Summary notes

🎯 Objectives: Overview summary

Data Analysis Full Notes

πŸ“Š Data Analysis - Complete Notes

Definition: Data analysis is the process of collecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.

πŸŽ₯ Introduction to Data Analysis (FreeCodeCamp - Full Course)

πŸ” 1. Types of Data

  • Qualitative (Categorical): Descriptive data (e.g. gender, country)
  • Quantitative (Numerical): Measurable data (e.g. income, age)
  • Discrete: Countable (e.g. number of students)
  • Continuous: Measurable but infinite (e.g. weight)

πŸ“‹ 2. Steps in Data Analysis

  1. Define the problem/question
  2. Collect the data (surveys, databases, APIs)
  3. Clean the data (handle missing/outliers)
  4. Analyze (using statistical tools or programming)
  5. Visualize the results
  6. Interpret and report

πŸ› οΈ 3. Tools for Data Analysis

  • Excel: Good for quick analysis & pivot tables
  • Python: Using Pandas, NumPy, Matplotlib, Seaborn
  • R: For statistical analysis & visualizations
  • SQL: Querying structured databases
  • Power BI / Tableau: Data dashboards

πŸ“‚ 4. Data Cleaning

Ensures that data is accurate, complete, and usable.

# Python (pandas)
df.dropna() # Remove missing values
df.fillna(0) # Replace missing with 0
df['age'] = df['age'].astype(int)
Example: In a survey dataset, replacing all missing gender values with "Unknown".

πŸ“ˆ 5. Data Visualization

Used to make data understandable using charts, graphs, and plots.

  • Bar Chart: Compare categories
  • Pie Chart: Show proportions
  • Histogram: Frequency distribution
  • Scatter Plot: Relationship between variables
  • Box Plot: Summary statistics and outliers

πŸ“Š 6. Descriptive Statistics

  • Mean: Average
  • Median: Middle value
  • Mode: Most frequent value
  • Standard Deviation: Spread of data
  • Variance: Square of SD

πŸ§ͺ 7. Sample Python Data Analysis Code

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('sales.csv')
print(df.describe())
df['revenue'].plot(kind='hist')
plt.show()

πŸ“˜ 8. Interpretation

After visualizing and analyzing, interpret what the patterns or anomalies mean in context.

Example: Sales drop every February β€” may indicate seasonality or poor marketing.

πŸ“Œ 9. Real-World Applications

  • Business intelligence & decision making
  • Healthcare (patient outcome analysis)
  • Finance (fraud detection, credit scoring)
  • Education (student performance tracking)
  • Retail (sales forecasting, market basket analysis)

🧠 10. Practice Tasks

  1. Analyze student scores dataset β€” find average, highest, and visualize performance
  2. Clean a CSV file with missing and duplicate values
  3. Use SQL to select top 5 products by sales
  4. Create bar chart of monthly expenses in Excel or Python

πŸ“ Sample Exam Questions

  1. Define data analysis and describe its key steps.
  2. What are the types of data and give an example of each?
  3. Explain the importance of data cleaning.
  4. Compare mean, median, and mode with examples.
  5. List three tools used for data analysis and their use cases.

πŸ“– Reference Book: N/A

πŸ“„ Page: 1.0