DATA ANALYTICS FOR SOCIAL SCIENCE STUDENTS
DATA ANALYTICS FOR SOCIAL SCIENCE STUDENTS
Introduction to Data Analytics
Data Analytics is the process of examining raw data to uncover trends, patterns, and insights that help organizations make informed decisions. By using various statistical and computational techniques, data analysts transform complex data sets into actionable knowledge. This field is essential for businesses, as it aids in improving operations, predicting future trends, and solving problems. Whether you're just starting out or looking to expand your skills, mastering data analytics opens up opportunities to drive success in a data-driven world. Join our training program to dive into the fundamentals of data analytics and learn how to turn data into valuable insights
Overview of Popular Data Analytics tool
1. Microsoft Excel
2. Tableau
3. Power BI
4. Google Analytics
5. Python
6. R
7. SPSS
8. Hadoop
Descriptive Statistics
1. Measures of Central Tendency (Mean, Median, Mode)
2. Measures of Variability (Range, Variance, Standard Deviation)
3. Data Visualization (Histograms, Box Plots, Scatter Plots)
Inferential Statistics
1. Hypothesis Testing (Z-test, T-test, ANOVA)
2. Confidence Intervals (CI)
3. Regression Analysis (Simple, Multiple, Logistic)
Probability Distributions
1. Normal Distribution
2. Binomial Distribution
3. Poisson Distribution
4. Exponential Distribution
Statistical Modeling Techniques
1. Linear Regression
2. Multiple Regression
3. Logistic Regression
4. Generalized Linear Models (GLM)
5. Generalized Additive Models (GAM)
Social Network Analysis
1. Network Visualization
2. Centrality Measures (Degree, Betweenness, Closeness)
3. Community Detection
4. Social Network Regression
Survey Research Methods
1. Sampling Techniques (Random, Stratified, Cluster)
2. Survey Design and Questionnaire Development
3. Survey Data Analysis (Descriptive, Inferential)
4. Non-Response Bias and Survey Weighting
Program Evaluation and Policy Analysis
1. Experimental and Quasi-Experimental Designs
2. Propensity Score Matching
3. Regression Discontinuity Design
4. Cost-Benefit Analysis
Time Series Analysis
1. Trend Analysis
2. Seasonal Decomposition
3. Autoregressive Integrated Moving Average (ARIMA) models
4. Exponential Smoothing (ES)
Machine Learning and Data Mining
1. Decision Trees and Random Forests
2. Neural Networks
3. Clustering and Dimensionality Reduction
4. Text Mining and Sentiment Analysis
Advanced Statistical Techniques
1. Bayesian Statistics
2. Multilevel Modeling
3. Structural Equation Modeling (SEM)
4. Item Response Theory (IRT)
These statistical models and techniques are essential for social science students to understand and apply in various social science disciplines, including
1. Sociology
2. Psychology
3. Political Science
4. Economics
5. Anthropology
6. Public Health
Expected Out Come
Mastering these statistical concepts will enable social science students to analyze complex social data, evaluate programs and policies, and inform decision-making.
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