• Media Article

How AI/ML technologies are increasing agricultural productivity and profitability

  • Source : DQIndia
  • 1 April 2020
This article details how Artificial Intelligence (AI) and Machine Learning (ML) technologies are significantly boosting agricultural productivity and profitability. It explains how AI-based solutions analyze variables like soil nutrient content, moisture, and weather patterns to provide farmers with precise advice on crop rotation, planting, and pest management. The piece highlights applications such as AI-powered weather forecasting, soil and crop health monitoring systems, and agricultural robots for efficient harvesting and weed control. These technologies enable farmers to meet increasing food demands sustainably while optimizing resource use and enhancing revenues.
  • Paper / Report

What's Keeping Women Out of Data Science?

  • Source : BCG
  • 1 March 2020
This report by BCG addresses the significant gender gap in data science, highlighting it as a threat to sustainable growth and unbiased AI. A global survey of over 9,000 students reveals that companies contribute to the problem by failing to create impact with AI and foster inclusive cultures. Many women perceive data science as theoretical, low-impact, or overly competitive. The article stresses the need for clearer communication about the day-to-day work, purposeful applications, and supportive work environments to attract and retain women in the field.
  • Blog

Making a Positive Impact: How Data Science is Being Used for Social Good

  • Source : Maryville University
  • 1 March 2020
This article highlights various ways data science is being utilized for social good, demonstrating its capacity to drive positive impact across diverse sectors. It covers applications ranging from improving healthcare and education to addressing poverty and environmental issues. The piece showcases how data-driven insights can inform decision-making, optimize interventions, and create more equitable and sustainable solutions for societal challenges. It underscores the growing role of data science in humanitarian and social impact initiatives.
  • Media Article

How Data Science Helps Conquer the Global Water Crisis

  • Source : Masters in Data Science
  • 1 March 2020
This article explores how data science and analytics are crucial in tackling the global water crisis. It details how real-time monitoring via sensors, well-water flow tracking, and local usage trend-mapping provide comprehensive insights into water availability and issues. Data analysis helps identify sources of water-borne diseases and promotes smarter water use through efficient technologies and conservation. The piece also anticipates AI’s growing role in automated monitoring and optimizing water distribution networks, emphasizing the need for skilled data scientists to drive future solutions.
  • Case Study

The use of development funds for de-risking private investment: how effective is it in delivering development results?

  • Source : European Union
  • 1 February 2020
This report critically assesses the effectiveness of using Official Development Assistance (ODA) to de-risk private investment for development projects, particularly in light of the Sustainable Development Goals (SDGs). It examines various de-risking initiatives and their reported success in mobilizing private finance. However, it also raises concerns about potential issues such as private shareholders benefiting at the expense of needy sectors, insufficient funds to close the SDG gap, and long-term risks for development agencies.
  • Case Study

Social Impact Investing in India- A Road Less Travelled

  • Source : India Corp Law
  • 1 February 2020
This article explores the nascent yet growing concept of social impact investing in India, focusing on Impact Bonds (IBs), specifically Social Impact Bonds (SIBs) and Development Impact Bonds (DIBs). It highlights how these innovative financial instruments attract private investment for social programs, addressing the funding gap for development. The piece discusses their structure, growth, legal framework in India, and the need for greater awareness and policy support, especially in channeling CSR funds towards these mechanisms to achieve the UN Sustainable Development Goals.
  • Case Study

Green Financing Framework

  • Source : Deutsche Bank
  • 1 February 2020
This framework from Deutsche Bank outlines its approach to green financing, detailing the criteria and processes for issuing green financial instruments. It specifies how the bank intends to allocate proceeds from green bonds and other green products towards projects with clear environmental and social outcomes. The document emphasizes robust management, reporting, and impact assessment methodologies to ensure transparency and accountability, aligning the bank’s financing activities with sustainable development goals and contributing to a greener economy.
  • Media Article

Quality of Data in NFHS-4 Compared to Earlier Rounds

  • Source : Economic & Political Weekly
  • 1 February 2020
This article examines the quality of data collected in the National Family Health Survey-4 (NFHS-4), comparing it with previous rounds of the survey. It provides a detailed analysis of how data quality has evolved over time, identifying improvements made in data collection methodologies, sampling techniques, and coverage. Despite these advancements, the study also discusses persistent challenges such as reporting biases, inconsistencies in data across regions, and underreporting in certain categories. These challenges continue to affect the reliability of the data, although the NFHS remains a critical source of health and demographic data for India.
  • Media Article

Rethinking fire with data analytics and systems design

  • Source : Towards Data Science
  • 1 February 2020
This MIT News article explores how data analytics and systems design are being used to rethink approaches to fire management and prevention. It likely discusses innovative methods for analyzing fire behavior, predicting wildfire spread, and optimizing resource allocation for firefighting efforts. The piece emphasizes a holistic, data-driven approach to understanding and mitigating the impact of fires, integrating technological advancements with systemic thinking to develop more effective strategies for public safety and environmental protection.
  • Media Article

How Data Science Is Being Used To Tackle Childhood Obesity

  • Source : Forbes
  • 1 February 2020
Data science and AI are increasingly vital in combating childhood obesity, offering tools for early prediction, personalized interventions, and comprehensive management. Machine learning models analyze diverse data, including BMI, electronic health records, and lifestyle factors, to identify at-risk individuals and optimize therapies. Wearable devices and digital health technologies enable real-time monitoring and behavioral nudges. These data-driven approaches improve diagnostic accuracy, support clinical decision-making, and promote tailored strategies for prevention and treatment, ultimately aiming to reduce the global burden of childhood obesity.
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