Mind Canvas: From Self to Society to Success.
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Analyzing and Coloring Personal, Social, and Corporate Minds with Insightful Brushes of Psychology.
Data Management, Backup, & Recovery
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Outline for Data Science in Social Sciences
Module 1: Introduction to Data Science
- Week 1: What is Data Science?
- Definition and key concepts of data science
- The role of data science in psychology and social sciences
- The data science process: data collection, cleaning, analysis, and visualization
- Week 2: Data Types and Sources
- Types of data (quantitative, qualitative, mixed)
- Data sources in psychology and social sciences (surveys, experiments, observations, administrative data)
- Data collection methods and ethical considerations
Module 2: Data Cleaning and Preparation
- Week 3: Data Cleaning
- Identifying and handling missing data
- Dealing with outliers and anomalies
- Data normalization and standardization
- Week 4: Data Preparation
- Data transformation and feature engineering
- Creating new variables and combining existing ones
- Data splitting for training and testing
Module 3: Statistical Analysis
- Week 5: Descriptive Statistics
- Measures of central tendency and dispersion
- Data visualization using graphs and charts
- Week 6: Inferential Statistics
- Hypothesis testing and p-values
- Confidence intervals
- Correlation and regression analysis
Module 4: Machine Learning in Psychology and Social Sciences
- Week 7: Supervised Learning
- Regression analysis (linear, logistic)
- Classification algorithms (decision trees, random forests, support vector machines)
- Evaluation metrics (accuracy, precision, recall, F1-score)
- Week 8: Unsupervised Learning
- Clustering algorithms (k-means, hierarchical clustering)
- Dimensionality reduction techniques (PCA, factor analysis)
Module 5: Data Visualization and Communication
- Week 9: Data Visualization
- Creating effective data visualizations
- Choosing appropriate charts and graphs
- Storytelling with data
- Week 10: Communicating Results
- Writing research reports and papers
- Presenting findings to different audiences
- Effective data communication strategies
Note:
This course will be supplemented with practical exercises, role-playing, and opportunities for supervised practice.
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