404.What happens if maintenance cost is accidentally stored as character data, and how does SAS expose this error?

What happens if maintenance cost is accidentally stored as character data, and how does SAS expose this error?

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HERE WE USED THESE SAS STATEMENTS AND FUNCTIONS FOR THIS PROJECT:

DATA STEP | SET | INPUT | DATALINES | FORMAT | KEEP | IF-THEN-ELSE | LENGTH | PUT | PROC CONTENTS | PROC DATASETS | PROC APPEND | PROC SQL | PROC MEANS | PROC UNIVARIATE | PROC FREQ | PROC CORR | PROC TRANSPOSE | PROC SGPLOT | MACRO | %MACRO / %MEND | INTCK | INTNX | MDY | CAT | CATX | STRIP | TRIM | UPCASE | LOWCASE | PROPCASE | COALESCE

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TABLE OF CONTENTS

1.     Introduction

2.     Business Context

3.     Project Objectives

4.     Dataset Design & Variables

5.     Raw Dataset Creation (with Intentional Error)

6.     Identifying & Explaining the Error

7.     Corrected Dataset – Full SAS Code

8.     Data Preparation & Formatting

9.     Date Handling (MDY, INTCK, INTNX)

10.  Character & Numeric Functions Usage

11.  Reliability & Utilization Classification (Macros)

12.  Fraud Detection Logic (Macros)

13.  Descriptive Analytics (PROC MEANS, UNIVARIATE)

14.  Frequency Analysis (PROC FREQ)

15.  Correlation Analysis (PROC CORR)

16.  SQL-Based Analytics

17.  Data Reshaping (SET, MERGE, APPEND, TRANSPOSE)

18.  Visualization (PROC SGPLOT)

19.  Dataset Cleanup (PROC DATASETS DELETE)

20.  Business Insights

21.  21 Key Points About This Project

22.  Conclusion

1.INTRODUCTION

Lighthouses are critical maritime safety infrastructure.
They guide ships, prevent accidents, and ensure smooth navigation during storms, fog, and night operations.

In this project, we treat lighthouses like operational assets, similar to data centers, telecom towers, or hospitals.
We analyze:

·       Reliability

·       Maintenance costs

·       Failure incidents

·       Power usage

·       Fraud or abnormal behavior

·       Operational efficiency

2. BUSINESS CONTEXT

Maritime authorities face challenges like:

·       High maintenance expenses

·       Power failures

·       Equipment aging

·       False maintenance claims

·       Inconsistent reliability

Using SAS, we can:

·       Detect risky lighthouses

·       Identify fraud-prone maintenance records

·       Optimize maintenance planning

·       Improve safety compliance

3. PROJECT OBJECTIVES

1. Create a lighthouse dataset (15+ observations)
2. Introduce intentional data error
3. Detect and fix the error
4. Perform full analytics using SAS
5. Use macros, SQL, dates, functions
6. Generate visual insights
7. Prepare interview-grade explanations

4. DATASET DESIGN

Variables Used

Variable	Description
Lighthouse_ID	Unique ID
Lighthouse_Name	Name
Coastline	East / West / South
Visibility_Range	Nautical miles
Maintenance_Cost	Annual cost
Failure_Incidents	Yearly failures
Power_Source	Solar / Electric / Hybrid
Installation_Date	Commission date
Last_Service_Date	Last maintenance
Reliability_Index	Calculated percentage
Utilization_Class	High / Medium / Low
Fraud_Flag	Yes / No

5. RAW DATASET (WITH INTENTIONAL ERROR)

Intentional Error:

Maintenance_Cost entered as character ("$45000") instead of numeric

data lighthouse_raw;

    input Lighthouse_ID Lighthouse_Name:$25. Coastline:$10.

          Visibility_Range Maintenance_Cost $ Failure_Incidents

          Power_Source:$10. Installation_Date : date9.

          Last_Service_Date : date9.;

    format Installation_Date Last_Service_Date date9.;

datalines;

101 Alpha_Point East 25 $45000 2 Solar 15JAN2015 12JAN2025

102 Beacon_Rock West 30 $52000 1 Electric 10FEB2016 05JAN2025

103 Coral_Light South 18 $38000 4 Hybrid 22MAR2014 20DEC2024

;

run;

proc print data=lighthouse_raw;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date
1	101	Alpha_Point	East	25	$45000	2	Solar	15JAN2015	12JAN2025
2	102	Beacon_Rock	West	30	$52000	1	Electric	10FEB2016	05JAN2025
3	103	Coral_Light	South	18	$38000	4	Hybrid	22MAR2014	20DEC2024

6.IDENTIFYING THE ERROR

·  Maintenance_Cost should be numeric

·  Stored as character

·  Causes:

·       PROC MEANS failure

·       Incorrect calculations

·       SQL aggregation errors

Detection

proc contents data=lighthouse_raw;

run;

OUTPUT:

The CONTENTS Procedure

Data Set Name	WORK.LIGHTHOUSE_RAW	Observations	3
Member Type	DATA	Variables	9
Engine	V9	Indexes	0
Created	02/17/2026 20:00:36	Observation Length	96
Last Modified	02/17/2026 20:00:36	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	SOLARIS_X86_64, LINUX_X86_64, ALPHA_TRU64, LINUX_IA64
Encoding	utf-8 Unicode (UTF-8)

Engine/Host Dependent Information
Data Set Page Size	131072
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	1363
Obs in First Data Page	3
Number of Data Set Repairs	0
Filename	/saswork/SAS_workFFCC00019F1A_odaws02-apse1-2.oda.sas.com/SAS_work313F00019F1A_odaws02-apse1-2.oda.sas.com/lighthouse_raw.sas7bdat
Release Created	9.0401M8
Host Created	Linux
Inode Number	67111648
Access Permission	rw-r--r--
Owner Name	u63247146
File Size	256KB
File Size (bytes)	262144

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Format
3	Coastline	Char	10
6	Failure_Incidents	Num	8
8	Installation_Date	Num	8	DATE9.
9	Last_Service_Date	Num	8	DATE9.
1	Lighthouse_ID	Num	8
2	Lighthouse_Name	Char	25
5	Maintenance_Cost	Char	8
7	Power_Source	Char	10
4	Visibility_Range	Num	8

7. CORRECTED DATASET (FULL & FINAL)

data lighthouse;

    input Lighthouse_ID Lighthouse_Name:$25. Coastline:$10.

          Visibility_Range Maintenance_Cost Failure_Incidents

          Power_Source:$10. Installation_Date : date9.

          Last_Service_Date : date9.;

    format Installation_Date Last_Service_Date date9.;

datalines;

101 Alpha_Point East 25 45000 2 Solar 15JAN2015 12JAN2025

102 Beacon_Rock West 30 52000 1 Electric 10FEB2016 05JAN2025

103 Coral_Light South 18 38000 4 Hybrid 22MAR2014 20DEC2024

104 Delta_Beam East 22 41000 3 Solar 11APR2017 18JAN2025

105 Eagle_Eye West 35 61000 0 Electric 09MAY2018 01JAN2025

106 Falcon_Tower South 20 39500 2 Hybrid 15JUN2016 10DEC2024

107 Gulf_Guide East 28 47000 1 Solar 12JUL2019 02JAN2025

108 Harbor_Light West 32 58000 0 Electric 18AUG2020 05JAN2025

109 Island_Beam South 16 36000 5 Hybrid 21SEP2013 22DEC2024

110 Jetty_Light East 26 45500 2 Solar 10OCT2015 12JAN2025

111 Keystone West 34 60000 1 Electric 15NOV2017 05JAN2025

112 Lagoon_Light South 19 39000 3 Hybrid 01DEC2016 20DEC2024

113 Marina_Beam East 27 46500 1 Solar 25JAN2019 02JAN2025

114 Nautical_Point West 33 59000 0 Electric 11FEB2021 05JAN2025

115 Ocean_Guard South 21 40500 4 Hybrid 18MAR2014 18DEC2024

;

run;

proc print data=lighthouse;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date
1	101	Alpha_Point	East	25	45000	2	Solar	15JAN2015	12JAN2025
2	102	Beacon_Rock	West	30	52000	1	Electric	10FEB2016	05JAN2025
3	103	Coral_Light	South	18	38000	4	Hybrid	22MAR2014	20DEC2024
4	104	Delta_Beam	East	22	41000	3	Solar	11APR2017	18JAN2025
5	105	Eagle_Eye	West	35	61000	0	Electric	09MAY2018	01JAN2025
6	106	Falcon_Tower	South	20	39500	2	Hybrid	15JUN2016	10DEC2024
7	107	Gulf_Guide	East	28	47000	1	Solar	12JUL2019	02JAN2025
8	108	Harbor_Light	West	32	58000	0	Electric	18AUG2020	05JAN2025
9	109	Island_Beam	South	16	36000	5	Hybrid	21SEP2013	22DEC2024
10	110	Jetty_Light	East	26	45500	2	Solar	10OCT2015	12JAN2025
11	111	Keystone	West	34	60000	1	Electric	15NOV2017	05JAN2025
12	112	Lagoon_Light	South	19	39000	3	Hybrid	01DEC2016	20DEC2024
13	113	Marina_Beam	East	27	46500	1	Solar	25JAN2019	02JAN2025
14	114	Nautical_Point	West	33	59000	0	Electric	11FEB2021	05JAN2025
15	115	Ocean_Guard	South	21	40500	4	Hybrid	18MAR2014	18DEC2024

8. RELIABILITY INDEX CALCULATION

data lighthouse_enriched;

    set lighthouse;

    Reliability_Index = (1 - (Failure_Incidents/10)) * 100;

run;

proc print data=lighthouse_enriched;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index
1	101	Alpha_Point	East	25	45000	2	Solar	15JAN2015	12JAN2025	80
2	102	Beacon_Rock	West	30	52000	1	Electric	10FEB2016	05JAN2025	90
3	103	Coral_Light	South	18	38000	4	Hybrid	22MAR2014	20DEC2024	60
4	104	Delta_Beam	East	22	41000	3	Solar	11APR2017	18JAN2025	70
5	105	Eagle_Eye	West	35	61000	0	Electric	09MAY2018	01JAN2025	100
6	106	Falcon_Tower	South	20	39500	2	Hybrid	15JUN2016	10DEC2024	80
7	107	Gulf_Guide	East	28	47000	1	Solar	12JUL2019	02JAN2025	90
8	108	Harbor_Light	West	32	58000	0	Electric	18AUG2020	05JAN2025	100
9	109	Island_Beam	South	16	36000	5	Hybrid	21SEP2013	22DEC2024	50
10	110	Jetty_Light	East	26	45500	2	Solar	10OCT2015	12JAN2025	80
11	111	Keystone	West	34	60000	1	Electric	15NOV2017	05JAN2025	90
12	112	Lagoon_Light	South	19	39000	3	Hybrid	01DEC2016	20DEC2024	70
13	113	Marina_Beam	East	27	46500	1	Solar	25JAN2019	02JAN2025	90
14	114	Nautical_Point	West	33	59000	0	Electric	11FEB2021	05JAN2025	100
15	115	Ocean_Guard	South	21	40500	4	Hybrid	18MAR2014	18DEC2024	60

·  Converts failures into percentage reliability

·  Standard KPI used in asset management

9. DATE FUNCTIONS (MDY, INTCK, INTNX)

data lighthouse_dates;

    set lighthouse_enriched;

    Next_Service_Date = intnx('month', Last_Service_Date, 6);

    Years_In_Service = intck('year', Installation_Date, today());

    format Next_Service_Date date9.;

run;

proc print data=lighthouse_dates;

 var Lighthouse_ID Lighthouse_Name Coastline Next_Service_Date Years_In_Service 

      Installation_Date;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Next_Service_Date	Years_In_Service	Installation_Date
1	101	Alpha_Point	East	01JUL2025	11	15JAN2015
2	102	Beacon_Rock	West	01JUL2025	10	10FEB2016
3	103	Coral_Light	South	01JUN2025	12	22MAR2014
4	104	Delta_Beam	East	01JUL2025	9	11APR2017
5	105	Eagle_Eye	West	01JUL2025	8	09MAY2018
6	106	Falcon_Tower	South	01JUN2025	10	15JUN2016
7	107	Gulf_Guide	East	01JUL2025	7	12JUL2019
8	108	Harbor_Light	West	01JUL2025	6	18AUG2020
9	109	Island_Beam	South	01JUN2025	13	21SEP2013
10	110	Jetty_Light	East	01JUL2025	11	10OCT2015
11	111	Keystone	West	01JUL2025	9	15NOV2017
12	112	Lagoon_Light	South	01JUN2025	10	01DEC2016
13	113	Marina_Beam	East	01JUL2025	7	25JAN2019
14	114	Nautical_Point	West	01JUL2025	5	11FEB2021
15	115	Ocean_Guard	South	01JUN2025	12	18MAR2014

10. CHARACTER & NUMERIC FUNCTIONS

data lighthouse_clean;

    set lighthouse_dates;

    Lighthouse_Name = propcase(strip(Lighthouse_Name));

    Coastline = upcase(Coastline);

    Power_Source = lowcase(Power_Source);

    Full_Name = catx(' - ', Lighthouse_Name, Coastline);

    Cost_Filled = coalesce(Maintenance_Cost,0);

run;

proc print data=lighthouse_clean;

 var Lighthouse_ID Lighthouse_Name Coastline Power_Source Full_Name Cost_Filled

     Maintenance_Cost;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Power_Source	Full_Name	Cost_Filled	Maintenance_Cost
1	101	Alpha_point	EAST	solar	Alpha_point - EAST	45000	45000
2	102	Beacon_rock	WEST	electric	Beacon_rock - WEST	52000	52000
3	103	Coral_light	SOUTH	hybrid	Coral_light - SOUTH	38000	38000
4	104	Delta_beam	EAST	solar	Delta_beam - EAST	41000	41000
5	105	Eagle_eye	WEST	electric	Eagle_eye - WEST	61000	61000
6	106	Falcon_tower	SOUTH	hybrid	Falcon_tower - SOUTH	39500	39500
7	107	Gulf_guide	EAST	solar	Gulf_guide - EAST	47000	47000
8	108	Harbor_light	WEST	electric	Harbor_light - WEST	58000	58000
9	109	Island_beam	SOUTH	hybrid	Island_beam - SOUTH	36000	36000
10	110	Jetty_light	EAST	solar	Jetty_light - EAST	45500	45500
11	111	Keystone	WEST	electric	Keystone - WEST	60000	60000
12	112	Lagoon_light	SOUTH	hybrid	Lagoon_light - SOUTH	39000	39000
13	113	Marina_beam	EAST	solar	Marina_beam - EAST	46500	46500
14	114	Nautical_point	WEST	electric	Nautical_point - WEST	59000	59000
15	115	Ocean_guard	SOUTH	hybrid	Ocean_guard - SOUTH	40500	40500

11. UTILIZATION CLASSIFICATION (MACRO)

%macro utilization;

data lighthouse_util;

    set lighthouse_clean;

length Utilization_Class $8.;

    if Reliability_Index >= 90 then Utilization_Class='HIGH';

    else if Reliability_Index >= 75 then Utilization_Class='MEDIUM';

    else Utilization_Class='LOW';

run;

proc print data=lighthouse_util;

run;

%mend;

%utilization;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index	Next_Service_Date	Years_In_Service	Full_Name	Cost_Filled	Utilization_Class
1	101	Alpha_point	EAST	25	45000	2	solar	15JAN2015	12JAN2025	80	01JUL2025	11	Alpha_point - EAST	45000	MEDIUM
2	102	Beacon_rock	WEST	30	52000	1	electric	10FEB2016	05JAN2025	90	01JUL2025	10	Beacon_rock - WEST	52000	HIGH
3	103	Coral_light	SOUTH	18	38000	4	hybrid	22MAR2014	20DEC2024	60	01JUN2025	12	Coral_light - SOUTH	38000	LOW
4	104	Delta_beam	EAST	22	41000	3	solar	11APR2017	18JAN2025	70	01JUL2025	9	Delta_beam - EAST	41000	LOW
5	105	Eagle_eye	WEST	35	61000	0	electric	09MAY2018	01JAN2025	100	01JUL2025	8	Eagle_eye - WEST	61000	HIGH
6	106	Falcon_tower	SOUTH	20	39500	2	hybrid	15JUN2016	10DEC2024	80	01JUN2025	10	Falcon_tower - SOUTH	39500	MEDIUM
7	107	Gulf_guide	EAST	28	47000	1	solar	12JUL2019	02JAN2025	90	01JUL2025	7	Gulf_guide - EAST	47000	HIGH
8	108	Harbor_light	WEST	32	58000	0	electric	18AUG2020	05JAN2025	100	01JUL2025	6	Harbor_light - WEST	58000	HIGH
9	109	Island_beam	SOUTH	16	36000	5	hybrid	21SEP2013	22DEC2024	50	01JUN2025	13	Island_beam - SOUTH	36000	LOW
10	110	Jetty_light	EAST	26	45500	2	solar	10OCT2015	12JAN2025	80	01JUL2025	11	Jetty_light - EAST	45500	MEDIUM
11	111	Keystone	WEST	34	60000	1	electric	15NOV2017	05JAN2025	90	01JUL2025	9	Keystone - WEST	60000	HIGH
12	112	Lagoon_light	SOUTH	19	39000	3	hybrid	01DEC2016	20DEC2024	70	01JUN2025	10	Lagoon_light - SOUTH	39000	LOW
13	113	Marina_beam	EAST	27	46500	1	solar	25JAN2019	02JAN2025	90	01JUL2025	7	Marina_beam - EAST	46500	HIGH
14	114	Nautical_point	WEST	33	59000	0	electric	11FEB2021	05JAN2025	100	01JUL2025	5	Nautical_point - WEST	59000	HIGH
15	115	Ocean_guard	SOUTH	21	40500	4	hybrid	18MAR2014	18DEC2024	60	01JUN2025	12	Ocean_guard - SOUTH	40500	LOW

12. FRAUD DETECTION LOGIC (MACRO)

%macro fraud_check;

data lighthouse_fraud;

    set lighthouse_util;

    if Maintenance_Cost > 55000 and Failure_Incidents < 2 then Fraud_Flag='YES';

    else Fraud_Flag='NO';

run;

proc print data=lighthouse_fraud;

run;

%mend;

%fraud_check;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index	Next_Service_Date	Years_In_Service	Full_Name	Cost_Filled	Utilization_Class	Fraud_Flag
1	101	Alpha_point	EAST	25	45000	2	solar	15JAN2015	12JAN2025	80	01JUL2025	11	Alpha_point - EAST	45000	MEDIUM	NO
2	102	Beacon_rock	WEST	30	52000	1	electric	10FEB2016	05JAN2025	90	01JUL2025	10	Beacon_rock - WEST	52000	HIGH	NO
3	103	Coral_light	SOUTH	18	38000	4	hybrid	22MAR2014	20DEC2024	60	01JUN2025	12	Coral_light - SOUTH	38000	LOW	NO
4	104	Delta_beam	EAST	22	41000	3	solar	11APR2017	18JAN2025	70	01JUL2025	9	Delta_beam - EAST	41000	LOW	NO
5	105	Eagle_eye	WEST	35	61000	0	electric	09MAY2018	01JAN2025	100	01JUL2025	8	Eagle_eye - WEST	61000	HIGH	YES
6	106	Falcon_tower	SOUTH	20	39500	2	hybrid	15JUN2016	10DEC2024	80	01JUN2025	10	Falcon_tower - SOUTH	39500	MEDIUM	NO
7	107	Gulf_guide	EAST	28	47000	1	solar	12JUL2019	02JAN2025	90	01JUL2025	7	Gulf_guide - EAST	47000	HIGH	NO
8	108	Harbor_light	WEST	32	58000	0	electric	18AUG2020	05JAN2025	100	01JUL2025	6	Harbor_light - WEST	58000	HIGH	YES
9	109	Island_beam	SOUTH	16	36000	5	hybrid	21SEP2013	22DEC2024	50	01JUN2025	13	Island_beam - SOUTH	36000	LOW	NO
10	110	Jetty_light	EAST	26	45500	2	solar	10OCT2015	12JAN2025	80	01JUL2025	11	Jetty_light - EAST	45500	MEDIUM	NO
11	111	Keystone	WEST	34	60000	1	electric	15NOV2017	05JAN2025	90	01JUL2025	9	Keystone - WEST	60000	HIGH	YES
12	112	Lagoon_light	SOUTH	19	39000	3	hybrid	01DEC2016	20DEC2024	70	01JUN2025	10	Lagoon_light - SOUTH	39000	LOW	NO
13	113	Marina_beam	EAST	27	46500	1	solar	25JAN2019	02JAN2025	90	01JUL2025	7	Marina_beam - EAST	46500	HIGH	NO
14	114	Nautical_point	WEST	33	59000	0	electric	11FEB2021	05JAN2025	100	01JUL2025	5	Nautical_point - WEST	59000	HIGH	YES
15	115	Ocean_guard	SOUTH	21	40500	4	hybrid	18MAR2014	18DEC2024	60	01JUN2025	12	Ocean_guard - SOUTH	40500	LOW	NO

13. DESCRIPTIVE STATISTICS

proc means data=lighthouse_fraud mean min max;

    var Maintenance_Cost Reliability_Index Failure_Incidents;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Maintenance_Cost Reliability_Index Failure_Incidents	47200.00 80.6666667 1.9333333	36000.00 50.0000000 0	61000.00 100.0000000 5.0000000

proc univariate data=lighthouse_fraud;

    var Reliability_Index;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Reliability_Index

Moments
N	15	Sum Weights	15
Mean	80.6666667	Sum Observations	1210
Std Deviation	15.7963227	Variance	249.52381
Skewness	-0.5016516	Kurtosis	-0.7252805
Uncorrected SS	101100	Corrected SS	3493.33333
Coeff Variation	19.5822182	Std Error Mean	4.07859297

Basic Statistical Measures
Location		Variability
Mean	80.66667	Std Deviation	15.79632
Median	80.00000	Variance	249.52381
Mode	90.00000	Range	50.00000
		Interquartile Range	20.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	19.77806	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	100
99%	100
95%	100
90%	100
75% Q3	90
50% Median	80
25% Q1	70
10%	60
5%	50
1%	50
0% Min	50

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
50	9	90	11
60	15	90	13
60	3	100	5
70	12	100	8
70	4	100	14

14. FREQUENCY ANALYSIS

proc freq data=lighthouse_fraud;

    tables Coastline Power_Source Utilization_Class Fraud_Flag;

run;

OUTPUT:

The FREQ Procedure

Coastline	Frequency	Percent	Cumulative Frequency	Cumulative Percent
EAST	5	33.33	5	33.33
SOUTH	5	33.33	10	66.67
WEST	5	33.33	15	100.00

Power_Source	Frequency	Percent	Cumulative Frequency	Cumulative Percent
electric	5	33.33	5	33.33
hybrid	5	33.33	10	66.67
solar	5	33.33	15	100.00

Utilization_Class	Frequency	Percent	Cumulative Frequency	Cumulative Percent
HIGH	7	46.67	7	46.67
LOW	5	33.33	12	80.00
MEDIUM	3	20.00	15	100.00

Fraud_Flag	Frequency	Percent	Cumulative Frequency	Cumulative Percent
NO	11	73.33	11	73.33
YES	4	26.67	15	100.00

15. CORRELATION

proc corr data=lighthouse_fraud;

    var Maintenance_Cost Failure_Incidents Reliability_Index;

run;

OUTPUT:

The CORR Procedure

3 Variables:	Maintenance_Cost Failure_Incidents Reliability_Index

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Maintenance_Cost	15	47200	8707	708000	36000	61000
Failure_Incidents	15	1.93333	1.57963	29.00000	0	5.00000
Reliability_Index	15	80.66667	15.79632	1210	50.00000	100.00000

Pearson Correlation Coefficients, N = 15 Prob > |r| under H0: Rho=0
	Maintenance_Cost	Failure_Incidents	Reliability_Index
Maintenance_Cost	1.00000	-0.87922 <.0001	0.87922 <.0001
Failure_Incidents	-0.87922 <.0001	1.00000	-1.00000 <.0001
Reliability_Index	0.87922 <.0001	-1.00000 <.0001	1.00000

16. SQL ANALYTICS

proc sql;

    select Coastline,

           avg(Maintenance_Cost) as Avg_Cost,

           avg(Reliability_Index) as Avg_Reliability

    from lighthouse_fraud

    group by Coastline;

quit;

OUTPUT:

Coastline	Avg_Cost	Avg_Reliability
EAST	45000	82
SOUTH	38600	64
WEST	58000	96

17. APPEND, TRANSPOSE

WHAT IS WRONG WITH THIS CODE?

Why this is incorrect

Issue	Explanation
Same BASE & DATA	You are appending a dataset into itself
Redundant	No new observations are added
Risky	In some cases, causes duplicate inflation
Interview red flag	Shows lack of data-flow understanding

**PROC APPEND is meant to combine TWO DIFFERENT datasets

·  Monthly → Yearly

·  Current → Historical

·  New batch → Master table

proc append base=lighthouse_fraud 

            data=lighthouse_fraud force;

run;

proc print data=lighthouse_fraud;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index	Next_Service_Date	Years_In_Service	Full_Name	Cost_Filled	Utilization_Class	Fraud_Flag
1	101	Alpha_point	EAST	25	45000	2	solar	15JAN2015	12JAN2025	80	01JUL2025	11	Alpha_point - EAST	45000	MEDIUM	NO
2	102	Beacon_rock	WEST	30	52000	1	electric	10FEB2016	05JAN2025	90	01JUL2025	10	Beacon_rock - WEST	52000	HIGH	NO
3	103	Coral_light	SOUTH	18	38000	4	hybrid	22MAR2014	20DEC2024	60	01JUN2025	12	Coral_light - SOUTH	38000	LOW	NO
4	104	Delta_beam	EAST	22	41000	3	solar	11APR2017	18JAN2025	70	01JUL2025	9	Delta_beam - EAST	41000	LOW	NO
5	105	Eagle_eye	WEST	35	61000	0	electric	09MAY2018	01JAN2025	100	01JUL2025	8	Eagle_eye - WEST	61000	HIGH	YES
6	106	Falcon_tower	SOUTH	20	39500	2	hybrid	15JUN2016	10DEC2024	80	01JUN2025	10	Falcon_tower - SOUTH	39500	MEDIUM	NO
7	107	Gulf_guide	EAST	28	47000	1	solar	12JUL2019	02JAN2025	90	01JUL2025	7	Gulf_guide - EAST	47000	HIGH	NO
8	108	Harbor_light	WEST	32	58000	0	electric	18AUG2020	05JAN2025	100	01JUL2025	6	Harbor_light - WEST	58000	HIGH	YES
9	109	Island_beam	SOUTH	16	36000	5	hybrid	21SEP2013	22DEC2024	50	01JUN2025	13	Island_beam - SOUTH	36000	LOW	NO
10	110	Jetty_light	EAST	26	45500	2	solar	10OCT2015	12JAN2025	80	01JUL2025	11	Jetty_light - EAST	45500	MEDIUM	NO
11	111	Keystone	WEST	34	60000	1	electric	15NOV2017	05JAN2025	90	01JUL2025	9	Keystone - WEST	60000	HIGH	YES
12	112	Lagoon_light	SOUTH	19	39000	3	hybrid	01DEC2016	20DEC2024	70	01JUN2025	10	Lagoon_light - SOUTH	39000	LOW	NO
13	113	Marina_beam	EAST	27	46500	1	solar	25JAN2019	02JAN2025	90	01JUL2025	7	Marina_beam - EAST	46500	HIGH	NO
14	114	Nautical_point	WEST	33	59000	0	electric	11FEB2021	05JAN2025	100	01JUL2025	5	Nautical_point - WEST	59000	HIGH	YES
15	115	Ocean_guard	SOUTH	21	40500	4	hybrid	18MAR2014	18DEC2024	60	01JUN2025	12	Ocean_guard - SOUTH	40500	LOW	NO
16	101	Alpha_point	EAST	25	45000	2	solar	15JAN2015	12JAN2025	80	01JUL2025	11	Alpha_point - EAST	45000	MEDIUM	NO
17	102	Beacon_rock	WEST	30	52000	1	electric	10FEB2016	05JAN2025	90	01JUL2025	10	Beacon_rock - WEST	52000	HIGH	NO
18	103	Coral_light	SOUTH	18	38000	4	hybrid	22MAR2014	20DEC2024	60	01JUN2025	12	Coral_light - SOUTH	38000	LOW	NO
19	104	Delta_beam	EAST	22	41000	3	solar	11APR2017	18JAN2025	70	01JUL2025	9	Delta_beam - EAST	41000	LOW	NO
20	105	Eagle_eye	WEST	35	61000	0	electric	09MAY2018	01JAN2025	100	01JUL2025	8	Eagle_eye - WEST	61000	HIGH	YES
21	106	Falcon_tower	SOUTH	20	39500	2	hybrid	15JUN2016	10DEC2024	80	01JUN2025	10	Falcon_tower - SOUTH	39500	MEDIUM	NO
22	107	Gulf_guide	EAST	28	47000	1	solar	12JUL2019	02JAN2025	90	01JUL2025	7	Gulf_guide - EAST	47000	HIGH	NO
23	108	Harbor_light	WEST	32	58000	0	electric	18AUG2020	05JAN2025	100	01JUL2025	6	Harbor_light - WEST	58000	HIGH	YES
24	109	Island_beam	SOUTH	16	36000	5	hybrid	21SEP2013	22DEC2024	50	01JUN2025	13	Island_beam - SOUTH	36000	LOW	NO
25	110	Jetty_light	EAST	26	45500	2	solar	10OCT2015	12JAN2025	80	01JUL2025	11	Jetty_light - EAST	45500	MEDIUM	NO
26	111	Keystone	WEST	34	60000	1	electric	15NOV2017	05JAN2025	90	01JUL2025	9	Keystone - WEST	60000	HIGH	YES
27	112	Lagoon_light	SOUTH	19	39000	3	hybrid	01DEC2016	20DEC2024	70	01JUN2025	10	Lagoon_light - SOUTH	39000	LOW	NO
28	113	Marina_beam	EAST	27	46500	1	solar	25JAN2019	02JAN2025	90	01JUL2025	7	Marina_beam - EAST	46500	HIGH	NO
29	114	Nautical_point	WEST	33	59000	0	electric	11FEB2021	05JAN2025	100	01JUL2025	5	Nautical_point - WEST	59000	HIGH	YES
30	115	Ocean_guard	SOUTH	21	40500	4	hybrid	18MAR2014	18DEC2024	60	01JUN2025	12	Ocean_guard - SOUTH	40500	LOW	NO

Approach 1:APPEND NEW MAINTENANCE DATA

Why this is correct

·  base= → existing master dataset

·  data= → new incoming records

·  force → allows structure mismatch 

Step 1: Create a NEW dataset

data lighthouse_new;

    input Lighthouse_ID Lighthouse_Name:$25. Coastline:$10. Visibility_Range

           Maintenance_Cost Failure_Incidents Power_Source:$10.

           Installation_Date : date9. Last_Service_Date : date9.

           Reliability_Index Utilization_Class:$6. Fraud_Flag:$3.;

    format Installation_Date Last_Service_Date date9.;

datalines;

116 Pearl_Light East 29 48000 1 solar 20APR2019 10JAN2026 90 HIGH NO

117 Reef_Beam West 36 62000 4 electric 15MAY2018 12JAN2026 60 LOW YES

;

run;

proc print data=lighthouse_new;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index	Utilization_Class	Fraud_Flag
1	116	Pearl_Light	East	29	48000	1	solar	20APR2019	10JAN2026	90	HIGH	NO
2	117	Reef_Beam	West	36	62000	4	electric	15MAY2018	12JAN2026	60	LOW	YES

Step 2: Append CORRECTLY

proc append base=lighthouse_fraud

            data=lighthouse_new force;

run;

proc print data=lighthouse_fraud;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index	Next_Service_Date	Years_In_Service	Full_Name	Cost_Filled	Utilization_Class	Fraud_Flag
1	101	Alpha_point	EAST	25	45000	2	solar	15JAN2015	12JAN2025	80	01JUL2025	11	Alpha_point - EAST	45000	MEDIUM	NO
2	102	Beacon_rock	WEST	30	52000	1	electric	10FEB2016	05JAN2025	90	01JUL2025	10	Beacon_rock - WEST	52000	HIGH	NO
3	103	Coral_light	SOUTH	18	38000	4	hybrid	22MAR2014	20DEC2024	60	01JUN2025	12	Coral_light - SOUTH	38000	LOW	NO
4	104	Delta_beam	EAST	22	41000	3	solar	11APR2017	18JAN2025	70	01JUL2025	9	Delta_beam - EAST	41000	LOW	NO
5	105	Eagle_eye	WEST	35	61000	0	electric	09MAY2018	01JAN2025	100	01JUL2025	8	Eagle_eye - WEST	61000	HIGH	YES
6	106	Falcon_tower	SOUTH	20	39500	2	hybrid	15JUN2016	10DEC2024	80	01JUN2025	10	Falcon_tower - SOUTH	39500	MEDIUM	NO
7	107	Gulf_guide	EAST	28	47000	1	solar	12JUL2019	02JAN2025	90	01JUL2025	7	Gulf_guide - EAST	47000	HIGH	NO
8	108	Harbor_light	WEST	32	58000	0	electric	18AUG2020	05JAN2025	100	01JUL2025	6	Harbor_light - WEST	58000	HIGH	YES
9	109	Island_beam	SOUTH	16	36000	5	hybrid	21SEP2013	22DEC2024	50	01JUN2025	13	Island_beam - SOUTH	36000	LOW	NO
10	110	Jetty_light	EAST	26	45500	2	solar	10OCT2015	12JAN2025	80	01JUL2025	11	Jetty_light - EAST	45500	MEDIUM	NO
11	111	Keystone	WEST	34	60000	1	electric	15NOV2017	05JAN2025	90	01JUL2025	9	Keystone - WEST	60000	HIGH	YES
12	112	Lagoon_light	SOUTH	19	39000	3	hybrid	01DEC2016	20DEC2024	70	01JUN2025	10	Lagoon_light - SOUTH	39000	LOW	NO
13	113	Marina_beam	EAST	27	46500	1	solar	25JAN2019	02JAN2025	90	01JUL2025	7	Marina_beam - EAST	46500	HIGH	NO
14	114	Nautical_point	WEST	33	59000	0	electric	11FEB2021	05JAN2025	100	01JUL2025	5	Nautical_point - WEST	59000	HIGH	YES
15	115	Ocean_guard	SOUTH	21	40500	4	hybrid	18MAR2014	18DEC2024	60	01JUN2025	12	Ocean_guard - SOUTH	40500	LOW	NO
16	116	Pearl_Light	East	29	48000	1	solar	20APR2019	10JAN2026	90	.	.		.	HIGH	NO
17	117	Reef_Beam	West	36	62000	4	electric	15MAY2018	12JAN2026	60	.	.		.	LOW	YES

APPROACH 2: SPLIT → APPEND

Step 1: Split

data east west south;

    set lighthouse_fraud;

    if Coastline='EAST' then output east;

    else if Coastline='WEST' then output west;

    else if Coastline='SOUTH' then output south;

run;

proc print data=east;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index	Next_Service_Date	Years_In_Service	Full_Name	Cost_Filled	Utilization_Class	Fraud_Flag
1	101	Alpha_point	EAST	25	45000	2	solar	15JAN2015	12JAN2025	80	01JUL2025	11	Alpha_point - EAST	45000	MEDIUM	NO
2	104	Delta_beam	EAST	22	41000	3	solar	11APR2017	18JAN2025	70	01JUL2025	9	Delta_beam - EAST	41000	LOW	NO
3	107	Gulf_guide	EAST	28	47000	1	solar	12JUL2019	02JAN2025	90	01JUL2025	7	Gulf_guide - EAST	47000	HIGH	NO
4	110	Jetty_light	EAST	26	45500	2	solar	10OCT2015	12JAN2025	80	01JUL2025	11	Jetty_light - EAST	45500	MEDIUM	NO
5	113	Marina_beam	EAST	27	46500	1	solar	25JAN2019	02JAN2025	90	01JUL2025	7	Marina_beam - EAST	46500	HIGH	NO

proc print data=west;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index	Next_Service_Date	Years_In_Service	Full_Name	Cost_Filled	Utilization_Class	Fraud_Flag
1	102	Beacon_rock	WEST	30	52000	1	electric	10FEB2016	05JAN2025	90	01JUL2025	10	Beacon_rock - WEST	52000	HIGH	NO
2	105	Eagle_eye	WEST	35	61000	0	electric	09MAY2018	01JAN2025	100	01JUL2025	8	Eagle_eye - WEST	61000	HIGH	YES
3	108	Harbor_light	WEST	32	58000	0	electric	18AUG2020	05JAN2025	100	01JUL2025	6	Harbor_light - WEST	58000	HIGH	YES
4	111	Keystone	WEST	34	60000	1	electric	15NOV2017	05JAN2025	90	01JUL2025	9	Keystone - WEST	60000	HIGH	YES
5	114	Nautical_point	WEST	33	59000	0	electric	11FEB2021	05JAN2025	100	01JUL2025	5	Nautical_point - WEST	59000	HIGH	YES

proc print data=south;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index	Next_Service_Date	Years_In_Service	Full_Name	Cost_Filled	Utilization_Class	Fraud_Flag
1	103	Coral_light	SOUTH	18	38000	4	hybrid	22MAR2014	20DEC2024	60	01JUN2025	12	Coral_light - SOUTH	38000	LOW	NO
2	106	Falcon_tower	SOUTH	20	39500	2	hybrid	15JUN2016	10DEC2024	80	01JUN2025	10	Falcon_tower - SOUTH	39500	MEDIUM	NO
3	109	Island_beam	SOUTH	16	36000	5	hybrid	21SEP2013	22DEC2024	50	01JUN2025	13	Island_beam - SOUTH	36000	LOW	NO
4	112	Lagoon_light	SOUTH	19	39000	3	hybrid	01DEC2016	20DEC2024	70	01JUN2025	10	Lagoon_light - SOUTH	39000	LOW	NO
5	115	Ocean_guard	SOUTH	21	40500	4	hybrid	18MAR2014	18DEC2024	60	01JUN2025	12	Ocean_guard - SOUTH	40500	LOW	NO

Step 2: Append Back

proc append base=east 

            data=west force;

run;

proc print data=east;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index	Next_Service_Date	Years_In_Service	Full_Name	Cost_Filled	Utilization_Class	Fraud_Flag
1	101	Alpha_point	EAST	25	45000	2	solar	15JAN2015	12JAN2025	80	01JUL2025	11	Alpha_point - EAST	45000	MEDIUM	NO
2	104	Delta_beam	EAST	22	41000	3	solar	11APR2017	18JAN2025	70	01JUL2025	9	Delta_beam - EAST	41000	LOW	NO
3	107	Gulf_guide	EAST	28	47000	1	solar	12JUL2019	02JAN2025	90	01JUL2025	7	Gulf_guide - EAST	47000	HIGH	NO
4	110	Jetty_light	EAST	26	45500	2	solar	10OCT2015	12JAN2025	80	01JUL2025	11	Jetty_light - EAST	45500	MEDIUM	NO
5	113	Marina_beam	EAST	27	46500	1	solar	25JAN2019	02JAN2025	90	01JUL2025	7	Marina_beam - EAST	46500	HIGH	NO
6	102	Beacon_rock	WEST	30	52000	1	electric	10FEB2016	05JAN2025	90	01JUL2025	10	Beacon_rock - WEST	52000	HIGH	NO
7	105	Eagle_eye	WEST	35	61000	0	electric	09MAY2018	01JAN2025	100	01JUL2025	8	Eagle_eye - WEST	61000	HIGH	YES
8	108	Harbor_light	WEST	32	58000	0	electric	18AUG2020	05JAN2025	100	01JUL2025	6	Harbor_light - WEST	58000	HIGH	YES
9	111	Keystone	WEST	34	60000	1	electric	15NOV2017	05JAN2025	90	01JUL2025	9	Keystone - WEST	60000	HIGH	YES
10	114	Nautical_point	WEST	33	59000	0	electric	11FEB2021	05JAN2025	100	01JUL2025	5	Nautical_point - WEST	59000	HIGH	YES

proc append base=east 

            data=south force;

run;

proc print data=east;

run;

OUTPUT:

Obs	Lighthouse_ID	Lighthouse_Name	Coastline	Visibility_Range	Maintenance_Cost	Failure_Incidents	Power_Source	Installation_Date	Last_Service_Date	Reliability_Index	Next_Service_Date	Years_In_Service	Full_Name	Cost_Filled	Utilization_Class	Fraud_Flag
1	101	Alpha_point	EAST	25	45000	2	solar	15JAN2015	12JAN2025	80	01JUL2025	11	Alpha_point - EAST	45000	MEDIUM	NO
2	104	Delta_beam	EAST	22	41000	3	solar	11APR2017	18JAN2025	70	01JUL2025	9	Delta_beam - EAST	41000	LOW	NO
3	107	Gulf_guide	EAST	28	47000	1	solar	12JUL2019	02JAN2025	90	01JUL2025	7	Gulf_guide - EAST	47000	HIGH	NO
4	110	Jetty_light	EAST	26	45500	2	solar	10OCT2015	12JAN2025	80	01JUL2025	11	Jetty_light - EAST	45500	MEDIUM	NO
5	113	Marina_beam	EAST	27	46500	1	solar	25JAN2019	02JAN2025	90	01JUL2025	7	Marina_beam - EAST	46500	HIGH	NO
6	102	Beacon_rock	WEST	30	52000	1	electric	10FEB2016	05JAN2025	90	01JUL2025	10	Beacon_rock - WEST	52000	HIGH	NO
7	105	Eagle_eye	WEST	35	61000	0	electric	09MAY2018	01JAN2025	100	01JUL2025	8	Eagle_eye - WEST	61000	HIGH	YES
8	108	Harbor_light	WEST	32	58000	0	electric	18AUG2020	05JAN2025	100	01JUL2025	6	Harbor_light - WEST	58000	HIGH	YES
9	111	Keystone	WEST	34	60000	1	electric	15NOV2017	05JAN2025	90	01JUL2025	9	Keystone - WEST	60000	HIGH	YES
10	114	Nautical_point	WEST	33	59000	0	electric	11FEB2021	05JAN2025	100	01JUL2025	5	Nautical_point - WEST	59000	HIGH	YES
11	103	Coral_light	SOUTH	18	38000	4	hybrid	22MAR2014	20DEC2024	60	01JUN2025	12	Coral_light - SOUTH	38000	LOW	NO
12	106	Falcon_tower	SOUTH	20	39500	2	hybrid	15JUN2016	10DEC2024	80	01JUN2025	10	Falcon_tower - SOUTH	39500	MEDIUM	NO
13	109	Island_beam	SOUTH	16	36000	5	hybrid	21SEP2013	22DEC2024	50	01JUN2025	13	Island_beam - SOUTH	36000	LOW	NO
14	112	Lagoon_light	SOUTH	19	39000	3	hybrid	01DEC2016	20DEC2024	70	01JUN2025	10	Lagoon_light - SOUTH	39000	LOW	NO
15	115	Ocean_guard	SOUTH	21	40500	4	hybrid	18MAR2014	18DEC2024	60	01JUN2025	12	Ocean_guard - SOUTH	40500	LOW	NO

Final combined dataset is east

TRANSPOSE

proc transpose data=lighthouse_fraud out=lighthouse_t;

    var Maintenance_Cost Reliability_Index;

run;

proc print data=lighthouse_t;

run;

OUTPUT:

Obs	_NAME_	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17
1	Maintenance_Cost	45000	52000	38000	41000	61000	39500	47000	58000	36000	45500	60000	39000	46500	59000	40500	48000	62000
2	Reliability_Index	80	90	60	70	100	80	90	100	50	80	90	70	90	100	60	90	60

18. VISUALIZATION

proc sgplot data=lighthouse_fraud;

    vbar Coastline / response=Maintenance_Cost stat=mean;

run;

OUTPUT:

19. DATASET CLEANUP

proc datasets lib=work;

    delete lighthouse_raw lighthouse_dates lighthouse_clean;

quit;

LOG:

NOTE: Deleting WORK.LIGHTHOUSE_RAW (memtype=DATA).

NOTE: Deleting WORK.LIGHTHOUSE_DATES (memtype=DATA).

NOTE: Deleting WORK.LIGHTHOUSE_CLEAN (memtype=DATA).

20. BUSINESS INSIGHTS

·  West coast lighthouses cost more but are more reliable

·  High maintenance cost with frequent failures indicates fraud risk

·  Solar-powered units show balanced cost-efficiency

·  Aging lighthouses require proactive servicing

21. 21 KEY POINTS ABOUT THIS PROJECT

1.     This project treats lighthouses as operational assets, similar to real-world infrastructure like data centers or telecom towers.

2.     A realistic dataset with more than 15 lighthouse records was created instead of using dummy or textbook data.

3.     Each lighthouse is uniquely identified, enabling tracking across multiple analytical steps.

4.     Operational variables such as visibility range, maintenance cost, power source, and failure incidents were carefully designed.

5.     Reliability Index was derived as a percentage to reflect operational health in a business-friendly metric.

6.     Intentional data errors were introduced to simulate real-world data quality issues.

7.     The project demonstrates how such errors are identified using PROC CONTENTS and logic checks.

8.     Error correction is explained step by step, mirroring actual industry practice.

9.     SAS Data Step programming is used extensively for data transformation and derivations.

10.  PROC SQL is applied for grouping, aggregation, and management-level summaries.

11.  Date functions such as MDY, INTCK, and INTNX are used to calculate service cycles and asset age.

12.  Character functions like STRIP, TRIM, UPCASE, LOWCASE, and CATX ensure clean and standardized data.

13.  Numeric functions such as COALESCE help manage missing and inconsistent values.

14.  Macros are used to automate utilization classification, improving code reusability and scalability.

15.  A rule-based fraud detection macro flags abnormal cost and failure combinations.

16.  PROC FREQ provides insight into coastline-wise and power-source-wise distributions.

17.  PROC MEANS and PROC UNIVARIATE support descriptive and distribution analysis.

18.  PROC CORR identifies relationships between maintenance cost, failures, and reliability.

19.  Data reshaping techniques using SET, MERGE, APPEND, and TRANSPOSE reflect real project workflows.

20.  Visualization using PROC SGPLOT converts analytics into decision-ready insights.

21.  The entire project is structured in an interview-ready, business-oriented format.

22. Conclusion

The Lighthouse Operations, Reliability & Fraud Analytics Using SAS project demonstrates a complete, real-world analytics lifecycle using SAS in a simple and understandable manner. Instead of focusing only on coding syntax, the project emphasizes business thinking, data quality awareness, and analytical reasoning. By modeling lighthouses as operational assets, the project mirrors how organizations monitor and manage critical infrastructure in industries such as transportation, energy, and public safety.

One of the strongest aspects of this project is the intentional inclusion of data errors. Real-world datasets are rarely clean, and the ability to identify, explain, and correct such issues is a key skill expected from a SAS programmer. The project clearly shows how incorrect data types or logic errors can affect analysis and how SAS tools can be used to resolve them effectively.

The use of macros adds automation and scalability, making the analysis efficient and reusable. Fraud detection logic highlights how analytics can go beyond reporting and support governance and compliance. Date functions, character handling, and numeric transformations further strengthen the project by addressing common data preparation challenges.

Overall, this project is not just a technical exercise but a practical demonstration of how SAS can be used to convert raw operational data into meaningful insights. It is especially valuable for interview preparation, as it showcases both programming proficiency and real-world problem-solving ability in a structured and professional way.

SAS INTERVIEW QUESTIONS

1.Why use INTCK vs INTNX?

2.How do macros help operational classification?

3.How do you detect data quality issues in SAS?

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About the Author:

SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.

Disclaimer:

The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent LIGHT HOUSE data.

Our Mission:

This blog provides industry-focused SAS programming tutorials and analytics projects covering finance, healthcare, and technology.

This project is suitable for:

·  Students learning SAS

·  Data analysts building portfolios

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