Analyzing HR Data for Improved Workforce Management: A Case Study

INTRODUCTION

HR analytics, also known as people analytics, is a data-driven approach to managing human resources. It involves gathering and analyzing data related to employees, such as recruitment, performance, engagement, and retention, to derive insights and make informed decisions. This case study explores the application of HR analytics in a hypothetical organization and showcases its benefits in optimizing workforce management.

CASE STUDY OVERVIEW

Organization Description: Let's consider a medium-sized technology company called "TechSolutions Inc." The company specializes in software development and has a diverse workforce across different departments, including engineering, marketing, sales, and customer support.

Objectives: The main objectives of this case study are as follows: 1. Understand the factors influencing employee attrition and job satisfaction. 2. Identify key predictors of employee performance. 3. Develop strategies to improve employee engagement and retention.

DATA COLLECTION AND ANALYSIS

Data Sources: To conduct HR analytics, the following data sources can be utilized: 1. HRIS (Human Resource Information System): Employee demographic information, employment history, and compensation details. 2. Performance Management System: Employee performance ratings, goals, and achievements. 3. Employee Surveys: Feedback on job satisfaction, work-life balance, and engagement. 4. Exit Interviews: Reasons for employee departures and feedback on their experiences.

Data Analysis Steps: 1. Data Preprocessing: Clean and prepare the collected data, handle missing values, and ensure data quality. 2. Attrition Analysis: Analyze historical data to understand factors contributing to employee attrition, such as department, job level, salary, tenure, performance ratings, and employee demographics. 3. Job Satisfaction Analysis: Explore survey data to identify key drivers of job satisfaction, including work environment, career growth opportunities, compensation, and employee benefits. 4. Performance Prediction: Utilize machine learning techniques, such as regression or classification models, to identify predictors of employee performance based on historical performance data, employee characteristics, and other relevant variables. 5. Employee Engagement Analysis: Analyze survey data and feedback to assess employee engagement levels and identify areas of improvement, such as communication, recognition programs, or training opportunities. 6. Actionable Insights: Derive actionable insights from the analysis results to develop targeted strategies for improving employee retention, job satisfaction, and performance.

RESULTS AND RECOMMENDATIONS

Based on the analysis conducted in the previous steps, let's assume the following findings and corresponding recommendations:

1. Attrition Analysis:

   • Identification: High employee turnover observed in the sales department, particularly among junior-level employees.
   • Recommendations: Implement mentoring programs, career development initiatives, and regular performance evaluations to support junior sales employees and enhance their job satisfaction.

2. Job Satisfaction Analysis:

   • Key Drivers: Compensation, opportunities for growth and advancement, and work-life balance identified as key factors affecting job satisfaction.
   • Recommendations: Conduct a salary benchmarking analysis to ensure competitive compensation. Implement performance-based incentives, career development programs, and flexible work arrangements to improve job satisfaction.

3. Performance Prediction:

   • Predictive Factors: Employee tenure, previous performance ratings, and engagement survey scores identified as key predictors of future performance.
   • Recommendations: Implement targeted onboarding programs to improve employee retention. Provide regular feedback and coaching to enhance performance. Identify high-potential employees for career advancement opportunities.

4. Employee Engagement Analysis:

   • Engagement Levels: Low engagement levels observed in the engineering department, possibly due to limited career growth opportunities and communication gaps.
   • Recommendations: Establish clear career paths, offer training and development opportunities, and foster a culture of open communication and feedback within the engineering department.

By implementing these recommendations, TechSolutions Inc. can enhance employee satisfaction, engagement, and retention, leading to a more productive and motivated workforce.

Dataset

This dataset was created by Joel Luma

Contents

Welcome to the Cyclistic bike-share analysis case study! In this case study, I have perform many real-world tasks of a junior data analyst. For this case study, I will work for a fictional company called, Cyclistic. I will meet different characters and team members. In order to answer the key business questions, I will follow the steps of the data analysis process: Ask, Prepare, Process, Analyze, Share and Act.

Scenario: I'm a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, my team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, my team will design a new marketing strategy to convert casual riders into annual members.

But first, Cyclistic executives must approve my recommendations, so they must be backed up with compelling data insights and professional data visualizations.

Collecting data and License:

The datasets have a different name because Cyclistic is a fictional company. For the purposes of this case study, the datasets are appropriate and will enable me to answer the business questions. The data has been made available by Motivate International Inc.

under this link: https://ride.divvybikes.com/data-license-agreement

The data I'm using was provided to me through the case study road map for the Coursera: Google Data Analytics Professional Certificate.

Under this link: https://divvy-tripdata.s3.amazonaws.com/index.html

This is public data that you can use to explore how different customer types are using Cyclistic bikes. But note that data-privacy issues prohibit you from using riders’ personally identifiable information. This means that you won’t be able to connect pass purchases to credit card numbers to determine if casual riders live in the Cyclistic service area or if they have purchased multiple single passes

sliding

Thorough knowledge of the structure of analyzed data allows to form detailed scientific hypotheses and research questions. The structure of data can be revealed with methods for exploratory data analysis. Due to multitude of available methods, selecting those which will work together well and facilitate data interpretation is not an easy task. In this work we present a well fitted set of tools for a complete exploratory analysis of a clinical dataset and perform a case study analysis on a set of 515 patients. The proposed procedure comprises several steps: 1) robust data normalization, 2) outlier detection with Mahalanobis (MD) and robust Mahalanobis distances (rMD), 3) hierarchical clustering with Ward’s algorithm, 4) Principal Component Analysis with biplot vectors. The analyzed set comprised elderly patients that participated in the PolSenior project. Each patient was characterized by over 40 biochemical and socio-geographical attributes. Introductory analysis showed that the case-study dataset comprises two clusters separated along the axis of sex hormone attributes. Further analysis was carried out separately for male and female patients. The most optimal partitioning in the male set resulted in five subgroups. Two of them were related to diseased patients: 1) diabetes and 2) hypogonadism patients. Analysis of the female set suggested that it was more homogeneous than the male dataset. No evidence of pathological patient subgroups was found. In the study we showed that outlier detection with MD and rMD allows not only to identify outliers, but can also assess the heterogeneity of a dataset. The case study proved that our procedure is well suited for identification and visualization of biologically meaningful patient subgroups.

Context

This table is a summary table of insights of my first data analyst project, a Google Data Analytics Professional Certificate Programme Case Study.

Content

It has nearly 5M rows and a 20 columns.

Case Study: How Does a Bike-Share Navigate Speedy Success?¶

Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members. In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path. By the end of this lesson, you will have a portfolio-ready case study. Download the packet and reference the details of this case study anytime. Then, when you begin your job hunt, your case study will be a tangible way to demonstrate your knowledge and skills to potential employers.

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations. Characters and teams ● Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. ● Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels. ● Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them. ● Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

About the company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs. Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends

Three questions will guide the future marketing program:

How do annual members and casual riders use Cyclistic bikes differently? Why would casual riders buy Cyclistic annual memberships? How can Cyclistic use digital media to influence casual riders to become members? Moreno has assigned you the first question to answer: How do annual members and casual rid...

This is the final presentation for the Google Data Analytics Certification (Case Study 1).

Penelitian ini memakai data berupa user-generated-content dari Twitter. Pengambilan data diambil dengan teknik crawling menggunakan tools Python. Proses analisis akan terdiri dari beberapa tahap, yaitu perception analysis, dan sentiment analysis. Analisis data ini akan menggunakan tools Google Collab dengan bahasa pemrograman Python dan Orange.

This dataset is a practical SQL case study designed for learners who are looking to enhance their SQL skills in analyzing sales, products, and marketing data. It contains several SQL queries related to a simulated business database for product sales, marketing expenses, and location data. The database consists of three main tables: Fact, Product, and Location.

Objective of the Case Study: The purpose of this case study is to provide learners with a variety of practical SQL exercises that involve real-world business problems. The queries explore topics such as:

• Aggregating data (e.g., sum, count, average)
• Filtering and sorting data
• Grouping and joining multiple tables
• Using SQL functions like AVG(), COUNT(), SUM(), and MIN/MAX()
• Handling advanced SQL features such as row numbering, transactions, and stored procedures

ABSTRACT Companies are encouraged by the big data trend to experiment with advanced analytics and many turn to specialist consultancies to help them get started where they lack the necessary competences. We investigate the program of one such consultancy, Advectas - in particular the advanced analytics Jumpstart. Using qualitative techniques including semi structured interviews and content analysis we investigate the nature and value of the Jumpstart concept through five cases in different companies. We provide a definition, a process model and a set of thirteen best practices derived from these experiences, and discuss the distinctive qualities of this approach.

This dataset is issued from the public repository TCGA (https://portal.gdc.cancer.gov/) and contain several files, each corresponding to a given omic on the same individuals with breast cancer. Raw data have been obtained from the mixOmics case study described in http://mixomics.org/mixdiablo/case-study-tcga/ [link accessed on August 18, 2021] and were made available by the package authors at http://mixomics.org/wp-content/uploads/2016/08/TCGA.normalised.mixDIABLO.RData_.zip (R data format). Data in the zip file had been normalised for technical biases by the package authors. Data from the train and test sets were exported as TXT/CSV files and completed with miRNA expression on the smae individuals and toy datasets to handle missing value cases and alike. They serve as a basis for the illustration of the web data analysis tool ASTERICS (Project 20008788 funded by Région Occitanie).

Dataset

This dataset was created by Johnnie R.

Contents

It contains the following files:

The Insurance Dataset project is an extensive initiative focused on collecting and analyzing insurance-related data from various sources.

Patient categorical and nominal attributes.

Abstract During analysis of scientific research data, it is customary to encounter anomalous values or missing data. Anomalous values can be the result of errors of recording, typing, measurement by instruments, or may be true outliers. This review discusses concepts, examples and methods for identifying and dealing with such contingencies. In the case of missing data, techniques for imputation of the values are discussed in, order to avoid exclusion of the research subject, if it is not possible to retrieve information from registration forms or to re-address the participant.

Note: I am a junior data analyst looking forward to improve my abilities. I would love to receive any suggestions or recommendations to help sharpen my skills. Any help would be appreciated. Thanks!

Bellabeat is a health and wellness technology company that manufactures health-focused smart products. The management of the company has asked the marketing analytics team to focus on a Bellabeat product and analyze smart device usage data in order to gain insight into how people are already using their smart devices. Then, using this information, the management of the company would like high-level recommendations for how these trends can inform Bellabeat marketing strategy.

Based on the Fitbit data obtained from the survey of 33 unique users, the data was cleaned, aggregated and analyzed to understand the user trends. A dashboard and presentation was compiled to tell the story of data.

Library of the 55 different classification and regression machine-learning algorithms used by the ensemble predictor SuperLearner (SL.library) in the CBDA 2.0 implementation.

Case study: Median (interquartile range) of the estimated values of η and p when we assume k = 5.

Original Dataset: data.cityofchicago.org/Public-Safety/Homicides/iyvd-p5ga/about_data

Used for Data Analytics Case Study