Exploratory Data Analysis (EDA)

EDA is the process of analyzing datasets to summarize key patterns, detect anomalies, and gain insights before applying machine learning or reporting.

1️⃣ Descriptive Statistics
Descriptive statistics help summarize and understand data distributions.

In SQL:

Calculate Mean (Average):

SELECT AVG(salary) AS average_salary FROM employees; 
Find Median (Using Window Functions) SELECT salary FROM ( SELECT salary, ROW_NUMBER() OVER (ORDER BY salary) AS row_num, COUNT(*) OVER () AS total_rows FROM employees ) subquery WHERE row_num = (total_rows / 2); 

Find Mode (Most Frequent Value)

SELECT department, COUNT(*) AS count FROM employees GROUP BY department ORDER BY count DESC LIMIT 1; 

Calculate Variance & Standard Deviation

SELECT VARIANCE(salary) AS salary_variance, STDDEV(salary) AS salary_std_dev FROM employees; 

In Python (Pandas):

Mean, Median, Mode

df['salary'].mean() df['salary'].median() df['salary'].mode()[0]

Variance & Standard Deviation

df['salary'].var() df['salary'].std()

2️⃣ Data Visualization

Visualizing data helps identify trends, outliers, and patterns.

In SQL (For Basic Visualization in Some Databases Like PostgreSQL):

Create Histogram (Approximate in SQL)

SELECT salary, COUNT(*) FROM employees GROUP BY salary ORDER BY salary; 

In Python (Matplotlib & Seaborn):

Bar Chart (Category-Wise Sales)

import matplotlib.pyplot as plt 
import seaborn as sns 
df.groupby('category')['sales'].sum().plot(kind='bar') 
plt.title('Total Sales by Category') 
plt.xlabel('Category') 
plt.ylabel('Sales') 
plt.show() 

Histogram (Salary Distribution)

sns.histplot(df['salary'], bins=10, kde=True) 
plt.title('Salary Distribution') 
plt.show() 

Box Plot (Outliers in Sales Data)

sns.boxplot(y=df['sales']) 
plt.title('Sales Data Outliers') 
plt.show()

Heatmap (Correlation Between Variables)

sns.heatmap(df.corr(), annot=True, cmap='coolwarm') plt.title('Feature Correlation Heatmap') plt.show() 

3️⃣ Detecting Anomalies & Outliers

Outliers can skew results and should be identified.

In SQL:

Find records with unusually high salaries

SELECT * FROM employees WHERE salary > (SELECT AVG(salary) + 2 * STDDEV(salary) FROM employees); 

In Python (Pandas & NumPy):

Using Z-Score (Values Beyond 3 Standard Deviations)

from scipy import stats df['z_score'] = stats.zscore(df['salary']) df_outliers = df[df['z_score'].abs() > 3] 

Using IQR (Interquartile Range)

Q1 = df['salary'].quantile(0.25) 
Q3 = df['salary'].quantile(0.75) 
IQR = Q3 - Q1 
df_outliers = df[(df['salary'] < (Q1 - 1.5 * IQR)) | (df['salary'] > (Q3 + 1.5 * IQR))] 

4️⃣ Key EDA Steps

Understand the Data → Check missing values, duplicates, and column types

Summarize Statistics → Mean, Median, Standard Deviation, etc.

Visualize Trends → Histograms, Box Plots, Heatmaps

Detect Outliers & Anomalies → Z-Score, IQR

Feature Engineering → Transform variables if needed

Mini Task for You: Write an SQL query to find employees whose salaries are above two standard deviations from the mean salary.

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Pick a messy dataset from Kaggle or public sources. Show your thought process.

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Data Cleaning Checklist:

If you're just starting out in the world of data analytics, hopefully this checklist helps demystify the concept of "data cleaning"...

☑ Missing data - Decide if you’re going to omit the datapoint, mathematically estimate the missing data using statistical methods, or use an external source to fill in the missing data.

☑ Duplicate data - Identify duplicate data and what it means in context. Is the duplicate an error that needs to be deleted? Or is it possible that you could have two of the same data point?

☑ Formatting errors - Ensure all data is rounded to the correct decimal place, all data is aligned correctly, and the data format is consistent within columns.

☑ Incorrect data types - Ensure all of your data is pulled as the correct data type (ex. making sure that integers are not used for money values).

☑ Outliers - Identify data points that are +/- 2 standard deviations from the mean, and double check that these values are correct. If they are correct, they may require further investigation.

Why is it require to split our data into three parts: train, validation, and test?

• The training set is used to fit the model, i.e. to train the model with the data.

• The validation set is then used to provide an unbiased evaluation of a model while fine-tuning hyperparameters. This improves the generalization of the model.

• Finally, a test data set which the model has never "seen" before should be used for the final evaluation of the model. This allows for an unbiased evaluation of the model. The evaluation should never be performed on the same data that is used for training. Otherwise the model performance would not be representative.

Data Analyst vs Data Scientist: Must-Know Differences

Data Analyst:
- Role: Primarily focuses on interpreting data, identifying trends, and creating reports that inform business decisions.
- Best For: Individuals who enjoy working with existing data to uncover insights and support decision-making in business processes.
- Key Responsibilities:
- Collecting, cleaning, and organizing data from various sources.
- Performing descriptive analytics to summarize the data (trends, patterns, anomalies).
- Creating reports and dashboards using tools like Excel, SQL, Power BI, and Tableau.
- Collaborating with business stakeholders to provide data-driven insights and recommendations.
- Skills Required:
- Proficiency in data visualization tools (e.g., Power BI, Tableau).
- Strong analytical and statistical skills, along with expertise in SQL and Excel.
- Familiarity with business intelligence and basic programming (optional).
- Outcome: Data analysts provide actionable insights to help companies make informed decisions by analyzing and visualizing data, often focusing on current and historical trends.

Data Scientist:
- Role: Combines statistical methods, machine learning, and programming to build predictive models and derive deeper insights from data.
- Best For: Individuals who enjoy working with complex datasets, developing algorithms, and using advanced analytics to solve business problems.
- Key Responsibilities:
- Designing and developing machine learning models for predictive analytics.
- Collecting, processing, and analyzing large datasets (structured and unstructured).
- Using statistical methods, algorithms, and data mining to uncover hidden patterns.
- Writing and maintaining code in programming languages like Python, R, and SQL.
- Working with big data technologies and cloud platforms for scalable solutions.
- Skills Required:
- Proficiency in programming languages like Python, R, and SQL.
- Strong understanding of machine learning algorithms, statistics, and data modeling.
- Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure).
- Outcome: Data scientists develop models that predict future outcomes and drive innovation through advanced analytics, going beyond what has happened to explain why it happened and what will happen next.

Data analysts focus on analyzing and visualizing existing data to provide insights for current business challenges, while data scientists apply advanced algorithms and machine learning to predict future outcomes and derive deeper insights. Data scientists typically handle more complex problems and require a stronger background in statistics, programming, and machine learning.

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