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Milan Janosov

Chief Data Scientist @Datapolis - Network and Data Science Researcher @CEU - Forbes 30u30

Milan Janosov is the Chief Data Scientist at Datapolis, a role that positions him at the forefront of data-driven strategies within the organization. His expertise lies in leveraging data science to enhance decision-making processes and optimize business operations.

Professional Background

Janosov has a robust academic foundation, holding a Ph.D. in Computer Science. His career has been marked by significant contributions to various projects that integrate machine learning and data analytics, making him a key player in advancing Datapolis's capabilities in these areas.

Contributions and Expertise

  • Data Science: He specializes in applying advanced analytics to solve complex problems, aiming to drive innovation and efficiency within Datapolis.
  • Leadership: As Chief Data Scientist, he leads a team of data professionals, fostering an environment of collaboration and continuous learning.

Online Presence

Janosov maintains a professional profile on LinkedIn under the username milan-janosov, where he shares insights related to data science and industry trends, contributing to discussions on best practices in the field.

His work at Datapolis exemplifies the integration of technology and business strategy, positioning the company as a leader in utilizing data for impactful decision-making.

Highlights

Aug 3 · linkedin

The book is gaining popularity among our furry friends as well!

𝐀𝐦𝐚𝐳𝐨𝐧: https://zurl.co/NHCu    𝐒𝐡𝐨𝐩𝐢𝐟𝐲 + 𝐨𝐮𝐭𝐥𝐢𝐧𝐞: https://zurl.co/s9JF

𝐕𝐢𝐝𝐞𝐨 𝐢𝐧𝐭𝐫𝐨: https://zurl.co/6eeK

#geodatascience101 #geospatialessentials #GIS #spatialanalytics

Jul 27 · linkedin

I am so glad to see you reaching 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬: 101 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐏𝐲𝐭𝐡𝐨𝐧 𝐓𝐢𝐩𝐬 𝐚𝐧𝐝 𝐓𝐫𝐢𝐜𝐤𝐬 so many people -- and below comes the outline of the second part!

𝘕𝘰𝘵𝘦: with the book comes an .epub version, a .pdf version, and all the codes in .ipynb!

𝐀𝐦𝐚𝐳𝐨𝐧: https://lnkd.in/dUkHadCW

𝐒𝐡𝐨𝐩𝐢𝐟𝐲: https://lnkd.in/dwEperZ7

𝐆𝐞𝐨𝐜𝐨𝐝𝐢𝐧𝐠 56. Geocoding a Single Address Using GeoPy 57. Reverse Geocoding Coordinates 58. Batch Geocoding Multiple Addresses 59. Handling Missing Geocodes 60. From Geocoding to GeoDataFrame 61. Geocoding within a DataFrame

𝐑𝐚𝐬𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 62. Reading Raster Data 63. Clip the raster data file using GeoPandas 64. Writing Raster Data 65. Visualizing Raster Data 66. Histogram Equalization on Raster Data 67. Applying Simple Functions on Raster Data 68. Reprojecting Raster Data 69. Compute Zonal Statistics 70. Convert a Raster Grid into Vector Data 71. Reading Large Raster Files Efficiently 72. Clipping Large Raster Files 73. Visualizing Large Raster Files 74. Downsampling a Large Raster File

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐎𝐩𝐞𝐧𝐒𝐭𝐫𝐞𝐞𝐭𝐌𝐚𝐩 𝐃𝐚𝐭𝐚 75. Downloading Administrative Areas from OpenStreetMap (OSM) 76. Using OSMnx to Download Points of Interest (POIs) 77. Using OSMnx to Download Parks as Polygons 78. Using OSMnx to Download Building Footprints 79. Using OSMnx to Download Road Networks 80. Visualizing Complex Urban Areas

𝐒𝐩𝐚𝐭𝐢𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 81. Road Networks in GeoPandas 82. From GeoDataFrames to Spatial Networks 83. Visualizing Spatial Networks 84. Calculating the Shortest Path 85. Visualizing the Shortest Path 86. Generating Walking Distance Isochrones 87. Obtaining spatial network statistics 88. Network Centrality Measures 89. Community Detection in Spatial Networks

𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 90. Descriptive Statistics with GeoPandas 91. Global Spatial Autocorrelation 92. Local Spatial Autocorrelation 93. Spatial Feature Generation 94. OLS Regression on Spatial Data 95. Spatial Regression Models 96. Spatial Random Forest 97. Comparing Spatial Regression Models 98. Hotspot Analysis 99. Kernel Density Estimation 100. Inverse Distance Weighting for Spatial Interpolation 101. Spatial Clustering

#geodatascience101 #geospatialessentials #GIS #spatialanalytics #geospatialdata #geospatial #datascience #datavisualization

Related Questions

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Milan Janosov
Milan Janosov, photo 1
Milan Janosov, photo 2
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Hungary