s API and documentation together	Parallelise feature extraction	Cache predictions	Async support for top-down chaining models
0	3	1	Strongly agree	Agree	Strongly agree	Neither agree nor disagree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Strongly agree	Neither agree nor disagree	Strongly agree
1	6	2	Neither agree nor disagree	Agree	Neither agree nor disagree	Agree	Agree	Neither agree nor disagree	Disagree	Strongly disagree	Disagree	Strongly disagree	Disagree	Strongly disagree	Disagree	Strongly agree	Strongly disagree
2	1	5	Strongly disagree	Disagree	Disagree	Strongly disagree	Disagree	Strongly disagree	Strongly disagree	Neither agree nor disagree	Strongly disagree	Disagree	Neither agree nor disagree	Agree	Strongly disagree	Strongly disagree	Disagree
3	3	3	Agree	Agree	Disagree	Neither agree nor disagree	Agree	Not applicable	Not applicable	Neither agree nor disagree	Agree	Disagree	Agree	Strongly agree	Not applicable	Neither agree nor disagree	Agree
4	1	7	Neither agree nor disagree	Disagree	Neither agree nor disagree	Disagree	Strongly disagree	Not applicable	Not applicable	Disagree	Strongly disagree	Disagree	Disagree	Neither agree nor disagree	Strongly disagree	Strongly agree	Not applicable
5	6	0	Strongly agree	Strongly agree	Agree	Neither agree nor disagree	Agree	Strongly agree	Agree	Strongly agree	Agree	Strongly agree	Disagree	Strongly agree	Agree	Strongly agree	Not applicable
6	2	2	Disagree	Neither agree nor disagree	Agree	Strongly agree	Neither agree nor disagree	Disagree	Strongly agree	Disagree	Disagree	Agree	Neither agree nor disagree	Neither agree nor disagree	Not applicable	Disagree	Strongly agree
7	1	2	Disagree	Neither agree nor disagree	Disagree	Agree	Disagree	Strongly disagree	Strongly agree	Strongly agree	Strongly disagree	Disagree	Agree	Strongly disagree	Not applicable	Strongly disagree	Strongly disagree
8	0	1	Strongly disagree	Disagree	Strongly disagree	Disagree	Strongly disagree	Disagree	Agree	Strongly disagree	Disagree	Strongly disagree	Disagree	Strongly disagree	Disagree	Disagree	Strongly disagree
9	7	1	Strongly agree	Strongly agree	Agree	Strongly agree	Agree	Agree	Strongly agree	Strongly disagree	Strongly agree	Not applicable	Agree	Agree	Strongly agree	Strongly agree	Agree

In [2]:

m = {
    "Strongly agree": 4 / 4,
    "Agree": 3 / 4,
    "Neither agree nor disagree": 2 / 4,
    "Disagree": 1 / 4,
    "Strongly disagree": 0 / 4,
}

best_practices_dicts = [v.to_dict() for _, v in best_practices.iterrows()]

best_practice_score_ds = []
best_practice_score_se = []

for d in best_practices_dicts:
    ds_experience = int(d["How many years of data science experience do you have?"])
    del d["How many years of data science experience do you have?"]

    se_experience = int(
        d["How many years of software engineering experience do you have?"]
    )
    del d["How many years of software engineering experience do you have?"]

    scores = [m[v] for v in d.values() if v != "Not applicable"]
    score = sum(scores) / len(scores)
    best_practice_score_ds.append((ds_experience, score))
    best_practice_score_se.append((se_experience, score))

best_practices[
    "How many years of software engineering experience do you have?"
].median(), best_practices[
    "How many years of data science experience do you have?"
].median()

m = { "Strongly agree": 4 / 4, "Agree": 3 / 4, "Neither agree nor disagree": 2 / 4, "Disagree": 1 / 4, "Strongly disagree": 0 / 4, } best_practices_dicts = [v.to_dict() for _, v in best_practices.iterrows()] best_practice_score_ds = [] best_practice_score_se = [] for d in best_practices_dicts: ds_experience = int(d["How many years of data science experience do you have?"]) del d["How many years of data science experience do you have?"] se_experience = int( d["How many years of software engineering experience do you have?"] ) del d["How many years of software engineering experience do you have?"] scores = [m[v] for v in d.values() if v != "Not applicable"] score = sum(scores) / len(scores) best_practice_score_ds.append((ds_experience, score)) best_practice_score_se.append((se_experience, score)) best_practices[ "How many years of software engineering experience do you have?" ].median(), best_practices[ "How many years of data science experience do you have?" ].median()

Out[2]:

(2.5, 2.0)

In [3]:

import matplotlib.pyplot as plt
from scipy import stats

best_practice_score_ds = sorted(best_practice_score_ds)
plt.scatter(
    [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds]
)
stats.pearsonr(
    [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds]
)

import matplotlib.pyplot as plt from scipy import stats best_practice_score_ds = sorted(best_practice_score_ds) plt.scatter( [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds] ) stats.pearsonr( [x for x, y in best_practice_score_ds], [y for x, y in best_practice_score_ds] )

Out[3]:

(-0.5440527060340572, 0.10399880919437814)

In [4]:

import re

best_practice_score_se = sorted(best_practice_score_se)

%matplotlib inline
plt.rcParams["figure.facecolor"] = "white"
plt.rcParams["font.size"] = 24
plt.rcParams["figure.figsize"] = (14, 10)

X = [x for x, y in best_practice_score_se]
Y = [y for x, y in best_practice_score_se]

print(sum(Y) / len(Y))
sc = plt.scatter(X, Y, c="black", s=[x * 50 for x, y in best_practice_score_ds])
plt.ylabel("Ratio of implemented deployment best practices")
plt.xlabel("Years of professional software engineering experience")


def best_fit(X, Y):
    xbar = sum(X) / len(X)
    ybar = sum(Y) / len(Y)
    n = len(X)

    numer = sum([xi * yi for xi, yi in zip(X, Y)]) - n * xbar * ybar
    denum = sum([xi**2 for xi in X]) - n * xbar**2

    b = numer / denum
    a = ybar - b * xbar
    return a, b


a, b = best_fit(X, Y)
yfit = [a + b * xi for xi in X]
plt.plot(X, yfit, "b", label="Best fit")
plt.legend(
    sc.legend_elements("sizes", num=5)[0],
    [
        re.sub(r"[^\d]*(\d+)[^\d]*", lambda v: f"{int(v.group(1)) // 40} years (DS)", t)
        for t in sc.legend_elements("sizes", num=5)[1]
    ],
)
plt.tight_layout()

plt.savefig("best-practices", dpi=150)

stats.pearsonr(
    [x for x, y in best_practice_score_se], [y for x, y in best_practice_score_se]
)

import re best_practice_score_se = sorted(best_practice_score_se) %matplotlib inline plt.rcParams["figure.facecolor"] = "white" plt.rcParams["font.size"] = 24 plt.rcParams["figure.figsize"] = (14, 10) X = [x for x, y in best_practice_score_se] Y = [y for x, y in best_practice_score_se] print(sum(Y) / len(Y)) sc = plt.scatter(X, Y, c="black", s=[x * 50 for x, y in best_practice_score_ds]) plt.ylabel("Ratio of implemented deployment best practices") plt.xlabel("Years of professional software engineering experience") def best_fit(X, Y): xbar = sum(X) / len(X) ybar = sum(Y) / len(Y) n = len(X) numer = sum([xi * yi for xi, yi in zip(X, Y)]) - n * xbar * ybar denum = sum([xi**2 for xi in X]) - n * xbar**2 b = numer / denum a = ybar - b * xbar return a, b a, b = best_fit(X, Y) yfit = [a + b * xi for xi in X] plt.plot(X, yfit, "b", label="Best fit") plt.legend( sc.legend_elements("sizes", num=5)[0], [ re.sub(r"[^\d]*(\d+)[^\d]*", lambda v: f"{int(v.group(1)) // 40} years (DS)", t) for t in sc.legend_elements("sizes", num=5)[1] ], ) plt.tight_layout() plt.savefig("best-practices", dpi=150) stats.pearsonr( [x for x, y in best_practice_score_se], [y for x, y in best_practice_score_se] )

0.5157738095238095

Out[4]:

(0.6722543326178704, 0.03321124881554773)

In [5]:

tam = pd.read_csv("Technology acceptance model questionnaire.csv")
tam

tam = pd.read_csv("Technology acceptance model questionnaire.csv") tam

Out[5]:

	I believe the use of GreatAI improves the quality of AI deployments.	I believe the use of GreatAI would increase my productivity.	I believe the use of GreatAI can lead to robust and trustworthy deployments.	Overall, I found GreatAI useful when working with AI.	I found the GreatAI easy to learn.	I found it is easy to employ GreatAI in practice.	I found it is easy to integrate GreatAI into an existing project.	Overall, I found GreatAI easy to use.	Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.	Overall, I intend to use the GreatAI in my personal or professional projects.
0	7	7	7	7	7	7	7	7	7	7
1	7	7	6	7	5	6	7	6	7	7
2	4	4	5	6	6	5	4	4	5	4
3	6	7	6	7	7	6	7	6	7	6
4	7	7	6	7	4	5	5	5	6	6
5	6	6	6	6	6	6	6	6	6	6
6	6	6	6	4	5	7	6	6	7	7
7	7	6	6	6	4	5	3	5	5	6
8	4	5	5	5	7	2	3	3	3	3
9	7	7	7	7	5	6	5	6	7	7

In [6]:

import pingouin

pu = [
    "I believe the use of GreatAI improves the quality of AI deployments.",
    "I believe the use of GreatAI would increase my productivity.",
    "I believe the use of GreatAI can lead to robust and trustworthy deployments.",
    "Overall, I found GreatAI useful when working with AI.",
]
pingouin.cronbach_alpha(tam[pu])

import pingouin pu = [ "I believe the use of GreatAI improves the quality of AI deployments.", "I believe the use of GreatAI would increase my productivity.", "I believe the use of GreatAI can lead to robust and trustworthy deployments.", "Overall, I found GreatAI useful when working with AI.", ] pingouin.cronbach_alpha(tam[pu])

Out[6]:

(0.8813771517996869, array([0.688, 0.967]))

In [7]:

peou = [
    "I found the GreatAI easy to learn.",
    "I found it is easy to employ GreatAI in practice.",
    "I found it is easy to integrate GreatAI into an existing project.",
    "Overall, I found GreatAI easy to use.",
]
pingouin.cronbach_alpha(tam[peou])

peou = [ "I found the GreatAI easy to learn.", "I found it is easy to employ GreatAI in practice.", "I found it is easy to integrate GreatAI into an existing project.", "Overall, I found GreatAI easy to use.", ] pingouin.cronbach_alpha(tam[peou])

Out[7]:

(0.7729220222793487, array([0.403, 0.937]))

In [8]:

itu = [
    "Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.",
    "Overall, I intend to use the GreatAI in my personal or professional projects.",
]
pingouin.cronbach_alpha(tam[itu])

itu = [ "Assuming GreatAI is applicable to my task, I predict that I will use it on a regular basis in the future.", "Overall, I intend to use the GreatAI in my personal or professional projects.", ] pingouin.cronbach_alpha(tam[itu])

Out[8]:

(0.9538950715421304, array([0.814, 0.989]))

In [9]:

tam["pu"] = tam[pu].mean(1)
tam["peou"] = tam[peou].mean(1)
tam["itu"] = tam[itu].mean(1)
tam[["pu", "peou", "itu"]]

tam["pu"] = tam[pu].mean(1) tam["peou"] = tam[peou].mean(1) tam["itu"] = tam[itu].mean(1) tam[["pu", "peou", "itu"]]

Out[9]:

	pu	peou	itu
0	7.00	7.00	7.0
1	6.75	6.00	7.0
2	4.75	4.75	4.5
3	6.50	6.50	6.5
4	6.75	4.75	6.0
5	6.00	6.00	6.0
6	5.50	6.00	7.0
7	6.25	4.25	5.5
8	4.75	3.75	3.0
9	7.00	5.50	7.0

In [10]:

tam[["pu", "peou", "itu"]].mean()

tam[["pu", "peou", "itu"]].mean()

Out[10]:

pu      6.125
peou    5.450
itu     5.950
dtype: float64

In [11]:

tam[["pu", "peou", "itu"]].median()

tam[["pu", "peou", "itu"]].median()

Out[11]:

pu      6.375
peou    5.750
itu     6.250
dtype: float64

In [12]:

tam[["pu", "peou", "itu"]].std()

tam[["pu", "peou", "itu"]].std()

Out[12]:

pu      0.859990
peou    1.039498
itu     1.321825
dtype: float64

In [13]:

stats.pearsonr(tam["peou"], tam["pu"])

stats.pearsonr(tam["peou"], tam["pu"])

Out[13]:

(0.5515422017785757, 0.09838124227663879)

In [14]:

stats.pearsonr(tam["peou"], tam["itu"])

stats.pearsonr(tam["peou"], tam["itu"])

Out[14]:

(0.8066270322592023, 0.004809023073123024)

In [15]:

stats.pearsonr(tam["pu"], tam["itu"])

stats.pearsonr(tam["pu"], tam["itu"])

Out[15]:

(0.7880605510627579, 0.006774486564715021)

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Last update: August 19, 2022