Statistics For Data Science and Machine Learning with Python
Practical Statistics with Python for Data Science & Machine Learning Statistical Modeling Using Sci-kit Learn and Scipy
- New
- Created by Taher Assaf
Preview this Course - GET COUPON CODE
This course is ideal for you if you want to gain knowledge in statistical methods required for Data Science and machine learning!
Learning Statistics is an essential part of becoming a professional data scientist. Most data science learners study python for data science and ignore or postpone studying statistics. One reason for that is the lack of resources and courses that teach statistics for data science and machine learning.
Statistics is a huge field of science, but the good news for data science learners is that not all statistics are required for data science and machine learning. However, this fact makes it more difficult for learners to study statistics because they are not sure where to start and what are the most relevant topics of statistics for data science.
What you'll learn
- You will learn to use data exploratory analysis in data science.
- You will learn the most common data types such as continuous and categorical data.
- You will learn the central tendency measures and the dispersion measures in statistics.
- You will learn the concepts of population data vs sample data.
- You will learn what random sampling means and how it affects data analysis.
- You will learn about outliers and sampling errors and how they are related to data analysis.
- You will learn how to visualize data distribution using boxplots, violin plots, histograms, and density plots.
- You will learn how to visualize categorical data using bar plots and pie charts.
- You will learn how to calculate correlation and covariance between features in the dataset.
- You will learn how to visualize a correlation matrix using heat maps.
- You will learn the most common probability distributions such as normal distribution and binomial distribution.
- You will learn how to perform normality tests to check for deviation from normality.
- You will learn how to test skewed distributions in real-world data.
- You will learn how to standardize and normalize data to have the same scale.
- You will learn how to transform skewed data to be normally distributed using different transformation methods such as log, square root, and power transformation
- You will learn how to calculate confidence intervals for statistical estimates such as model accuracy.
- You will learn bootstrapping in statistics and how it is used in machine learning.
- You will learn how to evaluate machine learning models.
- You will practically understand the concepts of bias and variance in data modeling.
- You will understand what we mean by underfitting and overfitting in machine leaning and statistical modeling.
- You will learn the most common evaluation metrics for regression models in machine learning.
- You will learn the evaluation metrics for classification models.
- You will learn how to validate predictive machine learning such as regression and classification models.
- You will learn how to use different validation techniques for machine learning such as hold-out validation and cross-validation techniques.
