Dive Deeper into the world of data science using Python

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In this tutorial, we’re going to dive deeper into the world of data science using Python, focusing on a more complex project that involves statistical analysis, regression, and hypothesis testing. This project is suitable for a bright 12th-grade student interested in STEM fields, combining both coding and advanced math concepts. We’ll be working with a more complex dataset and utilizing libraries such as pandas, matplotlib, numpy, and scipy for our analysis.

Tutorial Overview

  1. Advanced Project Setup on Replit
  2. Understanding the Dataset
  3. Data Preprocessing and Exploration
  4. Statistical Analysis and Hypothesis Testing
  5. Linear Regression
  6. Conclusion and Full Code

1. Advanced Project Setup on Replit

If you haven’t already, create a new Python project on Replit and name it something descriptive, like “AdvancedDataScienceProject.”

  • Installing Additional Modules:
  • Besides pandas, matplotlib, and numpy, you’ll also need to install scipy for this project. Follow the steps in the previous tutorial to add these packages.

2. Understanding the Dataset

For this project, let’s assume we’re working with a dataset related to educational outcomes, containing student grades, study time, health status, and family support. Your goal is to analyze how different factors influence final grades.

3. Data Preprocessing and Exploration

  • Importing the Dataset and Initial Exploration:
import pandas as pd

data = pd.read_csv('education_data.csv')
print(data.head())
  • Checking for Missing Values and Data Types:
print(data.info())
print(data.isnull().sum())
  • Visual Exploration:

Let’s visualize the relationship between study time and final grades.

import matplotlib.pyplot as plt

plt.scatter(data['studytime'], data['G3'])
plt.xlabel('Study Time')
plt.ylabel('Final Grade')
plt.title('Study Time vs Final Grade')
plt.show()

4. Statistical Analysis and Hypothesis Testing

Let’s hypothesize that students with higher levels of family support achieve higher final grades. We’ll use the t-test from the scipy library to test this hypothesis.

  • Hypothesis Testing:
from scipy import stats

# Splitting the data
support_high = data[data['famsup'] == 'yes']['G3']
support_low = data[data['famsup'] == 'no']['G3']

# Conducting a t-test
t_stat, p_val = stats.ttest_ind(support_high, support_low)

print(f"T-statistic: {t_stat}, P-value: {p_val}")

If the p-value is less than 0.05, we can reject the null hypothesis and conclude there’s a statistically significant difference in grades based on family support.

5. Linear Regression

Let’s now predict final grades based on several independent variables like study time, health, and family support. We’ll use numpy for this.

  • Coding the Linear Regression:
import numpy as np

# Encoding 'famsup' as 0 or 1
data['famsup'] = data['famsup'].apply(lambda x: 1 if x == 'yes' else 0)

# Defining our variables
X = data[['studytime', 'health', 'famsup']]
y = data['G3']

# Adding a column of ones to X
X = np.append(arr = np.ones((X.shape[0], 1)).astype(int), values = X, axis = 1)

# Calculating the coefficients
coefficients = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)

print(coefficients)

This code snippet performs a simple linear regression, providing us with coefficients that indicate the relationship between each independent variable and the dependent variable (final grades).

6. Conclusion and Full Code

In this tutorial, you’ve taken a more complex dive into data science, exploring statistical analysis, hypothesis testing, and linear regression, all while coding in Python on Replit. Here’s the full code for your project:

import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np

# Load the dataset
data = pd.read_csv('education_data.csv')

# Data exploration
print(data.head())
print(data.info())
print(data.isnull().sum())

# Visual exploration
plt.scatter(data['studytime'], data['G3'])
plt.xlabel('Study Time')
plt.ylabel('Final Grade')
plt.title('Study Time vs Final Grade')
plt.show()

# Hypothesis testing
support_high = data[data['famsup'] == 'yes']['G3']
support_low = data[data['famsup'] == 'no

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