📢 Registration is Open
Join our comprehensive online training program on
Trend Analysis and Seasonality Test Applications in R and ArcGIS.
This practical training is designed to provide hands-on
experience in environmental time-series analysis, trend
detection, seasonality testing, spatial analysis and
research publication.
Learn how to transform environmental datasets into
meaningful statistical results, maps and publication-quality
research outputs.
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📅 Course Starts
6 September 2026
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🔥 Early Bird Deadline
31 August 2026
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🇮🇳 Early Bird Fee
₹2,499
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🌎 Early Bird Fee
$30
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Regular Price
₹4,999
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Access
Lifetime Access
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*Early Bird pricing is valid until 31 August 2026.
Applicable taxes are included where applicable.
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🎓 Choose Your Registration Option
🇮🇳 Enroll Now – ₹2,499
🌎 Enroll Now – $30
Early Bird Offer valid until
31 August 2026
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📚 About the Course
Trend Analysis and Seasonality Test Applications in R and ArcGIS
is a comprehensive practical training program designed
for researchers, academicians, environmental scientists,
GIS professionals, PhD scholars and postgraduate students.
Participants will learn how to prepare environmental
time-series datasets, identify temporal trends, assess
seasonality, evaluate autocorrelation, quantify trend
magnitude and visualize spatial patterns using
Excel, R and ArcGIS.
The training follows a complete research workflow:
Data Preparation
→ Statistical Analysis
→ Trend Detection
→ Visualization
→ Spatial Mapping
→ Interpretation
→ Publication
The course is particularly useful for research involving
rainfall, temperature, groundwater, hydrology, climate,
vegetation and other environmental time-series datasets.
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📖 Course Curriculum
The course consists of
7 practical modules + 1 bonus module,
covering the complete workflow from environmental data
preparation to statistical analysis, spatial visualization
and research publication.
Module 1: Fundamentals of Trend & Seasonal Analysis
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Understanding environmental time-series data
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Trend, seasonality, variability and autocorrelation
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Parametric and non-parametric approaches
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Understanding ACF, MK and MMK
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Introduction to Seasonal Mann–Kendall Analysis
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Introduction to Sen's Slope Estimator
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Introduction to Excel, R and ArcGIS for trend analysis
Module 2: Environmental Data Preparation
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Downloading environmental datasets for India and global applications
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Rainfall, temperature and other environmental datasets
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Data validation and quality assessment
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Missing-data identification and handling
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Time-series formatting and preprocessing
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Preparation of datasets for R and ArcGIS
Module 3: Statistical Trend Analysis in R
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Descriptive statistics for environmental time-series
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Autocorrelation Function (ACF)
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Graphical interpretation of ACF
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Mann–Kendall (MK) Trend Test
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Modified Mann–Kendall (MMK) Test
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Sen's Slope Estimator
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Seasonal Mann–Kendall Test
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Significance assessment at 1% and 5% levels
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Interpretation of statistical outputs
Module 4: Innovative Trend Analysis & Visualization
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Concept and methodology of Innovative Trend Analysis (ITA)
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Preparation of ITA plots
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Identification of hidden trends
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Analysis of low-, medium- and high-value observations
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Interpretation of increasing and decreasing trends
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Significance and uncertainty considerations
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Publication-quality trend graphs
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Visualization using Excel and R
Module 5: Advanced Time-Series & Change-Point Analysis
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Time-series decomposition
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Identification of seasonal patterns
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Trend and seasonal components
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Pettitt Change-Point Test
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Detection of abrupt changes in environmental variables
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Gradual versus abrupt temporal changes
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Pre-whitening concepts for trend analysis
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Interpretation of change-point results
Module 6: Spatial Trend Analysis in ArcGIS
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Introduction to spatial trend analysis
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Preparation of spatial-temporal datasets
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Spatial representation of trend statistics
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Mapping trend magnitude and significance
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Spatial interpolation techniques
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Raster-based spatial trend visualization
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Preparation of publication-quality maps
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ArcGIS layout and figure preparation
Module 7: Research Publication & Practical Application
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Preparation of publication-ready tables
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Preparation of publication-quality figures
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Statistical result interpretation
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Interpretation of environmental significance
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Writing Results and Discussion
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Presenting trend and seasonality results in research papers
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Excel → R → ArcGIS → Research Paper workflow
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Common mistakes in environmental trend analysis
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Strategies for improving research-quality outputs
🎁 Bonus Module: Descriptive Statistics in R
- Mean
- Minimum
- Maximum
- Standard Deviation
- Coefficient of Variation
- Skewness
- Kurtosis
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🎯 Learning Outcomes
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Perform advanced statistical trend analysis.
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Analyze climatic and environmental time-series datasets.
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Assess autocorrelation and temporal dependence.
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Perform Mann–Kendall and Modified Mann–Kendall tests.
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Estimate trend magnitude using Sen's Slope.
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Identify seasonal and hidden trends.
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Apply Innovative Trend Analysis.
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Identify potential change points in environmental datasets.
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Develop spatial trend maps using ArcGIS.
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Create publication-quality tables, figures and maps.
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Interpret statistical outputs for scientific publications.
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Integrate Excel, R and ArcGIS into a complete research workflow.
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👥 Who Should Attend?
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Environmental Scientists and Researchers
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PhD Scholars and Research Scholars
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Faculty Members and Academicians
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GIS and Remote Sensing Professionals
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Postgraduate Students
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Climate and Hydrological Researchers
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Environmental Data Analysts
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Researchers working with Excel, R and ArcGIS
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Professionals involved in environmental monitoring
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⭐ Why Choose This Course?
✔ Hands-on Learning
Practical application using environmental datasets.
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✔ Research Focused
Designed around real research applications.
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✔ Publication Oriented
Learn to prepare research-quality figures,
tables and maps.
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✔ Complete Workflow
Excel → R → ArcGIS → Research Paper.
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✔ Lifetime Access
Learn at your own pace with lifetime access.
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✔ Expert Guidance
Training by Dr. Jayanta Das.
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📦 What's Included in the Course?
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Live online classes by Dr. Jayanta Das
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Practical application of trend and seasonal analysis
to environmental time-series data
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Hands-on training using Excel, R and ArcGIS
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Practical demonstration of MK, MMK, Sen's Slope,
Seasonal MK, ACF and ITA
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Audio-visual learning materials
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Real-world environmental datasets
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Publication-oriented tips for preparing research papers
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Publication-quality tables, graphs and maps
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Lifetime access to course recordings
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Unlimited viewing of recorded sessions
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Downloadable study materials
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Course Completion Certificate from AIGR
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Research and analytical support
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👨🏫 Resource Person
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Dr. Jayanta Das
Assistant Professor, Department of Geography
Rampurhat College, West Bengal, India
Dr. Jayanta Das holds a PhD from the University of North Bengal
and specializes in climate change, geomorphology, hazard management,
GIS/remote sensing and sustainable agriculture.
He is actively involved in geographical research, academic training,
research publications, editorial activities and international
research collaborations.
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🏆 Certification
Participants who successfully complete the training will receive
a Course Completion Certificate from Advances in
Geographical Research (AIGR).
The certificate recognizes successful completion of practical
training in trend analysis, seasonality analysis and environmental
time-series applications using R and ArcGIS.
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