• Blog

Data Science in Mental Health

  • Source : Towards Data Science
  • November 2020
This blog post explores the emerging role of data science in mental health. It discusses how data analytics and machine learning can be used to identify patterns in patient data, predict mental health conditions, personalize therapeutic interventions, and evaluate treatment effectiveness. The article highlights the potential for data-driven insights to improve diagnostic accuracy, support early intervention strategies, and enhance the accessibility and quality of mental healthcare services. It also touches upon the ethical considerations involved in handling sensitive mental health data.
  • Case Study

Introduction to Green Finance

  • Source : Global Environment Facility
  • October 2020
This document provides an introduction to green finance, illustrating its application through various case studies. It highlights different green finance instruments, including debt financing and equity investments, and discusses how these can be structured to support environmental projects. The examples showcase how organizations like the GEF use blended finance approaches to de-risk investments for private sector participation, encouraging capital flow into sustainable initiatives like forestry, fisheries, and energy efficiency.
  • Media Article

Why Social Workers Need Data Science

  • Source : socialworker.com
  • October 2020
This article emphasizes the increasing role of data science in the field of social work. It outlines how social workers can leverage data to improve outcomes in areas like case management, resource allocation, and decision-making. The piece discusses how data analytics can help social workers better understand the needs of their clients and improve service delivery.
  • Blog

How data science lets us do more than just chew the fat on pet obesity

  • Source : LinkedIn Pulse
  • October 2020
This article highlights how data science is crucial for understanding and combating pet obesity, moving beyond anecdotal observations to data-driven insights. It explains how analyzing factors like breed, age, diet, activity levels, and medical history helps identify at-risk pets and tailor intervention strategies. Data science enables the development of personalized nutritional plans and exercise recommendations, monitoring their effectiveness over time. By providing veterinarians and pet owners with actionable information, data science supports proactive health management, leading to improved well-being and longevity for pets, and ultimately reducing the prevalence of pet obesity.
  • Paper / Report

Centre for Strategic Philanthropy: A Conversation with Badr Jafar and Bill Gates

  • Source : CEP
  • September 2020
This video presentation provides an overview of how philanthropy acts as a driver of social change by funding innovative solutions and building community capacity. It features experts discussing examples of effective philanthropic initiatives, challenges in measuring impact, and the future trends in strategic giving. The video emphasizes collaboration, transparency, and adaptability as keys to successful philanthropy.
  • Case Study

Sustainable Finance: An Overview

  • Source : GIZ
  • September 2020
This overview defines sustainable finance and highlights its relevance for the financial sector from sustainability, risk, and efficiency perspectives. It emphasizes the need for unprecedented financial resources from both public and private sectors to achieve national and international sustainable development targets. The document assesses how the financial sector can mobilize and allocate capital for this transition, stressing the importance of appropriate policy and regulatory frameworks, and the integration of sustainability risks in investment decisions. It also examines current developments and future outlooks.
  • Blog

Role of Big Data Analytics in Solving Water Problems

  • Source : E3S Web of Conferences
  • September 2020
This Medium blog post delves into the pivotal role of big data analytics in addressing pressing global water problems. It discusses how analyzing diverse datasets related to water quality, consumption patterns, infrastructure, and climate can lead to more efficient water management, pollution control, and resource optimization. The article likely explores applications such as leak detection, smart irrigation, water scarcity prediction, and early warning systems for contamination, showcasing the power of data-driven solutions in ensuring sustainable water resources.
  • Academic / Journal Article

Geographic Distribution of US Cohorts Used to Train Deep Learning Algorithms

  • Source : JAMA Network
  • September 2020
This article investigates the geographic distribution of US cohorts utilized in training deep learning algorithms, particularly in medical imaging and diagnostics. It reveals a significant imbalance, with most cohorts originating from a limited number of states, potentially leading to algorithmic bias and reduced generalizability across diverse populations. The study emphasizes the critical need for more geographically diverse datasets to ensure fairness, accuracy, and equitable performance of AI in healthcare applications nationwide.
  • Media Article

A Practical Guide to Using Routine Data in Evaluation

  • Source : Data for Impact Project
  • August 2020
This guide offers practical strategies for using routine data in program evaluation, focusing on improving data quality and analytical rigor. It emphasizes the importance of integrating routine data into evaluation frameworks and outlines best practices for data analysis, interpretation, and incorporation into decision-making processes. By strengthening the quality of routine data, the guide helps organizations develop more robust evaluations, leading to better program outcomes and more evidence-based decision-making. The guide is particularly useful for development and health programs aiming to build stronger data-driven approaches to monitoring and evaluation, ultimately improving the impact of their interventions.
  • Blog

Data Science in the Indian Agriculture Industry

  • Source : Analytics Vidhya
  • July 2020
This article explores the growing role of data science in revolutionizing the Indian agriculture industry. It details how data-driven insights are being utilized to address various challenges, from optimizing crop yield and managing irrigation to predicting market prices and mitigating risks. The piece highlights specific applications such as precision agriculture, disease detection, and supply chain optimization. By leveraging data analytics, Indian agriculture can achieve greater efficiency, sustainability, and profitability, ultimately contributing to food security and farmer welfare.
We use essential and analytics cookies to operate this website and understand how visitors interact with it. As this site also functions as a login identity provider (IDP) for other ISDM portals, some cookies are necessary to enable secure authentication. By continuing to use this site, you consent to our use of cookies.
Accept & Continue