Apple

I lead the data science and engineering team at Apple Services Finance. The team focuses on translating business problems and requirements into data products using advanced statistical and machine learning models to support and help Apple leadership make data-driven decisions.

Senior Manager - Data Science and Engineering 

Managing a data science team focusing on cost optimization on Apple infrastructures with the use of statistical analysis and machine learning models.

Data Science Manager

Senior Data Scientist

Ernst & Young

Predictive analysis, time series models, forecasting and machine learning

Data Scientist

University of Michigan Law School

Data Analyst supporting the Empirical Legal Studies Center
Prediction Models, Regression Analysis and Data Visualization using R, JMP and Stata.

University of Michigan

Data Manipulation & Cleansing of a large database (India Census) using Stata and R
GIS Analysis with R
Data Visualization

Data Analyst

Senior Manager - Data Science and Engineering at Apple

I lead the data science and engineering team at Apple Finance - Services and Infra. The team focuses on translating business problems and requirements into data products using advanced statistical and machine learning models to support and help Apple leadership make data-driven decisions.

I am also an open-source contributor and the author of Hands-On Time Series Analysis with R (Packt publication) and several R packages for time series analysis and machine learning applications. Currently working on my next book, Applied Time Series Analysis and Forecasting, which focuses on forecasting at scale. 

Also available on:
Github: https://github.com/RamiKrispin
Medium: https://medium.com/@rami.krispin
Mastodon: https://mstdn.social/@ramikrispin

Rami Krispin

Geospatial Data Consulting

Founder

Central European University

Visiting Faculty

LinkedIn

Do you want to learn about geospatial data science? Join my first LinkedIn Learning course here:

https://www.linkedin.com/learning/geospatial-data-analytics-essential-training/analyzing-geospatial-data?shareModal=true

LinkedIn Learning Instructor

Udemy

Rolling out courses on data science with a particular focus on spatial analytics.

Data Science Instructor

Self Published

Author of Geospatial Data Science Essentials, a Python cookbook focusing on geospatial analytics, and a book that became #1 Amazon Best Seller and #1 New Release in four categories during its first week!

Geospatial Data Science Essentials is your hands-on guide to mastering the science of geospatial analytics using Python. Designed for practitioners and enthusiasts alike, this book distills years of experience by wrapping up 101 key concepts from theory to implementation, ensuring you gain a practical understanding of the tools and methods that define the geospatial data science landscape today.

Whether you are a seasoned data scientist, a GIS professional, a newcomer to spatial data, or simply a map lover, this book provides you solid foundation to level up your skills. The book is centered around practicalities, as you will explore real-world examples with compact code throughout ten topics and 101 sections. From understanding spatial data structures to leveraging advanced analytical techniques, from spatial networks to machine learning, this book equips you with a wide range of knowledge to navigate and succeed in the rapidly evolving field of geospatial data science.

Embrace the journey into geospatial data science with this essential guide and discover the power of Python in unlocking the potential of spatial analytics.

Author

Municipality of the city of Siklós

Serbian Minority Municipal Representative

Amazon KDP Publishing 

Author of 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐨𝐭𝐬: How data, networks, and algorithms shape our world. Kindle Edition

Author of the #1 Amazon Best Seller 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 - 101 Practical Python Tips and Tricks

Towards Data Science

Writing regular data science tutorials on geospatial data science, covering topics like urban sustainability, accessibility, remote sensing, transportation, urban networks, and more.

https://medium.com/@janosovm

Data Science Writer

Data Visualization Society

Correspondent writer

Open Books Publishing

Te is tucatnyi appot használsz éjjel-nappal? Tudtad, hogy legtöbbjük minden apró mozdulatodra figyel, amit az online térben teszel, rengeteg adatot rögzít, különféle algoritmusokat, akár mesterséges intelligenciát is használ, és mindeközben elképesztően sokféle hálózatot hoz létre? És azt, hogy ezek az appok egyszerre igyekeznek kiismerni téged és megérteni a korábbi lépéseidet azért, hogy a következőt már az ő segítségükkel tedd meg? Az adatalapú algoritmusok és hálózatok a háttérben digitális létünk megkerülhetetlen szereplőivé váltak, mindannyiunk zsebébe csempészve az adat- és hálózattudományt. Miért kaphat egy influenszer reggelije több lájkot, mint valamely relevánsabb tartalom? Hogyan segíthetnek a hálózatok megtalálni a következő sztár DJ-t, mi köze a hálózatoknak az élhető városokhoz, és hogy jön mindehhez a Trónok harca vagy a vállalati HR?

Olykor bonyolult és komplex jelenségek a tudomány és a pop-kult határán egyensúlyozva közérthetővé tehetők. Ehhez azonban sorra kell venni, mit is jelent az adat a mindennapjainkban, hogyan fest a hálózatok anatómiája, miként állnak össze a hálózatok.

A megszokott ismeretterjesztő könyvektől eltérően Janosov Milán első kötetében arra vállalkozott, hogy meghökkentő kérdésfelvetéssekkel és megközelítésekkel közelebb hozza hozzánk azt a tudományt, amely behálózza az életünket. Az adatok és a hálózatok szinte mindenütt jelen vannak, de lehetnek szórakoztatóak is, sőt számos korábban ismeretlen utat nyithatnak meg előttünk.

With a background in physics and biophysics, Milan earned his PhD in network and data science in 2020. He studied and researched at the Eötvös Loránd University and the Central European University in Budapest, the Barabási Lab in Boston, and the Bell Labs in Cambridge. In 2020, he was selected as Forbes 30u30 and featured on Data Science Connect's "99 Data Influencers to Follow 2023" list.

He is a regular author at Towards Data Science and the Data Visualization Society, while his first international book, "Geospatial Data Science Essentials," became a #1 Amazon Best Seller in four categories in 2024.

Milan is the founder of Geospatial Data Consulting, former chief data scientist at Baob and co-founder of Datapolis, a research affiliate at the Central European University, a research expert at the European Commission, and a senior data scientist at Maven7.

He was awarded the Scholarship of the Republic of Hungary three times, won multiple prizes at science competitions, and presented his work in peer-reviewed journals and conferences from Nature's Scientific Reports to MIT.

His work has been featured in Nature Social Science Research, GQ, Times Higher Education, New Scientist, New York Times, TechXplore, The Economic Times, Fossbytes, Futurism, The Times, Towards Data Science,Phys.org, Nightingale, Gamestar, the Miami Art Week, and more.

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Rami Krispin

Milan Janosov

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