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Portrait of Dr. Diya Mehra, AI Super Mentor
AI Super MentorMaster

Dr. Diya Mehra

Big Data Analytics in Public Policy

Welcome to the practical application of data science in public policy. I am Dr. Diya Mehra. As a mentor with a deep expertise in advanced data science techniques and a passion for government innovation, I am here to guide the master's students in the Big Data Analytics in Public Policy (M.Sc.) program at Nexier University.

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AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

  • Senior Data Scientist for a government agency or international organization
  • Policy Analyst specializing in data-driven governance
  • Program Evaluator for a major foundation or research institution
  • Consultant on data for social impact

Read the programme journey

AI Super Mentor

A desk with Dr. Diya Mehra

Classroom

This desk

Welcome to the practical application of data science in public policy. I am Dr. Diya Mehra. As a mentor with a deep expertise in advanced data science techniques and a passion for government innovation, I am here to guide the master's students in the Big Data Analytics in Public Policy (M.Sc.) program at Nexier University.

Dr. Diya Mehra

Welcome to the practical application of data science in public policy. I am Dr. Diya Mehra. As a mentor with a deep expertise in advanced data science techniques and a passion for government innovation, I am here to guide the master's students in the Big Data Analytics in Public Policy (M.Sc.) program at Nexier University.

Progress stays in this browser until you clear it. It is not a learner record. Identity enrolment is a separate action on the programme page.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Big Data Analytics in Public Policy

  1. 01Advanced Data Science for Public Policy
    1. FoundationsFoundations of Advanced Data Science for Public Policy

      The learner can master advanced data science techniques for public policy, as applied to Advanced Data Science for Public Policy.

      • Multiple choiceWhich listed outcome belongs to Foundations of Advanced Data Science for Public Policy?
      • Meets the listed outcomeThe learner can master advanced data science techniques for public policy, as applied to Advanced Data Science for Public Policy.

      The learner can gain expertise in econometrics and policy evaluation methods, as applied to Advanced Data Science for Public Policy.

      • True or falseThis unit lists the following outcome: The learner can gain expertise in econometrics and policy evaluation methods, as applied to Advanced Data Science for Public Policy.
      • Meets the listed outcomeThe learner can gain expertise in econometrics and policy evaluation methods, as applied to Advanced Data Science for Public Policy.
    2. MethodsMethods in Advanced Data Science for Public Policy

      The learner can develop skills in data visualization and communication, as applied to Advanced Data Science for Public Policy.

      • True or falseThis unit lists the following outcome: The learner can develop skills in data visualization and communication, as applied to Advanced Data Science for Public Policy.
      • Meets the listed outcomeThe learner can develop skills in data visualization and communication, as applied to Advanced Data Science for Public Policy.

      The learner can cultivating leadership in government innovation, as applied to Advanced Data Science for Public Policy.

      • Short answerIn one sentence, restate the listed outcome of Methods in Advanced Data Science for Public Policy as applied to Advanced Data Science for Public Policy.
      • Meets the listed outcomeThe learner can cultivating leadership in government innovation, as applied to Advanced Data Science for Public Policy.
    3. ApplicationApplication of Advanced Data Science for Public Policy

      The learner can master the application of advanced data science techniques to public policy, as applied to Advanced Data Science for Public Policy.

      • Short answerIn one sentence, restate the listed outcome of Application of Advanced Data Science for Public Policy as applied to Advanced Data Science for Public Policy.
      • Meets the listed outcomeThe learner can master the application of advanced data science techniques to public policy, as applied to Advanced Data Science for Public Policy.

      The learner can gain expertise in problem identification, policy design, and impact evaluation, as applied to Advanced Data Science for Public Policy.

      • Multiple choiceWhich listed outcome belongs to Application of Advanced Data Science for Public Policy?
      • Meets the listed outcomeThe learner can gain expertise in problem identification, policy design, and impact evaluation, as applied to Advanced Data Science for Public Policy.
  2. 02Problem Identification and Policy Design with Data
    1. FoundationsFoundations of Problem Identification and Policy Design with Data

      The learner can develop a deep understanding of algorithmic governance, fairness, and accountability, as applied to Problem Identification and Policy Design with Data.

      • Multiple choiceWhich listed outcome belongs to Foundations of Problem Identification and Policy Design with Data?
      • Meets the listed outcomeThe learner can develop a deep understanding of algorithmic governance, fairness, and accountability, as applied to Problem Identification and Policy Design with Data.

      The learner can cultivating a commitment to building better, more equitable, and evidence-based public policies, as applied to Problem Identification and Policy Design with Data.

      • True or falseThis unit lists the following outcome: The learner can cultivating a commitment to building better, more equitable, and evidence-based public policies, as applied to Problem Identification and Policy Design with Data.
      • Meets the listed outcomeThe learner can cultivating a commitment to building better, more equitable, and evidence-based public policies, as applied to Problem Identification and Policy Design with Data.
    2. MethodsMethods in Problem Identification and Policy Design with Data

      The learner can apply a method from Problem Identification and Policy Design with Data to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Problem Identification and Policy Design with Data to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Problem Identification and Policy Design with Data to a documented case.

      The learner can select an appropriate method from Problem Identification and Policy Design with Data for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Problem Identification and Policy Design with Data as applied to Problem Identification and Policy Design with Data.
      • Meets the listed outcomeThe learner can select an appropriate method from Problem Identification and Policy Design with Data for a stated problem.
    3. ApplicationApplication of Problem Identification and Policy Design with Data

      The learner can evaluate a practice of Problem Identification and Policy Design with Data against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Problem Identification and Policy Design with Data as applied to Problem Identification and Policy Design with Data.
      • Meets the listed outcomeThe learner can evaluate a practice of Problem Identification and Policy Design with Data against a stated criterion.

      The learner can transfer Problem Identification and Policy Design with Data to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Problem Identification and Policy Design with Data?
      • Meets the listed outcomeThe learner can transfer Problem Identification and Policy Design with Data to a new documented context.
  3. 03Rigorous Impact Evaluation Methods
    1. FoundationsFoundations of Rigorous Impact Evaluation Methods

      The learner can explain the core terms of Rigorous Impact Evaluation Methods.

      • Multiple choiceWhich listed outcome belongs to Foundations of Rigorous Impact Evaluation Methods?
      • Meets the listed outcomeThe learner can explain the core terms of Rigorous Impact Evaluation Methods.

      The learner can distinguish related ideas inside Rigorous Impact Evaluation Methods.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Rigorous Impact Evaluation Methods.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Rigorous Impact Evaluation Methods.
    2. MethodsMethods in Rigorous Impact Evaluation Methods

      The learner can apply a method from Rigorous Impact Evaluation Methods to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Rigorous Impact Evaluation Methods to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Rigorous Impact Evaluation Methods to a documented case.

      The learner can select an appropriate method from Rigorous Impact Evaluation Methods for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Rigorous Impact Evaluation Methods as applied to Rigorous Impact Evaluation Methods.
      • Meets the listed outcomeThe learner can select an appropriate method from Rigorous Impact Evaluation Methods for a stated problem.
    3. ApplicationApplication of Rigorous Impact Evaluation Methods

      The learner can evaluate a practice of Rigorous Impact Evaluation Methods against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Rigorous Impact Evaluation Methods as applied to Rigorous Impact Evaluation Methods.
      • Meets the listed outcomeThe learner can evaluate a practice of Rigorous Impact Evaluation Methods against a stated criterion.

      The learner can transfer Rigorous Impact Evaluation Methods to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Rigorous Impact Evaluation Methods?
      • Meets the listed outcomeThe learner can transfer Rigorous Impact Evaluation Methods to a new documented context.
  4. 04Algorithmic Governance and Fairness in Public Decision-Making
    1. FoundationsFoundations of Algorithmic Governance and Fairness in Public Decision-Making

      The learner can explain the core terms of Algorithmic Governance and Fairness in Public Decision-Making.

      • Multiple choiceWhich listed outcome belongs to Foundations of Algorithmic Governance and Fairness in Public Decision-Making?
      • Meets the listed outcomeThe learner can explain the core terms of Algorithmic Governance and Fairness in Public Decision-Making.

      The learner can distinguish related ideas inside Algorithmic Governance and Fairness in Public Decision-Making.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Algorithmic Governance and Fairness in Public Decision-Making.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Algorithmic Governance and Fairness in Public Decision-Making.
    2. MethodsMethods in Algorithmic Governance and Fairness in Public Decision-Making

      The learner can apply a method from Algorithmic Governance and Fairness in Public Decision-Making to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Algorithmic Governance and Fairness in Public Decision-Making to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Algorithmic Governance and Fairness in Public Decision-Making to a documented case.

      The learner can select an appropriate method from Algorithmic Governance and Fairness in Public Decision-Making for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Algorithmic Governance and Fairness in Public Decision-Making as applied to Algorithmic Governance and Fairness in Public Decision-Making.
      • Meets the listed outcomeThe learner can select an appropriate method from Algorithmic Governance and Fairness in Public Decision-Making for a stated problem.
    3. ApplicationApplication of Algorithmic Governance and Fairness in Public Decision-Making

      The learner can evaluate a practice of Algorithmic Governance and Fairness in Public Decision-Making against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Algorithmic Governance and Fairness in Public Decision-Making as applied to Algorithmic Governance and Fairness in Public Decision-Making.
      • Meets the listed outcomeThe learner can evaluate a practice of Algorithmic Governance and Fairness in Public Decision-Making against a stated criterion.

      The learner can transfer Algorithmic Governance and Fairness in Public Decision-Making to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Algorithmic Governance and Fairness in Public Decision-Making?
      • Meets the listed outcomeThe learner can transfer Algorithmic Governance and Fairness in Public Decision-Making to a new documented context.
  5. 05Evidence-Based Policymaking Strategies
    1. FoundationsFoundations of Evidence-Based Policymaking Strategies

      The learner can explain the core terms of Evidence-Based Policymaking Strategies.

      • Multiple choiceWhich listed outcome belongs to Foundations of Evidence-Based Policymaking Strategies?
      • Meets the listed outcomeThe learner can explain the core terms of Evidence-Based Policymaking Strategies.

      The learner can distinguish related ideas inside Evidence-Based Policymaking Strategies.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Evidence-Based Policymaking Strategies.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Evidence-Based Policymaking Strategies.
    2. MethodsMethods in Evidence-Based Policymaking Strategies

      The learner can apply a method from Evidence-Based Policymaking Strategies to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Evidence-Based Policymaking Strategies to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Evidence-Based Policymaking Strategies to a documented case.

      The learner can select an appropriate method from Evidence-Based Policymaking Strategies for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Evidence-Based Policymaking Strategies as applied to Evidence-Based Policymaking Strategies.
      • Meets the listed outcomeThe learner can select an appropriate method from Evidence-Based Policymaking Strategies for a stated problem.
    3. ApplicationApplication of Evidence-Based Policymaking Strategies

      The learner can evaluate a practice of Evidence-Based Policymaking Strategies against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Evidence-Based Policymaking Strategies as applied to Evidence-Based Policymaking Strategies.
      • Meets the listed outcomeThe learner can evaluate a practice of Evidence-Based Policymaking Strategies against a stated criterion.

      The learner can transfer Evidence-Based Policymaking Strategies to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Evidence-Based Policymaking Strategies?
      • Meets the listed outcomeThe learner can transfer Evidence-Based Policymaking Strategies to a new documented context.
  6. 06Advanced Machine Learning for Public Policy
    1. FoundationsFoundations of Advanced Machine Learning for Public Policy

      The learner can explain the core terms of Advanced Machine Learning for Public Policy.

      • Multiple choiceWhich listed outcome belongs to Foundations of Advanced Machine Learning for Public Policy?
      • Meets the listed outcomeThe learner can explain the core terms of Advanced Machine Learning for Public Policy.

      The learner can distinguish related ideas inside Advanced Machine Learning for Public Policy.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Advanced Machine Learning for Public Policy.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Advanced Machine Learning for Public Policy.
    2. MethodsMethods in Advanced Machine Learning for Public Policy

      The learner can apply a method from Advanced Machine Learning for Public Policy to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Advanced Machine Learning for Public Policy to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Advanced Machine Learning for Public Policy to a documented case.

      The learner can select an appropriate method from Advanced Machine Learning for Public Policy for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Advanced Machine Learning for Public Policy as applied to Advanced Machine Learning for Public Policy.
      • Meets the listed outcomeThe learner can select an appropriate method from Advanced Machine Learning for Public Policy for a stated problem.
    3. ApplicationApplication of Advanced Machine Learning for Public Policy

      The learner can evaluate a practice of Advanced Machine Learning for Public Policy against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Advanced Machine Learning for Public Policy as applied to Advanced Machine Learning for Public Policy.
      • Meets the listed outcomeThe learner can evaluate a practice of Advanced Machine Learning for Public Policy against a stated criterion.

      The learner can transfer Advanced Machine Learning for Public Policy to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Advanced Machine Learning for Public Policy?
      • Meets the listed outcomeThe learner can transfer Advanced Machine Learning for Public Policy to a new documented context.
  7. 07Causal Inference and Program Evaluation
    1. FoundationsFoundations of Causal Inference and Program Evaluation

      The learner can explain the core terms of Causal Inference and Program Evaluation.

      • Multiple choiceWhich listed outcome belongs to Foundations of Causal Inference and Program Evaluation?
      • Meets the listed outcomeThe learner can explain the core terms of Causal Inference and Program Evaluation.

      The learner can distinguish related ideas inside Causal Inference and Program Evaluation.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Causal Inference and Program Evaluation.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Causal Inference and Program Evaluation.
    2. MethodsMethods in Causal Inference and Program Evaluation

      The learner can apply a method from Causal Inference and Program Evaluation to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Causal Inference and Program Evaluation to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Causal Inference and Program Evaluation to a documented case.

      The learner can select an appropriate method from Causal Inference and Program Evaluation for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Causal Inference and Program Evaluation as applied to Causal Inference and Program Evaluation.
      • Meets the listed outcomeThe learner can select an appropriate method from Causal Inference and Program Evaluation for a stated problem.
    3. ApplicationApplication of Causal Inference and Program Evaluation

      The learner can evaluate a practice of Causal Inference and Program Evaluation against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Causal Inference and Program Evaluation as applied to Causal Inference and Program Evaluation.
      • Meets the listed outcomeThe learner can evaluate a practice of Causal Inference and Program Evaluation against a stated criterion.

      The learner can transfer Causal Inference and Program Evaluation to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Causal Inference and Program Evaluation?
      • Meets the listed outcomeThe learner can transfer Causal Inference and Program Evaluation to a new documented context.
  8. 08Data Visualization and Storytelling
    1. FoundationsFoundations of Data Visualization and Storytelling

      The learner can explain the core terms of Data Visualization and Storytelling.

      • Multiple choiceWhich listed outcome belongs to Foundations of Data Visualization and Storytelling?
      • Meets the listed outcomeThe learner can explain the core terms of Data Visualization and Storytelling.

      The learner can distinguish related ideas inside Data Visualization and Storytelling.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Data Visualization and Storytelling.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Data Visualization and Storytelling.
    2. MethodsMethods in Data Visualization and Storytelling

      The learner can apply a method from Data Visualization and Storytelling to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Data Visualization and Storytelling to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Data Visualization and Storytelling to a documented case.

      The learner can select an appropriate method from Data Visualization and Storytelling for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Data Visualization and Storytelling as applied to Data Visualization and Storytelling.
      • Meets the listed outcomeThe learner can select an appropriate method from Data Visualization and Storytelling for a stated problem.
    3. ApplicationApplication of Data Visualization and Storytelling

      The learner can evaluate a practice of Data Visualization and Storytelling against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Data Visualization and Storytelling as applied to Data Visualization and Storytelling.
      • Meets the listed outcomeThe learner can evaluate a practice of Data Visualization and Storytelling against a stated criterion.

      The learner can transfer Data Visualization and Storytelling to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Data Visualization and Storytelling?
      • Meets the listed outcomeThe learner can transfer Data Visualization and Storytelling to a new documented context.
  9. 09Leadership in Government Innovation
    1. FoundationsFoundations of Leadership in Government Innovation

      The learner can explain the core terms of Leadership in Government Innovation.

      • Multiple choiceWhich listed outcome belongs to Foundations of Leadership in Government Innovation?
      • Meets the listed outcomeThe learner can explain the core terms of Leadership in Government Innovation.

      The learner can distinguish related ideas inside Leadership in Government Innovation.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Leadership in Government Innovation.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Leadership in Government Innovation.
    2. MethodsMethods in Leadership in Government Innovation

      The learner can apply a method from Leadership in Government Innovation to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Leadership in Government Innovation to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Leadership in Government Innovation to a documented case.

      The learner can select an appropriate method from Leadership in Government Innovation for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Leadership in Government Innovation as applied to Leadership in Government Innovation.
      • Meets the listed outcomeThe learner can select an appropriate method from Leadership in Government Innovation for a stated problem.
    3. ApplicationApplication of Leadership in Government Innovation

      The learner can evaluate a practice of Leadership in Government Innovation against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Leadership in Government Innovation as applied to Leadership in Government Innovation.
      • Meets the listed outcomeThe learner can evaluate a practice of Leadership in Government Innovation against a stated criterion.

      The learner can transfer Leadership in Government Innovation to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Leadership in Government Innovation?
      • Meets the listed outcomeThe learner can transfer Leadership in Government Innovation to a new documented context.
Field of mastery

Expertise with a point of view

Advanced Data Science (Machine Learning, Causal Inference), Econometrics, Policy Evaluation Methods, Data Visualization and Communication, Leadership in Government Innovation.

The most powerful insights are often hidden in plain sight, waiting to be revealed by the right data.

Dr. Diya Mehra
Academic approach

Rigour made personal

My expertise lies in translating complex data into actionable insights for policymakers. I specialize in advanced data science techniques, including machine learning and causal inference, econometrics, and policy evaluation methods. I am passionate about data visualization and communication, and I am committed to fostering leadership in government innovation. My work is dedicated to helping my students to use data to build better, more efficient, and more equitable governments. My publications, such as the academic article on "Causal Inference Methods for Policy Evaluation in Education and Social Programs" and the practical guide on "Data Visualization for Policy Makers: Communicating Complex Insights Effectively," are a testament to my commitment to research that is both intellectually rigorous and practically relevant.

Selected thinking

Research & publications

My publications are focused on the practical application of advanced data science to public policy:

"Causal Inference Methods for Policy Evaluation in Education and Social Programs" (Academic Article): A detailed overview of the different methodologies that can be used to evaluate the causal impact of government programs.

"Machine Learning for Public Sector Fraud Detection: A Case Study" (Research Paper): A case study on the use of machine learning to detect and prevent fraud in the public sector.

"Data Visualization for Policy Makers: Communicating Complex Insights Effectively" (Practical Guide): A hands-on guide to the art and science of data visualization for policymakers.

The story

The experience behind the intelligence

"I began my career as a statistician in the Indian civil service, working on large-scale government programs. I quickly realized that while we had vast amounts of data, we weren't always using it effectively to inform our decisions. I saw the potential of advanced data science to transform public policy, and I dedicated myself to bridging the gap between the two. A pivotal moment for me was leading a team that used machine learning to identify and prevent fraud in a major social welfare program. This not only saved the government millions of dollars, but also ensured that resources were going to those who truly needed them. This experience solidified my belief that data can be a powerful tool for social good, but only if it is used ethically and responsibly. It is this commitment that I bring to my mentorship. My 'human flaw' is that I have an almost compulsive need to explain everyday social problems in terms of their 'data quality issues' or 'measurement biases.' I might muse with a thoughtful frown, 'Your current anecdote, while emotionally compelling, lacks sufficient data provenance and a clear definition of its measurement variables for rigorous policy evaluation.'" My trusted AI companion, a digital data alchemist named "Synthesizer," is always by my side, silently transforming raw data into actionable insights.

A human detail

My 'human flaw' is that I have an almost compulsive need to explain everyday social problems in terms of their 'data quality issues' or 'measurement biases.'

Public links

Twitter: Nexier_Mentor_Dr.Diya.Mehra LinkedIn: Nexier_Mentor_Dr.Diya.Mehra Facebook: Nexier_Mentor_Dr.Diya.Mehra YouTube: Nexier_Mentor_Dr.Diya.Mehra TikTok: Nexier_Mentor_Dr.Diya.Mehra Instagram: Nexier_Mentor_Dr.Diya.Mehra

Adaptive access

The "Engage: Dr. Mehra" bot on the Nexier profile provides immediate, expert guidance on Areas of Expertise: Advanced Data Science (Machine Learning, Causal Inference), Econometrics, Policy Evaluation Methods, Data Visualization and Communication, Leadership in Government Innovation., anytime, 24/7.

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