Python Statistical Methods: Machine Learning & Data Science
- Description
- Curriculum
- FAQ
- Reviews
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!
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1
Overview of Course CurriculumIn this Introductory lecture, I will show an overview of the course curriculum and the contents of this course.
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2
Installing Jupyter Notebook Environment
In this lecture, you will learn how to install anaconda on your local computer to access its integrated Jupyter notebooks.
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3
How to Download Exercises & Course Notebooks
In this lecture, you will learn how to download the course materials including course notebooks, exercises and projects of this course.
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4
Built-in Data Structures - Tuple and List
Students will learn how to create tuples and lists in python. Additionally, the most common methods and functions for manipulating tuples and lists will be explained.
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5
Built-in Data Structures - Dictionary and Set
Two built-in data structures will be explained, which are dictionary and set. Students will learn how to create and how to use python functions to manipulate data structured as dictionaries or sets.
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6
Numpy Arrays
Students will learn about NumPy arrays. They will learn how to create and manipulate NumPy arrays.
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7
Pandas Series and Dataframes
Students will learn how to create pandas series and pandas dataframe. In addition to the common functions and methods used by data scientists to manipulate pandas dataframes.
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8
Data Types (Numeric or Categorical)
Students will learn the common data types in data science. They will learn numerical and categorical data types.
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9
Exercise: Create Data Structures in Python
This is a hands-on exercise for students to apply the skills in this section.
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10
Mean (Average)
Students will learn what is the mean in statistics and how it can be calculated in python.
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11
Weighted Average
You will learn what is the weighted average in statistics and how it can be calculated in python.
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12
Median
Students will learn about the median and how it can be calculated in python.
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13
Population vs. Sample
You will learn two important concepts in statistics which are population and sample. And how they are related to t data analysis.
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14
Application in Data Science
In this lecture, you will learn how to apply the concepts explained in this module in data science using real-world data.
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15
Exercise: Calculate Central Tendency Measures
This is a hands-on exercise to apply the knowledge that you learned in this section.
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16
Range
You will learn what is the range and how to calculate the range of data using python.
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17
Variance and Standard Deviation
You will learn what are the variance and the standard deviation. You will also learn how to calculate variance and standard deviation with python.
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18
Percentile & Quartile
Students will learn about percentiles and quartiles in data analysis and how to calculate the percentiles in python.
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19
Outlier – part 1
In this lecture, you will learn about outliers and how they can be detected in data.
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20
Outlier – part 2
In this lecture, we will continue the discussion of the outliers where you will learn how to detect outliers in real-world data.
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21
Sampling Error
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22
Application in Data Science
In this lecture, we will apply what we discussed in this section to real-world data.
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23
Exercise: Calculate Variability Measures
This is an exercise for you to put your knowledge into practical skills by solving tasks in this exercise.
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24
Box Plot
In this lecture, you will learn how to create box plots in python. You will learn also how to read box plots in terms of data distribution.
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25
Violin Plot
In this lecture you will learn how to create and use violin plots to explore the distribution of data.
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26
Histogram and Density Plot
In this lecture, we will discuss histograms and density plots. You will learn how to create and read histograms and density plots in terms of data distribution.
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27
Bar Plot for Categorical Data
Students will learn how to create bar plots in python to describe categorical data.
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28
Pie Chart for Categorical Data
In this lecture, another visualization for categorical data will be explored which is pie charts. students will learn how to create pie charts with python.
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29
Application in Data Science
In this lecture, we will have more practical examples of using visualizations to describe data distribution.
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30
Exercise: Exploring Data Distribution
This lecture is an exercise for you to practice the skills explained in this section.
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31
Correlation and Covariance Coefficients
In this lecture, you will learn how to create correlations and covariance between variables in data sets. Also, you will learn how to create a correlation matrix.
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32
Correlation Using Scatter plot
In this lecture, you will learn how to visualize the correlation between variables using scatter plots.
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33
Mapping with Scatter plots
In this lecture, you will learn how to explore the relationship between more than two variables using mapping and scatter plots.
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34
Heat Maps
In this lecture, you will learn how to create heat maps in python to explore the relationship between three variables and to create heatmaps for correlation matrix.
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35
Application in Data Science
In this lecture, we will discuss more practical examples of exploring the relationships between variables in datasets using correlation, scatter plots, and heat maps.
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36
Exercise: Create Mapped Scatterplots and Heat Maps
This is an exercise for you to put the skills that you learned in this section into practice.
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39
Random Sampling and Bias
In this lecture, you will learn through practical examples what is random sampling in statistics, and you will learn about bias and how it can be calculated in python.
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40
Central Limit Theorem
Students will learn through practical examples about an important theorem in statistics and data analysis which is the central limit theorem.
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41
Normal distribution
Students will learn a very important probability distribution in statistical modeling which is the normal distribution.
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42
Normality Tests for Real-World Data
Students will learn how to perform the normality test to check if the data is normally distributed.
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43
Skewed Data: Real-life Distributions
Students will learn about skewed data, and how to measure skewness in real-world data.
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44
Probability: A Practical Introduction
This lecture is a practical introduction to probability in statistics.
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45
Common Probability Distributions
In this lecture, you will learn the common probability distributions in statistics such as the normal distribution and the binomial distribution.
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46
Exercise: Normal Distribution and Skewness
This lecture is an exercise for the students to apply the skills learned in this section.
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47
Data Scaling: Standardization
You will learn how to standardize data to have the same scale, which is a pre-requisite for some machine learning models.
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48
Data Scaling: Normalization
Students will learn how to normalize the data to have the same scale.
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49
Log and Square Root Transformations
Students will learn how to fix skewed data to have a normal distribution using log and square root transformations.
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50
Power Transformation (PowerTransformer)
Students will learn how to use the power transformer to transform the data to a more normal distribution and to fix skewed data.
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51
Application in Data Science
In this lecture, we will apply the skills in this section to solve real-world examples in data science and machine learning.
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52
Exercise: Data Scaling and Transformation
This lecture is an exercise for you to practice what you learned in this section about data scaling and data transformation.
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53
C.I for Continuous Data
In this lecture, you will learn how to calculate confidence intervals for continuous data in python.
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54
C.I for Classification Data
In this lecture, you will learn how to calculate confidence intervals for classification data in python.
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55
Bootstrapping For Unknown Distributions
Students will learn the bootstrapping method in statistics and how it can be used in machine learning and python.
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56
Nonparametric Confidence Interval with Bootstrapping
You will learn how to use bootstrapping to create non-parametric confidence intervals in python.
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57
Exercise: Create Confidence Interval
This lecture is an exercise for the students to apply what they learned about confidence intervals.
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58
Bias vs. Variance
Students will learn very important terms in statistical modeling which are bias and variance.
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59
Overfitting and Underfitting
Students will learn what is overfitting and underfitting in statistical and data modeling.
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60
Information Criteria for Model Selection
In this lecture, you will learn the common information criteria that are used in statistical modeling and machine learning for model selection.
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61
Evaluation Metrics for Regression Models
Students will learn how to calculate the information criteria for regression models in machine learning.
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62
Evaluation Metrics for Classification Models _Part One
Students will learn how to calculate the information criteria for classification models in machine learning.
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63
Evaluation Metrics for Classification Models – Part Two
In this lecture, you will learn how to calculate confusion matric and evaluation metrics for classification models such as accuracy, precision, recall, and f1-score metrics.
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64
Application in Data Science
In this lecture, we will apply the skills in this section to solve real-world examples in data science and machine learning.
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65
Exercise: Evaluating Machine Learning Models
This lecture is an exercise for the students to apply what they learned about confidence intervals.
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66
Hold Out Validation - Train/Test Split
Students will learn how to perform the K-fold cross-validation technique to evaluate machine learning models.
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67
K-Fold Cross-Validation
Students will learn how to perform the K-fold cross-validation technique to evaluate machine learning models.
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68
Leave-One-Out Cross-Validation (LOOCV)
Students will learn how to perform the Leave-One Out validation technique to evaluate machine learning models.
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69
Application in Data Science
In this lecture, we will apply the skills in this section to solve real-world examples in data science and machine learning.
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70
Exercise: Validation Techniques in Machine Learning
This lecture is an exercise for the students to apply what they learned about confidence intervals.
