Applied Statistics for Data Science: A Hands-On Approach!

Unlock the power of data with our comprehensive Statistics for Data Scientists course! This practical, hands-on course provides a solid foundation in key statistical concepts and demonstrates how to apply them effectively to real-world data analysis. We prioritize building an intuitive understanding of each topic, empowering you to confidently interpret and utilize statistical methods.

What You'll Learn:

• Core Statistical Concepts: Master fundamental statistical principles essential for data science success.
• Data Simulation: Learn to generate artificial datasets for testing and experimentation.
• The Central Limit Theorem: Understand this cornerstone of statistics and its implications.
• Distribution Functions: Explore and apply various distribution functions to model data patterns.
• Percentiles & Data Ranges: Gain proficiency in understanding data distribution through percentiles and data ranges.
• Cumulative Distribution Function (CDF): Learn to interpret and utilize the CDF for data analysis.
• Diverse Distribution Types: Deep dive into:
  • Normal Distribution: Understand its properties and applications.
  • Uniform Distribution: Explore its characteristics and use cases.
  • Exponential Distribution: Learn to model time-dependent phenomena.
  • Poisson Distribution: Apply it to count data analysis.
  • Bernoulli Distribution: Understand its role in binary outcomes.
  • Rayleigh Distribution: Explore its applications in modeling physical phenomena.
• Statistical Testing: Develop a strong understanding of statistical hypothesis testing.
  • Reasoning Behind Statistical Testing: Grasp the underlying principles of hypothesis testing.
  • P-value: Interpret and utilize p-values for decision-making.
  • Statistical Significance: Determine the significance of results.
• Comprehensive Statistical Tests: Gain expertise in:
  • Shapiro-Wilk Test: Assess normality.
  • Levene's Test: Evaluate equality of variances.
  • Student's T-test / Welch's T-test: Compare means of two groups.
  • ANOVA Test: Analyze means of multiple groups.
  • Kolmogorov-Smirnov Test: Compare distributions.
  • Non-parametric Tests: Apply tests without assuming data distribution.
• Real-World Applications: Apply statistical techniques to solve practical problems:
  • Bias Detection: Detect a biased coin with 95% certainty.
  • A/B Testing: Conduct and analyze real-life A/B tests.
• Correlation Analysis:
  • Linear Correlation (Pearson): Understand and apply Pearson correlation.
  • Categorical Correlation (Chi-Squared): Utilize Chi-Squared tests and contingency tables for categorical data.
• Bonus: Introduction to Machine Learning: Get a head start on machine learning concepts.
  • Linear Regression: Understand and apply linear regression models.
  • Logistic Regression: Learn logistic regression and the ML pipeline.

Course Materials:

• Detailed Course Notes: A 100-page textbook with over 50 illustrative figures.
• Extensive Slide Deck: A deck of 360 slides for easy reference.
• Video Lectures: Over 10 hours of content spread across 40+ videos.
• Jupyter Notebooks: All code provided in 7 Jupyter Notebooks with over 2000 lines of Python code – ready to run and adapt!

Who is this course for?

• Data Science Students: Perfect for students on a data science track.
• Career Changers: Ideal for professionals looking to pivot into a data science career.
• Data-Driven Managers: Empower yourself to make informed decisions based on data.
• Practicing Data Scientists: Enhance your skillset with valuable statistical techniques.

This course equips you with the statistical knowledge and practical skills needed to excel in data science and make data-driven decisions.