White Red Simple Modern Data Analysis Presentation

Data Science for Sustainable Development. By. Presentation Insights.

[Audio] Good morning everyone. We begin today with the 'Sustainable Development Goals,' or SDGs. These are a set of 17 global objectives that were adopted by 193 member countries of the UN General Assembly back in September 2015. The mission is ambitious: to end poverty, protect our planet, and ensure prosperity for everyone by the year 2030. However, achieving these goals isn't easy. We face significant challenges, such as instability and conflict between nations, and often a lack of proper planning at the local level. Furthermore, many non-profit organizations simply lack the skilled resources—like data scientists—needed to drive these changes effectively..

[Audio] Let's look at how technology intersects with the first few humanitarian goals. For Goal 1, No Poverty, we know that about 17.2% of the world is struggling to meet basic needs. AI is now being used to improve agriculture and assist in aid distribution, especially in war-torn areas. For Goal 2, Zero Hunger, AI helps by tracking food waste and identifying crop diseases to improve yields. And regarding Goal 3, Good Health, AI empowers healthcare professionals to analyze massive datasets to find cures, while wearable technology monitors patient health in real-time. Even for Gender Equality, AI tools are helping employers use gender-sensitive language during recruitment to reduce bias..

[Audio] Moving beyond basic needs, technology is vital for our economic and environmental survival. AI analyzes energy consumption data to make renewable energy more reliable and cheaper, supporting Affordable and Clean Energy. It also acts as a catalyst for Industry and Innovation by streamlining resources. For the environment, AI is a game-changer. It has the potential to reduce global greenhouse gas emissions by up to 4%. We are even using satellite imagery combined with machine learning to track illegal fishing in our oceans. But none of this happens in isolation. Goal 17 emphasizes 'Partnerships.' Governments, the private sector, and civil society must share knowledge and technology to make these goals a reality..

[Audio] So, what powers these solutions? The answer is Data Science. This is a field that combines domain expertise, programming skills, and mathematics to extract insights from the massive volumes of data organizations collect. When we talk about 'Big Data,' we aren't just talking about a lot of data. We are talking about datasets so complex that traditional databases can't handle them. We define Big Data by the '4 Vs': Volume: The sheer amount of data. Velocity: The speed at which data moves. Variety: The diversity of data types. Veracity: The authenticity and quality of that data..

[Audio] As data scientists, we deal with three main categories of data. First, there is Structured Data. Think of this like an Excel sheet or SQL database—it's highly organized in rows and columns, making it easy to search, like employee records. Then we have Unstructured Data, which is much harder to process. This includes text, videos, and images. For example, 'Natural Language Data' depends heavily on the speaker's mood, making it difficult for computers to interpret. Finally, there is Semi-structured Data, which has some organization, like tags, but no rigid structure—common in social media networks or streaming data..

[Audio] Why do we need to analyze all this data? Because it fuels industry growth and helps us measure performance. We use two main approaches: Descriptive Analytics looks at historical data to tell us what happened in the past, like analyzing stock market trends. Predictive Analytics uses machine learning to forecast what will happen, such as weather forecasting. This is a booming field. The number of data scientist roles has grown by 650% since 2012, and it's estimated that 11.5 million new jobs will be created by 2026..

[Audio] To wrap up, let's look at the toolkit we use. We rely on languages like SQL to manage databases and Python, which is incredibly popular for data mining and deep learning. We also use Tableau to visualize this data into interactive dashboards. Ultimately, Data Science and AI go hand in hand. AI components like computer vision rely entirely on the datasets that we prepare and analyze. Without data science, we cannot build the intelligent systems needed to solve the global challenges we discussed at the start of this presentation..

[Audio] Thank you for your time.. Thankyou. For Your Time.