Why Should Social Science Students Learn Python?

From Stata/R to Python — Exploring New Tools for Data Analysis

Limitations of Traditional Tools

As a social science student, you may have already encountered:

Stata: The mainstream tool in economics and political science, excels at panel data and econometric models
R: A powerful tool for statistics with a rich collection of statistical packages
SPSS: Commonly used in psychology and sociology, user-friendly interface but limited functionality

These tools are excellent in their respective fields, but in the AI era, they face some challenges:

Dimension	Stata	R	Python
Data Analysis	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Statistical Modeling	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Machine Learning	⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Deep Learning		⭐⭐	⭐⭐⭐⭐⭐
LLM API Calls		⭐⭐	⭐⭐⭐⭐⭐
Web Scraping		⭐⭐⭐	⭐⭐⭐⭐⭐
General Programming	⭐	⭐⭐	⭐⭐⭐⭐⭐
Community Ecosystem	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐

1. Unified Workflow

With Python, you can complete everything in one environment:

python

# Step 1: Scrape data
import requests
data = requests.get('https://api.worldbank.org/v2/country/all/indicator/NY.GDP.PCAP.CD')

# Step 2: Data cleaning and analysis
import pandas as pd
df = pd.read_csv('survey_data.csv')
df.groupby('country')['income'].mean()

# Step 3: Statistical modeling
from statsmodels.formula.api import ols
model = ols('income ~ education + age', data=df).fit()

# Step 4: Machine learning prediction
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_train, y_train)

# Step 5: Call LLM to generate text
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Summarize this research report"}]
)

Comparison with Stata/R:

Stata: Need external tools to scrape data → Import to Stata → Model → Cannot call LLMs
R: Can scrape data and model, but machine learning and LLM calling ecosystem is weaker

2. Essential Skills for the AI Era

Current frontier trends in social science research:

Text Analysis: Use LLMs to analyze policy documents, social media, historical documents
Causal Inference: New methods combining machine learning + causal inference (Double ML, Causal Forest)
Experimental Design: A/B testing, multi-armed bandit algorithms
Big Data Processing: Handle millions of rows of administrative data, network data

The best tool for all these frontier methods is Python.

3. Job Market Demand

Job Type	Stata	R
Academic Research (Economics)
Data Analyst	⭐
Data Scientist
AI Product Manager		⭐
Policy Analyst

4. Native Language of Large Language Models

If you've used ChatGPT, Doubao, or Qwen, you know that when you ask AI to solve a problem through programming, especially data science problems, Python is AI's default language. If you're also familiar with Python, communication with AI will naturally be more effective.

5. Econometric Modeling Capability: On Par with Stata

Many economics students worry that Python isn't as professional as Stata for econometric modeling. But in fact, through statsmodels and linearmodels packages, Python already has a complete econometric toolchain.

Case Study: Multi-Model Comparison Analysis of Wage Regression

Suppose we want to study the impact of education and work experience on wages, and compare different model specifications:

python

import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.formula.api import ols
from statsmodels.iolib.summary2 import summary_col
from linearmodels.panel import PanelOLS

# Step 1: Generate simulated data (replace with real data in actual research)
np.random.seed(42)
n = 1000

data = pd.DataFrame({
    'wage': np.random.normal(50000, 15000, n),
    'education': np.random.randint(12, 20, n),  # Years of education
    'experience': np.random.randint(0, 30, n),  # Work experience
    'age': np.random.randint(22, 60, n),
    'gender': np.random.choice([0, 1], n),  # 0=female, 1=male
    'region': np.random.choice(['East', 'West', 'South', 'North'], n)
})

# Add real causal relationships
data['wage'] = (
    20000 +
    3000 * data['education'] +
    800 * data['experience'] +
    5000 * data['gender'] +
    np.random.normal(0, 5000, n)
)

# Step 2: Build multiple regression models

# Model 1: Baseline OLS (education only)
model1 = ols('wage ~ education', data=data).fit()

# Model 2: Add work experience
model2 = ols('wage ~ education + experience', data=data).fit()

# Model 3: Add gender control variable
model3 = ols('wage ~ education + experience + gender', data=data).fit()

# Model 4: Add interaction term (education × experience)
model4 = ols('wage ~ education + experience + gender + education:experience',
             data=data).fit()

# Model 5: Add region fixed effects
model5 = ols('wage ~ education + experience + gender + C(region)',
             data=data).fit()

# Step 3: Use summary_col to consolidate output into one table
# This is Python's powerful econometric modeling feature: similar to Stata's esttab
results_table = summary_col(
    [model1, model2, model3, model4, model5],
    model_names=['Model(1)', 'Model(2)', 'Model(3)', 'Model(4)', 'Model(5)'],
    stars=True,  # Add significance stars
    float_format='%.2f',
    info_dict={
        'N': lambda x: f"{int(x.nobs)}",
        'R²': lambda x: f"{x.rsquared:.3f}"
    }
)

print(results_table)

Output (similar to Stata regression tables):

===============================================================================
                       Model(1)   Model(2)   Model(3)   Model(4)   Model(5)
-------------------------------------------------------------------------------
education            2891.35*** 2902.44*** 2903.12*** 2654.89*** 2897.85***
                     (138.99)   (99.87)    (79.45)    (156.32)   (79.82)
experience                      822.15***  819.43***  645.23***  816.78***
                                (45.23)    (35.98)    (89.45)    (36.15)
gender                                     4998.67*** 4987.34*** 4992.11***
                                           (298.76)   (299.12)   (299.34)
education:experience                                  12.56
                                                      (6.78)
C(region)[T.North]                                               -234.56
                                                                 (445.23)
C(region)[T.South]                                               156.78
                                                                 (438.91)
C(region)[T.West]                                                89.34
                                                                 (442.67)
R²                   0.293      0.456      0.612      0.615      0.613
N                    1000       1000       1000       1000       1000
===============================================================================
Standard errors in parentheses.
* p<.1, ** p<.05, ***p<.01

What does this case demonstrate?

Professional regression output: summary_col can generate regression tables as professional as Stata's esttab
Multi-model comparison: Easily display different model specifications for robustness checks
Flexible model specification: Supports interaction terms, fixed effects, clustered standard errors, and other advanced features
Complete ecosystem:
- statsmodels: OLS, Logit, Probit, time series (ARIMA), causal inference (DID, RDD)
- linearmodels: Panel data (fixed effects, random effects, instrumental variables), GMM estimation
- econml/dowhy: Combining machine learning with causal inference (Double ML, Causal Forest)

Comparison with Stata:

Python can also generate journal-quality regression tables
Supports all mainstream econometric methods (IV, DID, RDD, panel data, etc.)
Additional advantage: Seamless integration with machine learning, deep learning, and LLM analysis

Practical Usage Tips:

To export to LaTeX or Word format, use the stargazer package (Python version)
For panel data, linearmodels.PanelOLS provides the same functionality as Stata's xtreg
Clustered standard errors can be implemented via .fit(cov_type='cluster', cov_kwds={'groups': data['cluster_id']})

6. Jupyter Notebook: The Gold Standard for Research Reproducibility

In modern scientific research, reproducibility has become a core requirement for academic integrity and research quality. Top journals (such as AER, QJE, Nature, Science) all require authors to submit reproducible code and data. And Jupyter Notebook is the best tool for achieving research reproducibility.

Why is Jupyter Notebook the Best Tool for Presenting Data Analysis?

Core Advantage: Code, Results, and Explanations in One

Traditional workflow (like Stata):

Code file (.do) → Run → Output file (.log, .tex) → Manually organize into paper

Problem: Code and results are separated, after modifying code you need to re-run and manually update all outputs

Jupyter Notebook's Revolutionary Change:

One document (.ipynb) = Code + Execution results + Charts + Explanatory text

Real Case: A Complete Research Analysis Workflow

Suppose you want to analyze "the impact of education on income," with Jupyter Notebook you can present it like this:

Traditional Method (Stata):

Write analysis.do file
Run to get results.log
Use esttab to export regression table to .tex
Manually copy-paste to Word/LaTeX paper
If modifications needed, repeat steps 2-4

Jupyter Notebook Method:

markdown

# Analysis of Education's Impact on Income

## 1. Data Loading and Cleaning

```python
import pandas as pd
import numpy as np
from statsmodels.formula.api import ols
from statsmodels.iolib.summary2 import summary_col
import matplotlib.pyplot as plt

# Load data
data = pd.read_csv('wage_data.csv')

# View data overview
print(f"Sample size: {len(data)}")
print(f"Variable list: {data.columns.tolist()}")
data.describe()
```

**Output** (automatically displayed in notebook):
```
Sample size: 5000
Variable list: ['wage', 'education', 'experience', 'age', 'gender']
              wage   education  experience        age     gender
count    5000.000      5000.00     5000.00    5000.00    5000.00
mean    48234.56        15.23       12.45      38.67       0.51
std     18456.78         2.34        8.12      10.23       0.50
...
```

## 2. Descriptive Statistics Visualization

```python
# Plot scatter of education vs wage
plt.figure(figsize=(10, 6))
plt.scatter(data['education'], data['wage'], alpha=0.5)
plt.xlabel('Years of Education')
plt.ylabel('Annual Income')
plt.title('Relationship Between Education and Income')
plt.show()
```

**Output** (chart embedded directly):
[Scatter plot automatically displays in notebook, no manual insertion needed]

## 3. Regression Analysis

```python
# Build multiple models
model1 = ols('wage ~ education', data=data).fit()
model2 = ols('wage ~ education + experience', data=data).fit()
model3 = ols('wage ~ education + experience + gender', data=data).fit()

# Generate regression table
results = summary_col([model1, model2, model3],
                      model_names=['Model(1)', 'Model(2)', 'Model(3)'],
                      stars=True)
print(results)
```

**Output** (regression table displayed directly):
```
                       Model(1)   Model(2)   Model(3)
education            2850.34*** 2860.12*** 2855.67***
                     (145.23)   (102.34)   (85.12)
...
```

## 4. Conclusion

We found that each additional year of education increases income by approximately 2,850 yuan on average, and remains significant after controlling for work experience and gender.
This supports the predictions of human capital theory.

Core Advantages of Jupyter Notebook

1. One-Click Reproducibility

Recipients can open the .ipynb file and click "Run All" to reproduce all results
No need to manually run multiple files or manually organize outputs

2. Code and Results Update Synchronously

After modifying code, re-run the cell and output updates automatically
No mismatch between code and results

3. Interactive Exploration

Can modify parameters and re-run code blocks at any time
Convenient for debugging and trying different model specifications

4. Rich Output Formats

Supports tables, charts, mathematical formulas (LaTeX), Markdown text
Can export to PDF, HTML, Slides, and other formats

5. Meets Journal Reproducibility Standards

Mainstream journal reproducibility policies (2024):

Journal	Reproducibility Requirement	Jupyter Notebook Support
AER	Must provide runnable code and data	Explicitly accepts `.ipynb`
QJE	Requires replication package	Accepts Jupyter Notebook
Nature	Must provide Code availability statement	Recommends using Jupyter
Science	Requires code archived in public repository	Supports `.ipynb`

Real Examples:

2019 Nobel Economics Prize winner Abhijit Banerjee's team uses Jupyter Notebook to publish replication code
Over 60% of data science papers in Nature use Jupyter Notebook as supplementary material

Comparison with Traditional Tools

Dimension	Stata .do + .log	R Script + RMarkdown	Jupyter Notebook
Code-Result Integration	Separated	RMarkdown can	Native support
Interactive Execution	Must re-run everything	⭐ Partial support	Full support
Auto-Embedded Charts	Need manual export	RMarkdown supports	Auto display
Learning Curve	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Cross-Language Support	Stata only	R only	Python/R/Julia etc
Sharing Convenience	⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐
Version Control (Git)			(needs config)

RMarkdown vs Jupyter Notebook:

RMarkdown is popular in the R community, but Jupyter is more universal in data science
Jupyter supports 40+ programming languages (Python, R, Julia, Stata, etc.)
Google Colab, Kaggle, and other platforms are all based on Jupyter

Practical Use Cases

Scenario 1: Course Assignment Submission

Students submit .ipynb files, teachers can directly run to verify results
Clearer than submitting Word documents + code files

Scenario 2: Sharing Within Research Groups

Team members share analysis notebooks, everyone sees identical results
Avoids "works on my machine" problems

Scenario 3: Replication Package for Paper Submission

submission/
├── data/
│   └── analysis_data.csv
├── code/
│   └── main_analysis.ipynb    # Main analysis notebook
├── figures/
│   └── (automatically generated by notebook)
└── README.md

Reviewers/editors only need to run main_analysis.ipynb to reproduce all results

Scenario 4: Public Research Code (Boost Citation Rates)

Publish to GitHub, other researchers can directly view and run
Many highly-cited papers publicly share Jupyter Notebook code

Getting Started with Jupyter Notebook

Installation and startup are very simple:

bash

# Install Jupyter
pip install jupyter

# Start Jupyter Notebook
jupyter notebook

# Or use more modern JupyterLab
pip install jupyterlab
jupyter lab

Free Online Use (no installation required):

Google Colab: https://colab.research.google.com (free GPU)
Kaggle Notebooks: https://www.kaggle.com/code (free datasets and GPU)
Binder: Convert GitHub repositories to runnable Notebooks with one click

Summary

Jupyter Notebook is not just a programming tool, but the reproducibility standard for modern scientific research. It makes your research:

More transparent: Code and results fully correspond, impossible to fake
Easier to reproduce: Recipients can verify with one-click execution
Easier to share: Export to HTML/PDF, non-programmers can also view
Meets journal requirements: Satisfies reproducibility policies of top journals

In the AI and big data era, mastering Jupyter Notebook is an essential skill for social science students. All code examples in this tutorial will provide Jupyter Notebook versions to help you get started quickly.

Case 1: Large-Scale Text Analysis (Political Science)

Research Question: Analyze 1 million tweets to study political polarization

python

import pandas as pd
from transformers import pipeline

# Load pre-trained sentiment analysis model
sentiment_analyzer = pipeline("sentiment-analysis")

# Batch process Twitter data
tweets = pd.read_csv("twitter_data.csv")
tweets['sentiment'] = tweets['text'].apply(
    lambda x: sentiment_analyzer(x)[0]['label']
)

# Group analysis by party
result = tweets.groupby(['party', 'sentiment']).size()

Why not Stata/R?

Stata: Cannot call Transformer models
R: Can call, but ecosystem less mature than Python
Python: Has Hugging Face ecosystem, rich model selection

Case 2: Machine Learning in Causal Inference (Economics)

Research Question: Use Double Machine Learning to estimate returns to education

python

from econml.dml import LinearDML
from sklearn.ensemble import RandomForestRegressor

# Double ML estimation
dml = LinearDML(
    model_y=RandomForestRegressor(),
    model_t=RandomForestRegressor()
)
dml.fit(Y=wages, T=education, X=controls)

# Get causal effect
treatment_effect = dml.effect(X_test)
print(f"Causal effect of education: {treatment_effect.mean():.2f}")

Why Python?

EconML (Microsoft), DoWhy (Amazon) and other causal inference libraries
Best tool for combining machine learning and causal inference

Case 3: Real-Time Data Acquisition and Analysis (Financial Economics)

Research Question: Daily tracking of stock market data, analyzing policy shocks

python

import yfinance as yf

# Download S&P 500 data
sp500 = yf.download("^GSPC", start="2020-01-01")

# Calculate daily returns
sp500['returns'] = sp500['Close'].pct_change()

# Event study: analyze abnormal returns around policy date
event_date = "2020-03-15"
event_returns = sp500.loc[event_date]['returns']
normal_returns = sp500['returns'].rolling(30).mean()
abnormal_return = event_returns - normal_returns[event_date]

print(f"Event day abnormal return: {abnormal_return:.2%}")

Case 4: Network Analysis (Sociology)

Research Question: Analyze community structure in social networks

python

import networkx as nx
from networkx.algorithms import community

# Create social network graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4)])

# Detect communities
communities = community.greedy_modularity_communities(G)

# Calculate centrality
centrality = nx.betweenness_centrality(G)
print(f"Node importance: {centrality}")

Common Questions

Q1: I already know Stata/R, do I still need to learn Python?

Answer: Depends on your goals

If only doing traditional econometrics research → Stata is sufficient
If doing statistical methods research → R is powerful
If using machine learning, LLMs, or crossing over to tech industry → Must learn Python

Recommendation: The three are not replacement relationships, but complementary

Stata: Panel data, IV, DID and other econometric methods
R: Statistical inference, Bayesian analysis
Python: Machine learning, deep learning, LLMs, general programming

Real Data (based on 2024 Stack Overflow survey):

Data science positions requiring Python: 91%
Requiring R: 47%
Requiring Stata: 12%

Q2: Is Python difficult to learn?

Answer: Simpler than you think!

If you know Stata, you already understand:

Variable concepts (Stata's gen, replace)
Data frame concepts (Stata's dataset)
Loops and conditionals (Stata's foreach, if)

Python just uses different syntax to express the same logic.

Learning Curve Comparison (from zero to doing research):

Stata: ~4 weeks (simple syntax, but limited functionality)
R: ~6 weeks (powerful statistical functions, but inconsistent syntax)
Python: ~6-8 weeks (slightly harder initially, but high long-term returns)

Q3: How long does it take to learn Python?

Answer: Depends on goals

Data analysis basics (Pandas + statistics): 2-3 weeks
Machine learning intro (sklearn): Another 2-3 weeks
Deep learning/LLMs: Another 4-6 weeks

The goal of this tutorial is 6-8 weeks to take you from zero to doing research with Python.

Q4: Do top journals accept Python code?

Answer: Absolutely!

Code Policies of Mainstream Economics Journals (2024):

AER (American Economic Review): Accepts Python/R/Stata/Julia
QJE (Quarterly Journal of Economics): Accepts all mainstream languages
Econometrica: Accepts Python, requires reproducible code
JPE (Journal of Political Economy): Accepts Python

Trend: More and more top journal papers use Python, especially involving:

Machine learning methods
Text analysis
Network data
Real-time data acquisition

Q5: What are Python's disadvantages?

Answer: Honestly speaking, Python also has weaknesses

Compared to Stata:

Panel data commands not as concise as Stata (but functionality not weak)
Regression output needs manual formatting (Stata's esttab more convenient)
Slightly steeper learning curve

Compared to R:

Statistical packages not as comprehensive as R (some frontier statistical methods implemented faster in R)
Python version of ggplot2 (plotnine) less mature than original

But: These disadvantages are rapidly improving, and Python's advantages (machine learning, LLMs, versatility) far outweigh these weaknesses.

Learning Path of This Tutorial

Week 1-2: Python Basics → Can write simple scripts
   ↓
Week 3-4: Pandas Data Analysis → Can replicate all Stata operations
   ↓
Week 5-6: Statistical Modeling + Machine Learning → Can do regression, classification, clustering
   ↓
Week 7-8: LLM APIs + Advanced Applications → Can call GPT, Claude for text analysis

Next Steps

In the next section, we will compare in detail Python vs Stata vs R syntax differences to help you quickly build Python thinking patterns.

Ready? Let's begin!

Why Should Social Science Students Learn Python? ​

Evolution of Computing Tools in Social Science Research ​

Limitations of Traditional Tools ​

Unique Value of Python for Social Science Students ​

1. Unified Workflow ​

2. Essential Skills for the AI Era ​

3. Job Market Demand ​

4. Native Language of Large Language Models ​

5. Econometric Modeling Capability: On Par with Stata ​

6. Jupyter Notebook: The Gold Standard for Research Reproducibility ​

Why is Jupyter Notebook the Best Tool for Presenting Data Analysis? ​

Real Case: A Complete Research Analysis Workflow ​

Core Advantages of Jupyter Notebook ​

Comparison with Traditional Tools ​

Practical Use Cases ​

Getting Started with Jupyter Notebook ​

Summary ​

Real Cases: Python Applications in Social Science Research ​

Case 1: Large-Scale Text Analysis (Political Science) ​

Case 2: Machine Learning in Causal Inference (Economics) ​

Case 3: Real-Time Data Acquisition and Analysis (Financial Economics) ​

Case 4: Network Analysis (Sociology) ​

Common Questions ​

Q1: I already know Stata/R, do I still need to learn Python? ​

Q2: Is Python difficult to learn? ​

Q3: How long does it take to learn Python? ​

Q4: Do top journals accept Python code? ​

Q5: What are Python's disadvantages? ​

Learning Path of This Tutorial ​

Next Steps ​

Why Should Social Science Students Learn Python?

Evolution of Computing Tools in Social Science Research

Limitations of Traditional Tools

Unique Value of Python for Social Science Students

1. Unified Workflow

2. Essential Skills for the AI Era

3. Job Market Demand

4. Native Language of Large Language Models

5. Econometric Modeling Capability: On Par with Stata

6. Jupyter Notebook: The Gold Standard for Research Reproducibility

Why is Jupyter Notebook the Best Tool for Presenting Data Analysis?

Real Case: A Complete Research Analysis Workflow

Core Advantages of Jupyter Notebook

Comparison with Traditional Tools

Practical Use Cases

Getting Started with Jupyter Notebook

Summary

Real Cases: Python Applications in Social Science Research

Case 1: Large-Scale Text Analysis (Political Science)

Case 2: Machine Learning in Causal Inference (Economics)

Case 3: Real-Time Data Acquisition and Analysis (Financial Economics)

Case 4: Network Analysis (Sociology)

Common Questions

Q1: I already know Stata/R, do I still need to learn Python?

Q2: Is Python difficult to learn?

Q3: How long does it take to learn Python?

Q4: Do top journals accept Python code?

Q5: What are Python's disadvantages?

Learning Path of This Tutorial

Next Steps