Data Analysis Beginner Class

Description

Introduction to Data Analysis:

1. Overview of Data Analysis:
   • Definition and importance.
   • Real-world applications.
2. Types of Data:
   • Categorical vs. numerical data.
   • Cross-sectional vs. time-series data.

Data Collection:

1. Sources of Data:
   • Surveys, experiments, observational studies.
   • Public datasets and APIs.
2. Data Cleaning:
   • Dealing with missing values.
   • Handling outliers.
   • Data validation and sanity checks.

Exploratory Data Analysis (EDA):

1. Descriptive Statistics:
   • Measures of central tendency (mean, median, mode).
   • Measures of dispersion (range, variance, standard deviation).
2. Data Visualization:
   • Introduction to charts and graphs (bar charts, histograms, scatter plots).
   • Using tools like Matplotlib or Seaborn in Python.

Statistical Concepts:

1. Probability Basics:
   • Understanding probability distributions.
   • Probability rules and calculations.
2. Inferential Statistics:
   • Introduction to hypothesis testing.
   • Confidence intervals.

Data Analysis Tools:

1. Introduction to Excel or Google Sheets:
   • Basic formulas for data manipulation.
   • Creating charts and pivot tables.
2. Introduction to a Programming Language (e.g., Python):
   • Basics of Python for data analysis.
   • Introduction to libraries like Pandas and NumPy.

Practical Projects:

1. Case Studies:
   • Analyzing real-world datasets.
   • Solving practical problems.
2. Hands-On Projects:
   • Applying data analysis skills to a specific project.
   • Presenting findings.