Introduction to Data Analysis:

• Overview of data types, sources, and the importance of data analysis in decision-making.

• Understanding the data analysis process: collection, cleaning, analysis, and reporting.

Data Collection and Preparation:

• Techniques for gathering reliable and relevant data.

• Data cleaning methods to handle missing or inconsistent data.

Basic Statistical Concepts:

• Descriptive statistics: mean, median, mode, standard deviation.

• Introduction to probability and distributions.

Data Visualization:

• Creating effective charts and graphs (bar charts, scatter plots, histograms).

• Using tools like Excel, Google Sheets, or Tableau for visualization.

Introduction to Tools and Software:

• Overview of software like Microsoft Excel, Python (Pandas, NumPy), or R.

• Performing data analysis using these tools.

Exploratory Data Analysis (EDA):

• Identifying patterns, trends, and relationships in data.

• Detecting outliers and understanding data distributions.

Predictive Analysis and Modeling:

• Basics of regression analysis and forecasting.

• Introduction to machine learning models for data analysis.

Data Reporting and Decision-Making:

• Summarizing insights from data analysis for stakeholders.

• Creating dashboards and presentations to communicate results effectively.