Job Trends Using Data AnalyticsPRESENTED BY:

GOURAV ANVEKAR

RANJAN MELANTA

KUNAL SAVLANI

Agenda

Introduction

BI Tools/Techniques Used

Data Normalization

Analysis

Comparison

Business Value

Conclusion

Introduction

Introduction

The dataset is based on job market metrics like

salaries, job industries, occupation, countries

etc.

Contains historical data from year 1983 to 1999

Contains wage data of 132 countries

Data source EconomicsWebInstitute.org

Dataset Snapshot

BI Tools/Techniques Used

Tableau

Microsoft Excel Pivot table

Google Data Visualization API

Data Normalization

Cleaning the dataset

Removed entries with inappropriate values (1%)

Removed entries with NULL values (0.5%)

Consolidated dataset from different year ranges

Normalized dataset to meet single standard of comparison

Wage rate was standardized to USD

Exchange rate was taken care for country to country and time

to time

Analysis

Average Wages Vs Country

Average Wages Vs Country

Average Wages Vs Year

Average Wages Vs Occupation

Air transport pilot Miner Post office counter clerk Cook0.00

500.00

1000.00

1500.00

2000.00

2500.00

3000.00

Average Wage Vs Occupation

Comparison

Average Wage Comparison

Average Wage Comparison - Accountant

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 19990

500

1000

1500

2000

2500

3000

Average Wage Comparison - Accountant

United States China India

Data Visualization

Conclusion

Business Value

The analysis can be used by

Job boards like glassdoor, LinkedIn etc. to recommend

potential employers to candidates based on their profile and

the job industries

University career services department to help students

target specific occupation by considering average salary

metrics by locations, growing trends etc.

This analysis can further be extended to make future

predictions in job trends

Conclusion

The world has seen major shifts in the changing job

markets

Worlds largest economies have shown significant

rise in the average salaries in many job industries

Results will help us compare the historical data and

hence carry out predictive analysis to save cost and

build better solution useful to various organizations

Questions

Thank you