DataSharp Academy: Empowering YOU with practical data skills
Many data scientists complete intensive courses on the latest tools and methods but often lack a solid grasp of the fundamentals. Everything runs smoothly in the classroom — until they’re back at their desks.
“If only my data were as clean as the class examples, I could do this easily.”
The reality is, real-world data is messy. A large part of analysis involves curating and shaping data to your needs — and that can be hard. Really hard.
At DataSharp Academy, we believe precise thinking and powerful analyses start with clarity in your data and your goals. Our mission is to help you master data curation, analysis, and visualisation through practical, hands-on learning. Whether you want to streamline workflows, harness AI, or tell compelling data stories, we’ll help you unlock your full potential.
Our courses focus on everyday, actionable techniques to help you tackle real data challenges and work independently with clarity and purpose. We break down complex concepts into structured, hands-on lessons, so you can apply your skills immediately. And of course, we do not use pretty, curated datasets. We go straight for the challenging ones.
📅 Workshops this September
❶ R Fundamentals for Data Science: Master the basics, Enhance your work with AI.
Learn to structure analyses, write clean R scripts, understand complex code, build reproducible habits, and use AI to support — not replace — your thinking. This 4-day workshop combines coding skills with data-driven thinking so you can navigate projects with clarity and confidence.
🔗 https://datasharpacademy.com/workshop-rfundamentals/ [PDF flyer]
❷ Quantitative Palaeoclimate Reconstructions with CREST/r
Prepare your data, parameterise the CREST model, interpret outputs with confidence, and troubleshoot challenges. This 3-day interactive workshop blends technical training with structured thinking for robust climate reconstructions.
🔗 https://datasharpacademy.com/workshop-crest/ [PDF flyer]
📍 Visit our website: https://datasharpacademy.com
📧 Contact: manuel.chevalier@datasharpacademy.com
References and further reading
Chevalier, M., Gosling, W.D., Hooghiemstra, H., Cartapanis, O., Chase, B.M. and Kaboth-Bahr, S., 2025. Eccentricity-driven glacial climate variability and its influence on speciation in the tropical Andes. Quaternary Science Advances, 18, 100278.
Gosling, W.D., Chevalier, M., Fischer, M.L., Holewijn, M., Finch, J., Gil-Romera, G., Hill, T., Houngnon, A., Leonardi, M., Manica, A. and Kaboth-Bahr, S., 2025. A multi-model approach to the spatial and temporal characterization of the African Humid Period. Quaternary International, 744, 109933.
Chevalier, M., Dallmeyer, A., Weitzel, N., Li, C., Baudouin, J.P., Herzschuh, U., Cao, X. and Hense, A., 2023. Refining data–data and data–model vegetation comparisons using the Earth mover’s distance (EMD). Climate of the Past, 19(5), pp.1043-1060.
Chevalier, M., 2022. crestr: an R package to perform probabilistic climate reconstructions from palaeoecological datasets. Climate of the Past, 18(4), pp.821-844.
Chevalier, M., Cheddadi, R. and Chase, B.M., 2014. CREST: Climate REconstruction SofTware. Climate of the Past, 10, pp.625-663.
DataSharp Academy, 2025