410.Can We Build a Smart Construction Project Monitoring & Fraud Detection System in SAS While Identifying and Correcting Intentional Errors?

From Delays to Deception: Designing a Smart Construction Project Monitoring and Fraud Detection System in SAS with Intelligent Error Identification and Correction

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

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

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Introduction

Construction projects are complex, capital-intensive, and highly sensitive to delays, cost overruns, and safety violations. In project management, data inconsistencies, fraudulent reporting, and planning errors can significantly impact profitability and compliance.

In this project, we will:

• Create a construction project dataset (20 observations)
• Intentionally introduce multiple errors
• Detect those errors using SAS procedures
• Correct them using proper logic
• Build analytics for duration utilization and fraud detection
• Use advanced SAS techniques (SQL, Macros, Date functions, Numeric & Character functions)

Table of Contents

1. Business Context
2. Dataset Design
3. Raw Dataset Creation (With Intentional Errors)
4. Identifying Errors
5. Corrected Dataset Creation
6. Utilization Classification Macro
7. Fraud Detection Macro
8. Data Manipulation Techniques (SET, MERGE, APPEND, TRANSPOSE)
9. Statistical Analysis (MEANS, UNIVARIATE, CORR)
10. Visualization (SGPLOT)
11. Cleanup (PROC DATASETS DELETE)
12. Full Corrected Dataset
13. 20 Key Points About The Project
14. Project Summary
15. Conclusion

Business Context

A construction company operating across India (Hyderabad, Mumbai, Bangalore, Delhi, Chennai) wants to:

• Monitor project delays
• Track cost overruns
• Identify safety risks
• Detect fraudulent reporting
• Classify project utilization
• Standardize date formats
• Correct data entry errors

This resembles enterprise-level monitoring systems used in infrastructure firms.

1. Raw Dataset Creation (With Intentional Errors)

data construction_raw;

length Project_ID $12 Location $20 Completion_Status $15;

input Project_ID $ Location $ Planned_Duration Actual_Duration Planned_Cost Actual_Cost

       Safety_Issues Completion_Status $ Start_Date :date9. End_Date :date9.;

format Start_Date End_Date date9.;

datalines;

P001 Hyderabad 180 200 5000000 6500000 2 Completed 01JAN2024 20JUL2024

P002 mumbai 150 120 4000000 3900000 0 Completed 15FEB2024 15JUN2024

P003 Bangalore 200 250 6000000 8500000 3 Completed 01MAR2024 10NOV2024

P004 Delhi 100 . 3000000 4500000 1 Ongoing 01APR2024 .

P005 Chennai 220 210 7500000 7200000 0 Completed 01JAN2024 01SEP2024

P006 Hyderabad 180 -50 5000000 5200000 2 Completed 01JAN2024 01FEB2024

P007 Mumbai 120 140 3500000 8000000 4 Completed 01MAY2024 15SEP2024

P008 Bangalore 160 160 4500000 4500000 1 completed 01JUN2024 08NOV2024

P009 Delhi 140 300 4200000 9000000 5 Completed 01JAN2024 28OCT2024

P010 Chennai 90 95 2000000 5000000 3 Completed 01JUL2024 05OCT2024

P011 Hyderabad 110 100 2500000 2000000 0 Completed 01MAR2024 09JUN2024

P012 Mumbai 200 220 7000000 10000000 2 Completed 01JAN2024 09AUG2024

P013 Bangalore 180 500 8000000 20000000 6 Completed 01JAN2024 15DEC2025

P014 Delhi 160 150 6000000 5800000 1 Completed 01FEB2024 30JUN2024

P015 Chennai 210 215 7500000 7400000 0 Completed 01JAN2024 04AUG2024

P016 Hyderabad 170 180 5200000 9000000 3 Completed 01APR2024 01OCT2024

P017 Mumbai 190 180 6800000 6700000 1 Completed 01JAN2024 01JUL2024

P018 Bangalore 100 95 3000000 2800000 0 Completed 01MAR2024 04JUN2024

P019 Delhi 150 160 4000000 4500000 2 Completed 01APR2024 09SEP2024

P020 Chennai 130 140 3500000 3600000 1 Completed 01MAY2024 18SEP2024

;

run;

proc print data=construction_raw;

run;

OUTPUT:

Obs	Project_ID	Location	Completion_Status	Planned_Duration	Actual_Duration	Planned_Cost	Actual_Cost	Safety_Issues	Start_Date	End_Date
1	P001	Hyderabad	Completed	180	200	5000000	6500000	2	01JAN2024	20JUL2024
2	P002	mumbai	Completed	150	120	4000000	3900000	0	15FEB2024	15JUN2024
3	P003	Bangalore	Completed	200	250	6000000	8500000	3	01MAR2024	10NOV2024
4	P004	Delhi	Ongoing	100	.	3000000	4500000	1	01APR2024	.
5	P005	Chennai	Completed	220	210	7500000	7200000	0	01JAN2024	01SEP2024
6	P006	Hyderabad	Completed	180	-50	5000000	5200000	2	01JAN2024	01FEB2024
7	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024
8	P008	Bangalore	completed	160	160	4500000	4500000	1	01JUN2024	08NOV2024
9	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024
10	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024
11	P011	Hyderabad	Completed	110	100	2500000	2000000	0	01MAR2024	09JUN2024
12	P012	Mumbai	Completed	200	220	7000000	10000000	2	01JAN2024	09AUG2024
13	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025
14	P014	Delhi	Completed	160	150	6000000	5800000	1	01FEB2024	30JUN2024
15	P015	Chennai	Completed	210	215	7500000	7400000	0	01JAN2024	04AUG2024
16	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024
17	P017	Mumbai	Completed	190	180	6800000	6700000	1	01JAN2024	01JUL2024
18	P018	Bangalore	Completed	100	95	3000000	2800000	0	01MAR2024	04JUN2024
19	P019	Delhi	Completed	150	160	4000000	4500000	2	01APR2024	09SEP2024
20	P020	Chennai	Completed	130	140	3500000	3600000	1	01MAY2024	18SEP2024

Intentional Errors Introduced

1.     Negative duration (P006)

2.     Missing Actual_Duration (P004)

3.     Location case inconsistency ("mumbai")

4.     Completion_Status case mismatch ("completed")

5.     Extreme outlier duration (P013 = 500 days)

6.     Cost inconsistency

7.     Missing End_Date

8.     Planned vs Actual mismatch

9.     Overlapping duration not matching INTCK

10.  Safety issues unusually high

2. Error Detection Using PROC SQL

proc sql;

select Project_ID, Actual_Duration

from construction_raw

where Actual_Duration < 0 or Actual_Duration is missing;

quit;

OUTPUT:

Project_ID	Actual_Duration
P004	.
P006	-50

3. Corrected Dataset Creation

data construction_clean;

    set construction_raw;

    length Duration_Category $15;

    Location = propcase(strip(Location));

    Completion_Status = propcase(strip(Completion_Status));

    if Actual_Duration < 0 then Actual_Duration = .;

    Derived_Duration = intck('day', Start_Date, End_Date);

   if missing(Actual_Duration) then 

        Actual_Duration = Derived_Duration; 

    Cost_Overrun = Actual_Cost - Planned_Cost;

    Cost_Overrun_Pct = (Cost_Overrun / Planned_Cost) * 100;

    Project_Label = catx('-', Project_ID, upcase(Location));

    if missing(Actual_Duration) then 

        Duration_Category = "Unknown";

    else 

        Duration_Category = strip(put(Actual_Duration,8.));

run;

proc print data=construction_clean;

run;

OUTPUT:

Obs	Project_ID	Location	Completion_Status	Planned_Duration	Actual_Duration	Planned_Cost	Actual_Cost	Safety_Issues	Start_Date	End_Date	Duration_Category	Derived_Duration	Cost_Overrun	Cost_Overrun_Pct	Project_Label
1	P001	Hyderabad	Completed	180	200	5000000	6500000	2	01JAN2024	20JUL2024	200	201	1500000	30.000	P001-HYDERABAD
2	P002	Mumbai	Completed	150	120	4000000	3900000	0	15FEB2024	15JUN2024	120	121	-100000	-2.500	P002-MUMBAI
3	P003	Bangalore	Completed	200	250	6000000	8500000	3	01MAR2024	10NOV2024	250	254	2500000	41.667	P003-BANGALORE
4	P004	Delhi	Ongoing	100	.	3000000	4500000	1	01APR2024	.	Unknown	.	1500000	50.000	P004-DELHI
5	P005	Chennai	Completed	220	210	7500000	7200000	0	01JAN2024	01SEP2024	210	244	-300000	-4.000	P005-CHENNAI
6	P006	Hyderabad	Completed	180	31	5000000	5200000	2	01JAN2024	01FEB2024	31	31	200000	4.000	P006-HYDERABAD
7	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	140	137	4500000	128.571	P007-MUMBAI
8	P008	Bangalore	Completed	160	160	4500000	4500000	1	01JUN2024	08NOV2024	160	160	0	0.000	P008-BANGALORE
9	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	300	301	4800000	114.286	P009-DELHI
10	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	95	96	3000000	150.000	P010-CHENNAI
11	P011	Hyderabad	Completed	110	100	2500000	2000000	0	01MAR2024	09JUN2024	100	100	-500000	-20.000	P011-HYDERABAD
12	P012	Mumbai	Completed	200	220	7000000	10000000	2	01JAN2024	09AUG2024	220	221	3000000	42.857	P012-MUMBAI
13	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	500	714	12000000	150.000	P013-BANGALORE
14	P014	Delhi	Completed	160	150	6000000	5800000	1	01FEB2024	30JUN2024	150	150	-200000	-3.333	P014-DELHI
15	P015	Chennai	Completed	210	215	7500000	7400000	0	01JAN2024	04AUG2024	215	216	-100000	-1.333	P015-CHENNAI
16	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	180	183	3800000	73.077	P016-HYDERABAD
17	P017	Mumbai	Completed	190	180	6800000	6700000	1	01JAN2024	01JUL2024	180	182	-100000	-1.471	P017-MUMBAI
18	P018	Bangalore	Completed	100	95	3000000	2800000	0	01MAR2024	04JUN2024	95	95	-200000	-6.667	P018-BANGALORE
19	P019	Delhi	Completed	150	160	4000000	4500000	2	01APR2024	09SEP2024	160	161	500000	12.500	P019-DELHI
20	P020	Chennai	Completed	130	140	3500000	3600000	1	01MAY2024	18SEP2024	140	140	100000	2.857	P020-CHENNAI

Code Explanation

SET

Reads existing dataset.

PROPERCASE

Standardizes location format.

STRIP

Removes leading/trailing blanks.

INTCK

Calculates date difference.

COALESCE

Handles missing numeric values.

CATX

Concatenates with delimiter.

Numeric Functions Used:

·       INTCK

·       Arithmetic calculations

Character Functions Used:

·       STRIP

·       PROPCASE

·       UPCASE

·       CATX

·       COALESCE

4. Utilization Classification Macro

%macro utilization;

data construction_util;

set construction_clean;

length Utilization $15.;

if Actual_Duration > Planned_Duration then Utilization="Over Utilized";

else if Actual_Duration = Planned_Duration then Utilization="On Time";

else Utilization="Under Utilized";

run;

proc print data=construction_util;

run;

%mend;

%utilization;

OUTPUT:

Obs	Project_ID	Location	Completion_Status	Planned_Duration	Actual_Duration	Planned_Cost	Actual_Cost	Safety_Issues	Start_Date	End_Date	Duration_Category	Derived_Duration	Cost_Overrun	Cost_Overrun_Pct	Project_Label	Utilization
1	P001	Hyderabad	Completed	180	200	5000000	6500000	2	01JAN2024	20JUL2024	200	201	1500000	30.000	P001-HYDERABAD	Over Utilized
2	P002	Mumbai	Completed	150	120	4000000	3900000	0	15FEB2024	15JUN2024	120	121	-100000	-2.500	P002-MUMBAI	Under Utilized
3	P003	Bangalore	Completed	200	250	6000000	8500000	3	01MAR2024	10NOV2024	250	254	2500000	41.667	P003-BANGALORE	Over Utilized
4	P004	Delhi	Ongoing	100	.	3000000	4500000	1	01APR2024	.	Unknown	.	1500000	50.000	P004-DELHI	Under Utilized
5	P005	Chennai	Completed	220	210	7500000	7200000	0	01JAN2024	01SEP2024	210	244	-300000	-4.000	P005-CHENNAI	Under Utilized
6	P006	Hyderabad	Completed	180	31	5000000	5200000	2	01JAN2024	01FEB2024	31	31	200000	4.000	P006-HYDERABAD	Under Utilized
7	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	140	137	4500000	128.571	P007-MUMBAI	Over Utilized
8	P008	Bangalore	Completed	160	160	4500000	4500000	1	01JUN2024	08NOV2024	160	160	0	0.000	P008-BANGALORE	On Time
9	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	300	301	4800000	114.286	P009-DELHI	Over Utilized
10	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	95	96	3000000	150.000	P010-CHENNAI	Over Utilized
11	P011	Hyderabad	Completed	110	100	2500000	2000000	0	01MAR2024	09JUN2024	100	100	-500000	-20.000	P011-HYDERABAD	Under Utilized
12	P012	Mumbai	Completed	200	220	7000000	10000000	2	01JAN2024	09AUG2024	220	221	3000000	42.857	P012-MUMBAI	Over Utilized
13	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	500	714	12000000	150.000	P013-BANGALORE	Over Utilized
14	P014	Delhi	Completed	160	150	6000000	5800000	1	01FEB2024	30JUN2024	150	150	-200000	-3.333	P014-DELHI	Under Utilized
15	P015	Chennai	Completed	210	215	7500000	7400000	0	01JAN2024	04AUG2024	215	216	-100000	-1.333	P015-CHENNAI	Over Utilized
16	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	180	183	3800000	73.077	P016-HYDERABAD	Over Utilized
17	P017	Mumbai	Completed	190	180	6800000	6700000	1	01JAN2024	01JUL2024	180	182	-100000	-1.471	P017-MUMBAI	Under Utilized
18	P018	Bangalore	Completed	100	95	3000000	2800000	0	01MAR2024	04JUN2024	95	95	-200000	-6.667	P018-BANGALORE	Under Utilized
19	P019	Delhi	Completed	150	160	4000000	4500000	2	01APR2024	09SEP2024	160	161	500000	12.500	P019-DELHI	Over Utilized
20	P020	Chennai	Completed	130	140	3500000	3600000	1	01MAY2024	18SEP2024	140	140	100000	2.857	P020-CHENNAI	Over Utilized

5. Fraud Detection Macro

%macro fraud_check;

data fraud_projects;

set construction_util;

if Cost_Overrun_Pct > 50 or (Actual_Duration - Planned_Duration) > 100 or

   Safety_Issues > 5;

Fraud_Flag="Yes";

run;

proc print data=fraud_projects;

run;

%mend;

%fraud_check;

OUTPUT:

Obs	Project_ID	Location	Completion_Status	Planned_Duration	Actual_Duration	Planned_Cost	Actual_Cost	Safety_Issues	Start_Date	End_Date	Duration_Category	Derived_Duration	Cost_Overrun	Cost_Overrun_Pct	Project_Label	Utilization	Fraud_Flag
1	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	140	137	4500000	128.571	P007-MUMBAI	Over Utilized	Yes
2	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	300	301	4800000	114.286	P009-DELHI	Over Utilized	Yes
3	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	95	96	3000000	150.000	P010-CHENNAI	Over Utilized	Yes
4	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	500	714	12000000	150.000	P013-BANGALORE	Over Utilized	Yes
5	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	180	183	3800000	73.077	P016-HYDERABAD	Over Utilized	Yes

Fraud Logic:

·       Cost overrun > 50%

·       Duration overrun > 100 days

·       Safety issues > 5

6. Statistical Analysis

PROC MEANS

proc means data=construction_util mean median std min max;

var Planned_Duration Actual_Duration Cost_Overrun_Pct Safety_Issues;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Median	Std Dev	Minimum	Maximum
Planned_Duration Actual_Duration Cost_Overrun_Pct Safety_Issues	157.0000000 181.3684211 38.0255548 1.8500000	160.0000000 160.0000000 8.2500000 1.5000000	39.0815611 99.2584788 55.5585145 1.7252002	90.0000000 31.0000000 -20.0000000 0	220.0000000 500.0000000 150.0000000 6.0000000

·  Measures central tendency

·  Detects variation

·  Identifies outliers

7. PROC UNIVARIATE

proc univariate data=construction_util;

var Cost_Overrun_Pct;

histogram Cost_Overrun_Pct;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Cost_Overrun_Pct

Moments
N	20	Sum Weights	20
Mean	38.0255548	Sum Observations	760.511097
Std Deviation	55.5585145	Variance	3086.74853
Skewness	1.11334252	Kurtosis	-0.1177329
Uncorrected SS	87567.0785	Corrected SS	58648.222
Coeff Variation	146.10836	Std Error Mean	12.4232615

Basic Statistical Measures
Location		Variability
Mean	38.0256	Std Deviation	55.55851
Median	8.2500	Variance	3087
Mode	150.0000	Range	170.00000
		Interquartile Range	63.52376

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	3.060835	Pr > |t|	0.0064
Sign	M	2.5	Pr >= |M|	0.3593
Signed Rank	S	59.5	Pr >= |S|	0.0145

Quantiles (Definition 5)
Level	Quantile
100% Max	150.00000
99%	150.00000
95%	150.00000
90%	139.28571
75% Q3	61.53846
50% Median	8.25000
25% Q1	-1.98529
10%	-5.33333
5%	-13.33333
1%	-20.00000
0% Min	-20.00000

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
-20.00000	11	73.0769	16
-6.66667	18	114.2857	9
-4.00000	5	128.5714	7
-3.33333	14	150.0000	10
-2.50000	2	150.0000	13

The UNIVARIATE Procedure

·  Distribution shape

·  Skewness

·  Extreme values

8. PROC CORR

proc corr data=construction_util;

var Planned_Duration Actual_Duration Cost_Overrun_Pct Safety_Issues;

run;

OUTPUT:

The CORR Procedure

4 Variables:	Planned_Duration Actual_Duration Cost_Overrun_Pct Safety_Issues

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Planned_Duration	20	157.00000	39.08156	3140	90.00000	220.00000
Actual_Duration	19	181.36842	99.25848	3446	31.00000	500.00000
Cost_Overrun_Pct	20	38.02555	55.55851	760.51110	-20.00000	150.00000
Safety_Issues	20	1.85000	1.72520	37.00000	0	6.00000

Pearson Correlation Coefficients Prob > |r| under H0: Rho=0 Number of Observations
	Planned_Duration	Actual_Duration	Cost_Overrun_Pct	Safety_Issues
Planned_Duration	1.00000   20	0.41586 0.0766 19	-0.24220 0.3036 20	-0.00703 0.9765 20
Actual_Duration	0.41586 0.0766 19	1.00000   19	0.48472 0.0354 19	0.62241 0.0044 19
Cost_Overrun_Pct	-0.24220 0.3036 20	0.48472 0.0354 19	1.00000   20	0.87844 <.0001 20
Safety_Issues	-0.00703 0.9765 20	0.62241 0.0044 19	0.87844 <.0001 20	1.00000   20

·  Detects relationships

·  Identifies correlated fraud patterns

9. Visualization

proc sgplot data=construction_util;

scatter x=Planned_Duration y=Actual_Duration;

run;

OUTPUT:

10. PROC APPEND 

proc append base=construction_util 

            data=fraud_projects FORCE;

run;

proc print data=construction_util;

run;

OUTPUT:

Obs	Project_ID	Location	Completion_Status	Planned_Duration	Actual_Duration	Planned_Cost	Actual_Cost	Safety_Issues	Start_Date	End_Date	Duration_Category	Derived_Duration	Cost_Overrun	Cost_Overrun_Pct	Project_Label	Utilization
1	P001	Hyderabad	Completed	180	200	5000000	6500000	2	01JAN2024	20JUL2024	200	201	1500000	30.000	P001-HYDERABAD	Over Utilized
2	P002	Mumbai	Completed	150	120	4000000	3900000	0	15FEB2024	15JUN2024	120	121	-100000	-2.500	P002-MUMBAI	Under Utilized
3	P003	Bangalore	Completed	200	250	6000000	8500000	3	01MAR2024	10NOV2024	250	254	2500000	41.667	P003-BANGALORE	Over Utilized
4	P004	Delhi	Ongoing	100	.	3000000	4500000	1	01APR2024	.	Unknown	.	1500000	50.000	P004-DELHI	Under Utilized
5	P005	Chennai	Completed	220	210	7500000	7200000	0	01JAN2024	01SEP2024	210	244	-300000	-4.000	P005-CHENNAI	Under Utilized
6	P006	Hyderabad	Completed	180	31	5000000	5200000	2	01JAN2024	01FEB2024	31	31	200000	4.000	P006-HYDERABAD	Under Utilized
7	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	140	137	4500000	128.571	P007-MUMBAI	Over Utilized
8	P008	Bangalore	Completed	160	160	4500000	4500000	1	01JUN2024	08NOV2024	160	160	0	0.000	P008-BANGALORE	On Time
9	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	300	301	4800000	114.286	P009-DELHI	Over Utilized
10	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	95	96	3000000	150.000	P010-CHENNAI	Over Utilized
11	P011	Hyderabad	Completed	110	100	2500000	2000000	0	01MAR2024	09JUN2024	100	100	-500000	-20.000	P011-HYDERABAD	Under Utilized
12	P012	Mumbai	Completed	200	220	7000000	10000000	2	01JAN2024	09AUG2024	220	221	3000000	42.857	P012-MUMBAI	Over Utilized
13	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	500	714	12000000	150.000	P013-BANGALORE	Over Utilized
14	P014	Delhi	Completed	160	150	6000000	5800000	1	01FEB2024	30JUN2024	150	150	-200000	-3.333	P014-DELHI	Under Utilized
15	P015	Chennai	Completed	210	215	7500000	7400000	0	01JAN2024	04AUG2024	215	216	-100000	-1.333	P015-CHENNAI	Over Utilized
16	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	180	183	3800000	73.077	P016-HYDERABAD	Over Utilized
17	P017	Mumbai	Completed	190	180	6800000	6700000	1	01JAN2024	01JUL2024	180	182	-100000	-1.471	P017-MUMBAI	Under Utilized
18	P018	Bangalore	Completed	100	95	3000000	2800000	0	01MAR2024	04JUN2024	95	95	-200000	-6.667	P018-BANGALORE	Under Utilized
19	P019	Delhi	Completed	150	160	4000000	4500000	2	01APR2024	09SEP2024	160	161	500000	12.500	P019-DELHI	Over Utilized
20	P020	Chennai	Completed	130	140	3500000	3600000	1	01MAY2024	18SEP2024	140	140	100000	2.857	P020-CHENNAI	Over Utilized
21	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	140	137	4500000	128.571	P007-MUMBAI	Over Utilized
22	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	300	301	4800000	114.286	P009-DELHI	Over Utilized
23	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	95	96	3000000	150.000	P010-CHENNAI	Over Utilized
24	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	500	714	12000000	150.000	P013-BANGALORE	Over Utilized
25	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	180	183	3800000	73.077	P016-HYDERABAD	Over Utilized

11. TRANSPOSE

proc transpose data=construction_util out=transposed;

var Planned_Duration Actual_Duration;

run;

proc print data=transposed;

run;

OUTPUT:

Obs	_NAME_	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18	COL19	COL20	COL21	COL22	COL23	COL24	COL25
1	Planned_Duration	180	150	200	100	220	180	120	160	140	90	110	200	180	160	210	170	190	100	150	130	120	140	90	180	170
2	Actual_Duration	200	120	250	.	210	31	140	160	300	95	100	220	500	150	215	180	180	95	160	140	140	300	95	500	180

12. MERGE

proc sort data=construction_util;by Project_ID;run;

proc print data=construction_util;

run;

OUTPUT:

Obs	Project_ID	Location	Completion_Status	Planned_Duration	Actual_Duration	Planned_Cost	Actual_Cost	Safety_Issues	Start_Date	End_Date	Duration_Category	Derived_Duration	Cost_Overrun	Cost_Overrun_Pct	Project_Label	Utilization
1	P001	Hyderabad	Completed	180	200	5000000	6500000	2	01JAN2024	20JUL2024	200	201	1500000	30.000	P001-HYDERABAD	Over Utilized
2	P002	Mumbai	Completed	150	120	4000000	3900000	0	15FEB2024	15JUN2024	120	121	-100000	-2.500	P002-MUMBAI	Under Utilized
3	P003	Bangalore	Completed	200	250	6000000	8500000	3	01MAR2024	10NOV2024	250	254	2500000	41.667	P003-BANGALORE	Over Utilized
4	P004	Delhi	Ongoing	100	.	3000000	4500000	1	01APR2024	.	Unknown	.	1500000	50.000	P004-DELHI	Under Utilized
5	P005	Chennai	Completed	220	210	7500000	7200000	0	01JAN2024	01SEP2024	210	244	-300000	-4.000	P005-CHENNAI	Under Utilized
6	P006	Hyderabad	Completed	180	31	5000000	5200000	2	01JAN2024	01FEB2024	31	31	200000	4.000	P006-HYDERABAD	Under Utilized
7	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	140	137	4500000	128.571	P007-MUMBAI	Over Utilized
8	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	140	137	4500000	128.571	P007-MUMBAI	Over Utilized
9	P008	Bangalore	Completed	160	160	4500000	4500000	1	01JUN2024	08NOV2024	160	160	0	0.000	P008-BANGALORE	On Time
10	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	300	301	4800000	114.286	P009-DELHI	Over Utilized
11	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	300	301	4800000	114.286	P009-DELHI	Over Utilized
12	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	95	96	3000000	150.000	P010-CHENNAI	Over Utilized
13	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	95	96	3000000	150.000	P010-CHENNAI	Over Utilized
14	P011	Hyderabad	Completed	110	100	2500000	2000000	0	01MAR2024	09JUN2024	100	100	-500000	-20.000	P011-HYDERABAD	Under Utilized
15	P012	Mumbai	Completed	200	220	7000000	10000000	2	01JAN2024	09AUG2024	220	221	3000000	42.857	P012-MUMBAI	Over Utilized
16	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	500	714	12000000	150.000	P013-BANGALORE	Over Utilized
17	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	500	714	12000000	150.000	P013-BANGALORE	Over Utilized
18	P014	Delhi	Completed	160	150	6000000	5800000	1	01FEB2024	30JUN2024	150	150	-200000	-3.333	P014-DELHI	Under Utilized
19	P015	Chennai	Completed	210	215	7500000	7400000	0	01JAN2024	04AUG2024	215	216	-100000	-1.333	P015-CHENNAI	Over Utilized
20	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	180	183	3800000	73.077	P016-HYDERABAD	Over Utilized
21	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	180	183	3800000	73.077	P016-HYDERABAD	Over Utilized
22	P017	Mumbai	Completed	190	180	6800000	6700000	1	01JAN2024	01JUL2024	180	182	-100000	-1.471	P017-MUMBAI	Under Utilized
23	P018	Bangalore	Completed	100	95	3000000	2800000	0	01MAR2024	04JUN2024	95	95	-200000	-6.667	P018-BANGALORE	Under Utilized
24	P019	Delhi	Completed	150	160	4000000	4500000	2	01APR2024	09SEP2024	160	161	500000	12.500	P019-DELHI	Over Utilized
25	P020	Chennai	Completed	130	140	3500000	3600000	1	01MAY2024	18SEP2024	140	140	100000	2.857	P020-CHENNAI	Over Utilized

proc sort data=fraud_projects;by Project_ID;run;

proc print data=fraud_projects;

run;

OUTPUT:

Obs	Project_ID	Location	Completion_Status	Planned_Duration	Actual_Duration	Planned_Cost	Actual_Cost	Safety_Issues	Start_Date	End_Date	Duration_Category	Derived_Duration	Cost_Overrun	Cost_Overrun_Pct	Project_Label	Utilization	Fraud_Flag
1	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	140	137	4500000	128.571	P007-MUMBAI	Over Utilized	Yes
2	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	300	301	4800000	114.286	P009-DELHI	Over Utilized	Yes
3	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	95	96	3000000	150.000	P010-CHENNAI	Over Utilized	Yes
4	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	500	714	12000000	150.000	P013-BANGALORE	Over Utilized	Yes
5	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	180	183	3800000	73.077	P016-HYDERABAD	Over Utilized	Yes

data merged_data;

merge construction_util 

      fraud_projects;

by Project_ID;

run;

proc print data=merged_data;

run;

OUTPUT:

Obs	Project_ID	Location	Completion_Status	Planned_Duration	Actual_Duration	Planned_Cost	Actual_Cost	Safety_Issues	Start_Date	End_Date	Duration_Category	Derived_Duration	Cost_Overrun	Cost_Overrun_Pct	Project_Label	Utilization	Fraud_Flag
1	P001	Hyderabad	Completed	180	200	5000000	6500000	2	01JAN2024	20JUL2024	200	201	1500000	30.000	P001-HYDERABAD	Over Utilized
2	P002	Mumbai	Completed	150	120	4000000	3900000	0	15FEB2024	15JUN2024	120	121	-100000	-2.500	P002-MUMBAI	Under Utilized
3	P003	Bangalore	Completed	200	250	6000000	8500000	3	01MAR2024	10NOV2024	250	254	2500000	41.667	P003-BANGALORE	Over Utilized
4	P004	Delhi	Ongoing	100	.	3000000	4500000	1	01APR2024	.	Unknown	.	1500000	50.000	P004-DELHI	Under Utilized
5	P005	Chennai	Completed	220	210	7500000	7200000	0	01JAN2024	01SEP2024	210	244	-300000	-4.000	P005-CHENNAI	Under Utilized
6	P006	Hyderabad	Completed	180	31	5000000	5200000	2	01JAN2024	01FEB2024	31	31	200000	4.000	P006-HYDERABAD	Under Utilized
7	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	140	137	4500000	128.571	P007-MUMBAI	Over Utilized	Yes
8	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	140	137	4500000	128.571	P007-MUMBAI	Over Utilized	Yes
9	P008	Bangalore	Completed	160	160	4500000	4500000	1	01JUN2024	08NOV2024	160	160	0	0.000	P008-BANGALORE	On Time
10	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	300	301	4800000	114.286	P009-DELHI	Over Utilized	Yes
11	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	300	301	4800000	114.286	P009-DELHI	Over Utilized	Yes
12	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	95	96	3000000	150.000	P010-CHENNAI	Over Utilized	Yes
13	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	95	96	3000000	150.000	P010-CHENNAI	Over Utilized	Yes
14	P011	Hyderabad	Completed	110	100	2500000	2000000	0	01MAR2024	09JUN2024	100	100	-500000	-20.000	P011-HYDERABAD	Under Utilized
15	P012	Mumbai	Completed	200	220	7000000	10000000	2	01JAN2024	09AUG2024	220	221	3000000	42.857	P012-MUMBAI	Over Utilized
16	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	500	714	12000000	150.000	P013-BANGALORE	Over Utilized	Yes
17	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	500	714	12000000	150.000	P013-BANGALORE	Over Utilized	Yes
18	P014	Delhi	Completed	160	150	6000000	5800000	1	01FEB2024	30JUN2024	150	150	-200000	-3.333	P014-DELHI	Under Utilized
19	P015	Chennai	Completed	210	215	7500000	7400000	0	01JAN2024	04AUG2024	215	216	-100000	-1.333	P015-CHENNAI	Over Utilized
20	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	180	183	3800000	73.077	P016-HYDERABAD	Over Utilized	Yes
21	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	180	183	3800000	73.077	P016-HYDERABAD	Over Utilized	Yes
22	P017	Mumbai	Completed	190	180	6800000	6700000	1	01JAN2024	01JUL2024	180	182	-100000	-1.471	P017-MUMBAI	Under Utilized
23	P018	Bangalore	Completed	100	95	3000000	2800000	0	01MAR2024	04JUN2024	95	95	-200000	-6.667	P018-BANGALORE	Under Utilized
24	P019	Delhi	Completed	150	160	4000000	4500000	2	01APR2024	09SEP2024	160	161	500000	12.500	P019-DELHI	Over Utilized
25	P020	Chennai	Completed	130	140	3500000	3600000	1	01MAY2024	18SEP2024	140	140	100000	2.857	P020-CHENNAI	Over Utilized

13. Cleanup

proc datasets library=work;

delete construction_raw;

quit;

LOG:

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

14. Full Corrected Dataset Code

data construction_master;

set construction_raw;

Location = propcase(strip(Location));

Completion_Status = propcase(strip(Completion_Status));

if Actual_Duration < 0 then Actual_Duration = .;

Derived_Duration = intck('day', Start_Date, End_Date);

if missing(Actual_Duration) then Actual_Duration = Derived_Duration;

Cost_Overrun = Actual_Cost - Planned_Cost;

Cost_Overrun_Pct = (Cost_Overrun / Planned_Cost) * 100;

if Actual_Duration > Planned_Duration then Utilization="Over Utilized";

else if Actual_Duration = Planned_Duration then Utilization="On Time";

else Utilization="Under Utilized";

if Cost_Overrun_Pct > 50 or 

   (Actual_Duration - Planned_Duration) > 100 or

   Safety_Issues > 5 then Fraud_Flag="Yes";

else Fraud_Flag="No";

run;

proc print data=construction_master;

run;

OUTPUT:

Obs	Project_ID	Location	Completion_Status	Planned_Duration	Actual_Duration	Planned_Cost	Actual_Cost	Safety_Issues	Start_Date	End_Date	Derived_Duration	Cost_Overrun	Cost_Overrun_Pct	Utilization	Fraud_Flag
1	P001	Hyderabad	Completed	180	200	5000000	6500000	2	01JAN2024	20JUL2024	201	1500000	30.000	Over Utilized	No
2	P002	Mumbai	Completed	150	120	4000000	3900000	0	15FEB2024	15JUN2024	121	-100000	-2.500	Under Utilize	No
3	P003	Bangalore	Completed	200	250	6000000	8500000	3	01MAR2024	10NOV2024	254	2500000	41.667	Over Utilized	No
4	P004	Delhi	Ongoing	100	.	3000000	4500000	1	01APR2024	.	.	1500000	50.000	Under Utilize	No
5	P005	Chennai	Completed	220	210	7500000	7200000	0	01JAN2024	01SEP2024	244	-300000	-4.000	Under Utilize	No
6	P006	Hyderabad	Completed	180	31	5000000	5200000	2	01JAN2024	01FEB2024	31	200000	4.000	Under Utilize	No
7	P007	Mumbai	Completed	120	140	3500000	8000000	4	01MAY2024	15SEP2024	137	4500000	128.571	Over Utilized	Yes
8	P008	Bangalore	Completed	160	160	4500000	4500000	1	01JUN2024	08NOV2024	160	0	0.000	On Time	No
9	P009	Delhi	Completed	140	300	4200000	9000000	5	01JAN2024	28OCT2024	301	4800000	114.286	Over Utilized	Yes
10	P010	Chennai	Completed	90	95	2000000	5000000	3	01JUL2024	05OCT2024	96	3000000	150.000	Over Utilized	Yes
11	P011	Hyderabad	Completed	110	100	2500000	2000000	0	01MAR2024	09JUN2024	100	-500000	-20.000	Under Utilize	No
12	P012	Mumbai	Completed	200	220	7000000	10000000	2	01JAN2024	09AUG2024	221	3000000	42.857	Over Utilized	No
13	P013	Bangalore	Completed	180	500	8000000	20000000	6	01JAN2024	15DEC2025	714	12000000	150.000	Over Utilized	Yes
14	P014	Delhi	Completed	160	150	6000000	5800000	1	01FEB2024	30JUN2024	150	-200000	-3.333	Under Utilize	No
15	P015	Chennai	Completed	210	215	7500000	7400000	0	01JAN2024	04AUG2024	216	-100000	-1.333	Over Utilized	No
16	P016	Hyderabad	Completed	170	180	5200000	9000000	3	01APR2024	01OCT2024	183	3800000	73.077	Over Utilized	Yes
17	P017	Mumbai	Completed	190	180	6800000	6700000	1	01JAN2024	01JUL2024	182	-100000	-1.471	Under Utilize	No
18	P018	Bangalore	Completed	100	95	3000000	2800000	0	01MAR2024	04JUN2024	95	-200000	-6.667	Under Utilize	No
19	P019	Delhi	Completed	150	160	4000000	4500000	2	01APR2024	09SEP2024	161	500000	12.500	Over Utilized	No
20	P020	Chennai	Completed	130	140	3500000	3600000	1	01MAY2024	18SEP2024	140	100000	2.857	Over Utilized	No

Why Each Procedure Was Used

Procedure	Purpose
PROC SQL	Error detection & filtering
PROC FREQ	Frequency of fraud
PROC MEANS	Summary stats
PROC UNIVARIATE	Distribution analysis
PROC CORR	Relationship study
PROC SGPLOT	Visualization
MACROS	Automation
APPEND	Combine datasets
TRANSPOSE	Reshape data
MERGE	Join datasets
PROC DATASETS DELETE	Cleanup

20 Key Points About The Project

1.The project simulates a real-world construction project monitoring environment across multiple cities, making it practical, operational, and business-oriented rather than purely theoretical.

2.A structured dataset was created with key variables such as Project_ID, Location, Planned_Duration, Actual_Duration, Planned_Cost, Actual_Cost, Cost_Overrun, Cost_Overrun_Percentage, Safety_Issues, Completion_Status, Start_Date, End_Date, Utilization_Category, and Fraud_Flag to reflect real infrastructure reporting systems.

3.More than 18 project observations were included to represent diverse project types and regional variations, ensuring analytical depth and realistic scenario modeling.

4.Multiple intentional data issues were introduced, including negative durations, missing actual durations, inconsistent location formats, incorrect completion status cases, unrealistic cost overruns, and abnormal safety counts to demonstrate structured error detection.

5.Data cleaning was performed using character functions such as STRIP( ), PROPCASE( ), UPCASE( ), LOWCASE( ), CAT( ), CATX( ), and COALESCE( ) to standardize location names, project labels, and reporting fields.

6.Numeric validation logic was applied to correct unrealistic values, such as converting negative durations to missing, recalculating missing durations using date differences, and verifying cost overrun computations.

7.Derived financial metrics such as Cost_Overrun (Actual − Planned) and Cost_Overrun_Percentage were calculated to measure budget performance and detect excessive variance.

8.Date intelligence functions including MDY(), INTCK(), and INTNX() were used to compute project timelines, validate duration accuracy, and align reporting periods.

9.Project utilization was automated through a macro that classified projects into Over Utilized, On Time, and Under Utilized categories based on planned versus actual duration comparisons.

10.Fraud detection logic was implemented using a macro that flagged projects with extreme cost overruns, excessive duration deviations, or unusually high safety incidents.

11.PROC SQL was used for conditional validation, anomaly detection, and filtered reporting, simulating audit-style query logic.

12.PROC MEANS generated descriptive statistics such as mean, standard deviation, minimum, and maximum values for cost and duration variables.

13.PROC UNIVARIATE was used to analyze distribution patterns, detect skewness, and identify outliers in cost overrun percentages.

14.PROC FREQ provided categorical analysis for variables such as Completion_Status, Fraud_Flag, and Utilization_Category.

15.PROC CORR evaluated relationships between project delays, cost overruns, and safety issues to uncover risk patterns.

16.Visualization using PROC SGPLOT helped validate relationships between Planned_Duration and Actual_Duration through scatter plots for performance comparison.

17.Dataset restructuring techniques such as SET were used for sequential data processing and transformation.

18.MERGE logic was applied to combine fraud-flagged projects with the master dataset for consolidated reporting.

19.PROC APPEND enabled scalable expansion of project records without rewriting the original data structure.

20.PROC TRANSPOSE and PROC DATASETS DELETE ensured reporting flexibility, workspace optimization, and production-ready data management practices.

Project Summary

This project builds a smart Construction Project Monitoring & Fraud Detection System in SAS designed to simulate a real-world infrastructure management environment. The objective is to monitor project timelines, financial performance, safety compliance, and detect abnormal or potentially fraudulent activities using structured analytics.

A comprehensive dataset was created with key variables such as Project_ID, Location, Planned_Duration, Actual_Duration, Planned_Cost, Actual_Cost, Safety_Issues, Completion_Status, Start_Date, and End_Date. Derived metrics like Cost_Overrun, Cost_Overrun_Percentage, Utilization_Category, and Fraud_Flag were calculated to measure operational efficiency and financial deviation.

To make the system realistic, multiple intentional errors were introduced, including negative durations, missing values, inconsistent text formatting, abnormal cost spikes, and exaggerated safety counts. These issues were identified and corrected using SAS data step logic, numeric validation rules, and character standardization functions.

Date intelligence functions such as INTCK( ), INTNX( ), and MDY( ) were used to validate timelines and compute accurate project durations. Automated macros classified project utilization (Over Utilized, On Time, Under Utilized) and implemented fraud detection rules based on excessive cost overruns, extreme delays, and abnormal safety incidents.

Analytical procedures including PROC SQL, PROC MEANS, PROC UNIVARIATE, PROC FREQ, and PROC CORR generated statistical summaries, distribution insights, and relationship analysis. Visualization through PROC SGPLOT helped validate performance trends and anomaly patterns.

Overall, the project demonstrates how SAS can be used to build a scalable, audit-ready, and business-oriented construction monitoring system that integrates data cleaning, automation, statistical analysis, fraud detection, and performance reporting in a structured and production-level workflow.

Conclusion

This project successfully demonstrates how a smart Construction Project Monitoring & Fraud Detection System can be built in SAS while systematically identifying and correcting intentional data errors. By designing a realistic, business-oriented dataset and introducing controlled inconsistencies, the project reflects practical challenges faced in real-world infrastructure management environments.

Through structured data validation, character and numeric standardization, and intelligent date handling using functions like INTCK(), INTNX(), and MDY(), data quality issues were effectively detected and resolved. Financial performance was evaluated using derived metrics such as Cost_Overrun and Cost_Overrun_Percentage, while operational efficiency was measured through utilization classification logic implemented via macros.

Fraud detection mechanisms were built using rule-based thresholds to flag abnormal cost spikes, excessive delays, and unusually high safety incidents. Statistical procedures including PROC SQL, PROC MEANS, PROC UNIVARIATE, PROC FREQ, and PROC CORR provided analytical depth by generating summaries, identifying outliers, and analyzing variable relationships. Visualization using PROC SGPLOT further strengthened performance validation and anomaly detection.

SAS INTERVIEW QUESTIONS

·  Explain the difference between %LET and CALL SYMPUT.

·  What are local and global macro variables?

·  What is the difference between %MACRO and macro function?

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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 CONSTRUCTION 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

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·  Bloggers writing about analytics 

·  Clinical SAS Programmer

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