Motivation: To Uncover the factors that leads to employee Attrition
Goal:
1. To perform a data exploration in the data set by using SQL and R
2. Visualize the data using Tableau using interactive dashboard
3. Build a Random forest algorithm that could help us predict the factors leading to the employee attrition.
Data: IBM’s Employee attrition data:
The data is found in the below URL (Kaggle Repository)
https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset/data
As an engineer, I want to work in a company which understands the challenges of today and tomorrow. I wish to innovate all the time as it means better understanding the world around me.
I am enthusiastic about machine learning and I am studying for data science as it regroups mathematics, creating algorithms and investigating data.
This portfolio shows the projects I have worked on.
link to my linkedin account : https://fr.linkedin.com/in/pierre-masse
IBM HR Analytics Employee Attrition & PerformanceShivangiKrishna
- Help companies to be prepared for future employee-loss
- Evaluating possible trends and reasons for employee attrition, in order to prevent valuable employees from leaving.
- We analyzed the numeric and categorical data with the use of Machine Learning models to identify the main variables contributing to the attrition of employees
- This project was completed and carried out by three DSAI students Angelin Grace Wijaya, Agarwala Pratham, Krishna Shivangi
Delve into our students' project on employee retention, highlighting data-driven strategies to enhance workforce stability. Explore how analytics can predict turnover, identify key retention drivers, and improve employee engagement. Gain insights into HR analytics, predictive modeling, and innovative approaches to employee retention. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Workforce analytics, also called HR analytics or people analytics is getting much attention lately. And rightly so! Research has shown that companies using data to drive their decisions and actions are more succesfull than others. With (predictive) analytics an accurate view of the future requires predictions based on data rather than personal hunches or speculation.
In this comprehensive report, meticulous documentation has been undertaken to address the assessment questions and deliver meaningful insights. This detailed record aims to provide a transparent account of the analytical processes employed. The objective is to present not only answers to the posed questions but also valuable and actionable insights derived from a thorough examination of the data.
data science training in Hyderabad | Index IT |Data Science Classes in Hyde...index it
Data science is an interdisciplinary field about
processes and systems to extract knowledge or insights
from data in various forms, either structured or
unstructured, which is a continuation of some of the
data analysis fields such as statistics, data mining,
and predictive analytics
Index IT Provides best Data Science Training in Hyderabad
with 18 algorithms in R and 9 Algorithm in Python with 2
projects we are also covered in depth machine learning
with database concepts 15+Exp Faculty @ 8977802802
http://www.indexit.org/data-science-training-in-hyderabad-ameerpet
As an engineer, I want to work in a company which understands the challenges of today and tomorrow. I wish to innovate all the time as it means better understanding the world around me.
I am enthusiastic about machine learning and I am studying for data science as it regroups mathematics, creating algorithms and investigating data.
This portfolio shows the projects I have worked on.
link to my linkedin account : https://fr.linkedin.com/in/pierre-masse
IBM HR Analytics Employee Attrition & PerformanceShivangiKrishna
- Help companies to be prepared for future employee-loss
- Evaluating possible trends and reasons for employee attrition, in order to prevent valuable employees from leaving.
- We analyzed the numeric and categorical data with the use of Machine Learning models to identify the main variables contributing to the attrition of employees
- This project was completed and carried out by three DSAI students Angelin Grace Wijaya, Agarwala Pratham, Krishna Shivangi
Delve into our students' project on employee retention, highlighting data-driven strategies to enhance workforce stability. Explore how analytics can predict turnover, identify key retention drivers, and improve employee engagement. Gain insights into HR analytics, predictive modeling, and innovative approaches to employee retention. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Workforce analytics, also called HR analytics or people analytics is getting much attention lately. And rightly so! Research has shown that companies using data to drive their decisions and actions are more succesfull than others. With (predictive) analytics an accurate view of the future requires predictions based on data rather than personal hunches or speculation.
In this comprehensive report, meticulous documentation has been undertaken to address the assessment questions and deliver meaningful insights. This detailed record aims to provide a transparent account of the analytical processes employed. The objective is to present not only answers to the posed questions but also valuable and actionable insights derived from a thorough examination of the data.
data science training in Hyderabad | Index IT |Data Science Classes in Hyde...index it
Data science is an interdisciplinary field about
processes and systems to extract knowledge or insights
from data in various forms, either structured or
unstructured, which is a continuation of some of the
data analysis fields such as statistics, data mining,
and predictive analytics
Index IT Provides best Data Science Training in Hyderabad
with 18 algorithms in R and 9 Algorithm in Python with 2
projects we are also covered in depth machine learning
with database concepts 15+Exp Faculty @ 8977802802
http://www.indexit.org/data-science-training-in-hyderabad-ameerpet
Data AnalysisTeam A performed a series of analysis on behalf o.docxtheodorelove43763
Data Analysis
Team A performed a series of analysis on behalf of the management of Ballard Integrated Managed Services, Inc. (BIMS). These tasks were the result of an emerging trend of attrition and employee dissatisfaction within their organization. The initial actions taken involved data collection that were presented in the form of an internal employee survey. The data collection analysis revealed our hypothesis and we set out to prove that the increase in employee turnover was due to low employee morale and poor employee performance. The initial survey leads us to a very low response rate of 17.3%—we did not achieve our goal of obtaining the feedback of the vast majority of BIMS employees. By utilizing our initial findings we analyzed, displayed, and interpreted, the outcome shows that BIMS was experiencing high turnover due to lack of proper pay and poor communication within the business. This information provided seemed promising from the perspective that we were narrowing down the core issues within the company; it is never enough for management to determine an effective course of action or forecasting. The inferences made through our descriptive analysis approach made use of all three levels of measurement and dispersion and helped us rank the limited feedback on scale of one through five, changing the ordinal and ratio feedback into a numerical value, when necessary. The demographic based questions were significant collected data based on years of service, division, gender and role and facilitated in our manipulation of the survey data. In combination, we were able to scratch the surface on a pattern of data that ranked very negatively and that also met the condition of our hypothesis—so all was not lost in our initial attempt.
The research that was done for Ballard was to determine if the employees were satisfied with their work conditions and was there fair treatment from the company. The survey sent out would ask workers about working conditions, shifts, training, pay, fair treatment and the company itself. The survey was attached to their bi weekly paychecks asking them to return by a certain date. A reminder was sent out as well to encourage participation. The response to this survey was very low only 17.3 % took the time to even respond. However the data collected did provide was quantitative it provided numerical information to possibly give insight on correcting the current employment issues.
The survey sent out was a measurement on a scale from 1 to 5; five being positive and 1 negative rate the following questions. The questions consisted of do you enjoy working for the company? Do you like your shift? How was your training? Are you being paid fairly? Then basic questions to pinpoint your gender, department and position in the company.
The survey was given to determine the decrease in morale in the company and the increase of turnover of employees. Out of the 449 employees given the survey only 78 were returne.
Forming an effective compensation strategy is not as easy as it appears. Some managers might use
their instinct to throw a dollar figure at an employment contract, but successful salary
planning requires a careful understanding of factors that influence the amount required to secure
appropriate talent.
The market for talent in the tech field is tighter than others, heightening the importance of proper
compensation. In addition to salary tables, this salary guide provides a high-level overview of hiring,
a look at employment in IT, and several key hiring strategies for 2019.
Welcome to the 2021 Indigo’s C-Level Salary Guide. successful salary planning requires a thorough understanding of factors that influence the amount required to secure the appropriate talent.
The market for talent in the tech field is tighter than others, heightening the importance of proper compensation.
6 Cutting-Edge HR Metrics to Measure in 2019Namely
Are you thinking like a data scientist? While we’re all familiar with basic metrics like turnover and time to fill, there’s more that you can and should be measuring. Taking a more analytics-driven approach to your talent practices can help improve everything from hiring to workforce planning to employee development and retention.
Dr. Eric Knudsen, Manager of People Analytics at Namely, and Rita Patterson, Manager of Product Implementation, share how you can build, measure, and action these key metrics to drive HR and business results. In this webinar, you'll learn:
- How to use hiring data to measure and improve quality-of-hire.
- How to use career and skills data to anticipate workforce needs and facilitate tailored employee career growth.
- How to use compensation data to make improve workforce planning and prevent unwanted turnover.
Presenting this set of slides with name - Employee Annual Analysis Powerpoint Presentation Slides. This PPT deck displays twentysix slides with in depth research. Our topic oriented Employee Annual Analysis Powerpoint Presentation Slides presentation deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Employee Annual Analysis Powerpoint Presentation Slides presentation. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
Welcome to the 2019 Indigo’s C-Level Salary Guide. Forming an effective compensation strategy is not as easy as it appears. Some managers habitually throw a dollar figure at an employment contract. However, successful salary planning requires a thorough understanding of factors that influence the amount required to secure the appropriate talent.
The market for talent in the tech field is tighter than others, heightening the importance of proper compensation. In addition to salary tables, this salary guide provides a high-level overview of hiring, a look at employment in IT, and several key hiring strategies for 2020.
Unit 4 [GB513 Business Analytics] Assignment .docxdickonsondorris
Unit 4 [GB513: Business Analytics]
Assignment
This assignment requires you to use Excel. There is no template for this assignment. Make sure you
explain your answers and provide the regression output tables for questions 1 and 2.
Question 1
Shown below are rental and leasing revenue figures for office machinery and equipment in the United
States over a seven-year period according to the U.S. Census Bureau. Use these data to run a linear
regression and then forecast the rental and leasing revenue for the year 2012.
Year Rental and Leasing ($ millions)
2004 5,860
2005 6,632
2006 7,125
2007 6,000
2008 4,380
2009 3,326
2010 2,642
Question 2
Suppose a researcher gathered survey data from 19 employees and asked the employees to rate
their job satisfaction on a scale from 0 to 100 (with 100 being perfectly satisfied). Suppose the
following data represent the results of this survey. Assume that relationship with supervisor is rated
on a scale from 0 to 50 (0 represents poor relationship and 50 represents an excellent relationship),
overall quality of the work environment is rated on a scale from 0 to 100 (0 represents poor work
environment and 100 rep resents an excellent work environment), and opportunities for advancement
is rated on a scale from 0 to 50 (0 represents no opportunities and 50 represents excellent
opportunities).
Answer the following questions:
A) What is the regression formula?
B) How reliable do you think the estimates will be based on this formula? How can you tell?
C) Are there any variables that do not appear to be good predictors of Job satisfaction? How can
you tell?
Unit 4 [GB513: Business Analytics]
D) If a new employee reports that her relationship with her supervisor is 40, finds the quality of the
work environment to be scored at 75, works 60 hours per week and rates her opportunities for
advancement to be at 30, what would you expect her job satisfaction score to be?
Job
satisfaction
Relationship
with
supervisor
Overall
quality of
work
environment
Total
hours
worked
per week
Opportunities
for
advancement
55 27 65 50 42
20 12 13 60 28
85 40 79 45 7
65 35 53 65 48
45 29 43 40 32
70 42 62 50 41
35 22 18 75 18
60 34 75 40 32
95 50 84 45 48
65 33 68 60 11
85 40 72 55 33
10 5 10 50 21
75 37 64 45 42
80 42 82 40 46
50 31 46 60 48
90 47 95 55 30
75 36 82 70 39
45 20 42 40 22
65 32 73 55 12
Question 3
Unit 4 [GB513: Business Analytics]
Investment analysts generally believe the interest rate on bonds is inversely related to the prime
interest rate for loans; that is, bonds perform well when lending rates are down and perform poorly
when interest rates are up. Can the bond rate be predicted by the prime interest rate?
Use the following data to construct a scatter graph and then fit a regression ...
In this report we reveal the state of engagement in the world today. We will discuss how different contexts, from the macro- to the microlevel,can effect employee engagement.
Presenting this set of slides with name - Employee Monitoring PowerPoint Presentation Slides. Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of twentyseven slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed Employee Monitoring PowerPoint Presentation Slides complete deck.
Based on your reading ofThe Best-Performing CEOs in the World, cho.docxikirkton
Based on your reading ofThe Best-Performing CEOs in the World, choose four of the CEOs mentioned in the article to answer the following questions for each CEO and his or her organization.
1. In what ways has each leader proven to be ideal (e.g. performance, leadership, growth)?
2. How has each leader shaped his or her organizational culture?
3. Document and discuss the characteristics in this leader that you see in your own strategic leadership or that you want to add to your strategic leadership. How would these characteristics in you transfer to your organizational culture?
4. What do you believe this leader would do differently than, or the same as, you have done in your Capsim company to ensure positive organizational culture?
Your paper will be 5-6 pages with support from a minimum of two external sources.
at least 2 sources , apa style, 12point font, times new roman, double spaced,
5 or 6 pages, in text citations,
The knock on most business leaders is that they don’t take the
long view—that they’re fixated on achieving short-term goals
to lift their pay. So which global CEOs actually delivered solid
results over the long run? The 2013 version of the CEO Scorecard
provides an objective answer.
by Morten T. Hansen, Herminia Ibarra, and Urs Peyer
100
The Best-Performing
CEOs in the World
hBr.Org
January–February 2013 harvard Business review 81
The BesT-Performing Ceos in The World
I
t’s no accident that chief executives so
often focus on short-term financial re-
sults at the expense of longer-term per-
formance. They have every incentive to
do so. If they don’t make their quarterly
or annual numbers, their compensa-
tion drops and their jobs are in jeopardy.
Stock analysts, shareholders, and often
their own boards judge them harshly if
they miss near-term goals. And without
equally strong pressure to manage for a future that
stretches beyond 90 or 180 days, CEOs’ behavior is
unlikely to change. Developing a simple yet rigorous
way to gauge long-term performance is crucial; after
all, in business, leaders default to managing what’s
measured.
Five years ago we launched a global project to ad-
dress that challenge. But we wanted to do more than
just devise the right metrics. Our goal was to imple-
ment a scorecard that would not only get people
talking about long-term performance but also alter
the way that boards, executives, consultants, and
management scholars thought about and assessed
CEOs. We wanted this innovation to shine a spotlight
on the CEOs worldwide who had created long-term
value for their companies, and we wanted to give ex-
ecutives around the world critical benchmarks they
could aim for.
Three years ago, in the January–February 2010 is-
sue of HBR, we introduced such a scorecard. It evalu-
ated chief executives on their entire tenure in office.
We used it to rank the performance of nearly 2,000
CEOs. This month we are publishing a new version of
that analysis. We have expand ...
Forming an effective compensation strategy is not as easy as it appears. Some managers might use
their instinct to throw a dollar figure at an employment contract, but successful salary
planning requires a careful understanding of factors that influence the amount required to secure
appropriate talent.
The market for talent in the tech field is tighter than others, heightening the importance of proper
compensation. In addition to salary tables, this salary guide provides a high-level overview of hiring,
a look at employment in IT, and several key hiring strategies for 2019.
Over the past year, we have closed 30 top-level positions for IT companies and found that neither
candidates nor employers are confident in numbers. For instance, $ 5000 for the service station - is it a lot
or a little? Who should provide options? Is flexible scheduling motivating?
This prompted us to create a salary and compensation package survey for top managers.
167 top managers shared information about their income and other types of remuneration and motivation.
Our biggest thanks to our partner Vitaliy Luzhentsov for the competent help with statistical analysis
required for the report.
We hope the content herein will help you to make informed life and management decisions.
Whitepaper | The Impact of Valuing Employee Effort | Sapience AnalyticsSapience Analytics
Most organizations will agree that employees are working harder than ever before while also agreeing that employees are less engaged than ever before. What’s wrong with this picture? In this insightful whitepaper you can find an answer.
This whitepaper addresses 3 basic issues:
--Identifying if the employee’s efforts are in line with the value the organization desires
--Can knowing one’s productivity contribute to greater employee engagement?
--How can effort and value be measured?
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Data AnalysisTeam A performed a series of analysis on behalf o.docxtheodorelove43763
Data Analysis
Team A performed a series of analysis on behalf of the management of Ballard Integrated Managed Services, Inc. (BIMS). These tasks were the result of an emerging trend of attrition and employee dissatisfaction within their organization. The initial actions taken involved data collection that were presented in the form of an internal employee survey. The data collection analysis revealed our hypothesis and we set out to prove that the increase in employee turnover was due to low employee morale and poor employee performance. The initial survey leads us to a very low response rate of 17.3%—we did not achieve our goal of obtaining the feedback of the vast majority of BIMS employees. By utilizing our initial findings we analyzed, displayed, and interpreted, the outcome shows that BIMS was experiencing high turnover due to lack of proper pay and poor communication within the business. This information provided seemed promising from the perspective that we were narrowing down the core issues within the company; it is never enough for management to determine an effective course of action or forecasting. The inferences made through our descriptive analysis approach made use of all three levels of measurement and dispersion and helped us rank the limited feedback on scale of one through five, changing the ordinal and ratio feedback into a numerical value, when necessary. The demographic based questions were significant collected data based on years of service, division, gender and role and facilitated in our manipulation of the survey data. In combination, we were able to scratch the surface on a pattern of data that ranked very negatively and that also met the condition of our hypothesis—so all was not lost in our initial attempt.
The research that was done for Ballard was to determine if the employees were satisfied with their work conditions and was there fair treatment from the company. The survey sent out would ask workers about working conditions, shifts, training, pay, fair treatment and the company itself. The survey was attached to their bi weekly paychecks asking them to return by a certain date. A reminder was sent out as well to encourage participation. The response to this survey was very low only 17.3 % took the time to even respond. However the data collected did provide was quantitative it provided numerical information to possibly give insight on correcting the current employment issues.
The survey sent out was a measurement on a scale from 1 to 5; five being positive and 1 negative rate the following questions. The questions consisted of do you enjoy working for the company? Do you like your shift? How was your training? Are you being paid fairly? Then basic questions to pinpoint your gender, department and position in the company.
The survey was given to determine the decrease in morale in the company and the increase of turnover of employees. Out of the 449 employees given the survey only 78 were returne.
Forming an effective compensation strategy is not as easy as it appears. Some managers might use
their instinct to throw a dollar figure at an employment contract, but successful salary
planning requires a careful understanding of factors that influence the amount required to secure
appropriate talent.
The market for talent in the tech field is tighter than others, heightening the importance of proper
compensation. In addition to salary tables, this salary guide provides a high-level overview of hiring,
a look at employment in IT, and several key hiring strategies for 2019.
Welcome to the 2021 Indigo’s C-Level Salary Guide. successful salary planning requires a thorough understanding of factors that influence the amount required to secure the appropriate talent.
The market for talent in the tech field is tighter than others, heightening the importance of proper compensation.
6 Cutting-Edge HR Metrics to Measure in 2019Namely
Are you thinking like a data scientist? While we’re all familiar with basic metrics like turnover and time to fill, there’s more that you can and should be measuring. Taking a more analytics-driven approach to your talent practices can help improve everything from hiring to workforce planning to employee development and retention.
Dr. Eric Knudsen, Manager of People Analytics at Namely, and Rita Patterson, Manager of Product Implementation, share how you can build, measure, and action these key metrics to drive HR and business results. In this webinar, you'll learn:
- How to use hiring data to measure and improve quality-of-hire.
- How to use career and skills data to anticipate workforce needs and facilitate tailored employee career growth.
- How to use compensation data to make improve workforce planning and prevent unwanted turnover.
Presenting this set of slides with name - Employee Annual Analysis Powerpoint Presentation Slides. This PPT deck displays twentysix slides with in depth research. Our topic oriented Employee Annual Analysis Powerpoint Presentation Slides presentation deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Employee Annual Analysis Powerpoint Presentation Slides presentation. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
Welcome to the 2019 Indigo’s C-Level Salary Guide. Forming an effective compensation strategy is not as easy as it appears. Some managers habitually throw a dollar figure at an employment contract. However, successful salary planning requires a thorough understanding of factors that influence the amount required to secure the appropriate talent.
The market for talent in the tech field is tighter than others, heightening the importance of proper compensation. In addition to salary tables, this salary guide provides a high-level overview of hiring, a look at employment in IT, and several key hiring strategies for 2020.
Unit 4 [GB513 Business Analytics] Assignment .docxdickonsondorris
Unit 4 [GB513: Business Analytics]
Assignment
This assignment requires you to use Excel. There is no template for this assignment. Make sure you
explain your answers and provide the regression output tables for questions 1 and 2.
Question 1
Shown below are rental and leasing revenue figures for office machinery and equipment in the United
States over a seven-year period according to the U.S. Census Bureau. Use these data to run a linear
regression and then forecast the rental and leasing revenue for the year 2012.
Year Rental and Leasing ($ millions)
2004 5,860
2005 6,632
2006 7,125
2007 6,000
2008 4,380
2009 3,326
2010 2,642
Question 2
Suppose a researcher gathered survey data from 19 employees and asked the employees to rate
their job satisfaction on a scale from 0 to 100 (with 100 being perfectly satisfied). Suppose the
following data represent the results of this survey. Assume that relationship with supervisor is rated
on a scale from 0 to 50 (0 represents poor relationship and 50 represents an excellent relationship),
overall quality of the work environment is rated on a scale from 0 to 100 (0 represents poor work
environment and 100 rep resents an excellent work environment), and opportunities for advancement
is rated on a scale from 0 to 50 (0 represents no opportunities and 50 represents excellent
opportunities).
Answer the following questions:
A) What is the regression formula?
B) How reliable do you think the estimates will be based on this formula? How can you tell?
C) Are there any variables that do not appear to be good predictors of Job satisfaction? How can
you tell?
Unit 4 [GB513: Business Analytics]
D) If a new employee reports that her relationship with her supervisor is 40, finds the quality of the
work environment to be scored at 75, works 60 hours per week and rates her opportunities for
advancement to be at 30, what would you expect her job satisfaction score to be?
Job
satisfaction
Relationship
with
supervisor
Overall
quality of
work
environment
Total
hours
worked
per week
Opportunities
for
advancement
55 27 65 50 42
20 12 13 60 28
85 40 79 45 7
65 35 53 65 48
45 29 43 40 32
70 42 62 50 41
35 22 18 75 18
60 34 75 40 32
95 50 84 45 48
65 33 68 60 11
85 40 72 55 33
10 5 10 50 21
75 37 64 45 42
80 42 82 40 46
50 31 46 60 48
90 47 95 55 30
75 36 82 70 39
45 20 42 40 22
65 32 73 55 12
Question 3
Unit 4 [GB513: Business Analytics]
Investment analysts generally believe the interest rate on bonds is inversely related to the prime
interest rate for loans; that is, bonds perform well when lending rates are down and perform poorly
when interest rates are up. Can the bond rate be predicted by the prime interest rate?
Use the following data to construct a scatter graph and then fit a regression ...
In this report we reveal the state of engagement in the world today. We will discuss how different contexts, from the macro- to the microlevel,can effect employee engagement.
Presenting this set of slides with name - Employee Monitoring PowerPoint Presentation Slides. Enhance your audiences knowledge with this well researched complete deck. Showcase all the important features of the deck with perfect visuals. This deck comprises of total of twentyseven slides with each slide explained in detail. Each template comprises of professional diagrams and layouts. Our professional PowerPoint experts have also included icons, graphs and charts for your convenience. All you have to do is DOWNLOAD the deck. Make changes as per the requirement. Yes, these PPT slides are completely customizable. Edit the colour, text and font size. Add or delete the content from the slide. And leave your audience awestruck with the professionally designed Employee Monitoring PowerPoint Presentation Slides complete deck.
Based on your reading ofThe Best-Performing CEOs in the World, cho.docxikirkton
Based on your reading ofThe Best-Performing CEOs in the World, choose four of the CEOs mentioned in the article to answer the following questions for each CEO and his or her organization.
1. In what ways has each leader proven to be ideal (e.g. performance, leadership, growth)?
2. How has each leader shaped his or her organizational culture?
3. Document and discuss the characteristics in this leader that you see in your own strategic leadership or that you want to add to your strategic leadership. How would these characteristics in you transfer to your organizational culture?
4. What do you believe this leader would do differently than, or the same as, you have done in your Capsim company to ensure positive organizational culture?
Your paper will be 5-6 pages with support from a minimum of two external sources.
at least 2 sources , apa style, 12point font, times new roman, double spaced,
5 or 6 pages, in text citations,
The knock on most business leaders is that they don’t take the
long view—that they’re fixated on achieving short-term goals
to lift their pay. So which global CEOs actually delivered solid
results over the long run? The 2013 version of the CEO Scorecard
provides an objective answer.
by Morten T. Hansen, Herminia Ibarra, and Urs Peyer
100
The Best-Performing
CEOs in the World
hBr.Org
January–February 2013 harvard Business review 81
The BesT-Performing Ceos in The World
I
t’s no accident that chief executives so
often focus on short-term financial re-
sults at the expense of longer-term per-
formance. They have every incentive to
do so. If they don’t make their quarterly
or annual numbers, their compensa-
tion drops and their jobs are in jeopardy.
Stock analysts, shareholders, and often
their own boards judge them harshly if
they miss near-term goals. And without
equally strong pressure to manage for a future that
stretches beyond 90 or 180 days, CEOs’ behavior is
unlikely to change. Developing a simple yet rigorous
way to gauge long-term performance is crucial; after
all, in business, leaders default to managing what’s
measured.
Five years ago we launched a global project to ad-
dress that challenge. But we wanted to do more than
just devise the right metrics. Our goal was to imple-
ment a scorecard that would not only get people
talking about long-term performance but also alter
the way that boards, executives, consultants, and
management scholars thought about and assessed
CEOs. We wanted this innovation to shine a spotlight
on the CEOs worldwide who had created long-term
value for their companies, and we wanted to give ex-
ecutives around the world critical benchmarks they
could aim for.
Three years ago, in the January–February 2010 is-
sue of HBR, we introduced such a scorecard. It evalu-
ated chief executives on their entire tenure in office.
We used it to rank the performance of nearly 2,000
CEOs. This month we are publishing a new version of
that analysis. We have expand ...
Forming an effective compensation strategy is not as easy as it appears. Some managers might use
their instinct to throw a dollar figure at an employment contract, but successful salary
planning requires a careful understanding of factors that influence the amount required to secure
appropriate talent.
The market for talent in the tech field is tighter than others, heightening the importance of proper
compensation. In addition to salary tables, this salary guide provides a high-level overview of hiring,
a look at employment in IT, and several key hiring strategies for 2019.
Over the past year, we have closed 30 top-level positions for IT companies and found that neither
candidates nor employers are confident in numbers. For instance, $ 5000 for the service station - is it a lot
or a little? Who should provide options? Is flexible scheduling motivating?
This prompted us to create a salary and compensation package survey for top managers.
167 top managers shared information about their income and other types of remuneration and motivation.
Our biggest thanks to our partner Vitaliy Luzhentsov for the competent help with statistical analysis
required for the report.
We hope the content herein will help you to make informed life and management decisions.
Whitepaper | The Impact of Valuing Employee Effort | Sapience AnalyticsSapience Analytics
Most organizations will agree that employees are working harder than ever before while also agreeing that employees are less engaged than ever before. What’s wrong with this picture? In this insightful whitepaper you can find an answer.
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As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
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Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
1. IS6030
NAME: AYANK GUPTA UCID:M12388639
Background: IBM’s HR Analytics
Motivation: To Uncover the factors that leads to employee Attrition
Goal:
1. To perform a data exploration in the data set by using SQL and R
2. Visualize the data using Tableau using interactive dashboard
3. Build a Random forest algorithm that could help us predict the factors leading to the
employee attrition.
Data: IBM’s Employee attrition data:
The data is found in the below URL (Kaggle Repository)
https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset/data
Description on the data:
Contains Various employee Identifiers as Age, Gender,ID
And various metrices like length of stay in the company,Average Monthly Salary
In total it has around 37 columns for us to explore and make the data a little bit more
meaningful
2. PROJECT INDEX
➢ CHAPTER 1: DATA PREPARATION
➢ Performing the completeness check of each variable – examine if missing values are present;
➢ Performing the validity check of each variable – examine if abnormal values are present;
➢ Cleaning the data based on the results of Steps 2 and 3;
➢ Summarizing the distribution of each variable (what tables and figures will you present?)
➢ CHAPTER 2: Descriptive Study (XY plots and correlation studies)
➢ Studying the X-Y plot between the different variables.
➢ Performing Various data exploration analysis
➢ CHAPTER 3: Statistical Modelling
➢ Preparing a model to predict the relationship between the independent variable and the dependent
variables
➢ CHAPTER 4: Visualizing Using Tableau
➢ CHAPTER 5: Project Summary (report)
3. CHAPTER 1: DATA PREPARATION
➢ Data Explanation:
S.No Column Name Column Definition Data Type
1 Age Age of Employees Numeric
2 Attrition Employee still in company status Categorical
3 BusinessTravel Opportunity of Travel Categorical
4 DailyRate Daily rate Numeric
5 Department Employee's Department Categorical
6 DistanceFromHome Employee's Distance from home Categorical
7 Education Level Eductaion Categorical
8 EducationField Field of the education Categorical
10 EmployeeNumber Unique Employee Identifier Numeric
11 EnvironmentSatisfaction Factor for Employee Satisfaction Categorical
12 Gender Employee gender Categorical
13 HourlyRate HourlyRate Numeric
14 JobInvolvement Involvment in the Job Categorical
15 JobLevel Level of the Job Categorical
16 JobRole Role in the Job Categorical
17 JobSatisfaction Satisfaction score of the employee Numeric
18 MaritalStatus Married or Not Categorical
19 MonthlyIncome Monthly income Categorical
20 MonthlyRate Monthl Salary Numeric
21 NumCompaniesWorked
Number of companies worked
before Numeric
22 Over18 whether 18+ ? Categorical
23 OverTime whether used to work overtime Numeric
24 PercentSalaryHike % Salary Hike Categorical
25 PerformanceRating
Performanceo rating of the
Employee Numeric
26 RelationshipSatisfaction Relationship satisfaction rating Categorical
27 StandardHours Standard working hours Numeric
28 StockOptionLevel StockOptionLevel available ? Categorical
29 TotalWorkingYears # Workingyears Numeric
30 TrainingTimesLastYear # Trainings Numeric
31 WorkLifeBalance Work life balance Numeric
32 YearsAtCompany
# years wrking for the same
company Numeric
33 YearsInCurrentRole # Years in current role Numeric
34
YearsSinceLastPromotio
n # years since last year Numeric
35 YearsWithCurrManager # years with the current manager Numeric
4. ➢ Data Normalization:
Data is fine form , as it has all the required columns for analysis and prediction.
The data can be randomly divided into 2 data sets i.e Test and training data sets for the prediction
algorithm
➢ Data Cleaning:
1. Performing the completeness check of each variable
a. The whole data is unique at the Employee number level.
b. Are there, in any missing value ?
c. Bad columns
All the columns are aptly named , Except I had to make a age bucket columns
i.e above 30 and below 30 to have planned analysis on the age group.
Inconsistency in data types corrected:
I observed few of the data types were not consistent
5. ➢ Using SQL for genera statistics, data description and data manipulation
After loading in the excel file in SQL, lets try to do some basic statistics
We will finding the statistics of the below variables
1. YearsWithCurrManager
2. YearsSinceLastPromotion
3. YearsInCurrentRole
4. YearsAtCompany
5. WorkLifeBalance
6. PerformanceRating
7. MonthlyIncome
6.
7. Note: As opposite to the popular belief female on an average gets paid more than males.
Note: Another shocker all the people below 30 earn more on an average that their experienced
counterpart
Now let’s move our analysis to R , Firstly we need to connect our sql data base in to R.
Now let’s check the structure of the data base
8. Finally lets check the the statistically summary of the data sets to check for any discrepancies if any
9. A few basic summaries
Lets look at few of the visualizations in R
10. Creating a Machine learning algorithm-Random Forest for prediction Employees Attrition
Now use the VarImplot function to find out the most important factors
11. As we can see a few important factors in predicting the attritionis OverTime, MonthlyIncome,Total
Working Income and Job Roles
And hence we can study these factors in detail to explore more about in detail in the tableau
dashboard
12. Learning about the insights by using Tableau dashboards.
I tried to make the dashboard completely interactive, so that even a common man could drive
insights through it.
Few of the observations:
1. Most of the Employees are from the Life Sciences closely followed by Medical and
Marketing.
a. Least number of employees belongs to the HR
2. ~16% of the Employees in general leave the company per year.
3. Employees above is 30 are more in number as compared to employees in less than 30.
a. Maximum Employees are mail above 30.
b. And Minimum employees are female 30
In the interactive big boxes above we can also look at various metrices that will be ultra helpful to
the HR like
13. 1. Avg Working hours of the selected employees
2. Avg years in the company
3. Average salary hike
4. Avg salary
Now we select the population that left company and we will be able to see a drastic change
And if we compare the above results with the people who have stayed in the company the
difference will be clear
14.
15. Summary or the conclusion of the findings in the analysis
Below points will help uncover the reason why the employees left the company
1. The Average Salary of the employees who left was almost 33% less than the person who
stayed.
2. The Average Salary hike of the people that stayed in the office was marginally more that
people who left.
3. The Average Working years of the people who stayed were ~3 years more that people who
left
a. This means experienced people are reluctant to switch companies
4. Years with manager: On an average the people who stayed had more time with manager as
compared to the who left
Difficulties faced
1. The Assignment was at the time of other examinations so that to take out time in
completing the assignment
2. It was challenging but good to master Tableau as well.
3. Finding the dataset was also difficult.