Data Science in Public Policy

As part of my coursework, I have made a short presentation about the applications of data science in the field of public policy.

Transcript

1. DATA SCIENCE IN PUBLIC POLICY A LOOK AT HOW DATA DRIVES POLICY DECISIONS Submitted by Roshni K Mathew 2237052
2. the basics UNDERSTANDING WHAT PUBLIC POLICY IS AND DATA'S ROLE WITHIN IT A public policy is simply what the government does or does not do about a problem that comes before them for consideration & possible action WHAT IS PUBLIC POLICY An interdisciplinary field that focuses on extracting knowledge from data sets. This includes analysis, interpreting findings etc. WHAT IS DATA SCIENCE HOW IS DATA SCIENCE APPLIED IN THE FIELD OF PUBLIC POLICY Data science has improved the way in which public policies are designed & implemented. Policymakers can acquire and analyze data in real-time, and develop more evidence based solutions. Data science also lends policymakers fresh insights into an issue
3. DESCRIPTIVE STATISTICS - Summarizing & describing data through summary measures like mean, median mode etc. techniques by which data is used to drive policy decisions INFERENTIAL STATISTICS - Making predictions or inferences about a population based on a sample of data. REGRESSION ANALYSIS- Examining the relationship between two or more variables and can be used to identfiy factors that may influence a particular policy outcome. COST-BENEFIT ANALYSIS - Weighing the costs of a policy against its potential benefits and is often used to determine the feasibility of a particular policy. REGRESSION ANALYSIS- Examining the relationship between two or more variables and can be used to identfiy factors that may influence policy outcome PREDICTIVE MODELLING - Involves using algorithms to analyse data & make predictions about future outcomes which can inform policy decisions. TEXT MINING & SENTIMENT ANALYSIS - Using natural language processing & machine learning to analyze unstructured data such as news articles etc., to identify patterns & sentiments that may inform policy decisions.
4. How does data science help policy making Data Science has the potential to solve a wide variety of problems and can help policymakers make more informed decisions, improve the effectiveness of policies and ultimately improve the lives of citizens IDENTIFYING PATTERNS & PREDICTING OUTCOMES Data Science can help in identifying patterns in large datasets that may not be obvious to the policymakers. By analyzing this data, the policymakers can predict the outcomes of different policy interventions IMPROVING RESOURCE ALLOCATION With limited resources available at hand, data science can help identify areas of greatest need and allocate resources accordingly. IMPROVING PUBLIC SERVICE DELIVERY Data Science can be used to improve the efficiency & the effectiveness of government programs. MONITORING & EVALUATING POLICIES Data Science can help policymakers to monitor & evaluate the policy interventions and this information can help policymakers make adjustments as needed & ensure that policies are achieving their intended outcomes
5. Predictive modelling in public Policy Traffic Management : Predictive models are used to predict traffic patterns & congestions allowing transportation agencies to make real-time adjustments to traffic signals & routing to improve traffic flow Disaster Management : Predictive models are used to predict the likelihood & severity of natural disasters. This allows emergency responders to plan & allocate resources aacordingly Child Welfare : In this field, predictive models are used to identify children who are at risk of abuse or neglect, allowing social workers to intervene early and prevent harm Predictive modelling is used by policymakers to help understand complex social problems. It helps policymakers make data-driven decisions. Here are some of the ways in which predictive modelling is used in public policy PREDICTIVE MODELLING : A method of predicting future events or outcomes by analyzing patterns in a given set of input data. It is a crucial component of predictive analytics, a type of data analytics which uses current and historical data to forecast activity, behavior and trends
6. PRIVACY & CONFIDENTIALITY - With sensitive data being involved (health records, criminal history, financial information) policymakers should take care to ensure that this data is protected & is only used for legitimate purposes. ETHICAL ISSUES BIAS & DISCRIMINATION - The use of data can inadvertently perpetuate or amplify biases if not carefully analysed & interpreted. Policymakers should be aware of the potential biases in data collection, analysis and interpretation and take steps to mitigate them. TRANSPARENCY & ACCOUNTABILITY - Policymakers should be transparent about their use of data and the methods used to analyze it and be accountable for the decisions they make based on this data. DATA QUALITY & RELIABILITY - Policymakers should ensure that their sources are accurate & should analyze the data in such a way that is appropriate for the policy question at hand. Data driven decision making in public policy raises important ethical considerations that should be carefully considered to ensure the integrity & fairness of policy outcomes INFORMED CONSENT - In cases where personal data is being collected, policymakers should ensure that individuals are fully informed about how their data will be used & obtain their informed consent before collecting or sharing data.
7. THANK YOU !