Daha fazla veri eklemek

Fazla mesai saatleri yıllar arasında anlamlı bir değişim göstermediğine göre, başka hangi değişkenleri inceleyebileceğine bakmalısın. Ekibinle yaptığın görüşmede biri, etkileşim (engagement) puanlarını ve özellikle de lokasyondaki ilgisiz (disengaged) çalışan sayısını denemeyi öneriyor. Anket verileri henüz hazır değil, bu yüzden onları içeri aktarıp üzerinde çalıştığın verilerle birleştirmen gerekecek. Birden fazla join yapmak, HR analitiğinde yaygındır.

Join işleminden sonra year değişkenini factor(year) olarak değiştir. Yılı sayısal değeriyle değil, yalnızca bir gruplama değişkeni olarak umursadığın için, faktöre çevirmek sonraki analizleri kolaylaştıracaktır.

Bu egzersiz

İK Analitiği: R ile Çalışan Verilerini Keşfetme

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

read_csv() kullanarak "survey_data_2.csv" dosyasını survey_data olarak içe aktar.
Etkileşim verilerini left_join() ile hr_joined üzerine ekle. Önce yıl, ardından çalışan kimliği üzerinden join yap.
Join işleminden sonra %>% kullanarak mutate() ile disengaged değişkenini ekle: engagement 2 ya da daha azsa 1, aksi halde 0 olsun; ayrıca year'ı factor(year) ile değiştir. Bu adımların sonucunu safety'ye ata.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import the survey data
survey_data <- ___

# Create the safety dataset
safety <- ___

Kodu Düzenle ve Çalıştır

Bu egzersiz

İK Analitiği: R ile Çalışan Verilerini Keşfetme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

In this chapter, you will get an introduction to how data science is used in a human resources context. Then you will dive into a case study where you'll analyze and visualize recruiting data to determine which source of new candidates ultimately produces the best new hires. The dataset you'll use in this and the other chapters in this course is synthetic, to maintain the privacy of actual employees.

Exercise 1: Welcome to the course!Exercise 2: Applications of human resources (HR) analytics Exercise 3: Looking at the recruiting data Exercise 4: Recruiting and quality of hire Exercise 5: Identifying groups in data Exercise 6: Sales numbers by recruiting source Exercise 7: Attrition rates by recruiting source Exercise 8: Visualizing the recruiting data Exercise 9: Visualizing the sales performance differences Exercise 10: Visualizing the attrition differences Exercise 11: Drawing conclusions

Gallup defines engaged employees as those who are involved in, enthusiastic about and committed to their work and workplace. There is disagreement about the strength of the connection between employee engagement and business outcomes, but the idea is that employees that are more engaged will be more productive and stay with the organization longer. In this chapter, you'll look into potential reasons that one department's engagement scores are lower than the rest.

Exercise 1: Analyzing employee engagement Exercise 2: Importing the survey data Exercise 3: Which department has the lowest engagement?Exercise 4: Comparing other factors by department Exercise 5: Visualizing the engagement data Exercise 6: Visualizing several variables at once Exercise 7: Visualizing several variables at once with facets Exercise 8: Drawing conclusions from graphs Exercise 9: Is that difference meaningful?Exercise 10: Statistical significance - disengaged employees Exercise 11: Statistical significance - vacation days Exercise 12: Drawing conclusions from the tests

When employers make a new hire, they must determine what the new employee will be paid. If the employer is not careful, the new hires can come in with a higher salary than the employees that currently work at the same job, which can cause employee turnover and dissatisfaction. In this chapter, you will check whether new hires are really getting paid more than current employees, and how to double-check your initial observations.

Exercise 1: Paying new hires fairly Exercise 2: Importing the pay data Exercise 3: Is the difference significant?Exercise 4: Omitted variable bias Exercise 5: Omitted variable bias examples Exercise 6: What other differences exist?Exercise 7: What does the pay difference look like now?Exercise 8: Are hourly hires paid more?Exercise 9: Linear regression Exercise 10: New hire pay: a simple regression Exercise 11: New hire pay: accounting for job levels Exercise 12: Drawing conclusions from the tests

Performance management helps an organization keep track of which employees are providing extra value, or below-average value, and compensating them accordingly. Whether performance is a rating or the result of a questionnaire, whether employees are rated each year or more often than that, the process is somewhat subjective. An organization should check that ratings are being given with regard to performance, and not individual managers' preferences, or even biases (conscious or subconscious).

Exercise 1: Joining HR data Exercise 2: Importing the two datasets Exercise 3: Joining performance data Exercise 4: Performance ratings and fairness Exercise 5: Focus on high performers Exercise 6: Comparing distributions of high performers Exercise 7: Checking for omitted variable bias Exercise 8: Logistic regression Exercise 9: Linear and logistic regression Exercise 10: Performance ratings: a simple logistic regression Exercise 11: Performance ratings: accounting for job levels Exercise 12: Conclusions and recommendations

In many industries, workplace safety is a critical consideration. Maintaining a safe workplace provides employees with confidence and reduces costs for workers' compensation and legal liabilities. In this chapter, you'll look for explanations for an increase in workplace accidents.

Exercise 1: Çalışan güvenliği Exercise 2: Kaza verilerini içe aktarma ve birleştirme Exercise 3: En yüksek kaza oranı nerede?Exercise 4: Kaza oranı en çok nerede arttı?Exercise 5: İlgi duyulan konuma odaklanma Exercise 6: Sorunun yaşandığı konuma odaklanma Exercise 7: Daha fazla veri eklemek

Geçerli Egzersiz

Exercise 8: Atlanan değişkenleri kontrol etme Exercise 9: Bu değişim sadece sorunlu lokasyona mı özgü?Exercise 10: Kazalardaki artışı açıklamak Exercise 11: Değişim etkenlerini belirlemek için regresyon kullanma Exercise 12: Ne sonuç çıkarabilirsin?Exercise 13: Sonuç