Evaluation Report of the Applied Statistics Master’s Program Curriculum 2020
Applied Statistics Master’s Program
2024-08-04
Introduction
In the rapidly evolving landscape of data science and analytics, the importance of a robust and adaptive curriculum for a Master’s in Applied Statistics cannot be overstated. As we navigate the complexities of the 5.0 society—a term that encapsulates the fusion of digital, physical, and biological realms driven by technological advancements—the necessity for an education system that remains relevant, responsive, and innovative is paramount. To this end, we undertake a rigorous and systematic evaluation of our Master’s in Applied Statistics curriculum to ensure it meets the ever-changing demands of our stakeholders and aligns with the latest technological developments.
The Evolution of Educational Demands
The field of statistics is experiencing an unprecedented transformation driven by advances in technology and data analytics. With the advent of big data, machine learning, artificial intelligence, and advanced computational techniques, the skills and knowledge required of statisticians have expanded significantly. In response to these changes, our educational programs must not only incorporate these new tools and methodologies but also anticipate future trends to prepare our students effectively.
Our routine curriculum evaluations are designed to address these dynamic educational demands. This process involves a comprehensive assessment of the curriculum’s relevance, rigor, and effectiveness. By engaging with industry professionals, academic experts, and other key stakeholders, we ensure that our program remains aligned with the latest developments and best practices in the field. This collaborative approach helps us understand the skills and competencies that are in demand, allowing us to adapt our curriculum accordingly.
Aligning with Stakeholder Requirements
Stakeholders—including employers, industry leaders, alumni, and current students—play a crucial role in shaping the direction of our curriculum. Their feedback provides valuable insights into the practical skills and knowledge that are essential for success in the field of applied statistics. Regular consultations with these stakeholders help us identify gaps in our curriculum and areas for improvement.
Our evaluation process includes surveys, focus groups, and advisory board meetings with stakeholders. These interactions provide a platform for stakeholders to express their expectations and requirements. For example, employers may highlight the need for proficiency in emerging statistical software, familiarity with advanced data visualization techniques, or the ability to apply statistical methods to complex real-world problems. By incorporating these insights into our curriculum, we ensure that our graduates are well-equipped to meet the expectations of the job market.
Incorporating Technological Advancements
The integration of new technologies into our curriculum is another critical aspect of our evaluation process. The 5.0 society is characterized by the convergence of digital technology, artificial intelligence, and the Internet of Things (IoT), all of which have significant implications for the field of applied statistics. As these technologies continue to evolve, it is essential that our curriculum reflects the latest advancements and prepares students to leverage these tools effectively.
Our curriculum evaluation includes a thorough review of emerging technologies and their applications in statistics. This involves assessing the relevance of existing courses, identifying new areas of focus, and incorporating cutting-edge tools and methodologies. For instance, we may introduce new modules on machine learning algorithms, data mining techniques, or advanced statistical software. By staying abreast of technological developments, we ensure that our program remains current and that our students are proficient in the tools and techniques that are shaping the future of statistics.
Fostering Continuous Improvement
Routine curriculum evaluations are not a one-time endeavor but an ongoing process of continuous improvement. By regularly reviewing and updating our curriculum, we maintain a dynamic educational environment that adapts to the changing needs of the field. This iterative approach allows us to address emerging trends, incorporate new knowledge, and refine our teaching methods.
Our evaluation process includes periodic reviews, feedback loops, and benchmarking against other leading programs. We also monitor the career progression of our alumni to assess the long-term impact of our curriculum on their professional development. This comprehensive approach ensures that our program remains relevant and effective in preparing students for successful careers in applied statistics.
The survey was conducted via gform with the following link: https://bit.ly/Evaluasi_Kurikulum_MStat
Data Analysis
1. Year of Admission to the Master’s Program in Applied Statistics.
# Hitung persentase
Year = tabyl(data, Tahun.Masuk.S2.Statistika.Terapan) %>%
adorn_totals("row") %>%
adorn_pct_formatting(digits = 2)
names(Year) = c("Year", "Frequency", "Percentage")
Year %>%
flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
vline() %>% vline_left() %>% align(align="center",part="all") %>%
add_header_row(values="Table 1: Distribution of Admission Years for the Master's Program in Applied Statistics", colwidths=3) %>%
align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")Table 1: Distribution of Admission Years for the Master's Program in Applied Statistics | ||
|---|---|---|
Year | Frequency | Percentage |
2010 | 1 | 3.57% |
2013 | 1 | 3.57% |
2015 | 2 | 7.14% |
2016 | 9 | 32.14% |
2018 | 5 | 17.86% |
2020 | 4 | 14.29% |
2021 | 4 | 14.29% |
2022 | 2 | 7.14% |
Total | 28 | 100.00% |
# Grafik Kepuasan Keseluruhan Terhadap Kurikulum
Year_plot <- ggplot(Year[-nrow(Year),], aes(x = as.factor(Year), y = Frequency, fill = as.factor(Year))) +
geom_bar(stat = "identity") +
geom_text(aes(label = Frequency), vjust = -0.5) + # Add labels above the bars
xlab("Year") +
ylab("Frequency") +guides(fill="none", color="none")+
ggtitle("Figure 1. Distribution of Admission Years for the Master's Program in Applied Statistics") + theme(legend.position = "none")+theme_bw()+
theme(plot.title = element_text(size = 15, face = "bold"))Figure 1 shows, the largest proportion of respondents came from the 2016 batch, with a total of 9 alumni participating in the survey.
2. Alumni positions
data$Jabatan<-factor(data$Jabatan)
data$Jabatan<-recode(data$Jabatan,
"Analis Kebijakan"="Policy Analyst",
"Asisten Ahli"="Assistant Expert",
"Data Analyst"="Data Analyst",
"Dosen Tetap"= "Lecturer",
"Dosen/Ketua Program Studi"="Head of Study Program",
"Fungsional"="Statistician",
"Fungsional Dosen Lektor"="Statistician",
"Fungsional Prakom Muda"="Statistician",
"Fungsional Statistik Ahli Pertama"="Statistician",
"Fungsional Statistisi"="Statistician",
"Fungsional Statistisi Ahli Muda"="Statistician",
"Guru"="Lecturer",
"Kepala BPS Kabupaten"="Head of District Statistics Office",
"Kepala bps kabupaten"="Head of District Statistics Office",
"Staf"="Actuarial Staff",
"Staff Aktuaria"="Actuarial Staff",
"Statistisi"="Statistician",
"Statistisi "="Statistician",
"Statistisi Ahli Madya"="Statistician",
"Statistisi Ahli Muda"="Statistician",
"Statistisi Ahli Pertama"="Statistician",
"Statistisi Muda"="Statistician",
"Total"="Total")
Position = tabyl(data, Jabatan) %>%
adorn_totals("row") %>%
adorn_pct_formatting(digits = 2)
names(Position) = c("Position", "Frequency", "Percentage")
Position %>%
flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
vline() %>% vline_left() %>% align(align="center",part="all") %>%
add_header_row(values="Table 2: Distribution of Admission Positions for the Master's Program in Applied Statistics", colwidths=3) %>%
align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")Table 2: Distribution of Admission Positions for the Master's Program in Applied Statistics | ||
|---|---|---|
Position | Frequency | Percentage |
Policy Analyst | 1 | 3.57% |
Assistant Expert | 1 | 3.57% |
Data Analyst | 2 | 7.14% |
Lecturer | 2 | 7.14% |
Head of Study Program | 1 | 3.57% |
Statistician | 17 | 60.71% |
Head of District Statistics Office | 2 | 7.14% |
Actuarial Staff | 2 | 7.14% |
Total | 28 | 100.00% |
Position_plot <- ggplot(Position[-nrow(Position),], aes(x = as.factor(Position), y = Frequency, fill = as.factor(Position))) +
geom_bar(stat = "identity") +
geom_text(aes(label = Frequency), vjust = -0.5) + # Add labels above the bars
xlab("Position") +
ylab("Frequency") +guides(fill="none", color="none")+
ggtitle("Figure 2. Alumni positions") + theme(legend.position = "none")+theme_bw()+
theme(plot.title = element_text(size = 15, face = "bold"))+scale_x_discrete(guide = guide_axis(angle = 90))
Position_plot
Based on Figure 2, the majority of respondents are statisticians.
3. Relevance of the Statistics Curriculum to the Professional Workforce
data$Relevance<-factor(data$Bagaimana.relevansi.materi.yang.diajarkan.dengan.kebutuhan.di.tempat.kerja.Anda.)
data$Relevance<-recode(data$Relevance,
"Sangat Relevan"="Excellence",
"Relevan"="Good",
"Cukup Relevan"="Average",
"Kurang Relevan"="Poor")
Relevance = tabyl(data, Relevance) %>%
adorn_totals("row") %>%
adorn_pct_formatting(digits = 2)
names(Relevance) = c("Relevance", "Frequency", "Percentage")
Relevance %>%
flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
vline() %>% vline_left() %>% align(align="center",part="all") %>%
add_header_row(values="Table 3: Relevance of the Statistics Curriculum to the Professional Workforce", colwidths=3) %>%
align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")Table 3: Relevance of the Statistics Curriculum to the Professional Workforce | ||
|---|---|---|
Relevance | Frequency | Percentage |
Average | 4 | 14.29% |
Poor | 2 | 7.14% |
Good | 15 | 53.57% |
Excellence | 7 | 25.00% |
Total | 28 | 100.00% |
# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Relevance<- plot_ly(Relevance[-nrow(Relevance),], labels = ~Relevance, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
layout(title = 'Figure 3: Relevance of the Statistics Curriculum ')
pie_Relevance4. The current curriculum accommodate the latest advancements in science and technology
data$Updating<-factor(data$Apakah.kurikulum.saat.ini.sudah.mengakomodasi.perkembangan.ilmu.dan.teknologi.terkini.)
data$Updating<-recode(data$Updating,
"Sangat Memadai"="Excellence",
"Memadai"="Good",
"Cukup Memadai"="Average",
"Kurang Memadai"="Poor")
Updating = tabyl(data, Updating) %>%
adorn_totals("row") %>%
adorn_pct_formatting(digits = 2)
names(Updating) = c("Updating", "Frequency", "Percentage")
Updating %>%
flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
vline() %>% vline_left() %>% align(align="center",part="all") %>%
add_header_row(values="Table 4: The current curriculum accommodate the latest advancements in science and technology", colwidths=3) %>%
align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")Table 4: The current curriculum accommodate the latest advancements in science and technology | ||
|---|---|---|
Updating | Frequency | Percentage |
Average | 7 | 25.00% |
Good | 19 | 67.86% |
Excellence | 2 | 7.14% |
Total | 28 | 100.00% |
# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Updating<- plot_ly(Updating[-nrow(Updating),], labels = ~Updating, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
layout(title = 'Figure 3: The current curriculum accommodate science and technology')
pie_Updating5. Technical Competence of Graduates from the Master’s Program in Statistics
data$Competency<-factor(data$Bagaimana.penilaian.Anda.terhadap.kompetensi.teknis.lulusan.Program.S2.Statistika.)
data$Competency<-recode(data$Competency,
"Sangat Baik"="Excellence",
"Baik"="Good",
"Cukup Baik"="Average",
"Kurang Baik"="Poor")
Competency = tabyl(data, Competency) %>%
adorn_totals("row") %>%
adorn_pct_formatting(digits = 2)
names(Competency) = c("Competency", "Frequency", "Percentage")
Competency %>%
flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
vline() %>% vline_left() %>% align(align="center",part="all") %>%
add_header_row(values="Table 5: Technical Competence of Graduates from the Master's Program in Statistics", colwidths=3) %>%
align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")Table 5: Technical Competence of Graduates from the Master's Program in Statistics | ||
|---|---|---|
Competency | Frequency | Percentage |
Good | 19 | 67.86% |
Average | 2 | 7.14% |
Excellence | 7 | 25.00% |
Total | 28 | 100.00% |
# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Competency<- plot_ly(Competency[-nrow(Competency),], labels = ~Competency, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
layout(title = 'Figure 5: Technical Competence')
pie_Competency6. The curriculum integrated Society 5.0 concepts, such as the use of big data, artificial intelligence (AI), and the Internet of Things (IoT)
data$Society_5<-factor(data$Apakah.kurikulum.telah.mengintegrasikan.konsep.Society.5.0..seperti.penggunaan.big.data..artificial.intelligence..AI...dan.Internet.of.Things..IoT..)
data$Society_5<-recode(data$Society_5,
"Sangat Memadai"="Excellence",
"Memadai"="Good",
"Cukup Memadai"="Average",
"Kurang Memadai"="Poor")
Society_5 = tabyl(data, Society_5) %>%
adorn_totals("row") %>%
adorn_pct_formatting(digits = 2)
names(Society_5) = c("Society_5", "Frequency", "Percentage")
Society_5 %>%
flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
vline() %>% vline_left() %>% align(align="center",part="all") %>%
add_header_row(values="Table 6: The curriculum integrated Society 5.0 concepts, such as the use of big data, artificial intelligence (AI), and the Internet of Things (IoT)", colwidths=3) %>%
align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")Table 6: The curriculum integrated Society 5.0 concepts, such as the use of big data, artificial intelligence (AI), and the Internet of Things (IoT) | ||
|---|---|---|
Society_5 | Frequency | Percentage |
Average | 9 | 32.14% |
Poor | 4 | 14.29% |
Good | 14 | 50.00% |
Excellence | 1 | 3.57% |
Total | 28 | 100.00% |
# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Society_5<- plot_ly(Society_5[-nrow(Society_5),], labels = ~Society_5, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
layout(title = 'Figure 6: The curriculum integrated Society 5.0 concepts')
pie_Society_5Suggestions for new courses that are relevant to the Society 5.0 era
# Create the data frame with the provided text data
data$Suggestions <-data$Apakah.Anda.memiliki.saran.untuk.mata.kuliah.baru.yang.relevan.dengan.era.Society.5.0.
# Clean and preprocess the text data
data_clean <- data %>%
mutate(Suggestions = tolower(Suggestions)) %>% # Convert to lowercase
mutate(Suggestions = removePunctuation(Suggestions)) %>% # Remove punctuation
mutate(Suggestions = removeNumbers(Suggestions)) %>% # Remove numbers
mutate(Suggestions = removeWords(Suggestions, stopwords("en"))) %>% # Remove common stopwords
mutate(Suggestions = stripWhitespace(Suggestions)) # Remove extra whitespace
# Remove empty rows
data_clean <- data_clean %>% filter(Suggestions != "" & Suggestions != "-")
# Display the cleaned text data
# Create a term-document matrix
corpus <- Corpus(VectorSource(data_clean$Suggestions))
tdm <- TermDocumentMatrix(corpus)
# Convert the term-document matrix to a matrix
m <- as.matrix(tdm)
word_freqs <- sort(rowSums(m), decreasing = TRUE)
word_freqs <- data.frame(word = names(word_freqs), freq = word_freqs)
# Display the most frequent words
# Create a word cloud
wordcloud(words = word_freqs$word, freq = word_freqs$freq, min.freq = 1,
scale = c(3, 0.5), colors = brewer.pal(8, "Dark2"))
7. Overall Satisfaction with the Master’s Program in Statistics Curriculum
data$Satistication<-factor(data$Seberapa.puas.Anda.dengan.keseluruhan.kurikulum.Program.S2.Statistika.)
data$Satistication<-recode(data$Satistication,
"Sangat Puas"="Excellence",
"Puas"="Good",
"Cukup Puas"="Average",
"Kurang Puas"="Poor")
Satistication = tabyl(data, Satistication) %>%
adorn_totals("row") %>%
adorn_pct_formatting(digits = 2)
names(Satistication) = c("Satistication", "Frequency", "Percentage")
Satistication %>%
flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
vline() %>% vline_left() %>% align(align="center",part="all") %>%
add_header_row(values="Table 7: Overall Satisfaction with the Master's Program in Statistics Curriculum", colwidths=3) %>%
align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")Table 7: Overall Satisfaction with the Master's Program in Statistics Curriculum | ||
|---|---|---|
Satistication | Frequency | Percentage |
Average | 3 | 10.71% |
Good | 18 | 64.29% |
Excellence | 7 | 25.00% |
Total | 28 | 100.00% |
# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Satistication<- plot_ly(Satistication[-nrow(Satistication),], labels = ~Satistication, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
layout(title = 'Figure 7: Overall Satisfaction with the Master Program in Statistics Curriculum')
pie_Satistication8. Would you recommend the Master’s Program in Statistics to others?
data$Recomendation<-factor(data$Seberapa.besar.kemungkinan.Anda.merekomendasikan.Program.S2.Statistika.kepada.orang.lain.)
data$Recomendation<-recode(data$Recomendation,
"Sangat Puas"="Excellence",
"Puas"="Good",
"Cukup Puas"="Average",
"Kurang Puas"="Poor")
Recomendation = tabyl(data, Recomendation) %>%
adorn_totals("row") %>%
adorn_pct_formatting(digits = 2)
names(Recomendation) = c("Recomendation", "Frequency", "Percentage")
Recomendation %>%
flextable() %>% width(j=1,width=1.8) %>% width(j=2:3,width=1.5) %>%
vline() %>% vline_left() %>% align(align="center",part="all") %>%
add_header_row(values="Table 7: Would you recommend the Master's Program in Statistics to others?", colwidths=3) %>%
align(i=2,j=1, align="left",part="header") %>% align(j=1, align="left",part="body")Table 7: Would you recommend the Master's Program in Statistics to others? | ||
|---|---|---|
Recomendation | Frequency | Percentage |
Besar | 14 | 50.00% |
Cukup Besar | 1 | 3.57% |
Kecil | 1 | 3.57% |
Sangat Besar | 12 | 42.86% |
Total | 28 | 100.00% |
# Pie Chart Kepuasan Keseluruhan Terhadap Kurikulum
pie_Recomendation<- plot_ly(Recomendation[-nrow(Recomendation),], labels = ~Recomendation, values = ~ Frequency, type = 'pie', textinfo = 'label+percent') %>%
layout(title = 'Figure 8: Would you recommend the Master Program in Statistics to others?')
pie_Recomendation# Create the data frame with the provided text data
data$Suggestions <-data$Apakah.ada.hal.lain.yang.ingin.Anda.sampaikan.terkait.kurikulum.Program.S2.Statistika.
# Clean and preprocess the text data
data_clean <- data %>%
mutate(Suggestions = tolower(Suggestions)) %>% # Convert to lowercase
mutate(Suggestions = removePunctuation(Suggestions)) %>% # Remove punctuation
mutate(Suggestions = removeNumbers(Suggestions)) %>% # Remove numbers
mutate(Suggestions = removeWords(Suggestions, stopwords("en"))) %>% # Remove common stopwords
mutate(Suggestions = stripWhitespace(Suggestions)) # Remove extra whitespace
# Remove empty rows
data_clean <- data_clean %>% filter(Suggestions != "" & Suggestions != "-")
# Create a term-document matrix
corpus <- Corpus(VectorSource(data_clean$Suggestions))
tdm <- TermDocumentMatrix(corpus)
# Convert the term-document matrix to a matrix
m <- as.matrix(tdm)
word_freqs <- sort(rowSums(m), decreasing = TRUE)
word_freqs <- data.frame(word = names(word_freqs), freq = word_freqs)
# Display the most frequent words
# Create a word cloud
wordcloud(words = word_freqs$word, freq = word_freqs$freq, min.freq = 1,
scale = c(3, 0.5), colors = brewer.pal(8, "Dark2"))
Conclusion
Based on the results from our alumni satisfaction survey, we have found that, overall, alumni consider the curriculum of the Applied Statistics Master’s Program at FMIPA Unpad to be well-aligned with their professional needs and current technological advancements in the Society 5.0 era. Alumni expressed satisfaction with the program’s updated curriculum and indicated they would recommend it to their colleagues. However, there remains a need for ongoing improvements to keep pace with the rapidly evolving field of Artificial Intelligence.”
The routine evaluation of our Master’s in Applied Statistics curriculum is a critical component of our commitment to excellence in education. By aligning our curriculum with stakeholder requirements and technological advancements, we ensure that our program remains relevant, rigorous, and responsive to the needs of the 5.0 society. This ongoing process of evaluation and improvement enables us to provide our students with the skills and knowledge they need to excel in a rapidly evolving field, ultimately contributing to their success and the advancement of the statistical profession.
As we continue to navigate the complexities of the modern data landscape, our dedication to curriculum evaluation underscores our commitment to delivering a high-quality education that meets the demands of today’s world and prepares our students for the challenges and opportunities of tomorrow.