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Statistics For Data Science and Machine Learning with Python

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Statistics For Data Science and Machine Learning with Python, available at $54.99, has an average rating of 4.7, with 74 lectures, based on 26 reviews, and has 2108 subscribers.

You will learn about 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. This course is ideal for individuals who are This course is for students who want to learn statistics from data science perspective. It is particularly useful for This course is for students who want to learn statistics from data science perspective.

Enroll now: Statistics For Data Science and Machine Learning with Python

Summary

Title: Statistics For Data Science and Machine Learning with Python

Price: $54.99

Average Rating: 4.7

Number of Lectures: 74

Number of Published Lectures: 74

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

  • This course is for students who want to learn statistics from data science perspective.

Target Audiences

  • This course is for students who want to learn statistics from data science perspective.

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.

This course comes to close this gap.

This course is designed for both beginners with no background in statistics for data science or for those looking to extend their knowledge in the field of statistics for data science.

I have organized this course to be used as a video library for you so that you can use it in the future as a reference. Every lecture in this comprehensive course covers a single topic.

In this comprehensive course, I will guide you to learn the most common and essential methods of statistics for data analysis and data modeling.

My course is equivalent to a college-level course in statistics for data science and machine learning that usually cost thousands of dollars. Here, I give you the opportunity to learn all that information at a fraction of the cost! With 77 HD video lectures, many exercises, and two projects with solutions.

All materials presented in this course are provided in detailed downloadable notebooks for every lecture.

Most students focus on learning python codes for data science, however, this is not enough to be a proficient data scientist. You also need to understand the statistical foundation of python methods. Models and data analysis can be easily created in python, but to be able to choose the correct method or select the best model you need to understand the statistical methods that are used in these models. Here are a few of the topics that you will be learning in this comprehensive course:

· Data Types and Structures

· Exploratory Data Analysis

· Central Tendency Measures

· Dispersion Measures

· Visualizing Data Distributions

· Correlation, Scatterplots, and Heat Maps

· Data Distribution and Data Sampling

· Data Scaling and Transformation

· Data Scaling and Transformation

· Confidence Intervals

· Evaluation Metrics for Machine Learning

· Model Validation Techniques in Machine Learning

Enroll in the course and gain the essential knowledge of statistical methods for data science today!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Overview of Course Curriculum

Lecture 2: Installing Jupyter Notebook Environment

Lecture 3: How to Download Exercises & Course Notebooks

Chapter 2: Data Types and Structures

Lecture 1: Built-in Data Structures – Tuple and List

Lecture 2: Built-in Data Structures – Dictionary and Set

Lecture 3: Numpy Arrays

Lecture 4: Pandas Series and Dataframes

Lecture 5: Data Types (Numeric or Categorical)

Lecture 6: Exercise: Create Data Structures in Python

Chapter 3: Exploratory Data Analysis (1): Central Tendency Measures

Lecture 1: Mean (Average)

Lecture 2: Weighted Average

Lecture 3: Median

Lecture 4: Population vs. Sample

Lecture 5: Application in Data Science

Lecture 6: Exercise: Calculate Central Tendency Measures

Chapter 4: Exploratory Data Analysis (2): Variability Measures

Lecture 1: Range

Lecture 2: Variance and Standard Deviation

Lecture 3: Percentile & Quartile

Lecture 4: Outlier – part 1

Lecture 5: Outlier – part 2

Lecture 6: Sampling Error

Lecture 7: Application in Data Science

Lecture 8: Exercise: Calculate Variability Measures

Chapter 5: Visualizing Data Distributions

Lecture 1: Box Plot

Lecture 2: Violin Plot

Lecture 3: Histogram and Density Plot

Lecture 4: Bar Plot for Categorical Data

Lecture 5: Pie Chart for Categorical Data

Lecture 6: Application in Data Science

Lecture 7: Exercise: Exploring Data Distribution

Chapter 6: Correlation, Scatterplots, and Heat Maps

Lecture 1: Correlation and Covariance Coefficients

Lecture 2: Correlation Using Scatter plot

Lecture 3: Mapping with Scatter plots

Lecture 4: Heat Maps

Lecture 5: Application in Data Science

Lecture 6: Exercise: Create Mapped Scatterplots and Heat Maps

Chapter 7: Capstone Project for Exploratory Analysis

Lecture 1: Project Description

Lecture 2: Solution walk-through of The Project

Chapter 8: Data Distributions and Data Sampling

Lecture 1: Random Sampling and Bias

Lecture 2: Central Limit Theorem

Lecture 3: Normal distribution

Lecture 4: Normality Tests for Real-World Data

Lecture 5: Skewed Data: Real-life Distributions

Lecture 6: Probability: A Practical Introduction

Lecture 7: Common Probability Distributions

Lecture 8: Exercise: Normal Distribution and Skewness

Chapter 9: Data Scaling and Transformation

Lecture 1: Data Scaling: Standardization

Lecture 2: Data Scaling: Normalization

Lecture 3: Log and Square Root Transformations

Lecture 4: Power Transformation (PowerTransformer)

Lecture 5: Application in Data Science

Lecture 6: Exercise: Data Scaling and Transformation

Chapter 10: Confidence Intervals (CI)

Lecture 1: C.I for Continuous Data

Lecture 2: C.I for Classification Data

Lecture 3: Bootstrapping For Unknown Distributions

Lecture 4: Nonparametric Confidence Interval with Bootstrapping

Lecture 5: Exercise: Create Confidence Interval

Chapter 11: Evaluation Metrics for Machine Learning

Lecture 1: Bias vs. Variance

Lecture 2: Overfitting and Underfitting

Lecture 3: Information Criteria for Model Selection

Lecture 4: Evaluation Metrics for Regression Models

Lecture 5: Evaluation Metrics for Classification Models _Part One

Lecture 6: Evaluation Metrics for Classification Models – Part Two

Lecture 7: Application in Data Science

Lecture 8: Exercise: Evaluating Machine Learning Models

Chapter 12: Model Validation Techniques in Machine Learning

Lecture 1: Hold Out Validation – Train/Test Split

Lecture 2: K-Fold Cross-Validation

Lecture 3: Leave-One-Out Cross-Validation (LOOCV)

Lecture 4: Application in Data Science

Lecture 5: Exercise: Validation Techniques in Machine Learning

Chapter 13: Final project

Lecture 1: Project Description

Lecture 2: Walk-through Solution of the Project – Part One

Lecture 3: Walk-through Solution of the Project – Part Two

Lecture 4: Walk-through Solution of the Project – Part Three

Instructors

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  • 4 stars: 8 votes
  • 5 stars: 17 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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