Why I Took the Course and What I Learned

📖 Why I Took the Course

I enrolled in the Statistics Bootcamp with Python: Zero to Mastery course to:

• Strengthen my foundation in statistics, essential for my goals in data engineering, data science, and machine learning.
• Gain practical experience in applying statistical concepts using Python.
• Build confidence in performing tasks like exploratory data analysis (EDA), hypothesis testing, and regression modeling.
• Enhance my portfolio with real-world statistical analysis and Python-driven projects.

This course offered a blend of theoretical knowledge and hands-on practice, making it a perfect fit for my learning objectives.

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📚 What I Learned

1. Core Statistical Concepts

• Calculated measures of central tendency: mean, median, and mode.
• Understood measures of dispersion: variance and standard deviation.
• Recognized different data types and scales of measurement for appropriate analysis.

2. Exploratory Data Analysis (EDA)

• Visualized datasets using Python libraries like Matplotlib and Seaborn.
• Summarized data with descriptive statistics to uncover trends and insights.

3. Probability and Distributions

• Mastered fundamental probability concepts and rules (e.g., Bayes' Theorem).
• Explored statistical distributions:
  • Normal distribution
  • Binomial distribution
  • Poisson distribution
• Understood and applied the Central Limit Theorem.

4. Hypothesis Testing

• Formulated null and alternative hypotheses.
• Conducted statistical tests:
  • t-tests
  • z-tests
  • chi-square tests
• Interpreted p-values and confidence intervals for informed decision-making.

5. Regression Analysis

• Built and interpreted linear regression models using Python.
• Differentiated between correlation and causation.
• Applied regression techniques to predict outcomes and analyze relationships.

6. Python for Statistics

• Leveraged Python libraries for statistical analysis:
  • NumPy
  • Pandas
  • SciPy
  • Statsmodels
• Created data visualizations using Matplotlib and Seaborn to present insights effectively.

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🏆 Overall Outcome

This course provided me with a solid foundation in both statistics and Python-driven data analysis, giving me the tools I need to excel in data engineering and data science roles.