Applying Statistical Methods in Data Science

1. Introduction

This tutorial aims at providing a comprehensive guide on how to apply statistical methods in Data Science. It focuses on explaining the fundamental statistical concepts that are often implemented in Data Science, and how to apply them in real-world situations using Python.

By the end of this tutorial, you will:

• Understand key statistical methods used in Data Science
• Learn how to apply these methods using Python
• Gain insights into how these methods can be used to analyze and interpret data

Prerequisites:
- Basic understanding of Python programming
- Familiarity with basic statistical concepts

2. Step-by-Step Guide

One of the most common statistical methods used in Data Science is descriptive statistics. It provides simple summaries about the sample and the measures. These measures can be either a simple quantitative summary, or a more sophisticated understanding of the distribution of the data.

Best practices and tips:
- Always check your data for any inconsistencies or missing values before applying any statistical methods.
- Understand the nature of your data. Different types of data may require different statistical methods.

3. Code Examples

Below are some examples of how to apply statistical methods in Python:

Example 1: Descriptive Statistics

import pandas as pd

# Create a simple dataset
data = {'Name': ['John', 'Anna', 'Peter', 'Linda', 'James'], 'Age': [23, 45, 35, 62, 18], 'Income': [40000, 55000, 80000, 70000, 30000]}
df = pd.DataFrame(data)

# Using pandas describe() method to get the descriptive statistics of the data
df.describe()

This code will output the count, mean, standard deviation, minimum and maximum values, and the 25th, 50th, and 75th percentiles of the 'Age' and 'Income' columns.

4. Summary

In this tutorial, we've covered the basics of applying statistical methods in Data Science using Python. You've learned how to use descriptive statistics to summarize and understand your data.

Next steps for learning:
- Explore other statistical methods such as inferential statistics and hypothesis testing.
- Learn about various data visualization techniques to represent your statistical findings.

Additional resources:
- Python for Data Analysis
- Statistics for Data Science

5. Practice Exercises

Exercise 1: Generate a dataset of 100 random ages and find their mean, median and standard deviation.

Exercise 2: Create a dataset of 1000 random incomes and calculate their quartiles.

Solutions and explanations:
- For exercise 1, you can use the random module in Python to generate random ages. To calculate mean, median and standard deviation, you can use the mean(), median() and stdev() functions respectively from the statistics module.
- For exercise 2, you can still use the random module to generate random incomes. To calculate quartiles, you can use the quantile() function from pandas.

Tips for further practice:
- Try working with larger datasets
- Practice with different types of data such as categorical data, time series data etc.