Data Skills Can Make a Big Difference in Non-Data-Science Careers
Sustainability and energy analyst Himalaya Bir Shrestha reflects on self-learning and the numerous benefits of learning to code
Author Spotlight
In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Himalaya Bir Shrestha.
Himalaya is an energy system analyst based in Germany. With a background in Renewable Energy Management, he started his career as a climate policy analyst at a science policy institute in Berlin. In his free time, Himalaya loves to read articles about data science and implement relevant techniques in his daily work related to assessing climate policies, building energy system models, and developing long-term decarbonization scenarios at different levels.
How did you discover your interest in data-related topics?
In 2018, I interned at the NewClimate Institute based in Cologne, Germany. As part of this internship, I developed an Excel-based energy system model of Mongolia for a project. The model utilized extensive datasets of energy balance and emissions statistics from different sources, such as the International Energy Agency (IEA). Using this data, I could analyze the trends and drivers of primary energy, final energy, electricity supply, and emissions trajectories in Mongolia for different sectors. Moreover, I used the model to construct long-term decarbonization scenarios by playing with various socio-economic levers. It was the first time I developed an affinity towards leveraging data to extract insights on energy and sustainability-related topics.
What kinds of problems and challenges are you drawn to?
These days, I am most interested in analyzing the transformations of the power sector and energy sector on a granular level. For this purpose, I am learning different techniques for time series analyses, clustering, and spatial analyses.
Did your formal training prepare you well for the kind of work you do, or was a lot self-learning necessary as well?
I had a basic introduction to Python during my graduate studies. My internship provided me with a strong foundation for working with Excel. One time, while I was working at Climate Analytics two years ago, I saw my team lead automate several tasks in a Jupyter notebook – tasks that I was doing manually in Excel. I was amazed by the productivity one could gain by using Python.
It instilled a curiosity and ignited a passion in me to learn programming. Prior to this moment, I had taken some online courses. But it was difficult for me to stick with the syllabus of these courses, because I’d prefer to learn only those areas that I’d implement at work. Also, I used to get frustrated and demotivated when I encountered programming errors that I could not comprehend, and gave up the course without completing them.
However, at some point, I decided to start a "100 days of code" challenge on my own. I searched for different resources for learning topics such as data analysis, data visualization, machine leaning, statistics, optimization, and spatial analysis. In a sense, I created my own pathway. During this journey, I discovered the Medium platform and Towards Data Science. I gained a lot of knowledge by going through the blog posts and tutorials and implementing them on my own. Plus, I kept note of the things I learned every day.
Besides self-study, I learned a good deal at my job through reviewing the code of my colleagues, and receiving their feedback on mine.
As someone who works at the intersection of analytics, policy, climate, and sustainability, what are the advantages of having a strong foundation in programming and data science?
There are multifold advantages of having a strong technical, data-informed background. It enables you to see the data not just as some arbitrary numbers, but as resources from which you can extract valuable insights. It provides you the knowledge, skills, and confidence to perform different quantitative analyses, which are vital in this field.
In my work, a sound technical and data-informed background is imperative for assessing the effectiveness and implications of different energy and climate policies, such as Nationally Determined Contributions (NDCs). The NDCs embody the efforts of any country to reduce national emissions and adapt to the impacts of climate change. Furthermore, a sound data-informed background is essential to extract insights from the data—insights that are relevant for policy recommendations, business, and investment decisions around energy system planning, climate litigation, and so on.
Do you have any advice for data professionals who might be interested in working on social and environmental issues (whether climate-related or not), but are not sure how to go about it?
Studies suggest that data science lies at the intersection of mathematics, computer science (programming skills), and domain expertise. It is incomplete without one of the three aspects.
There are different websites that offer open source datasets in social and environmental issues, such as Our World in Data, World Bank Open Data, climate data from NASA’s Goddard Institute for Space Studies, or datasets from Kaggle. If a data professional is interested in working on social and environmental issues, they can go to these websites, select a dataset of their choice, and start working with them. There are also analyses that these sources provide, for example, Our World in Data’s take on Renewable Energy, or the global surface temperature analysis by NASA.
One can get a broad analytical knowledge simply by reading and trying to comprehend these analyses. Besides, I’d also suggest searching for relevant topics on Medium and on Towards Data Science, go through them, create your own story about a topic, and and share it.
Relatedly, are there ways for industry data professionals to contribute around these topics even if they remain in the corporate world?
I have seen different independent research publications and blog posts from people who work in industry or in the corporate world. It is always interesting to read through their perspectives. People who work in these sectors can always contribute through platforms such as Medium, TDS, or social media.
As an author on TDS, you’ve covered topics related to your field of expertise, but also shared guides and tutorials on more general topics. Why did you start writing for a wide audience?
I love keeping notes on programming and visualization techniques that I find unique and interesting. During the course of my learning, I realized that I could create different interesting stories by combining my programming knowledge with my domain – renewable-energy management and climate-policy analysis. Therefore, I wrote stories about the application of data science in my field.
Writing helps me organize knowledge in one place. There are several instances when I refer back to my own stories to execute a specific algorithm or visualization technique. I started writing and posting on TDS because I thought my content could be helpful to readers, just like it has been for me when I went through the stories of others.
Looking ahead to the next year or two, what changes do you hope to see in your sector?
In the data science/policy ecosystem, I see different modelling and analytical works going open source in the coming years. Today, readers want to check the data and methodology on their own, rather than seeing only the results of any analytical work. For example, in January 2022, the IEA stated their willingness to make their data public after a campaign by several scientists. It is a significant announcement because several government bodies and researchers depend on IEA’s data to analyze the energy system and formulate energy policies and investment decisions.
I also see an increase in the acceptance and uptake of programming tools such as Python, and Business Intelligence (BI) tools for visualization in different organizations that previously relied on basic Microsoft Office applications. I think this relates to the multifold applications, advantages, and automation possibilities of using these tools, which could significantly boost efficiency across organizational levels.
To learn more about Himalaya’s work and to explore her latest articles, follow him on Medium. For a quick introduction to his work on TDS, here are a few highlights from our archives:
- 4 Key Figures to Understand the Climate Crisis (October 2021, 7 minutes)
- Geoplotting Emissions Intensity of Electricity Generation in Europe (March 2021, 10 minutes)
- Solving the Travelling Salesman Problem for Germany using NetworkX in Python (June 2022, 12 minutes)
- Network and Interconnection in Python Maps (March 2022, 8 minutes)
- Wind Energy Physics and Resource Assessment with Python (January 2022, 8 minutes)
Feeling inspired to share some of your own writing with a wide audience? We’d love to hear from you.
This Q&A was lightly edited for length and clarity.
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