325.E-LEARNING PLATFORM ANALYTICS USING DATA STEP | PROC SQL | PROC MEANS | PROC RANK | PROC UNIVARIATE | PROC SGPLOT | DATE FUNCTIONS AND MACROS

E-LEARNING PLATFORM ANALYTICS USING DATA STEP | PROC SQL | PROC MEANS | PROC RANK | PROC UNIVARIATE | PROC SGPLOT | DATE FUNCTIONS AND MACROS

options nocenter;

1) CREATE RAW DATASET: elearning_raw

data work.elearning_raw;

  informat Launch_Date date9. Last_Active_Date date9.;

  format   Launch_Date date9. Last_Active_Date date9.;

  length Platform_Name $30 Country $20 Category $20;

  input Platform_Name :$30. Active_Users Avg_Course_Price Completion_Rate Country :$20.

       Category :$20. Launch_Date :date9. Last_Active_Date :date9.;

datalines;

"LearnSphere"       125000 49.99 72.5 "USA"        "General"  15JAN2015 10NOV2025

"CodeHive"          90000  79.00 65.3 "India"      "Tech"     02MAR2017 20OCT2025

"SkillUp"           45000  29.99 58.1 "UK"         "Business" 10SEP2019 05NOV2025

"EduPulse"          30000  59.50 80.2 "Canada"     "Design"   22JUN2016 01NOV2025

"BrightLearn"       175000 19.99 45.7 "USA"        "Kids"     01JAN2014 29OCT2025

"DataTrail"         60000  99.00 70.0 "Germany"    "Tech"     15AUG2018 12NOV2025

"MyTutor"           25000  15.00 55.0 "India"      "Tutoring" 03APR2020 03NOV2025

"ProCert"           85000  149.0 82.6 "USA"        "Professional" 12DEC2013 08NOV2025

"LanguageLoop"      42000  39.00 67.2 "Spain"      "Language" 17MAY2016 30OCT2025

"CreativeCamp"      22000  34.50 60.5 "Australia"  "Design"   11NOV2019 11NOV2025

"ExamReady"         98000  9.99  50.0 "India"      "TestPrep" 07SEP2015 02NOV2025

"UpSkillNow"        52000  24.99 61.8 "UK"         "Business" 29FEB2016 06NOV2025

;

run;

proc print data=work.elearning_raw;

run;

OUTPUT:

Obs	Launch_Date	Last_Active_Date	Platform_Name	Country	Category	Active_Users	Avg_Course_Price	Completion_Rate
1	15JAN2015	10NOV2025	"LearnSphere"	"USA"	"General"	125000	49.99	72.5
2	02MAR2017	20OCT2025	"CodeHive"	"India"	"Tech"	90000	79.00	65.3
3	10SEP2019	05NOV2025	"SkillUp"	"UK"	"Business"	45000	29.99	58.1
4	22JUN2016	01NOV2025	"EduPulse"	"Canada"	"Design"	30000	59.50	80.2
5	01JAN2014	29OCT2025	"BrightLearn"	"USA"	"Kids"	175000	19.99	45.7
6	15AUG2018	12NOV2025	"DataTrail"	"Germany"	"Tech"	60000	99.00	70.0
7	03APR2020	03NOV2025	"MyTutor"	"India"	"Tutoring"	25000	15.00	55.0
8	12DEC2013	08NOV2025	"ProCert"	"USA"	"Professional"	85000	149.00	82.6
9	17MAY2016	30OCT2025	"LanguageLoop"	"Spain"	"Language"	42000	39.00	67.2
10	11NOV2019	11NOV2025	"CreativeCamp"	"Australia"	"Design"	22000	34.50	60.5
11	07SEP2015	02NOV2025	"ExamReady"	"India"	"TestPrep"	98000	9.99	50.0
12	29FEB2016	06NOV2025	"UpSkillNow"	"UK"	"Business"	52000	24.99	61.8

2) Macro to compute date-based metrics using INTCK and INTNX

%macro compute_date_metrics(data=work.elearning_raw, out=work.elearning_dates);

  data &out;

    set &data;

    /* Today's date - use TODAY() to get system date */

    Today = today();

    format Today date9.;

    /* Days, months, years since launch */

    Days_Since_Launch = intck('day', Launch_Date, Today);

    Months_Since_Launch = intck('month', Launch_Date, Today);

    Years_Since_Launch = intck('year', Launch_Date, Today);

    /* Days between launch and last active (platform lifetime so far) */

    Lifetime_Days = intck('day', Launch_Date, Last_Active_Date);

    /* Next billing cycle date example: shift last active by 1 month to next cycle */

    Next_Billing_Cycle = intnx('month', Last_Active_Date, 1, 'same');

    /* Use INTNX 'samemonth' alignment or 'beginning','end' as required */

    format Next_Billing_Cycle date9.;

    /* Create a flag for recently_active: last active within last 30 days */

    Recently_Active_Flag = (intck('day', Last_Active_Date, Today) <= 30);

    /* Clean up odd values (safeguard) */

    if Days_Since_Launch < 0 then Days_Since_Launch = .;

    if Lifetime_Days < 0 then Lifetime_Days = .;

    label Days_Since_Launch = "Days since Launch (INTCK day)"

          Months_Since_Launch = "Months since Launch (INTCK month)"

          Years_Since_Launch = "Years since Launch (INTCK year)"

          Lifetime_Days = "Days between Launch and Last Active"

          Next_Billing_Cycle = "Next Billing Cycle (INTNX month +1)";

  run;

  proc print data=&out;

  run;

%mend compute_date_metrics;

%compute_date_metrics();

OUTPUT:

Obs	Launch_Date	Last_Active_Date	Platform_Name	Country	Category	Active_Users	Avg_Course_Price	Completion_Rate	Today	Days_Since_Launch	Months_Since_Launch	Years_Since_Launch	Lifetime_Days	Next_Billing_Cycle	Recently_Active_Flag
1	15JAN2015	10NOV2025	"LearnSphere"	"USA"	"General"	125000	49.99	72.5	01DEC2025	3973	131	10	3952	10DEC2025	1
2	02MAR2017	20OCT2025	"CodeHive"	"India"	"Tech"	90000	79.00	65.3	01DEC2025	3196	105	8	3154	20NOV2025	0
3	10SEP2019	05NOV2025	"SkillUp"	"UK"	"Business"	45000	29.99	58.1	01DEC2025	2274	75	6	2248	05DEC2025	1
4	22JUN2016	01NOV2025	"EduPulse"	"Canada"	"Design"	30000	59.50	80.2	01DEC2025	3449	114	9	3419	01DEC2025	1
5	01JAN2014	29OCT2025	"BrightLearn"	"USA"	"Kids"	175000	19.99	45.7	01DEC2025	4352	143	11	4319	29NOV2025	0
6	15AUG2018	12NOV2025	"DataTrail"	"Germany"	"Tech"	60000	99.00	70.0	01DEC2025	2665	88	7	2646	12DEC2025	1
7	03APR2020	03NOV2025	"MyTutor"	"India"	"Tutoring"	25000	15.00	55.0	01DEC2025	2068	68	5	2040	03DEC2025	1
8	12DEC2013	08NOV2025	"ProCert"	"USA"	"Professional"	85000	149.00	82.6	01DEC2025	4372	144	12	4349	08DEC2025	1
9	17MAY2016	30OCT2025	"LanguageLoop"	"Spain"	"Language"	42000	39.00	67.2	01DEC2025	3485	115	9	3453	30NOV2025	0
10	11NOV2019	11NOV2025	"CreativeCamp"	"Australia"	"Design"	22000	34.50	60.5	01DEC2025	2212	73	6	2192	11DEC2025	1
11	07SEP2015	02NOV2025	"ExamReady"	"India"	"TestPrep"	98000	9.99	50.0	01DEC2025	3738	123	10	3709	02DEC2025	1
12	29FEB2016	06NOV2025	"UpSkillNow"	"UK"	"Business"	52000	24.99	61.8	01DEC2025	3563	118	9	3538	06DEC2025	1

3) PROC SQL: Aggregations by Country and Category

proc sql;

  create table work.country_summary as

  select Country,

         count(*) as Platform_Count,

         sum(Active_Users) as Total_Active_Users,

         mean(Completion_Rate) as Avg_Completion_Rate format=6.2,

         mean(Avg_Course_Price) as Avg_Price format=6.2

  from work.elearning_dates

  group by Country

  order by Total_Active_Users desc;

quit;

proc print data=work.country_summary;

run;

OUTPUT:

Obs	Country	Platform_Count	Total_Active_Users	Avg_Completion_Rate	Avg_Price
1	"USA"	3	385000	66.93	72.99
2	"India"	3	213000	56.77	34.66
3	"UK"	2	97000	59.95	27.49
4	"Germany"	1	60000	70.00	99.00
5	"Spain"	1	42000	67.20	39.00
6	"Canada"	1	30000	80.20	59.50
7	"Australia"	1	22000	60.50	34.50

4) PROC MEANS: descriptive statistics for numeric fields

proc means data=work.elearning_dates n mean median std min max nmiss;

  var Active_Users Avg_Course_Price Completion_Rate Days_Since_Launch Lifetime_Days;

  output out=work.means_summary mean= / autoname;

run;

OUTPUT:

The MEANS Procedure

Variable	Label	N	Mean	Median	Std Dev	Minimum	Maximum	N Miss
Active_Users Avg_Course_Price Completion_Rate Days_Since_Launch Lifetime_Days	Days since Launch (INTCK day) Days between Launch and Last Active	12 12 12 12 12	70750.00 50.8291667 64.0750000 3278.92 3251.58	56000.00 36.7500000 63.5500000 3467.00 3436.00	45938.94 40.7191604 11.2504646 808.4332163 807.2529691	22000.00 9.9900000 45.7000000 2068.00 2040.00	175000.00 149.0000000 82.6000000 4372.00 4349.00	0 0 0 0 0

5) PROC RANK usage: macro to rank platforms by chosen variable

%macro rank_platforms(data=work.elearning_dates, out=work.ranked_platforms, byvar=Active_Users, ties=low);

  proc rank data=&data out=&out ties=&ties descending;

    var &byvar;

    ranks Rank_&byvar;

  run;

  proc print data=&out;

  run;

%mend rank_platforms;

%rank_platforms(byvar=Active_Users, out=work.rank_by_users);

OUTPUT:

Obs	Launch_Date	Last_Active_Date	Platform_Name	Country	Category	Active_Users	Avg_Course_Price	Completion_Rate	Today	Days_Since_Launch	Months_Since_Launch	Years_Since_Launch	Lifetime_Days	Next_Billing_Cycle	Recently_Active_Flag	Rank_Active_Users
1	15JAN2015	10NOV2025	"LearnSphere"	"USA"	"General"	125000	49.99	72.5	01DEC2025	3973	131	10	3952	10DEC2025	1	2
2	02MAR2017	20OCT2025	"CodeHive"	"India"	"Tech"	90000	79.00	65.3	01DEC2025	3196	105	8	3154	20NOV2025	0	4
3	10SEP2019	05NOV2025	"SkillUp"	"UK"	"Business"	45000	29.99	58.1	01DEC2025	2274	75	6	2248	05DEC2025	1	8
4	22JUN2016	01NOV2025	"EduPulse"	"Canada"	"Design"	30000	59.50	80.2	01DEC2025	3449	114	9	3419	01DEC2025	1	10
5	01JAN2014	29OCT2025	"BrightLearn"	"USA"	"Kids"	175000	19.99	45.7	01DEC2025	4352	143	11	4319	29NOV2025	0	1
6	15AUG2018	12NOV2025	"DataTrail"	"Germany"	"Tech"	60000	99.00	70.0	01DEC2025	2665	88	7	2646	12DEC2025	1	6
7	03APR2020	03NOV2025	"MyTutor"	"India"	"Tutoring"	25000	15.00	55.0	01DEC2025	2068	68	5	2040	03DEC2025	1	11
8	12DEC2013	08NOV2025	"ProCert"	"USA"	"Professional"	85000	149.00	82.6	01DEC2025	4372	144	12	4349	08DEC2025	1	5
9	17MAY2016	30OCT2025	"LanguageLoop"	"Spain"	"Language"	42000	39.00	67.2	01DEC2025	3485	115	9	3453	30NOV2025	0	9
10	11NOV2019	11NOV2025	"CreativeCamp"	"Australia"	"Design"	22000	34.50	60.5	01DEC2025	2212	73	6	2192	11DEC2025	1	12
11	07SEP2015	02NOV2025	"ExamReady"	"India"	"TestPrep"	98000	9.99	50.0	01DEC2025	3738	123	10	3709	02DEC2025	1	3
12	29FEB2016	06NOV2025	"UpSkillNow"	"UK"	"Business"	52000	24.99	61.8	01DEC2025	3563	118	9	3538	06DEC2025	1	7

%rank_platforms(byvar=Completion_Rate, out=work.rank_by_completion);

OUTPUT:

Obs	Launch_Date	Last_Active_Date	Platform_Name	Country	Category	Active_Users	Avg_Course_Price	Completion_Rate	Today	Days_Since_Launch	Months_Since_Launch	Years_Since_Launch	Lifetime_Days	Next_Billing_Cycle	Recently_Active_Flag	Rank_Completion_Rate
1	15JAN2015	10NOV2025	"LearnSphere"	"USA"	"General"	125000	49.99	72.5	01DEC2025	3973	131	10	3952	10DEC2025	1	3
2	02MAR2017	20OCT2025	"CodeHive"	"India"	"Tech"	90000	79.00	65.3	01DEC2025	3196	105	8	3154	20NOV2025	0	6
3	10SEP2019	05NOV2025	"SkillUp"	"UK"	"Business"	45000	29.99	58.1	01DEC2025	2274	75	6	2248	05DEC2025	1	9
4	22JUN2016	01NOV2025	"EduPulse"	"Canada"	"Design"	30000	59.50	80.2	01DEC2025	3449	114	9	3419	01DEC2025	1	2
5	01JAN2014	29OCT2025	"BrightLearn"	"USA"	"Kids"	175000	19.99	45.7	01DEC2025	4352	143	11	4319	29NOV2025	0	12
6	15AUG2018	12NOV2025	"DataTrail"	"Germany"	"Tech"	60000	99.00	70.0	01DEC2025	2665	88	7	2646	12DEC2025	1	4
7	03APR2020	03NOV2025	"MyTutor"	"India"	"Tutoring"	25000	15.00	55.0	01DEC2025	2068	68	5	2040	03DEC2025	1	10
8	12DEC2013	08NOV2025	"ProCert"	"USA"	"Professional"	85000	149.00	82.6	01DEC2025	4372	144	12	4349	08DEC2025	1	1
9	17MAY2016	30OCT2025	"LanguageLoop"	"Spain"	"Language"	42000	39.00	67.2	01DEC2025	3485	115	9	3453	30NOV2025	0	5
10	11NOV2019	11NOV2025	"CreativeCamp"	"Australia"	"Design"	22000	34.50	60.5	01DEC2025	2212	73	6	2192	11DEC2025	1	8
11	07SEP2015	02NOV2025	"ExamReady"	"India"	"TestPrep"	98000	9.99	50.0	01DEC2025	3738	123	10	3709	02DEC2025	1	11
12	29FEB2016	06NOV2025	"UpSkillNow"	"UK"	"Business"	52000	24.99	61.8	01DEC2025	3563	118	9	3538	06DEC2025	1	7

/* Merge ranks into a single table using PROC SQL */

proc sql;

  create table work.platforms_ranked as

  select a.Platform_Name, a.Active_Users, a.Avg_Course_Price, a.Completion_Rate,

         a.Country, a.Category, a.Launch_Date, a.Last_Active_Date,

         b.Rank_Active_Users, c.Rank_Completion_Rate

  from work.elearning_dates a

  left join work.rank_by_users b on a.Platform_Name = b.Platform_Name

  left join work.rank_by_completion c on a.Platform_Name = c.Platform_Name

  order by Rank_Active_Users;

quit;

proc print data=work.platforms_ranked;

run;

OUTPUT:

Obs	Platform_Name	Active_Users	Avg_Course_Price	Completion_Rate	Country	Category	Launch_Date	Last_Active_Date	Rank_Active_Users	Rank_Completion_Rate
1	"BrightLearn"	175000	19.99	45.7	"USA"	"Kids"	01JAN2014	29OCT2025	1	12
2	"LearnSphere"	125000	49.99	72.5	"USA"	"General"	15JAN2015	10NOV2025	2	3
3	"ExamReady"	98000	9.99	50.0	"India"	"TestPrep"	07SEP2015	02NOV2025	3	11
4	"CodeHive"	90000	79.00	65.3	"India"	"Tech"	02MAR2017	20OCT2025	4	6
5	"ProCert"	85000	149.00	82.6	"USA"	"Professional"	12DEC2013	08NOV2025	5	1
6	"DataTrail"	60000	99.00	70.0	"Germany"	"Tech"	15AUG2018	12NOV2025	6	4
7	"UpSkillNow"	52000	24.99	61.8	"UK"	"Business"	29FEB2016	06NOV2025	7	7
8	"SkillUp"	45000	29.99	58.1	"UK"	"Business"	10SEP2019	05NOV2025	8	9
9	"LanguageLoop"	42000	39.00	67.2	"Spain"	"Language"	17MAY2016	30OCT2025	9	5
10	"EduPulse"	30000	59.50	80.2	"Canada"	"Design"	22JUN2016	01NOV2025	10	2
11	"MyTutor"	25000	15.00	55.0	"India"	"Tutoring"	03APR2020	03NOV2025	11	10
12	"CreativeCamp"	22000	34.50	60.5	"Australia"	"Design"	11NOV2019	11NOV2025	12	8

6) PROC UNIVARIATE: detailed distribution checks (Active_Users)

proc univariate data=work.elearning_dates;

  var Active_Users Avg_Course_Price Completion_Rate;

  histogram Active_Users / normal;

  inset mean median std / pos=ne;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Active_Users

Moments
N	12	Sum Weights	12
Mean	70750	Sum Observations	849000
Std Deviation	45938.9417	Variance	2110386364
Skewness	1.11957429	Kurtosis	0.97488514
Uncorrected SS	8.3281E10	Corrected SS	2.32143E10
Coeff Variation	64.9313663	Std Error Mean	13261.4302

Basic Statistical Measures
Location		Variability
Mean	70750.00	Std Deviation	45939
Median	56000.00	Variance	2110386364
Mode	.	Range	153000
		Interquartile Range	58000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	5.33502	Pr > |t|	0.0002
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	175000
99%	175000
95%	175000
90%	125000
75% Q3	94000
50% Median	56000
25% Q1	36000
10%	25000
5%	22000
1%	22000
0% Min	22000

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
22000	10	85000	8
25000	7	90000	2
30000	4	98000	11
42000	9	125000	1
45000	3	175000	5

The UNIVARIATE Procedure

The UNIVARIATE Procedure

Fitted Normal Distribution for Active_Users

Parameters for Normal Distribution
Parameter	Symbol	Estimate
Mean	Mu	70750
Std Dev	Sigma	45938.94

Goodness-of-Fit Tests for Normal Distribution
Test	Statistic		p Value
Kolmogorov-Smirnov	D	0.17584327	Pr > D	>0.150
Cramer-von Mises	W-Sq	0.06677709	Pr > W-Sq	>0.250
Anderson-Darling	A-Sq	0.44404111	Pr > A-Sq	0.240

Quantiles for Normal Distribution
Percent	Quantile
	Observed	Estimated
1.0	22000.0	-36119.96
5.0	22000.0	-4812.83
10.0	25000.0	11876.88
25.0	36000.0	39764.65
50.0	56000.0	70750.00
75.0	94000.0	101735.35
90.0	125000.0	129623.12
95.0	175000.0	146312.83
99.0	175000.0	177619.96

The UNIVARIATE Procedure

Variable: Avg_Course_Price

Moments
N	12	Sum Weights	12
Mean	50.8291667	Sum Observations	609.95
Std Deviation	40.7191604	Variance	1658.05003
Skewness	1.46257027	Kurtosis	1.98284584
Uncorrected SS	49241.8005	Corrected SS	18238.5503
Coeff Variation	80.1098328	Std Error Mean	11.7546091

Basic Statistical Measures
Location		Variability
Mean	50.82917	Std Deviation	40.71916
Median	36.75000	Variance	1658
Mode	.	Range	139.01000
		Interquartile Range	46.76000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	4.32419	Pr > |t|	0.0012
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	149.00
99%	149.00
95%	149.00
90%	99.00
75% Q3	69.25
50% Median	36.75
25% Q1	22.49
10%	15.00
5%	9.99
1%	9.99
0% Min	9.99

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
9.99	11	49.99	1
15.00	7	59.50	4
19.99	5	79.00	2
24.99	12	99.00	6
29.99	3	149.00	8

The UNIVARIATE Procedure

Variable: Completion_Rate

Moments
N	12	Sum Weights	12
Mean	64.075	Sum Observations	768.9
Std Deviation	11.2504646	Variance	126.572955
Skewness	0.10555216	Kurtosis	-0.5589518
Uncorrected SS	50659.57	Corrected SS	1392.3025
Coeff Variation	17.5582749	Std Error Mean	3.24772939

Basic Statistical Measures
Location		Variability
Mean	64.07500	Std Deviation	11.25046
Median	63.55000	Variance	126.57295
Mode	.	Range	36.90000
		Interquartile Range	14.70000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	19.72917	Pr > |t|	<.0001
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	82.60
99%	82.60
95%	82.60
90%	80.20
75% Q3	71.25
50% Median	63.55
25% Q1	56.55
10%	50.00
5%	45.70
1%	45.70
0% Min	45.70

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
45.7	5	67.2	9
50.0	11	70.0	6
55.0	7	72.5	1
58.1	3	80.2	4
60.5	10	82.6	8

7) PROC SGPLOT: visualizations

/* Bar: Total Active Users by Category */

proc sgplot data=work.elearning_dates;

  vbar Category / response=Active_Users stat=sum datalabel;

  title "Total Active Users by Category";

run;

OUTPUT:

/* Scatter: Avg Course Price vs Completion Rate (bubble sized by Active Users) */

proc sgplot data=work.elearning_dates;

  bubble x=Avg_Course_Price y=Completion_Rate size=Active_Users / datalabel=Platform_Name;

  xaxis label="Average Course Price (USD)";

  yaxis label="Completion Rate (%)";

  title "Price vs Completion Rate (bubble size = Active Users)";

run;

OUTPUT:

/* Box plot: Completion rate by Category */

proc sgplot data=work.elearning_dates;

  vbox Completion_Rate / category=Category;

  title "Completion Rate Distribution by Category";

run;

OUTPUT:

8) Example: create top N macro using ranked table

%macro top_n_by(data=work.platforms_ranked, byrank=Rank_Active_Users, n=5, out=work.topN);

  data &out;

    set &data;

    if &byrank < &n then output;

  run;

%mend top_n_by;

%top_n_by(n=5, out=work.top5_by_users);

/* show top 5 by users */

proc print data=work.top5_by_users label noobs;

  var Platform_Name Active_Users Rank_Active_Users Completion_Rate Rank_Completion_Rate Country Category;

  title "Top 5 Platforms by Active Users";

run;

 OUTPUT:

Top 5 Platforms by Active Users

Platform_Name	Active_Users	Rank for Variable Active_Users	Completion_Rate	Rank for Variable Completion_Rate	Country	Category
"BrightLearn"	175000	1	45.7	12	"USA"	"Kids"
"LearnSphere"	125000	2	72.5	3	"USA"	"General"
"ExamReady"	98000	3	50.0	11	"India"	"TestPrep"
"CodeHive"	90000	4	65.3	6	"India"	"Tech"

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