Application of Data Science on Public Policy

1. APPLICATION OF DATA
SCIENCE ON PUBLIC
POLICY
LYNDA THANKAM VARGHESE
2237042
MA. APPLIED ECONOMICS

2. DATA SCIENCE AND PUBLIC POLICY
• Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and
systems to extract knowledge and insights from structured and unstructured data.
• It is a concept to unify statistics, data analysis, machine learning, and their related methods in
order to understand and analyze actual phenomena with data.
• Employs techniques and theories drawn from many fields within the context of mathematics,
information science, etc.
• Public policy is a purposive course of action or inactions undertaken by an actor or set of actors in
dealing with a particular problem or matter of concern.
• The policy is a process as well as a product.

3. DATA SCIENCE AND GOVERNMENT
Data Science can find ample application in government. It can help in
extracting useful information and knowledge from large volumes of data in
order to improve government decision-making or provide insights to make
data-driven decisions, making use of predictive causal analytics,
prescriptive analytics, and machine learning. The government can harness
the data to address implementation gaps, detect overlaps and target the right
beneficiaries and feed into smart policymaking, for instance, through
improved predictive analytics.

4. HOW CAN DATA SCIENCE BE APPLIED IN PUBLIC
POLICIES
• Here are a few ways that data science can be applied in public policy:
1. Evidence-based policy-making: Data science can provide evidence to support policy decisions. By analyzing large
datasets, data scientists can identify patterns and trends that can inform policy decisions. This approach can help
ensure that policies are based on sound evidence rather than intuition or guesswork.
2. Predictive modeling: Data scientists can build models that predict the outcomes of policy decisions. For example, a
predictive model could be used to estimate the impact of a new tax policy on the economy or to predict the likelihood
of a particular disease outbreak.
3. Performance evaluation: Data science can help evaluate the effectiveness of policies. By analyzing data on outcomes,
data scientists can determine whether a policy is achieving its intended goals and make recommendations for
improvement.
4. Resource allocation: Data science can help governments allocate resources more efficiently. By analyzing data on
population demographics, health outcomes, and other factors, data scientists can identify areas where resources are
most needed and allocate resources accordingly.

5. • Data science mechanisms can be used in public policy in several ways. Here are a few examples:
1. Data Collection and Analysis: The first step in applying data science to public policy is to collect and analyze relevant
data. This can involve gathering data from various sources, such as surveys, administrative records, and social media.
Once the data is collected, data scientists can use statistical and machine learning techniques to analyze the data and
extract meaningful insights.
2. Predictive Modeling: Predictive modeling can be used to forecast the potential impact of policy decisions. By using
historical data and statistical models, data scientists can predict how a policy change will affect various outcomes,
such as economic growth, public health, or crime rates.
3. Performance Evaluation: Data science can be used to evaluate the performance of existing policies. By comparing
outcomes before and after a policy change, data scientists can assess the effectiveness of the policy and identify areas
for improvement.
4. Resource Allocation: Data science can be used to optimize the allocation of resources. By analyzing data on
population demographics, health outcomes, and other factors, data scientists can identify areas where resources are
most needed and allocate resources accordingly.
DATA SCIENCE MECHANISM IN PUBLIC POLICY

6. PUBLIC POLICY ANALYSIS