Data Science Methods and Techniques [2025]
- Descrição
- Currículo
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Welcome to the course Data Science Methods and Techniques for Data Analysis and Machine Learning!
Data Science is expanding and developing on a massive and global scale. Everywhere in society, there is a movement to implement and use Data Science Methods and Techniques to develop and optimize all aspects of our lives, businesses, societies, governments, and states.
This course will teach you a large selection of Data Science methods and techniques, which will give you an excellent foundation for Data Science jobs and studies. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, Data Analyst, or Machine Learning Engineer.
This is a three-in-one master class video course which will teach you to master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, and Unsupervised Learning.
You will learn to master Regression, Regression analysis, Prediction and supervised learning. This course has the most complete and fundamental master-level regression content packages on Udemy, with hands-on, useful practical theory, and also automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.
You will learn to master Classification and supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifiers Ensembles and Voting Classifier Ensembles.
You will learn to master Cluster Analysis and unsupervised learning. This part of the course is about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and some useful Machine Learning clustering algorithms ranging from hierarchical cluster models to density-based cluster models.
You will learn
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Knowledge about Data Science methods, techniques, theory, best practices, and tasks
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Deep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidence
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Detailed and deep Master knowledge of Regression, Regression analysis, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised Learning
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Hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python libraries
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Advanced knowledge of A.I. prediction models and automatic model creation
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Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
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Option: To use the Anaconda Distribution (for Windows, Mac, Linux)
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Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages – golden nuggets to improve your quality of work life
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And much more…
This course includes
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an easy-to-follow guide for using the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). You may learn to use Cloud Computing resources in this course
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an easy-to-follow optional guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able to install a Python Data Science environment useful for this course or for any Data Science or coding task
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content that will teach you many new things, regardless of if you are a beginner or an experienced Data Scientist, Data Analyst, or Machine Learning Engineer
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a large collection of unique content, and this course will teach you many new things that only can be learned from this course on Udemy
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a course structure built on a proven and professional framework for learning.
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a compact course structure and no killing time
This course is an excellent way to learn to master Regression, Prediction, Classification, and Cluster analysis!
These are the most important and useful tools for modeling, AI, and forecasting.
Is this course for you?
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This course is for you, regardless if you are a beginner or an experienced Data Scientist
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This course is for you, regardless if you have a Ph.D. or no education or experience at all
This course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, and unsupervised learning.
Course requirements
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Basic knowledge of the Python programming language and preferably the Pandas library
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The four ways of counting (+-*/)
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Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
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Access to a computer with an internet connection
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The course only uses costless software
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Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included
Enroll now to receive 15+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!
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1
IntroductionIntroduction and overview of the course
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2
Setup of the Anaconda Cloud Notebook
This video describes the setup procedures for using the Anaconda Cloud Notebook
Using Anaconda Cloud Notebook requires internet access and an email address
Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
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3
Download and installation of the Anaconda Distribution (optional)
This video describes the procedures to download and install the Anaconda Distribution for use with this course
Download requires internet access
Video is optional
Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
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4
The Conda Package Management System (optional)
This video describes the Conda Package Management System
Conda requires internet access
Video is optional
Note: Conda is a speedily developing environment and this may cause minor differences in graphics and procedures
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5
Regression, Prediction, and Supervised Learning. Section Overview (I)
This video provides an overview of this section with a table of contents. The concepts of Regression, Prediction, and Supervised Learning are described
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6
The Traditional Simple Regression Model (II)
Learn to use the traditional simple regression model, some fundamental theory and to create a regression model in a theoretically correct environment with the Scikit-learn and Statsmodels libraries
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7
The Traditional Simple Regression Model (III)
Learn to use the traditional simple regression model, more fundamental theory, and tools to check and inspect model-fit-to-data, and model assumptions. Learn to create powerful residual plots with Pandas and Matplotlib, and learn to use the R-squared and Durbin-Watson statistics from the Statsmodels summary output
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8
Some practical and useful modelling concepts (IV)
Learn some practical and useful modeling concepts. Learn about Overfitting, Underfitting, and the Bias-Variance tradeoff
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9
Some practical and useful modelling concepts (V)
Learn some practical and useful modeling concepts. Learn to use Generalizations with Interpolation and extrapolation. Learn about model interpretation and learn about the fake sample or non-causality concept and about simple or advanced models
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10
Linear Multiple Regression model (VI)
Create a Linear Multiple Regression Model using correlation matrixes and heatmaps. Learn model Diagnostics and Residual Analysis using both standard package Residual plots and more advanced designed Residual plots
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11
Linear Multiple Regression model (VII)
Deepen your knowledge about Linear Multiple Regression Models. Introduction to Machine Learning Automatic Model Creation with Forward Selection and Probability-Values
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12
Multivariate Polynomial Multiple Regression models (VIII)
Learn theory about Multivariate Polynomial Regression Models and Regression terminology. Learn some theory about Automatic model creation (AI) using Machine Learning backward elimination and Regression Models
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13
Multivariate Polynomial Multiple Regression models (VIIII)
Learn to code Multivariate Polynomial Multiple Regression Models combined with the Backward Elimination Feature Selection Algorithm for Machine Learning Automatic Model Creation. Learn to make Feature transformations, Residual Analysis, and some about how to plot advanced high-dimensional model predictions in low dimensional spaces, in a simplified fashion
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14
Regression Regularization, Lasso and Ridge models (X)
Learn about Regularization and to Regularize regression models using Lasso and Ridge Regression. Example regularizing an overfit Polynomial Multiple Regression Model
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15
Decision Tree Regression models (XI)
Learn Decision Tree Regression theory and to implement and regularize Decision Tree Regression models with Scikit-learn. Learn to prepare a dataset for use with Decision Tree Regression models and how to plot Decision Tree graphs and the output of Decision Tree Regression models
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16
Random Forest Regression (XII)
Learn to use Random Forest Regression / Ensembles for Prediction and Regularization. Learn to use importances for model creation and feature selection. Learn how importances change over different subsets of a dataset
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17
Voting Regression (XIII)
Learn to use the Voting Ensemble Regression model for prediction. Learn to use Voting Regression to augment and modify standard Regression models for extended functionality and advanced prediction
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18
Classification and Supervised Learning, overview
An overview of the Classification section of the video course. A description of the Classification theory and process
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19
Logistic Regression Classifier
Learn to use the Logistic Regression Classifier with a practical example, learn to create advanced decision surface plots, use exploratory seaborn pair plots, and learn to create useful classification reports and much more…
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20
The Naive Bayes Classifier
Learn to use the Naive Bayes Classifier. Learn some about Bayes theorem, conditional probability, model extrapolations, data quality effect on accuracy, practical modeling theory and more…
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21
K-Nearest Neighbor Classifier (KNN) [Extra Video]
Learn to use K-Nearest Neighbor Classifier (KNN). Learn to use heuristics and graphs to determine a useful number of neighbors and learn practical hands-on classification skills for datasets with complex data structures
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22
The Decision Tree Classifier
Learn to use the Decision Tree Classifier. Learn to Visualize Decision trees and to create corresponding Decision Surfaces.
Learn some tricks to enhance Decision Tree Classifiers performance and more...
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23
The Random Forest Classifier
Learn to use the Random Forest Classifier. Learn some theory about Random Forest Classifiers and importances. Learn to extract Decision Trees from a Random Forest and learn to graph importances and decision surfaces
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24
Linear Discriminant Analysis (LDA) [Extra Video]
Learn to use Linear Discriminant Analysis (LDA). Learn to use permutation importances for feature selection to overcome the complexity of environments with many features.
Learn to use ROC-curves, DET-curves, Precision-Recall graphs, and more…
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25
The Voting Classifier
Learn to use the Voting Classifier Ensemble. Learn to use the Voting Classifier as a tool to create almost arbitrary decision surfaces, Classification models, and more...
