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Climate Data Analysis & Visualization with Python
Denis Slabakov
Denis Slabakov
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Climate Data Analysis & Visualization with Python

Analyze and visualize real-world climate data using Python to uncover trends in global temperature and CO₂ levels, creating a professional climate data report.

Register today
Apply now
Wednesdays
 at
3:00
P.M.
 ET /
12:00
P.M.
PT
8 weeks, 2-3 hours per week
Intermediate
No experience required
No experience required
Some experience required
Degree and experience required

Description

Data science plays a crucial role in understanding climate change, allowing scientists and policymakers to analyze trends, make predictions, and develop solutions. This project introduces you to the fundamentals of climate data analysis using Python, focusing on real-world datasets from NASA and NOAA.

In this Build Project, you will step into the role of a Data Scientist working in the environmental sector. You will learn how to access, clean, and analyze climate data, create compelling visualizations, and interpret key indicators such as global temperature rise and CO₂ levels. The project is designed to be accessible to students with basic Python knowledge, focusing on step-by-step learning and practical implementation. By the end, you will have a structured portfolio piece, showcasing your ability to analyze and communicate climate data effectively.

Session timeline

  • Applications open
    May 1, 2025
  • Application deadline
    June 10, 2025
  • Project start date
    Week of July 8, 2024
    Week of
    July 7, 2025
  • Project end date
    Week of

What you will learn

  • Use Python to load, clean, and analyze real-world climate datasets from NASA and NOAA.
  • Create effective data visualizations (graphs, heatmaps, and trend analysis) using Matplotlib and Seaborn.
  • Interpret and identify long-term climate trends from historical data.
  • Present findings in a clear and professional format, using Jupyter Notebooks, GitHub, and slide decks
  • Gain insight into climate data science and its role in environmental decision-making.

Project workshops

1
Introduction to Climate Data
2
Python for Data Analysis
3
Cleaning & Preprocessing Data
4
Creating Graphs & Heatmaps
5
Identifying Climate Trends
6
Storytelling with Data
7
Finalizing Reports
8
Presenting Findings

Prerequisites

  • Basic Python knowledge: ability to use loops, functions, and Pandas for data manipulation.
  • Introductory knowledge of data visualization: basic experience with Matplotlib or Seaborn is helpful.
  • Basic understanding of climate science concepts (helpful but not required).

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About the expert

Denis Slabakov is a Mathematics Build Fellow at DREAM Venture Labs, where he works with students leading projects in mathematics and applied problem-solving.

Denis is a co-founder and managing director at New Mining, a cryptocurrency mining company engaged in developing and managing high-performance data centers. He focuses on expanding the business internationally, specifically in the U.S. market, and exploring new technologies to optimize mining operations.

Denis has over 15 years of experience in IT, business development, and entrepreneurship. Over his career, he has co-founded and managed ventures in cryptocurrency, real estate, and media. He specializes in leading teams, solving complex business challenges, and scaling operations globally.

He holds a degree in Applied Mathematics from Moscow Institute of Physics and Technology and has completed business administration studies at The Open University.

A fun fact about Denis is that he has visited over 50 countries and has a passion for mountain skiing and exploring new cultures.

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