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W4/Final-project.ipynb

@@ -987,8 +987,48 @@
     "\n",
     "<a>Nima Raeisi</a> has a MSc in Hydrogeology, his research focused on using machine learning to pridict the groundwater level by using hydrologic and groundwater quality data.\n",
     "\n",
-    "<a>Mahdi Farmahini Farahani</a> has a BSc in Geology, his research focused on using Computer Science skills to solve geophysical problems.\n"
+    "<a>Mahdi Farmahini Farahani</a> pursuing a Bachelor of Science degree in Geology, his research focused on using Computer Science skills to solve geophysical problems.\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Just a friendly reminder:\n",
+    "\n",
+    "## Deadline Alert! 📅\n",
+    "\n",
+    "Don’t forget to submit your final project by July 15th. This is your chance to showcase all the hard work and knowledge you’ve acquired. Make sure to review your work, check for completeness, and give it your best shot. Good luck, and I’m looking forward to seeing your amazing projects! 💼🌟"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Acknowledgments\n",
+    "We would like to thank the Geological Society of Kharazmi University and the Water Research Center for their support in making this workshop possible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reference\n",
+    "* [Petrelli, M. (2021). Introduction to python in earth science data analysis: from descriptive statistics to machine learning. Springer Nature.](https://link.springer.com/book/10.1007/978-3-030-78055-5)\n",
+    "\n",
+    "* [Petrelli, M. (2023). Machine Learning for Earth Sciences. Springer Nature.](https://www.springerprofessional.de/en/machine-learning-for-earth-sciences/26078378)\n",
+    "  \n",
+    "* [Trauth, M. H. (2022). Python recipes for earth sciences. Springer Nature.](https://link.springer.com/book/10.1007/978-3-031-07719-7)\n",
+    "  \n",
+    "* [Muñoz Sabater, J. (2019): ERA5-Land monthly averaged data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.68d2bb30 (Accessed on 03-06-2024)](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {