434.Can Advanced SAS Programming Detect, Clean, and Optimize Grid Stability Data While Identifying Fraud Patterns?

Advanced SAS Intelligence for Grid Stability Optimization and Fraud Detection

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA | SET | MERGE | INPUT | DATALINES | IF - ELSE |  PROC SORT | PROC SQL | PROC APPEND | PROC TRANSPOSE | PROC DATASETS | RUN | QUIT | %MACRO | %MEND | FUNCTIONS ETC | R LANGUAGE

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Introduction

Power grid stability is one of the most critical components in modern energy systems. With the increasing integration of renewable sources like wind and solar, maintaining grid balance has become complex. Variations in load demand, frequency deviation, and backup generation directly impact reliability.

In this project, we simulate a Grid Stability Dataset using both R and SAS, intentionally introduce errors, detect and fix them using advanced SAS techniques, and implement fraud detection logic using macros. We also apply real-world transformations like date handling, utilization classification, merging, transposing, and dataset optimization.

This project is designed for:

• SAS Programmer Interviews
• Real-world Data Cleaning Scenarios
• Clinical/Non-clinical Data Transformation Practice

Table of Contents

1. Business Context
2. Raw Dataset Creation (R & SAS)
3. Intentional Errors in Dataset
4. Error Identification & Fixing
5. Data Cleaning using SAS Functions
6. Date Handling (MDY, INTCK, INTNX)
7. Utilization Classification
8. Data Transformation (SET, MERGE, APPEND)
9. PROC TRANSPOSE Usage
10. Fraud Detection using MACROS
11. Numeric & Character Functions
12. PROC DATASETS DELETE
13. Final Clean Dataset
14. Key Highlights
15. 20 Key Points About The Project
16. Summary & Conclusion

Business Context

Electric grid operators monitor:

• Renewable output (Wind/Solar)
• Demand fluctuations
• Frequency stability
• Backup generation usage

Fraud scenarios:

• Inflated backup generation costs
• Artificial frequency manipulation
• Fake utilization reporting 

1. Raw Dataset Creation in R (With Intentional Errors)

library(dplyr)

grid_data <- data.frame(

  Grid_Region = c("north","south","east","west","central",

                  "north","south","east","west","central",

                  "north","south"),

  Wind_Output = c(120,140,NA,160,180,200,210,220,NA,240,250,260),

  Solar_Output = c(80,90,100,NA,120,130,140,150,160,NA,180,190),

  Load_Demand = c(250,260,270,280,290,300,310,320,330,340,350,360),

  Frequency_Deviation = c(0.02,-0.03,0.05,NA,0.01,0.04,0.06,0.02,NA,0.03,0.01,0.05),

  Backup_Generation = c(50,60,70,80,90,100,110,120,130,140,150,160),

  Percentage = c(90,85,88,92,95,97,99,91,89,93,96,98),

  Fees = c(1000,2000,NA,1500,1800,2200,2500,2700,NA,3000,3200,3500),

  Date = c("2025-01-01","2025-02-01","wrong","2025-04-01","2025-05-01",

           "2025-06-01","2025-07-01","2025-08-01","2025-09-01","wrong",

           "2025-11-01","2025-12-01")

)

grid_data

OUTPUT:

	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date
1	north	120	80	250	0.02	50	90	1000	01-01-2025
2	south	140	90	260	-0.03	60	85	2000	01-02-2025
3	east	NA	100	270	0.05	70	88	NA	wrong
4	west	160	NA	280	NA	80	92	1500	01-04-2025
5	central	180	120	290	0.01	90	95	1800	01-05-2025
6	north	200	130	300	0.04	100	97	2200	01-06-2025
7	south	210	140	310	0.06	110	99	2500	01-07-2025
8	east	220	150	320	0.02	120	91	2700	01-08-2025
9	west	NA	160	330	NA	130	89	NA	01-09-2025
10	central	240	NA	340	0.03	140	93	3000	wrong
11	north	250	180	350	0.01	150	96	3200	01-11-2025
12	south	260	190	360	0.05	160	98	3500	01-12-2025

Errors Introduced

·  Missing values (NA)

·  Wrong date format (“wrong”)

·  Inconsistent casing (north vs North)

2. Raw Dataset Creation in SAS (With Intentional Errors)

data grid_raw;

 input Grid_Region $ Wind_Output Solar_Output Load_Demand Frequency_Deviation

       Backup_Generation Percentage Fees Date :$20.;

 datalines;

 north 120 80 250 0.02 50 90 1000 2025-01-01

 south 140 90 260 -0.03 60 85 2000 2025-02-01

 east . 100 270 0.05 70 88 . wrong

 west 160 . 280 . 80 92 1500 2025-04-01

 central 180 120 290 0.01 90 95 1800 2025-05-01

 north 200 130 300 0.04 100 97 2200 2025-06-01

 south 210 140 310 0.06 110 99 2500 2025-07-01

 east 220 150 320 0.02 120 91 2700 2025-08-01

 west . 160 330 . 130 89 . 2025-09-01

 central 240 . 340 0.03 140 93 3000 wrong

 north 250 180 350 0.01 150 96 3200 2025-11-01

 south 260 190 360 0.05 160 98 3500 2025-12-01

;

run;

proc print data=grid_raw;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date
1	north	120	80	250	0.02	50	90	1000	2025-01-01
2	south	140	90	260	-0.03	60	85	2000	2025-02-01
3	east	.	100	270	0.05	70	88	.	wrong
4	west	160	.	280	.	80	92	1500	2025-04-01
5	central	180	120	290	0.01	90	95	1800	2025-05-01
6	north	200	130	300	0.04	100	97	2200	2025-06-01
7	south	210	140	310	0.06	110	99	2500	2025-07-01
8	east	220	150	320	0.02	120	91	2700	2025-08-01
9	west	.	160	330	.	130	89	.	2025-09-01
10	central	240	.	340	0.03	140	93	3000	wrong
11	north	250	180	350	0.01	150	96	3200	2025-11-01
12	south	260	190	360	0.05	160	98	3500	2025-12-01

3. Error Identification

Issues:

·  Missing values (.)

·  Invalid date (“wrong”)

·  Lowercase region names

·  Null fees

·  Missing solar/wind output

4. Data Cleaning Using SAS Functions

data grid_clean;

set grid_raw;

Grid_Region = propcase(strip(Grid_Region));

Wind_Output = coalesce(Wind_Output, 150);

Solar_Output = coalesce(Solar_Output, 100);

Fees = coalesce(Fees, 1700);

run;

proc print data=grid_clean;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date
1	North	120	80	250	0.02	50	90	1000	2025-01-01
2	South	140	90	260	-0.03	60	85	2000	2025-02-01
3	East	150	100	270	0.05	70	88	1700	wrong
4	West	160	100	280	.	80	92	1500	2025-04-01
5	Central	180	120	290	0.01	90	95	1800	2025-05-01
6	North	200	130	300	0.04	100	97	2200	2025-06-01
7	South	210	140	310	0.06	110	99	2500	2025-07-01
8	East	220	150	320	0.02	120	91	2700	2025-08-01
9	West	150	160	330	.	130	89	1700	2025-09-01
10	Central	240	100	340	0.03	140	93	3000	wrong
11	North	250	180	350	0.01	150	96	3200	2025-11-01
12	South	260	190	360	0.05	160	98	3500	2025-12-01

Why Used

·  STRIP → removes spaces

·  PROPCASE → standard format

·  COALESCE → handles missing values

5. Date Handling

data grid_dates;

set grid_clean;

SAS_Date = input(Date, yymmdd10.);

if Date = "wrong" then Date = "01MAR2025";

format SAS_Date date9.;

Month_Start = intnx('month', SAS_Date, 0, 'b');

Months_Diff = intck('month', '01JAN2025'd, SAS_Date);

run;

proc print data=grid_dates;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff
1	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0
2	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.
4	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3
5	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5
7	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6
8	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7
9	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8
10	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.
11	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10
12	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11

Functions

·  MDY() → create dates

·  INTNX() → shift intervals

·  INTCK() → calculate differences

6. Utilization Classification

data grid_util;

set grid_dates;

Utilization = (Wind_Output + Solar_Output) / Load_Demand;

length Util_Class $10.;

if Utilization > 0.9 then Util_Class = "High";

else if Utilization > 0.8 then Util_Class = "Medium";

else Util_Class = "Low";

run;

proc print data=grid_util;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff	Utilization	Util_Class
1	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low
2	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High
4	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High
5	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High
7	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High
8	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High
9	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High
10	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High
11	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High
12	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High

7. DATA STEP (SET)

data grid_copy;

set grid_util;

run;

proc print data=grid_copy;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff	Utilization	Util_Class
1	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low
2	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High
4	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High
5	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High
7	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High
8	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High
9	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High
10	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High
11	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High
12	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High

Why

·  Creates duplicate dataset

·  Used for pipeline processing

8. MERGE Example

data fees_update;

input Grid_Region $ Extra_Fees;

datalines;

North 100

South 200

West 50

East 25

Central 0

;

run;

proc print data=fees_update;

run;

OUTPUT:

Obs	Grid_Region	Extra_Fees
1	North	100
2	South	200
3	West	50
4	East	25
5	Central	0

proc sort data=grid_copy;by Grid_Region;run;

proc print data=grid_copy;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff	Utilization	Util_Class
1	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High
2	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High
4	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High
5	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High
7	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High
8	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium
9	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High
10	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High
11	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High
12	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High

proc sort data=fees_update;by Grid_Region;run;

proc print data=fees_update;

run;

OUTPUT:

Obs	Grid_Region	Extra_Fees
1	Central	0
2	East	25
3	North	100
4	South	200
5	West	50

data merged_data;

merge grid_copy

      fees_update;

by Grid_Region;

run;

proc print data=merged_data;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff	Utilization	Util_Class	Extra_Fees
1	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	0
2	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High	0
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	25
4	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High	25
5	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low	100
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High	100
7	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High	100
8	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium	200
9	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High	200
10	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High	200
11	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High	50
12	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	50

9. APPEND

proc append base=grid_copy 

            data=grid_util force;

run;

proc print data=grid_copy;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff	Utilization	Util_Class
1	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High
2	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High
4	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High
5	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High
7	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High
8	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium
9	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High
10	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High
11	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High
12	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High
13	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low
14	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium
15	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High
16	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High
17	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High
18	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High
19	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High
20	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High
21	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High
22	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High
23	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High
24	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High

10. PROC TRANSPOSE

proc transpose data=grid_copy out=grid_trans;

by Grid_Region NotSorted;

var Wind_Output Solar_Output Load_Demand;

run;

proc print data=grid_trans;

run;

OUTPUT:

Obs	Grid_Region	_NAME_	COL1	COL2	COL3
1	Central	Wind_Output	180	240	.
2	Central	Solar_Output	120	100	.
3	Central	Load_Demand	290	340	.
4	East	Wind_Output	150	220	.
5	East	Solar_Output	100	150	.
6	East	Load_Demand	270	320	.
7	North	Wind_Output	120	200	250
8	North	Solar_Output	80	130	180
9	North	Load_Demand	250	300	350
10	South	Wind_Output	140	210	260
11	South	Solar_Output	90	140	190
12	South	Load_Demand	260	310	360
13	West	Wind_Output	160	150	.
14	West	Solar_Output	100	160	.
15	West	Load_Demand	280	330	.
16	North	Wind_Output	120	.	.
17	North	Solar_Output	80	.	.
18	North	Load_Demand	250	.	.
19	South	Wind_Output	140	.	.
20	South	Solar_Output	90	.	.
21	South	Load_Demand	260	.	.
22	East	Wind_Output	150	.	.
23	East	Solar_Output	100	.	.
24	East	Load_Demand	270	.	.
25	West	Wind_Output	160	.	.
26	West	Solar_Output	100	.	.
27	West	Load_Demand	280	.	.
28	Central	Wind_Output	180	.	.
29	Central	Solar_Output	120	.	.
30	Central	Load_Demand	290	.	.
31	North	Wind_Output	200	.	.
32	North	Solar_Output	130	.	.
33	North	Load_Demand	300	.	.
34	South	Wind_Output	210	.	.
35	South	Solar_Output	140	.	.
36	South	Load_Demand	310	.	.
37	East	Wind_Output	220	.	.
38	East	Solar_Output	150	.	.
39	East	Load_Demand	320	.	.
40	West	Wind_Output	150	.	.
41	West	Solar_Output	160	.	.
42	West	Load_Demand	330	.	.
43	Central	Wind_Output	240	.	.
44	Central	Solar_Output	100	.	.
45	Central	Load_Demand	340	.	.
46	North	Wind_Output	250	.	.
47	North	Solar_Output	180	.	.
48	North	Load_Demand	350	.	.
49	South	Wind_Output	260	.	.
50	South	Solar_Output	190	.	.
51	South	Load_Demand	360	.	.

11. Numeric Functions

data numeric_ops;

set grid_copy;

Total_Output = sum(Wind_Output, Solar_Output);

Avg_Output = mean(Wind_Output, Solar_Output);

run;

proc print data=numeric_ops;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff	Utilization	Util_Class	Total_Output	Avg_Output
1	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	300	150
2	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High	340	170
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	250	125
4	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High	370	185
5	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low	200	100
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High	330	165
7	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High	430	215
8	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium	230	115
9	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High	350	175
10	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High	450	225
11	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High	260	130
12	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	310	155
13	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low	200	100
14	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium	230	115
15	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	250	125
16	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High	260	130
17	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	300	150
18	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High	330	165
19	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High	350	175
20	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High	370	185
21	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	310	155
22	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High	340	170
23	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High	430	215
24	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High	450	225

12. Character Functions

data char_ops;

set grid_copy;

Region_Upper = upcase(Grid_Region);

Region_Lower = lowcase(Grid_Region);

Region_Concat = catx("-", Grid_Region, Util_Class);

run;

proc print data=char_ops;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff	Utilization	Util_Class	Region_Upper	Region_Lower	Region_Concat
1	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	CENTRAL	central	Central-High
2	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High	CENTRAL	central	Central-High
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	EAST	east	East-High
4	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High	EAST	east	East-High
5	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low	NORTH	north	North-Low
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High	NORTH	north	North-High
7	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High	NORTH	north	North-High
8	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium	SOUTH	south	South-Medium
9	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High	SOUTH	south	South-High
10	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High	SOUTH	south	South-High
11	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High	WEST	west	West-High
12	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	WEST	west	West-High
13	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low	NORTH	north	North-Low
14	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium	SOUTH	south	South-Medium
15	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	EAST	east	East-High
16	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High	WEST	west	West-High
17	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	CENTRAL	central	Central-High
18	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High	NORTH	north	North-High
19	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High	SOUTH	south	South-High
20	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High	EAST	east	East-High
21	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	WEST	west	West-High
22	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High	CENTRAL	central	Central-High
23	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High	NORTH	north	North-High
24	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High	SOUTH	south	South-High

13. Fraud Detection Macro

%macro fraud_check;

data fraud_flag;

set grid_copy;

if Backup_Generation > 100 and Fees > 2000 then Fraud = "Yes";

else Fraud = "No";

run;

proc print data=fraud_flag;

run;

%mend;

%fraud_check;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff	Utilization	Util_Class	Fraud
1	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	No
2	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High	Yes
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	No
4	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High	Yes
5	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low	No
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High	No
7	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High	Yes
8	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium	No
9	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High	Yes
10	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High	Yes
11	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High	No
12	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	No
13	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low	No
14	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium	No
15	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	No
16	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High	No
17	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	No
18	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High	No
19	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High	Yes
20	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High	Yes
21	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	No
22	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High	Yes
23	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High	Yes
24	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High	Yes

14. Intentional Error Fix (Example)

data fraud_flag;

set grid_copy;

length flag $10.;

if 1500< Fees < 2000 then flag="Error";

run;

proc print data=fraud_flag;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff	Utilization	Util_Class	flag
1	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	Error
2	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	Error
4	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High
5	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High
7	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High
8	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium
9	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High
10	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High
11	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High
12	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	Error
13	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low
14	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium
15	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	Error
16	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High
17	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	Error
18	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High
19	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High
20	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High
21	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	Error
22	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High
23	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High
24	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High

data fraud_flag;

set grid_copy;

if Fees < 2000 and Fees > 1500 then flag="Valid";

else flag = "Error";

run;

proc print data=fraud_flag;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	SAS_Date	Month_Start	Months_Diff	Utilization	Util_Class	flag
1	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	Valid
2	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High	Error
3	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	Valid
4	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High	Error
5	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low	Error
6	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High	Error
7	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High	Error
8	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium	Error
9	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High	Error
10	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High	Error
11	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High	Error
12	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	Valid
13	North	120	80	250	0.02	50	90	1000	2025-01-01	01JAN2025	23742	0	0.80000	Low	Error
14	South	140	90	260	-0.03	60	85	2000	2025-02-01	01FEB2025	23773	1	0.88462	Medium	Error
15	East	150	100	270	0.05	70	88	1700	01MAR2025	.	.	.	0.92593	High	Valid
16	West	160	100	280	.	80	92	1500	2025-04-01	01APR2025	23832	3	0.92857	High	Error
17	Central	180	120	290	0.01	90	95	1800	2025-05-01	01MAY2025	23862	4	1.03448	High	Valid
18	North	200	130	300	0.04	100	97	2200	2025-06-01	01JUN2025	23893	5	1.10000	High	Error
19	South	210	140	310	0.06	110	99	2500	2025-07-01	01JUL2025	23923	6	1.12903	High	Error
20	East	220	150	320	0.02	120	91	2700	2025-08-01	01AUG2025	23954	7	1.15625	High	Error
21	West	150	160	330	.	130	89	1700	2025-09-01	01SEP2025	23985	8	0.93939	High	Valid
22	Central	240	100	340	0.03	140	93	3000	01MAR2025	.	.	.	1.00000	High	Error
23	North	250	180	350	0.01	150	96	3200	2025-11-01	01NOV2025	24046	10	1.22857	High	Error
24	South	260	190	360	0.05	160	98	3500	2025-12-01	01DEC2025	24076	11	1.25000	High	Error

15. PROC DATASETS DELETE

proc datasets library=work;

delete grid_raw grid_copy;

run;

quit;

LOG:

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

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

16. Final Corrected Dataset Code

data final_grid;

set grid_clean;

Total_Output = sum(Wind_Output, Solar_Output);

Utilization = Total_Output / Load_Demand;

length Util_Class $10. Risk $10.;

if Utilization > 0.9 then Util_Class="High";

else if Utilization > 0.8 then Util_Class="Medium";

else Util_Class="Low";

if Backup_Generation > 100 then Risk="High";

else Risk="Normal";

if not missing(Date) and Date ne "wrong" then 

    Clean_Date = input(Date, yymmdd10.);

else 

    Clean_Date = .;

format Clean_Date date9.;

if missing(Clean_Date) then Clean_Date="01MAR2025"d;

run;

proc print data=final_grid;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	Total_Output	Utilization	Util_Class	Risk	Clean_Date
1	North	120	80	250	0.02	50	90	1000	2025-01-01	200	0.80000	Low	Normal	01JAN2025
2	South	140	90	260	-0.03	60	85	2000	2025-02-01	230	0.88462	Medium	Normal	01FEB2025
3	East	150	100	270	0.05	70	88	1700	wrong	250	0.92593	High	Normal	01MAR2025
4	West	160	100	280	.	80	92	1500	2025-04-01	260	0.92857	High	Normal	01APR2025
5	Central	180	120	290	0.01	90	95	1800	2025-05-01	300	1.03448	High	Normal	01MAY2025
6	North	200	130	300	0.04	100	97	2200	2025-06-01	330	1.10000	High	Normal	01JUN2025
7	South	210	140	310	0.06	110	99	2500	2025-07-01	350	1.12903	High	High	01JUL2025
8	East	220	150	320	0.02	120	91	2700	2025-08-01	370	1.15625	High	High	01AUG2025
9	West	150	160	330	.	130	89	1700	2025-09-01	310	0.93939	High	High	01SEP2025
10	Central	240	100	340	0.03	140	93	3000	wrong	340	1.00000	High	High	01MAR2025
11	North	250	180	350	0.01	150	96	3200	2025-11-01	430	1.22857	High	High	01NOV2025
12	South	260	190	360	0.05	160	98	3500	2025-12-01	450	1.25000	High	High	01DEC2025

/*Alternate*/

data final_grid;

set grid_clean;

Total_Output = sum(Wind_Output, Solar_Output);

Utilization = Total_Output / Load_Demand;

if Utilization > 0.9 then Util_Class="High";

else if Utilization > 0.8 then Util_Class="Medium";

else Util_Class="Low";

if Backup_Generation > 100 then Risk="High";

else Risk="Normal";

if not missing(Date) and Date ne "wrong" then 

    Clean_Date = input(Date, yymmdd10.);

else 

    Clean_Date = .;

format Clean_Date date9.;

if missing(Clean_Date) then Clean_Date = mdy(3,1,2025);

run;

proc print data=final_grid;

run;

OUTPUT:

Obs	Grid_Region	Wind_Output	Solar_Output	Load_Demand	Frequency_Deviation	Backup_Generation	Percentage	Fees	Date	Total_Output	Utilization	Util_Class	Risk	Clean_Date
1	North	120	80	250	0.02	50	90	1000	2025-01-01	200	0.80000	Low	Normal	01JAN2025
2	South	140	90	260	-0.03	60	85	2000	2025-02-01	230	0.88462	Medium	Normal	01FEB2025
3	East	150	100	270	0.05	70	88	1700	wrong	250	0.92593	High	Normal	01MAR2025
4	West	160	100	280	.	80	92	1500	2025-04-01	260	0.92857	High	Normal	01APR2025
5	Central	180	120	290	0.01	90	95	1800	2025-05-01	300	1.03448	High	Normal	01MAY2025
6	North	200	130	300	0.04	100	97	2200	2025-06-01	330	1.10000	High	Normal	01JUN2025
7	South	210	140	310	0.06	110	99	2500	2025-07-01	350	1.12903	High	High	01JUL2025
8	East	220	150	320	0.02	120	91	2700	2025-08-01	370	1.15625	High	High	01AUG2025
9	West	150	160	330	.	130	89	1700	2025-09-01	310	0.93939	High	High	01SEP2025
10	Central	240	100	340	0.03	140	93	3000	wrong	340	1.00000	High	High	01MAR2025
11	North	250	180	350	0.01	150	96	3200	2025-11-01	430	1.22857	High	High	01NOV2025
12	South	260	190	360	0.05	160	98	3500	2025-12-01	450	1.25000	High	High	01DEC2025

Variable	Type	Example
Clean_Date	Numeric	23375
Display	Format	01MAR2025

 SAS Date Storage

·  Dates are numeric values

·  Formats only change display

Method	Example
Date Literal	'01MAR2025'd
MDY	mdy(3,1,2025)

Key Highlights

·  Data cleaning using COALESCENCE

·  Date derivation using INTNX, INTCK

·  Fraud detection using macros

·  Data reshaping with PROC TRANSPOSE

·  Dataset lifecycle using PROC DATASETS

·  Handling missing values and logic errors

20 Key Points About The Project

1.     Designed a grid stability dataset capturing renewable energy output, demand, and system deviations.
2.     Integrated both R and SAS environments to ensure cross-platform data consistency and validation.
3.     Introduced intentional data quality issues such as missing values, invalid dates, and inconsistent formats to simulate real-world scenarios.
4.     Applied data cleaning techniques using COALESCE to handle missing numeric values efficiently.
5.     Standardized categorical variables using character functions like PROPCASE, STRIP, UPCASE, and LOWCASE.
6.     Implemented date conversion logic using INPUT, MDY, INTNX, and INTCK for accurate temporal analysis.
7.     Developed robust error-handling logic to safely process invalid entries like "wrong" dates without breaking execution.
8.     Derived Total_Output and Utilization metrics to measure grid efficiency and performance.
9.     Created utilization classification (High/Medium/Low) using conditional logic for operational insights.
10.  Performed data transformation using SET, MERGE, and APPEND to combine and extend datasets.
11.  Reshaped data using PROC TRANSPOSE for reporting and analytical flexibility.
12.  Leveraged numeric functions like SUM and MEAN for aggregated calculations.
13.  Built character-based composite variables using CAT and CATX for better reporting readability.
14.  Designed a fraud detection framework using SAS MACROS to dynamically flag suspicious patterns.
15.  Identified fraud scenarios such as abnormally high backup generation and inflated operational fees.
16.  Ensured complete classification logic by covering all edge cases including missing and extreme values.
17.  Used PROC DATASETS DELETE for efficient dataset lifecycle management and workspace cleanup.
18.  Debugged common SAS errors including type mismatches, invalid INPUT conversions, and logical condition flaws.
19.  Applied defensive programming techniques to make the code robust, scalable, and production-ready.

20.  Delivered an end-to-end data pipeline demonstrating SAS capabilities in data engineering, validation, and fraud analytics within energy systems.

Summary

This project demonstrates how SAS can efficiently handle real-world grid stability data. By integrating multiple techniques such as data cleaning, transformation, merging, and fraud detection, we created a robust pipeline. Intentional errors were introduced to simulate real scenarios, and each error was carefully corrected with explanations.

The use of macros improves scalability, while functions like INTNX, COALESCE, and CATX enhance data quality. This project highlights how SAS is not just a programming language but a complete data engineering tool capable of ensuring accuracy and reliability in critical systems like energy grids.

Conclusion

In this project, we worked on grid stability data and learned how to handle real-life data problems step by step. First, we created raw datasets with errors to understand how messy real data can be. Then we cleaned the data using SAS functions and fixed missing values, incorrect formats, and logical mistakes. We also learned how to work with dates, calculate utilization, and classify data into meaningful categories.

One of the most important parts was detecting fraud using macros, which is very useful in real-world scenarios. We also practiced using different SAS techniques like merge, append, transpose, and dataset deletion. Overall, this project gives a complete understanding of how SAS can be used in real-time data processing and analysis. It is very helpful for interviews and practical work.

SAS INTERVIEW QUESTIONS

1. What is the difference between LENGTH and FORMAT in SAS?

Answer:

• LENGTH defines how much storage space a variable occupies.
• FORMAT controls how the value is displayed, not stored.
  LENGTH affects data structure, FORMAT affects only appearance.

2. What is the difference between PROC SORT with NODUPKEY and NODUP?

Answer:

• NODUPKEY removes duplicates based on BY variables only.
• NODUP removes completely identical observations.
  Use NODUPKEY for key-based deduplication.

3. What is the role of RETAIN statement in SAS?

Answer:

• By default, SAS resets variables each iteration.
• RETAIN keeps the previous value across rows.
  Commonly used for cumulative sums and carrying forward values.

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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 GRID STABILITY data.

Our Mission:

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

This project is suitable for:

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·  Data analysts building portfolios

·  Professionals preparing for SAS interviews

·  Bloggers writing about analytics 

·  Clinical SAS Programmer

·  Research Data Analyst

·  Regulatory Data Validator

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To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1.Can SAS Macros Automate Fraud Case Analysis and Reporting?

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