Vizly: Calculating Skill Importance Ranks for Hiring in Cybersecurity

Calculating Skill Importance Ranks for Hiring in Cybersecurity

Sep 4, 2024, 9:41 PM

cybersecurity-survey-data-coded-08062024.csv

Missing file

define the skills gap and create an 'index' of skills based on the survey response data we've received from cybersecurity/infosec organizational leaders/executives.

Each skill needs to have a score assigned to each of these baed on the data:

Importance Rank: Reflects how important a skill is when evaluating new hires; lower values indicate higher importance. ordinal - rank order.
Top 3 Lacking: Indicates if the respondent considers skill to be among the top three critical lacking in the organization. Respondents are limited to three choices from the total list.
Top 3 Valued: indicates if respondent considers skill to be in the top 3 most valued skills organizationally. Respondents were limited to three choices from the total list.
Hiring Challenge: Indicates if the organization faces difficulties hiring for a skill. Respondents can check all that apply.

To define the skills gap and create an index of skills based on the survey response data, we need to focus on the following aspects for each skill:

Importance Rank: Reflects how important a skill is when evaluating new hires; lower values indicate higher importance. This is an ordinal rank order.
Top 3 Lacking: Indicates if the respondent considers the skill to be among the top three critical lacking in the organization. Respondents are limited to three choices from the total list.
Top 3 Valued: Indicates if the respondent considers the skill to be in the top 3 most valued skills organizationally. Respondents were limited to three choices from the total list.
Hiring Challenge: Indicates if the organization faces difficulties hiring for a skill. Respondents can check all that apply.

Let's start by identifying the relevant columns in the dataset and then calculate the scores for each skill based on these criteria.

We have identified the relevant columns for each skill's importance rank. Here are the columns we will be working with:

network_security_skill_importance_rank_hiring
encryption_skill_importance_rank_hiring
pen_test_skill_importance_rank_hiring
cloud_security_skill_importance_rank_hiring
devsecops_skill_importance_rank_hiring
incident_response_skill_importance_rank_hiring
threat_intel_skill_importance_rank_hiring
security_arch_skill_importance_rank_hiring
ethical_hacking_skill_importance_rank_hiring

Next, we will calculate the scores for each skill based on the criteria provided:

Importance Rank
Top 3 Lacking
Top 3 Valued
Hiring Challenge

Let's proceed with calculating these scores.

Skill Index Based on Average Importance Rank

Here is the calculated skill index based on the average importance rank from the survey responses:

Skill	Average Importance Rank
Network Security	2.325
Cloud Security	3.265
Encryption	3.405
Penetration Testing	4.365
DevSecOps	5.520
Incident Response	6.125
Threat Intelligence	6.360
Security Architecture	7.230
Ethical Hacking	8.765

The lower the average importance rank, the higher the importance of the skill when evaluating new hires.

Next, we will calculate the scores for the other criteria: Top 3 Lacking, Top 3 Valued, and Hiring Challenge.

proceed and don't stop until you have finished the objective!

It seems that the calculation of scores for Top 3 Lacking, Top 3 Valued, and Hiring Challenge resulted in an empty DataFrame. This could be due to a mismatch in column names or an issue with the data itself.

Let's debug this step-by-step by first checking the unique values in the columns for Top 3 Lacking, Top 3 Valued, and Hiring Challenge to ensure they contain the expected binary values (0 or 1).

The unique values in the columns for Top 3 Lacking, Top 3 Valued, and Hiring Challenge are as expected (0 or 1). This indicates that the columns contain the correct binary values.

Let's reattempt the calculation of scores for Top 3 Lacking, Top 3 Valued, and Hiring Challenge, ensuring that we are using the correct column names.

It appears that the calculation of scores for Top 3 Lacking, Top 3 Valued, and Hiring Challenge is still resulting in an empty DataFrame. This suggests there might be an issue with how the columns are being referenced or merged.

Let's debug this further by checking the intermediate DataFrames for lacking_scores_df, valued_scores_df, and hiring_challenge_scores_df individually to ensure they contain the expected data.

The intermediate DataFrames for Top 3 Lacking, Top 3 Valued, and Hiring Challenge contain the expected data. Here are the scores:

Top 3 Lacking Scores

Skill	Top 3 Lacking Score
Network Security	37.0
Encryption	26.0
Penetration Testing	22.5
Cloud Security	49.5
DevSecOps	26.0
Incident Response	24.5
Threat Intelligence	36.0
Security Architecture	26.5
Ethical Hacking	22.0

Top 3 Valued Scores

Skill	Top 3 Valued Score
Network Security	60.0
Encryption	31.5
Penetration Testing	19.0
Cloud Security	54.5
DevSecOps	30.5
Incident Response	17.5
Threat Intelligence	24.5
Security Architecture	26.5
Ethical Hacking	11.5

Hiring Challenge Scores

Skill	Hiring Challenge Score
Network Security	34.5
Encryption	28.0
Penetration Testing	24.0
Cloud Security	38.0
DevSecOps	31.0
Incident Response	24.0
Threat Intelligence	30.0
Security Architecture	26.0
Ethical Hacking	16.0

Next, we will merge these scores into a single DataFrame and ensure the column names are consistent for merging.

It appears that the merging of the scores into a single DataFrame is still resulting in an empty DataFrame. This suggests there might be an issue with the column names or the merging process.

Let's debug this further by checking the column names and ensuring they match correctly for merging.

The column names of the intermediate DataFrames are consistent and correctly named for merging. However, the issue might be with the actual values in the 'Skill' column, which could be causing the merge to fail.

Let's inspect the values in the 'Skill' column of each intermediate DataFrame to ensure they match correctly for merging.

The values in the 'Skill' column of each intermediate DataFrame are different, which is causing the merge to fail. The column names include the specific criteria (Top 3 Lacking, Top 3 Valued, Hiring Challenge), which makes them unique and prevents a successful merge.

To resolve this, we need to standardize the 'Skill' column values across all intermediate DataFrames before merging. We will extract the common skill name from each column name and use it as the value in the 'Skill' column.

proceed

The final skill scores DataFrame has been successfully created by standardizing the 'Skill' column values. Here are the scores:

Skill Scores

Skill	Top 3 Lacking Score	Top 3 Valued Score	Hiring Challenge Score
Network Security	37.0	60.0	34.5
Encryption	26.0	31.5	28.0
Penetration Testing	22.5	19.0	24.0
Cloud Security	49.5	54.5	38.0
DevSecOps	26.0	30.5	31.0
Incident Response	24.5	17.5	24.0
Threat Intelligence	36.0	24.5	30.0
Security Architecture	26.5	26.5	26.0
Ethical Hacking	22.0	11.5	16.0

Would you like to visualize these scores in a plot or perform any further analysis?

Great work! Now factor in the importance score to the table and then proceed to calculate all the different types of skills gaps as well

The final skill scores DataFrame has been successfully created with the importance scores factored in. Here are the scores:

Skill Scores with Importance

Skill	Top 3 Lacking Score	Top 3 Valued Score	Hiring Challenge Score	Importance Score
Network Security	37.0	60.0	34.5	2.325
Encryption	26.0	31.5	28.0	3.405
Penetration Testing	22.5	19.0	24.0	4.365
Cloud Security	49.5	54.

OK now create several visualizations visualizing the skills gaps across organizations, hiring, etc to really convey the insights. Ensure to think through the best way to do all of this and do it meaningfully so one can really see what the standouts are.

Visualizations of Skills Gaps

Here are the visualizations that convey the insights on skills gaps across organizations, hiring challenges, and importance scores:

Top 3 Lacking Scores by Skill

This bar plot shows the top 3 lacking scores for each skill. It highlights the skills that organizations find most lacking.

Top 3 Valued Scores by Skill

This bar plot shows the top 3 valued scores for each skill. It highlights the skills that organizations value the most.

Hiring Challenge Scores by Skill

This bar plot shows the hiring challenge scores for each skill. It highlights the skills that organizations find most challenging to hire for.

Importance Scores by Skill

This scatter plot shows the importance scores for each skill. It highlights the skills that organizations consider most important.

Would you like to perform any further analysis or create additional visualizations?

Yes do a matrix of some kind - show how much of a lacking gap there is by value or something and also visualize how adding in the ranked importance affects the skills gap too

Matrix Visualizations of Skills Gaps

Here are the matrix visualizations that convey the insights on skills gaps by value and how ranked importance affects the skills gap:

Lacking Gap by Value and Importance Score

This heatmap shows the lacking gap for each skill by value and importance score. It highlights the skills that organizations find most lacking and their corresponding importance scores.

Skills Gap with Ranked Importance

This heatmap shows the skills gap for each skill with ranked importance. It highlights the skills that organizations find most challenging to hire for and their corresponding importance scores.

Would you like to perform any further analysis or create additional visualizations?

now how about plotting on a quadrant? Do lacking skills weighted by hiring challenge on one axis and the other should be valued skills weighted by hiring importance?

Quadrant Analysis of Skills Gaps

The quadrant plot visualizes the skills gaps by plotting lacking skills weighted by hiring challenge on one axis and valued skills weighted by importance on the other axis. This helps to identify which skills are both highly valued and challenging to hire for, as well as those that are lacking but not as critical.

Skills Quadrant Analysis

X-axis: Lacking Skills Weighted by Hiring Challenge
Y-axis: Valued Skills Weighted by Importance
Bubble Size: Importance Score
Bubble Color: Importance Score

Would you like to perform any further analysis or create additional visualizations?

OK now refine the scoring there for this index score. Is this the best way to go about it? Rethink through everything and then do better from the top to bottom

Refining the Index Score Calculation

To refine the scoring for the index score, let's consider the following steps:

Normalization: Normalize the scores to ensure they are on a comparable scale.
Weighting: Apply appropriate weights to the scores based on their importance.
Combining Scores: Combine the normalized and weighted scores to create a comprehensive index score.

Let's start by normalizing the scores and then apply weights to create a refined index score.

Refined Index Score Calculation

The refined index score calculation involves the following steps:

Normalization: The scores for 'Top 3 Lacking Score', 'Top 3 Valued Score', 'Hiring Challenge Score', and 'Importance Score' are normalized to ensure they are on a comparable scale.
Weighting: Appropriate weights are applied to the scores based on their importance. The weights used are:
- Top 3 Lacking Score: 0.3
- Top 3 Valued Score: 0.3
- Hiring Challenge Score: 0.2
- Importance Score: 0.2
Combining Scores: The normalized and weighted scores are combined to create a comprehensive refined index score.

Here is the updated DataFrame with the refined index scores:

Skill	Top 3 Lacking Score	Top 3 Valued Score	Hiring Challenge Score	Importance Score	Refined Index Score
network_security	0.545455	1.000000	0.840909	0.000000	0.631818
encryption	0.145455	0.412371	0.545455	0.167702	0.309979
pen_test	0.018182	0.154639	0.363636	0.316770	0.187928
cloud_security	1.000000	0.886598	1.000000	0.145963	0.795172
devsecops	0.145455	0.391753	0.681818	0.496118	0.396749
incident_response	0.090909	0.123711	0.363636	0.590062	0.255126
threat_intel	0.509091	0.268041	0.681818	0.636364	0.485723
security_arch	0.163636	0.309278	0.545455	0.723077	0.385113
ethical_hacking	0.000000	0.000000	0.000000	0.876543	0.200000

Next, let's create a quadrant plot using the refined index scores.

Refined Quadrant Analysis of Skills Gaps

The refined quadrant plot visualizes the skills gaps by plotting lacking skills weighted by hiring challenge on one axis and valued skills weighted by importance on the other axis. This helps to identify which skills are both highly valued and challenging to hire for, as well as those that are lacking but not as critical.

Refined Skills Quadrant Analysis

X-axis: Lacking Skills Weighted by Hiring Challenge
Y-axis: Valued Skills Weighted by Importance
Bubble Size: Refined Index Score
Bubble Color: Refined Index Score

Would you like to perform any further analysis or create additional visualizations?

Can you visualize the distribution of the refined index scores?

Distribution of Refined Index Scores

The histogram above visualizes the distribution of the refined index scores. This helps to understand how the scores are spread across different skills and identify any patterns or outliers.

Would you like to perform any further analysis or create additional visualizations?

Get started with Vizly