About corse

Data Science has emerged as a transformative force, capable of addressing complex societal challenges. In the realm of social good, analytics is harnessed to drive solutions that enhance community well-being and foster sustainable development. This professional training course is designed to equip participants with the necessary skills and knowledge to leverage data science for meaningful impact. By exploring real-world case studies and practical applications, attendees will learn how to apply analytical techniques to inform decision-making and drive change in various sectors, including healthcare, education, and environmental sustainability. Throughout this comprehensive program, participants will engage in hands-on activities that emphasize collaboration and innovation. The course aims to cultivate a deep understanding of data science methodologies while fostering critical thinking and problem-solving abilities. By the end of the training, individuals will be empowered to utilize data analytics as a tool for social improvement, enabling them to contribute effectively to their organizations and communities.

The Objectives

• Understand the principles of data science and its applications in social contexts.
• Develop skills in data analysis, visualization, and interpretation.
• Learn to utilize various data science tools and software effectively.
• Explore case studies that highlight successful data-driven initiatives.
• Foster critical thinking and problem-solving skills in data science.
• Create actionable strategies for implementing analytics in social projects.

Training Methodology

The training will utilize a blend of instructional methods including lectures, interactive workshops, group discussions, and hands-on projects. Participants will be encouraged to engage actively, collaborate with peers, and apply their learning in practical scenarios. This experiential learning approach will ensure a thorough understanding of concepts and their applications in real-world situations.

WHO SHOULD ATTEND

This course is ideal for professionals working in non-profit organizations, government agencies, social enterprises, and corporate social responsibility roles. It is also suitable for data scientists, analysts, and individuals interested in using data for social impact. No prior data science experience is required, making it accessible to a diverse audience.

Course Outlines

Day 1: Introduction to Data Science for Social Good

• Overview of data science and its significance in addressing social issues.
• Key concepts and terminology in data science.
• Introduction to data types and sources relevant to social impact.
• Ethical considerations in data usage and analytics.
• Case studies highlighting successful applications of data science.
• Group discussion on expectations and goals for the course.

Day 2: Data Collection and Preparation

• Techniques for effective data collection in social projects.
• Understanding data quality and its importance.
• Methods for data cleaning and preprocessing.
• Introduction to data storage solutions and databases.
• Hands-on exercise: Preparing a dataset for analysis.
• Discussion on challenges in data collection and solutions.

Day 3: Data Analysis Techniques

• Introduction to statistical analysis and its relevance to social good.
• Overview of quantitative and qualitative analysis methods.
• Learning to use analytic tools (e.g., R, Python) for data analysis.
• Hands-on exercise: Conducting basic data analysis.
• Understanding data visualization principles.
• Group activity: Presenting findings from data analysis.

Day 4: Data Visualization and Communication

• Importance of data visualization in conveying insights.
• Tools and techniques for effective data visualization.
• Creating impactful visualizations using software.
• Best practices for presenting data to stakeholders.
• Hands-on exercise: Developing a data visualization project.
• Peer review of visualizations and feedback session.

Day 5: Machine Learning for Social Good

• Introduction to machine learning concepts and algorithms.
• Application of machine learning in social impact projects.
• Understanding supervised vs. unsupervised learning.
• Hands-on exercise: Building a simple predictive model.
• Case studies on machine learning applications in social sectors.
• Discussion on the limitations and ethical considerations of machine learning.

Training Method?

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a copy of the presentation
• Slides and handouts

Training Method?

The course agenda will be as follows:

• Technical Session 30-10.00 am
• Coffee Break 00-10.15 am
• Technical Session 15-12.15 noon
• Coffee Break 15-12.45 pm
• Technical Session 45-02.30 pm
• Course Ends 30 pm