When Bombay Meets Mumbai: Transforming Inconsistent City Data into Analytical Gold

Cities Reborn, Data Refined: Turning Chaotic Global City Name Changes into Analytical Intelligence with SAS & R

1. Introduction 

Imagine you’re working on a global pharmaceutical trial. Patient data is coming in from multiple countries India, USA, Japan, Russia. Everything looks fine until you run a regional analysis. Suddenly, your output shows Madras and Chennai as two different locations. Bombay appears separately from Mumbai. Worse, some records show “NULL,” others “unknown,” and some are blank.

Now your regional efficacy results are split across duplicate geographies.

That’s not just messy that’s dangerous.

In clinical trials, business intelligence, or financial systems, bad data leads to:

• Incorrect aggregations
• Misleading trends
• Faulty decision-making
• Regulatory risks

This is where SAS and R become critical. SAS provides structured, enterprise-grade data processing (ideal for clinical environments), while R offers flexible, modern data wrangling capabilities.

In this project, we simulate a global dataset of old vs new city names, intentionally inject errors, and then clean, transform, and combine datasets using:

• SAS DATA Step vs PROC SQL (MERGE vs JOIN)
• R dplyr workflows

2. Raw Data Creation (With Intentional Errors)

SAS Code (Raw Dataset)

data city_raw;

length Old_Name $20 New_Name $20 Country $15 Region $15 

       Update_Date $12 Source $20 Status $10;

input ID Old_Name $ New_Name $ Country $ Region $ Update_Date $ 

      Population Source $ Status $;

datalines;

1 Bombay Mumbai India West 2020-01-01 20000000 govt active

2 Madras Chennai India South 2019-05-10 11000000 govt active

3 Calcutta Kolkata India East 2018-07-21 15000000 govt active

4 NULL Delhi India North 2021-02-01 18000000 private active

5 Peking Beijing China North 2017-06-15 21000000 govt active

6 Saigon HoChiMinh Vietnam South 2016-03-12 9000000 govt active

7 Leningrad StPetersburg Russia West 2015-09-30 5000000 govt active

8 Bombay Mumbai India West 2020-01-01 20000000 govt active

9 -999 Karachi Pakistan South 2018-11-11 14000000 govt inactive

10 Edo Tokyo Japan East wrongdate 13000000 govt active

;

run;

proc print data = city_raw;

run;

OUTPUT:

Obs	Old_Name	New_Name	Country	Region	Update_Date	Source	Status	ID	Population
1	Bombay	Mumbai	India	West	2020-01-01	govt	active	1	20000000
2	Madras	Chennai	India	South	2019-05-10	govt	active	2	11000000
3	Calcutta	Kolkata	India	East	2018-07-21	govt	active	3	15000000
4	NULL	Delhi	India	North	2021-02-01	private	active	4	18000000
5	Peking	Beijing	China	North	2017-06-15	govt	active	5	21000000
6	Saigon	HoChiMinh	Vietnam	South	2016-03-12	govt	active	6	9000000
7	Leningrad	StPetersburg	Russia	West	2015-09-30	govt	active	7	5000000
8	Bombay	Mumbai	India	West	2020-01-01	govt	active	8	20000000
9	-999	Karachi	Pakistan	South	2018-11-11	govt	inactive	9	14000000
10	Edo	Tokyo	Japan	East	wrongdate	govt	active	10	13000000

Explanation (SAS Raw Data)

This dataset intentionally includes:

• Missing values (NULL)
• Invalid numeric (-999)
• Wrong date format (wrongdate)
• Duplicate records (Bombay → Mumbai)
• Inconsistent naming

This mirrors real-world ingestion issues. Notice the use of length this is critical to prevent truncation. Without it, SAS may default to smaller lengths and silently corrupt strings.

R Code – Equivalent Raw Dataset

options(scipen=999)

city_raw <- data.frame(

  ID = 1:10,

  Old_Name = c("Bombay","Madras","Calcutta","NULL","Peking","Saigon",

               "Leningrad","Bombay","-999","Edo"),

  New_Name = c("Mumbai","Chennai","Kolkata","Delhi","Beijing",

               "HoChiMinh","StPetersburg","Mumbai","Karachi","Tokyo"),

  Country = c("India","India","India","India","China","Vietnam","Russia",

              "India","Pakistan","Japan"),

  Region = c("West","South","East","North","North","South","West","West",

             "South","East"),

  Update_Date = c("2020-01-01","2019-05-10","2018-07-21","2021-02-01",

                  "2017-06-15","2016-03-12","2015-09-30","2020-01-01",

                  "2018-11-11","wrongdate"),

  Population = c(20000000,11000000,15000000,18000000,21000000,9000000,

                 5000000,20000000,14000000,13000000),

  Source = c("govt","govt","govt","private","govt","govt","govt","govt",

             "govt","govt"),

  Status = c("active","active","active","active","active","active","active",

             "active","inactive","active")

)

OUTPUT:

	ID	Old_Name	New_Name	Country	Region	Update_Date	Population	Source	Status
1	1	Bombay	Mumbai	India	West	01-01-2020	20000000	govt	active
2	2	Madras	Chennai	India	South	10-05-2019	11000000	govt	active
3	3	Calcutta	Kolkata	India	East	21-07-2018	15000000	govt	active
4	4	NULL	Delhi	India	North	01-02-2021	18000000	private	active
5	5	Peking	Beijing	China	North	15-06-2017	21000000	govt	active
6	6	Saigon	HoChiMinh	Vietnam	South	12-03-2016	9000000	govt	active
7	7	Leningrad	StPetersburg	Russia	West	30-09-2015	5000000	govt	active
8	8	Bombay	Mumbai	India	West	01-01-2020	20000000	govt	active
9	9	-999	Karachi	Pakistan	South	11-11-2018	14000000	govt	inactive
10	10	Edo	Tokyo	Japan	East	wrongdate	13000000	govt	active

Explanation (R Raw Data)

R’s data.frame() mirrors SAS input but allows more flexibility. However:

• Strings like "NULL" are not automatically treated as missing
• "wrongdate" remains a string
• No strict typing enforcement like SAS

This flexibility is powerful but dangerous if not controlled.

Section 1: Phase 1 – The SAS Engineering Layer

Data Cleaning in SAS

data city_clean;

set city_raw;

/* Fix missing names */

if strip(upcase(Old_Name)) in ("NULL","-999") then Old_Name = "";

Old_Name = coalescec(Old_Name, "UNKNOWN");

/* Handle invalid numeric */

if Population < 0 then Population = abs(Population);

/* Date conversion */

New_Date =input(Update_Date,??  yymmdd10.);

format New_Date yymmdd10.;

/* Standardize text */

Old_Name = upcase(strip(Old_Name));

New_Name = propcase(strip(New_Name));

/* Category assignment */

select(Status);

  when("active") Flag = 1;

  when("inactive") Flag = 0;

  otherwise Flag = .;

end;

run;

proc print data = city_clean;

run;

OUTPUT:

Obs	Old_Name	New_Name	Country	Region	Update_Date	Source	Status	ID	Population	New_Date	Flag
1	BOMBAY	Mumbai	India	West	2020-01-01	govt	active	1	20000000	2020-01-01	1
2	MADRAS	Chennai	India	South	2019-05-10	govt	active	2	11000000	2019-05-10	1
3	CALCUTTA	Kolkata	India	East	2018-07-21	govt	active	3	15000000	2018-07-21	1
4	UNKNOWN	Delhi	India	North	2021-02-01	private	active	4	18000000	2021-02-01	1
5	PEKING	Beijing	China	North	2017-06-15	govt	active	5	21000000	2017-06-15	1
6	SAIGON	Hochiminh	Vietnam	South	2016-03-12	govt	active	6	9000000	2016-03-12	1
7	LENINGRAD	Stpetersburg	Russia	West	2015-09-30	govt	active	7	5000000	2015-09-30	1
8	BOMBAY	Mumbai	India	West	2020-01-01	govt	active	8	20000000	2020-01-01	1
9	UNKNOWN	Karachi	Pakistan	South	2018-11-11	govt	inactive	9	14000000	2018-11-11	0
10	EDO	Tokyo	Japan	East	wrongdate	govt	active	10	13000000	.	1

Explanation

• COALESCEC fills missing character values
• ABS() fixes invalid numeric values
• INPUT() converts string → date
• SELECT-WHEN is cleaner than IF-ELSE for categorical logic
• STRIP + UPCASE ensures standardization
• Convert "NULL" → blank → then COALESCEC() works.

      This is advanced SAS defensive programming

• ? → suppress warning
• ?? → suppress warning + NOTE

👉 If "wrongdate" appears:

• No log clutter
• Value safely becomes .

/* Remove duplicates */

proc sort data=city_clean nodupkey;

by Old_Name New_Name Country;

run;

proc print data = city_clean;

run;

OUTPUT:

Obs	Old_Name	New_Name	Country	Region	Update_Date	Source	Status	ID	Population	New_Date	Flag
1	BOMBAY	Mumbai	India	West	2020-01-01	govt	active	1	20000000	2020-01-01	1
2	CALCUTTA	Kolkata	India	East	2018-07-21	govt	active	3	15000000	2018-07-21	1
3	EDO	Tokyo	Japan	East	wrongdate	govt	active	10	13000000	.	1
4	LENINGRAD	Stpetersburg	Russia	West	2015-09-30	govt	active	7	5000000	2015-09-30	1
5	MADRAS	Chennai	India	South	2019-05-10	govt	active	2	11000000	2019-05-10	1
6	PEKING	Beijing	China	North	2017-06-15	govt	active	5	21000000	2017-06-15	1
7	SAIGON	Hochiminh	Vietnam	South	2016-03-12	govt	active	6	9000000	2016-03-12	1
8	UNKNOWN	Delhi	India	North	2021-02-01	private	active	4	18000000	2021-02-01	1
9	UNKNOWN	Karachi	Pakistan	South	2018-11-11	govt	inactive	9	14000000	2018-11-11	0

Explanation

NODUPKEY removes duplicate combinations. This is critical in preventing double counting.

MERGE vs JOIN (SAS)

data region_map;

input Country $ Region_Code $;

datalines;

India IN

China CN

Japan JP

Pakistan PAK

Russia Rus

Vietnam Viet

;

run;

proc print data = region_map;

run;

OUTPUT:

Obs	Country	Region_Code
1	India	IN
2	China	CN
3	Japan	JP
4	Pakistan	PAK
5	Russia	Rus
6	Vietnam	Viet

/*Sorting Before Merging*/

proc sort data=region_map;

by Country;

run;

proc print data = region_map;

run;

OUTPUT:

Obs	Country	Region_Code
1	China	CN
2	India	IN
3	Japan	JP
4	Pakistan	PAK
5	Russia	Rus
6	Vietnam	Viet

proc sort data=city_clean;

by Country;

run;

proc print data = city_clean;

run;

OUTPUT:

Obs	Old_Name	New_Name	Country	Region	Update_Date	Source	Status	ID	Population	New_Date	Flag
1	PEKING	Beijing	China	North	2017-06-15	govt	active	5	21000000	2017-06-15	1
2	BOMBAY	Mumbai	India	West	2020-01-01	govt	active	1	20000000	2020-01-01	1
3	CALCUTTA	Kolkata	India	East	2018-07-21	govt	active	3	15000000	2018-07-21	1
4	MADRAS	Chennai	India	South	2019-05-10	govt	active	2	11000000	2019-05-10	1
5	UNKNOWN	Delhi	India	North	2021-02-01	private	active	4	18000000	2021-02-01	1
6	EDO	Tokyo	Japan	East	wrongdate	govt	active	10	13000000	.	1
7	UNKNOWN	Karachi	Pakistan	South	2018-11-11	govt	inactive	9	14000000	2018-11-11	0
8	LENINGRAD	Stpetersburg	Russia	West	2015-09-30	govt	active	7	5000000	2015-09-30	1
9	SAIGON	Hochiminh	Vietnam	South	2016-03-12	govt	active	6	9000000	2016-03-12	1

/* MERGE */

data merged_data;

merge city_clean(in=a) region_map(in=b);

by Country;

if a;

run;

proc print data = merged_data;

run;

OUTPUT:

Obs	Old_Name	New_Name	Country	Region	Update_Date	Source	Status	ID	Population	New_Date	Flag	Region_Code
1	PEKING	Beijing	China	North	2017-06-15	govt	active	5	21000000	2017-06-15	1	CN
2	BOMBAY	Mumbai	India	West	2020-01-01	govt	active	1	20000000	2020-01-01	1	IN
3	CALCUTTA	Kolkata	India	East	2018-07-21	govt	active	3	15000000	2018-07-21	1	IN
4	MADRAS	Chennai	India	South	2019-05-10	govt	active	2	11000000	2019-05-10	1	IN
5	UNKNOWN	Delhi	India	North	2021-02-01	private	active	4	18000000	2021-02-01	1	IN
6	EDO	Tokyo	Japan	East	wrongdate	govt	active	10	13000000	.	1	JP
7	UNKNOWN	Karachi	Pakistan	South	2018-11-11	govt	inactive	9	14000000	2018-11-11	0	PAK
8	LENINGRAD	Stpetersburg	Russia	West	2015-09-30	govt	active	7	5000000	2015-09-30	1	Rus
9	SAIGON	Hochiminh	Vietnam	South	2016-03-12	govt	active	6	9000000	2016-03-12	1	Viet

/* PROC SQL JOIN */

proc sql;

create table joined_data as

select a.*, b.Region_Code

from city_clean a

left join region_map b

on a.Country = b.Country

order by ID;

quit;

proc print data = joined_data;

run;

OUTPUT:

Obs	Old_Name	New_Name	Country	Region	Update_Date	Source	Status	ID	Population	New_Date	Flag	Region_Code
1	BOMBAY	Mumbai	India	West	2020-01-01	govt	active	1	20000000	2020-01-01	1	IN
2	MADRAS	Chennai	India	South	2019-05-10	govt	active	2	11000000	2019-05-10	1	IN
3	CALCUTTA	Kolkata	India	East	2018-07-21	govt	active	3	15000000	2018-07-21	1	IN
4	UNKNOWN	Delhi	India	North	2021-02-01	private	active	4	18000000	2021-02-01	1	IN
5	PEKING	Beijing	China	North	2017-06-15	govt	active	5	21000000	2017-06-15	1	CN
6	SAIGON	Hochiminh	Vietnam	South	2016-03-12	govt	active	6	9000000	2016-03-12	1	Viet
7	LENINGRAD	Stpetersburg	Russia	West	2015-09-30	govt	active	7	5000000	2015-09-30	1	Rus
8	UNKNOWN	Karachi	Pakistan	South	2018-11-11	govt	inactive	9	14000000	2018-11-11	0	PAK
9	EDO	Tokyo	Japan	East	wrongdate	govt	active	10	13000000	.	1	JP

Explanation

• MERGE requires sorted data and works row-wise
• JOIN is flexible and does not require sorting
• SQL JOIN is preferred for complex relationships

Section 2: Phase 2 – R Refinement Layer

library(dplyr)

city_clean <- city_raw %>%

  mutate(

    Old_Name = ifelse(Old_Name %in% c("NULL","-999"), "UNKNOWN", Old_Name),

    Population = ifelse(Population < 0, abs(Population), Population),

    Update_Date = as.Date(Update_Date, format="%Y-%m-%d"),

    Old_Name = toupper(trimws(Old_Name)),

    New_Name = tools::toTitleCase(trimws(New_Name)),

    Flag = case_when(

      Status == "active" ~ 1,

      Status == "inactive" ~ 0,

      TRUE ~ NA_real_

    )

  ) %>%

  distinct(Old_Name, New_Name, .keep_all = TRUE)

 OUTPUT:

	ID	Old_Name	New_Name	Country	Region	Update_Date	Population	Source	Status	Flag
1	1	BOMBAY	Mumbai	India	West	01-01-2020	20000000	govt	active	1
2	2	MADRAS	Chennai	India	South	10-05-2019	11000000	govt	active	1
3	3	CALCUTTA	Kolkata	India	East	21-07-2018	15000000	govt	active	1
4	4	UNKNOWN	Delhi	India	North	01-02-2021	18000000	private	active	1
5	5	PEKING	Beijing	China	North	15-06-2017	21000000	govt	active	1
6	6	SAIGON	HoChiMinh	Vietnam	South	12-03-2016	9000000	govt	active	1
7	7	LENINGRAD	StPetersburg	Russia	West	30-09-2015	5000000	govt	active	1
8	9	UNKNOWN	Karachi	Pakistan	South	11-11-2018	14000000	govt	inactive	0
9	10	EDO	Tokyo	Japan	East	NA	13000000	govt	active	1

Logic Bridge (SAS vs R)

• mutate() = DATA step transformation
• case_when() = SELECT-WHEN
• distinct() = PROC SORT NODUPKEY
• as.Date() = INPUT()
• trimws() before as.Date()
• Removes hidden spaces → avoids conversion failure
• %>% requires dplyr library.checks multiple values correctly
• %in% is safer than chained OR conditions

Section 3: Business Logic & The “Why”

In a financial system, if Mumbai and Bombay are treated separately:

• Revenue aggregation splits
• Regional performance becomes inaccurate
• Decision-makers misinterpret market size

In clinical trials:

• Duplicate site names distort patient counts
• Regulatory submissions fail validation

A single missing value treated incorrectly can lead to:

• Wrong dosage recommendations
• Financial misreporting

Section 4: 20 Key Implementation Practices

1. Always define LENGTH in SAS
2. Standardize before merging
3. Never trust raw input
4. Deduplicate early
5. Use audit logs
6. Validate joins
7. Avoid implicit conversions
8. Check missing explicitly
9. Use consistent casing
10. Separate raw and clean layers
11. Validate dates
12. Flag anomalies
13. Avoid hardcoding
14. Use reusable macros
15. Test edge cases
16. Use metadata-driven design
17. Validate counts before/after joins
18. Keep backup datasets
19. Document transformations
20. Automate QC checks

3. Phase 3: Extended SAS Analysis

data file_import;

infile datalines dlm=',' firstobs=2;

input Old_Name $ New_Name $ Population;

datalines;

Bombay,Mumbai,20000000

Madras,Chennai,11000000

Calcutta,Kolkata,15000000

Peking,Beijing,21000000

Saigon,HoChiMinh,9000000

Leningrad,StPetersburg,5000000

Bombay,Mumbai,20000000

Edo,Tokyo,13000000

;

run;

proc print data = file_import;

run;

OUTPUT:

Obs	Old_Name	New_Name	Population
1	Madras	Chennai	11000000
2	Calcutta	Kolkata	15000000
3	Peking	Beijing	21000000
4	Saigon	HoChiMin	9000000
5	Leningra	StPeters	5000000
6	Bombay	Mumbai	20000000
7	Edo	Tokyo	13000000

/* Flag high population */

data flagged;

set file_import;

if Population > 15000000 then High_Flag = 1;

else High_Flag = 0;

run;

proc print data = flagged;

run;

OUTPUT:

Obs	Old_Name	New_Name	Population	High_Flag
1	Madras	Chennai	11000000	0
2	Calcutta	Kolkata	15000000	0
3	Peking	Beijing	21000000	1
4	Saigon	HoChiMin	9000000	0
5	Leningra	StPeters	5000000	0
6	Bombay	Mumbai	20000000	1
7	Edo	Tokyo	13000000	0

/* Aggregation */

proc means data=flagged;

var Population;

class High_Flag;

run;

OUTPUT:

The MEANS Procedure

Analysis Variable : Population
High_Flag	N Obs	N	Mean	Std Dev	Minimum	Maximum
0	5	5	10600000.00	3847076.81	5000000.00	15000000.00
1	2	2	20500000.00	707106.78	20000000.00	21000000.00

/* Deduplication */

proc sort data=flagged nodupkey;

by Old_Name;

run;

proc print data=flagged;

run;

OUTPUT:

Obs	Old_Name	New_Name	Population	High_Flag
1	Bombay	Mumbai	20000000	1
2	Calcutta	Kolkata	15000000	0
3	Edo	Tokyo	13000000	0
4	Leningra	StPeters	5000000	0
5	Madras	Chennai	11000000	0
6	Peking	Beijing	21000000	1
7	Saigon	HoChiMin	9000000	0

Explanation

• Import
• Transform
• Aggregate
• Deduplicate
• Report

4. 20 Additional Data Cleaning Best Practices

1. Follow CDISC standards
2. Maintain SDTM compliance
3. Validate ADaM derivations
4. Track lineage
5. Ensure reproducibility
6. Maintain audit trails
7. Validate controlled terminology
8. Perform double programming
9. Use version control
10. Validate joins
11. Cross-check outputs
12. Use QC datasets
13. Document assumptions
14. Handle partial dates
15. Normalize units
16. Validate ranges
17. Track missing patterns
18. Ensure traceability
19. Maintain regulatory logs
20. Perform independent review

5. Business Logic Behind Data Cleaning

Data cleaning is not cosmetic it directly impacts decision-making. Missing values are replaced to ensure continuity in analysis. For example, if a patient’s age is missing, imputing it based on median prevents bias in demographic summaries. Similarly, unrealistic values such as negative population or age must be corrected using functions like ABS() because they distort statistical distributions.

Date imputation is critical in longitudinal studies. If a visit date is missing, incorrect sequencing may lead to wrong conclusions about treatment efficacy. In financial datasets, salary normalization ensures comparability across regions.

Incorrect handling of missing values (e.g., treating NULL as zero) can significantly bias results. For instance, in clinical trials, a missing lab value treated as zero might falsely indicate abnormality.

Thus, data cleaning ensures:

• Accuracy
• Consistency
• Regulatory compliance
• Reliable analytics

6. 20 Sharp Insights

• Dirty data leads to wrong conclusions
• Standardization ensures reproducibility
• Missing ≠ zero
• Deduplication prevents inflation
• Dates drive timelines
• Validation prevents disasters
• Merge carefully
• Always audit transformations
• Clean before analysis
• Consistency is key
• Logic errors are silent killers
• Documentation is power
• Automation saves time
• QC is non-negotiable
• Data lineage matters
• Format before logic
• Always test edge cases
• Clean data builds trust
• Reproducibility is science
• Accuracy beats speed

7. Summary

SAS and R both provide powerful frameworks for data cleaning, but they serve different operational philosophies. SAS excels in structured, enterprise-level processing, making it ideal for regulated industries like pharmaceuticals. Its DATA step offers deterministic execution, while PROC SQL enables flexible joins and aggregations.

R, on the other hand, thrives in exploratory and flexible environments. Packages like dplyr simplify transformations, while stringr enhances text cleaning capabilities. The “tidyverse” ecosystem allows analysts to write expressive, readable pipelines.

In this project, we demonstrated:

• How messy global city name data can distort analytics
• How SAS handles structured cleaning using DATA steps, SELECT-WHEN, and PROC SORT
• How R achieves similar outcomes using mutate(), case_when(), and distinct()
• The difference between MERGE (row-wise, sorted) and JOIN (relational, flexible)

The key takeaway:
SAS ensures reliability and compliance, while R provides agility and speed.

A hybrid approach is often the best strategy in real-world projects.

8. Conclusion

Data is only as valuable as its quality. Whether you are analyzing global city transformations, financial transactions, or clinical trial outcomes, the underlying principle remains the same: clean, structured, and validated data is the foundation of trustworthy analytics.

Through this project, we explored how seemingly simple inconsistencies like “Bombay” vs “Mumbai” can cascade into major analytical errors. These issues are not hypothetical; they occur daily in real-world systems. Without proper cleaning frameworks, organizations risk making flawed decisions that can impact revenue, compliance, and even human lives.

SAS provides a robust, auditable environment where every transformation is controlled and reproducible. This is why it remains the gold standard in clinical and regulatory domains. R complements this by offering flexibility and speed, allowing analysts to iterate quickly and explore data deeply.

The combination of:

• Structured SAS pipelines
• Agile R workflows
• Strong business logic
• Rigorous validation

creates a powerful data engineering ecosystem.

Ultimately, data cleaning is not a preliminary step it is a core analytical function. Investing time in cleaning ensures accuracy, builds trust, and enables scalable analytics.

9. Interview Questions

1. Difference between MERGE and JOIN in SAS?

Answer: MERGE works row-wise and requires sorting; JOIN is relational and flexible.

2. How do you handle missing values in SAS?

Answer: Use COALESCEC for character and IF conditions for numeric.

3. R equivalent of SELECT-WHEN?

Answer: case_when()

4. What happens if LENGTH is not defined?

Answer: SAS may truncate variables, causing data loss.

5. Real-world debugging scenario?

Answer: Duplicate city names causing double counting resolved using PROC SORT NODUPKEY and standardization.

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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. Here we learn about CITY NAME 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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·  Bloggers writing about analytics and smart cities

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