Table of Contents

Basic Concepts of Statistics
- Fundamental Theory of Statistics
- Data Collection and Quality Control
Data Analysis Using Excel
Advanced Analytical Techniques
- Random Numbers and Simulations
- Cross-Tabulation of Data
Practical Applications and Career Aspirations

1. Basic Concepts of Statistics

Through university courses, I learned the foundational theories and practical methods of statistics. Below are the key points I covered:

Fundamental Theory of Statistics

● Purpose and Importance of Data Analysis
The goal of studying statistics is to collect data, analyze it, and make decisions based on the results. Through this, I learned methods to derive valuable insights from data.

● Types of Data and Measurement Scales
Data is mainly divided into "discrete data" and "continuous data," and different analysis techniques are required for each. I also learned about different measurement scales: nominal, ordinal, interval, and ratio scales, and understood their differences.

Data Collection and Quality Control

● Survey Design
I learned how to design surveys effectively, including choosing between single-choice or multiple-choice questions and methods to increase respondent engagement.
● Data Quality Management
I studied how to manage the quality of primary and secondary data and methods to minimize errors. Practical exercises were conducted to check the quality of real data.

2. Data Analysis Using Excel

In my courses, a significant focus was placed on using Excel for data analysis. The following topics were covered:

Basic Excel Operations

● Cell Input and Calculations
I learned the basic operations in Excel, including how to use functions and formulas and how to handle spreadsheet operations.

Data Visualization

● Creating Graphs
I learned how to visually represent data using Excel by creating graphs (bar graphs, line graphs, histograms, etc.). This is an essential skill to easily understand data trends.

Basic Statistical Analysis

● Calculating Mean, Median, Variance, and Standard Deviation
I learned how to compute basic statistical measures and understand the variability and trends of data.

● Chi-Square Test and Correlation Analysis
I studied how to use the chi-square test for independence testing and correlation analysis to explore relationships between variables. This helped me develop skills to validate correlations and significance in data.

3. Advanced Analytical Techniques

Random Numbers and Simulations

● Simulation with Random Numbers
I learned how to generate random numbers and conduct simulations using Excel's analysis tools. This deepened my understanding of probability theory and statistical testing.

Cross-Tabulation of Data

● Cross-Tabulation and Pivot Tables
I learned how to perform cross-tabulation and analyze data combinations using pivot tables in Excel. This technique is crucial for efficiently summarizing and analyzing large datasets.

4. Practical Applications and Career Aspirations

The statistical knowledge and analytical techniques I learned are highly applicable in practical settings. Data-driven decision-making is essential in various fields such as business, marketing, and research.

Studying at an overseas university also helped me develop an international perspective.

Through my hands-on experience in public relations and marketing communications, I developed a strong interest in applying data analysis to solve business problems.

I am eager to take on roles where I can analyze data, measure performance, and derive actionable insights to support business decision-making.

My goal is to develop advanced analytical skills to provide data-driven recommendations that optimize performance and strategy. I am actively enhancing my expertise and look forward to applying these skills in a professional setting.