Ogden College Study Hub
Classes in the Ogden College of Science and Engineering may challenge you to develop new study strategies that you might not have needed in high school classes or in other college courses. These resources have been compiled by your peers, teaching assistants, faculty, and staff to help you achieve academic success @ Ogden.
STEM courses can have difficult content that even the brightest students may struggle with. The tabs here provide guidance, advice, and resources for some common issues shared by students across Ogden College.
All your tech needs will be more easily met if you follow the guidance on our Ogden Tech Reqs page. PCs are much preferred over MACs. You will also need the basic Office Suite (available for free here). Please use the software program rather than the online versions. Online programs (e.g., online Word or Google Docs) are excellent for group work but lack functionality.
Get Started with WKU AppStreaming today: Anywhere at any time, from any mobile device/tablet/computer you already have, you can securely access the apps you need for school without having to purchase, download, or install them. They are always available, ready within moments of logging in.
Direct links to commonly used programs across Ogden College are provided below:
JAMOVI
- From jamovi: https://www.jamovi.org/
- Cloud version: https://www.jamovi.org/cloud.html
JASP
- From JASP: https://www.jasp-stats.org
MATLAB and Simulink
R
- Homepage: https://www.r-project.org
- Download R: https://cran.rstudio.com/
SPSS
- Install SPSS (windows): https://td.wku.edu/TDClient/34/Portal/KB/ArticleDet?ID=3924
- Install SPSS (MAC): https://td.wku.edu/TDClient/34/Portal/KB/ArticleDet?ID=3923 (please don't)
Data analysis and figure creation are important for all Ogden majors; statistics help us test our hypotheses and figures help us illustrate our data and convey our results. The specific ways to report the results of your statistical tests, and the formatting guidelines for your tables and figures (e.g., titles, captions, etc.) can vary and are discipline- and journal-specific. Be sure to confirm these details with your professor.
Why Statistics Matter for STEM Majors: Statistics are foundational to scientific thinking. Whether you're designing experiments, analyzing data, or interpreting results, a solid understanding of statistics allows you to draw valid conclusions, evaluate reliability, and identify patterns in complex data. In STEM fields, statistics support evidence-based decision-making, hypothesis testing, risk assessment, and predictive modeling. From biology and chemistry to engineering and computer science, statistics are not just helpful—they're essential for advancing knowledge and solving real-world problems.
- Khan Academy – Statistics and Probability
Foundational concepts: descriptive stats, distributions, hypothesis testing
🔗 https://www.khanacademy.org/math/statistics-probability - Harvard Online: Statistics and R for the Life Sciences (edX)
Free course tailored to STEM and biomedical students
🔗 https://www.edx.org/learn/r-programming/harvard-university-statistics-and-r - Coursera: Statistics with Python (University of Michigan)
Applied stats course using Python (good for data science majors)
🔗 https://www.coursera.org/specializations/statistics-with-python - Harvard Online Stats Courses (many for free)
From general principles of probability to complex stats tools
🔗https://pll.harvard.edu/subject/statistics
- “The Art of Statistics: How to Learn from Data” by David Spiegelhalter
A readable introduction with real scientific examples
🔗 https://www.amazon.com/Art-Statistics-How-Learn-Data/dp/1541618513 - “Statistics Done Wrong: The Woefully Complete Guide” by Alex Reinhart
Focuses on common mistakes scientists make in statistical reasoning
🔗 https://www.statisticsdonewrong.com/
Why Graphs and Figures Matter for STEM Majors: Creating clear, accurate graphs and figures is a vital skill for STEM majors. Visual representations help convey complex data, patterns, and relationships in a way that’s easier to understand than raw numbers alone. Whether presenting lab results, analyzing trends, or publishing research, effective figures enhance communication, strengthen arguments, and support scientific integrity. In both academic and professional settings, strong graphing skills demonstrate attention to detail and the ability to interpret and present data meaningfully.
- Excel – Widely used for basic graphing and data analysis: Microsoft Excel Graph Tutorial
- Python (Matplotlib Docs & Seaborn Tutorials): Popular in scientific computing:
- R (ggplot2) – Powerful for statistics and publication-quality plots: R Graph Gallery
- “Data Visualization: A Practical Introduction” by Kieran Healy
- Overview: Teaches principles of effective data visualization using R (ggplot2) with a focus on clarity and aesthetics.
- Link to publisher: https://socviz.co/(Free online version available)
- Purchase: https://www.amazon.com/Data-Visualization-Introduction-Kieran-Healy/dp/0691181624
- “The Visual Display of Quantitative Information” by Edward Tufte
- Overview: A foundational book on graph design, focusing on data integrity, clarity, and avoiding “chartjunk.”
- Official site: https://www.edwardtufte.com/tufte/books_vdqi
- Purchase: https://www.amazon.com/Visual-Display-Quantitative-Information/dp/0961392142
- Scientific American – Graphic Science Archive
- Overview: A collection of beautifully designed infographics on scientific topics, useful for studying layout, color use, and storytelling through data.
- Link: https://www.scientificamerican.com/article/see-the-best-science-graphics-of-2023
- DataCamp (Python, R) – https://www.datacamp.com/
- Khan Academy – Statistics and Graphing – https://www.khanacademy.org/math/statistics-probability
- LinkedIn Learning – Excel Data Visualization (free with many university logins)
Scientific literature can be...well, a lot. It often uses technical language, complex methodologies, and assumes background knowledge that not all readers may have. Additionally, searching for journal articles can be overwhelming due to the sheer volume of publications and the challenge of identifying credible, relevant sources amid paywalls and varying formats.
TIPS
- Start with review articles – These provide summaries of research in a field and are a great way to build background knowledge before diving into primary studies.
- Use university library databases – Tools like PubMed, JSTOR, or Google Scholar are more reliable than a basic web search, and your school may provide free access.
- Read strategically – Focus first on the abstract, introduction, and conclusion to decide if the article is relevant before tackling the full text.
- Look up unfamiliar terms – Don’t be discouraged by jargon; keeping a glossary or using resources like Khan Academy or Wikipedia can help build understanding.
- Ask for help – Professors, librarians, and even peer tutors can guide you in finding and interpreting articles.
- Practice regularly – Like any skill, reading scientific papers gets easier with repetition and exposure.
Writing in STEM can be tough too! It requires a clear, concise, and objective style that may differ from the writing style you are used to in other subjects. It involves not just reporting facts, but also analyzing data, using discipline-specific terminology accurately, and following strict formatting and citation conventions. For many students, the challenge is learning to think like a scientist while also mastering how to communicate that thinking in a structured, evidence-based way. The resources below can be a big help.
General Resources
Discipline Specific Resources
- CSE (Council of Science Editors) Style Manual (common Ag & Env Sci)
- American Society of Agronomy (ASA), Crop Science Society of America (CSSA), and Soil Science Society of America (SSSA) Publications Handbook and Style Manual
- Writing in Environmental Sciences (Writing@CSU)
- CSE (Council of Science Editors) Style Manual (common in Biology)
- Writing in Biology (Colorado State University Writing Studio)
- Scientific Writing Made Easy: A Step-by-Step Guide to Undergraduate Writing in the Biological Sciences (ESA)
- How to Write a Technical Paper (MIT Computer Science)
- ACM (Association for Computing Machinery) Author Guidelines
- IEEE Editorial Style Manual (for CS and Engineering)
- Writing in the Earth & Climate Sciences (Duke University Libraries)
- Science Communication in Earth and Mineral Sciences (Penn State)
- Writing in the Environmental Sciences (Get access, by L.Baker)
- A Guide to Writing Mathematics (UC Davis, Dr. Kevin P. Lee)
- AIP Style Manual (American Institute of Physics)
- Writing in the Sciences: Physics (Dartmouth College Writing Program)
- Astrobites (A daily blog written by graduate students)
- APA Style Website (American Psychological Association)
- Purdue Online Writing Lab – Psychology Writing
- Writing in Psychology: A Student Guide (Mark Mitchell & Janina Jolley)
- Engineering Writing Lab
- IEEE Author Center (The Institute of Electrical and Electronics Engineers)
- Writing in Engineering (Colorado State University Writing Studio)
STEM fields use the metric system because it is a standardized, internationally recognized system based on units of 10, making calculations simpler and more consistent. Its universal adoption in science and engineering promotes clear communication and collaboration across countries and disciplines. The metric system’s logical structure and ease of conversion between units make it especially well-suited for precise measurements and data analysis in research and technical work.
Understanding the Metric System
NIST Metric Program (National Institute of Standards and Technology): NIST Metric Program
Khan Academy – Video Tutorials on Metric Units and Conversions: Khan Academy – Metric Conversions
A downloadable reference guide with definitions, examples, and tables: The Metric Handbook (PDF)
Metric Conversion Tools and Help
- UnitConverters.net: Easy-to-use online tool for converting between metric and U.S. customary units in many categories (length, mass, temperature, etc.).
- Google Search or WolframAlpha: Type directly into Google or WolframAlpha (e.g., “10 cm to inches”) for instant, reliable conversions with explanations.
Some of the links on this page may require additional software to view.