Data Science Methods and Algorithms [2024]
- Description
- Curriculum
- FAQ
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Welcome to the course Data Science Methods and Algorithms with Pandas and Python!
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 Algorithms 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 algorithms, 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.
This is a five-in-one master class video course which will teach you to master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, Unsupervised Learning, Python 3, Pandas 2 + 3, and advanced Data Handling.
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 to master the Python 3 programming language, which is one of the most popular and useful programming languages in the world, and you will learn to use it for Data Handling.
You will learn to master the Pandas 2 and future 3 library and to use Pandas powerful Data Handling techniques for advanced Data Handling tasks. The Pandas library is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation tool, which is directly usable with the Python programming language, and combined creates the world’s most powerful coding environment for Data Handling and Advanced Data Handling…
You will learn
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Knowledge about Data Science methods, algorithms, 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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Master the Python 3 programming language for Data Handling
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Master Pandas 2 and 3 for Advanced Data Handling
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And much more…
This course includes
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a comprehensive and easy-to-follow teaching package for Mastering Python and Pandas for Data Handling, which makes anyone able to learn the course contents regardless of beforehand knowledge of programming, tabulation software, Python, Data Science, or Machine Learning
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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
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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, Cluster analysis, Python, Pandas and Data Handling! These are the most important and useful tools for modeling, AI, and forecasting. Data Handling is the process of making data useful and usable for regression, prediction, classification, cluster analysis, and data analysis.
Most Data Scientists and Machine Learning Engineers spends about 80% of their working efforts and time on Data Handling tasks. Being good at Python, Pandas, and Data Handling are extremely useful and time-saving skills that functions as a force multiplier for productivity.
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, Python, Pandas, and Data Handling.
Course requirements
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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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Programming experience is not needed and you will be taught everything you need
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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 35+ 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 to use Anaconda Cloud Notebook
Using Anaconda Cloud Notebook requires internet access
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
Overview of Python for Data Handling
This video provides an overview of "Python for data handling", teaches you some Python and Data Handling theory, and presents a table of contents for Python for Data Handling as well as some basic information about the Jupyter IDE with dynamic typing, Python programs organization, and some fundamental Python language syntax
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6
Python Integers
Learn to use Python Integers
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7
Python Floats
Learn to use Python Floats
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8
Python Strings
Learn to use Python Strings
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9
Python String Methods
Learn to use some Python string methods to test, search, transform, change, and manipulate string data
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10
Python Strings III: DateTime Objects and Strings
Learn to use date and time data with Python's Datetime module. Learn to calculate time durations and time event data
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11
Overview of Python Native Data Storage Structures
This video provides an overview of the part of this section about Python's data storage abstractions, the set, tuple, dictionary, and the list
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12
Python Set
Learn to use Python's native Set
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13
Python Tuple
Learn to use Python's native Tuple and how to unpack Tuples
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14
Python Dictionary
Learn to use Python's native Dictionary
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15
Python List
Learn to use Python's native List
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16
Overview of Python Data Transformers and Functions
An overview of the contents of this subpart of the section, Python's data transformers, and functions
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17
Python While-loop
Learn to use Python's native while-loop with some practical examples
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18
Python For-loop
Learn to use Python's native for-loop with some practical examples
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19
Python Logic Operators and conditional code branching
Learn to use some of Python's logic operators and conditional code branching. Use your learned knowledge to edit and tailor basic descriptive statistics at a detailed level
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20
Python Functions I: Some theory
This video lecture describes the theoretical advantages of Python's functions
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21
Python Functions II: create your own functions
Learn practical coding with Python's functions. You are introduced to functions and basic protections for functions. You will learn how to create functions from code-examples from earlier video lectures, and you will learn how to generalize functions up to advanced uneven-multitype-object 2-dimensional list of lists
Learn to create your own functions!
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22
Python Object Oriented Programming I: Some theory
Learn Python OOP theory relevant for data handling tasks and how object-oriented data structures may affect data handling
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23
Python Object Oriented Programming II: create your own custom objects
Learn to code object-oriented programming with Python, and to handle Python object-oriented code and custom objects within the ambit of data handling
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24
Python Object Oriented Programming III: Files and Tables
Learn to save files in Python and the practical process of converting custom Python objects to tabular form and saving these into .csv, and Excel files and to load files to Pandas Data Frames
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25
Python Object Oriented Programming IV: Recap and More
This video lecture is a recap and extension of earlier video lectures. You will assemble knowledge from earlier lectures into more powerful knowledge. You will learn to construct a tabular data form with additional calculated variables and how to use the tabular data form for plotting, etc. You will learn how Data Handling fits with advanced object-oriented program structures.
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26
Master Pandas for Data Handling: Overview
This video provides and introduction and overview of this section of the video course. "Master Pandas for Data Handling" is updated to current Pandas 2.2 and all known new changes in the future Pandas 3 version.
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27
Pandas theory and terminology
Learn the fundamental concepts and language of the Pandas DataFrame, the Pandas Series, and the data or object content of a DataFrame/Series object.
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28
Creating a Pandas DataFrame from scratch
Learn to create Pandas DataFrame from scratch using Python and Pandas. You will learn how to create Pandas DataFrames using Python Dictionaries, Lists, and lots more.
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29
Pandas File Handling: Overview
This video contains an overview of the Pandas File Handing part of this section.
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30
Pandas File Handling: The .csv file format
Learn to load and save files from/to Pandas DataFrames from .csv files.
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31
Pandas File Handling: The .xlsx file format
Learn to load and save files from/to Pandas DataFrames from .xlsx files and hierarchical .xlsx files.
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32
Pandas File Handling: SQL-database files and Pandas DataFrame
Learn to load and save files from/to Pandas Dataframes from a SQL-database file.
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33
Pandas Operations & Techniques: Overview
This video contains an overview of the Pandas Operations and Techniques part of this section.
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34
Pandas Operations & Techniques: Object Inspection
Learn to inspect Pandas Dataframes and Dataframe content with Pandas .info() method, Python's .type() method, and more
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35
Pandas Operations & Techniques: DataFrame Inspection
Learn to inspect the contents of large-sized Pandas DataFrames. Learn to use the .head, .tail, and other general methods to inspect the contents of a DataFrame
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36
Pandas Operations & Techniques: Column Selections
Learn to select subsets of Columns from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame
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37
Pandas Operations & Techniques: Row Selections
Learn to select subsets of Rows from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame
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38
Pandas Operations & Techniques: Conditional Selections
Learn to make conditional selections of subsets from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame
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39
Pandas Operations & Techniques: Scalers and Standardization
Learn about Scalers, Normalization, and Standardization. Learn to use mean-correction, normalization, and zero-one unity-based normalization
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40
Pandas Operations & Techniques: Concatenate DataFrames
Learn to Concatenate Pandas DataFrames. Learn to use Pandas .concat() function to add DataFrames together horizontally and vertically. Learn to use the .concat() function with Inner and Outer joins
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41
Pandas Operations & Techniques: Joining DataFrames
Learn to join Pandas DataFrames. Learn to use Pandas DataFrames .join() method. Learn to use "left joins", "right joins", "inner joins", "outer joins", and "cross joins"
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42
Pandas Operations & Techniques: Merging DataFrames
Learn to merge Pandas DataFrames. Learn to use Pandas DataFrames .merge() method. Learn to use "left joins", "right joins", "inner joins", and "outer joins" to merge different DataFrames on column variables
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43
Pandas Operations & Techniques: Transpose & Pivot Functions
Learn to Transpose and Pivot Pandas DataFrames. Learn to use the transpose, pivot, pivot_table, and melt functions
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44
Pandas Data Preparation I: Overview & workflow
This video has an overview of the Data Preparation part of the course and includes a workflow for Data preparation or so-called data cleaning
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45
Pandas Data Preparation II: Edit DataFrame labels
Learn to edit Pandas DataFrame column names, index, and index labels
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46
Pandas Data Preparation III: Duplicates
Learn about Duplicates. Duplicate rows or observations may impact the quality of data products. Learn how to properly handle Duplicates with Pandas functionality
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47
Pandas Data Preparation IV: Missing Data & Imputation
Learn to handle Missing data and Missing values with Pandas functionality. Learn Imputation and to augment Pandas with scikit-learn to use advanced model-based imputation of missing data
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48
Pandas Data Preparation V: Data Binnings [Extra Video]
Learn Data Binning with Pandas. Learn to use Administrative Data Binning, Algorithmic Data Binning, and subjective Data Binning. Learn to use Pandas .qcut() and .cut() functions.
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49
Pandas Data Preparation VI: Indicator Features [Extra Video]
Learn to create Indicator Features or Dummy Features with Pandas
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50
Pandas Data Description I: Overview
This video provides an overview of the part of this section about Pandas Data Description
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51
Pandas Data Description II: Sorting and Ranking
Learn to use Pandas functions for Sorting and Ranking data
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52
Pandas Data Description III: Descriptive Statistics
Learn to create useful descriptive statistics with Pandas .agg() and .describe() functions. Learn to augment Pandas functions with the powerful .apply() and .value_counts() functions
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53
Pandas Data Description IV: Crosstabulations & Groupings
Learn to create crosstabulations with Pandas .crosstab() function and to use the powerful Pandas .groupby() operation. Learn to augment these functions with a selection of Pandas functionality
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54
Pandas Data Visualization I: Overview
This video contains an overview of Pandas Data Visualization and gives an overview of the contents of this part of the section Master Pandas for Data Handling
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55
Pandas Data Visualization II: Histograms
Learn to make Histograms with Pandas, Matplotlib, and Seaborn. You will learn to make simple Histograms, advanced Histograms, multi-dimensional Histograms, and advanced Jointgrid Histograms
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56
Pandas Data Visualization III: Boxplots
Learn to make traditional and modern Boxplots with Pandas, Matplotlib, and Seaborn. You will learn to make Boxplots, Boxenplots, Violinplots, Swarmplots and to create graphs consisting of many types of boxplots
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57
Pandas Data Visualization IV: Scatterplots
Learn to make scatterplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple scatterplots, advanced scatterplots, advanced multi-scatterplots, and advanced pairplots of scatterplots
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58
Pandas Data Visualization V: Pie Charts
Learn to make Pie Charts with Pandas, Matplotlib, and support from Seaborn. You will learn to make Pie Charts, detailed Pie Charts, multiple Pie Charts, and how to properly use Pie Charts for effect
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59
Pandas Data Visualization VI: Line plots
Learn to make Lineplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple Lineplots, advanced Lineplots, advanced Line-area plots, and advanced multidimensional Line-area plots
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60
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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61
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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62
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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63
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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64
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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65
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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66
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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67
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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68
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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69
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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70
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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71
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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72
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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73
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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74
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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75
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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76
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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77
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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78
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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79
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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80
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...
