Data Science Methods and Algorithms [2024]
Data Science Methods and Algorithms [2024], available at $54.99, has an average rating of 5, with 85 lectures, based on 42 reviews, and has 112 subscribers.
You will learn about Knowledge about Data Science methods, algorithms, theory, best practices, and tasks Deep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidence Detailed and deep Master knowledge of Regression, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised Learning Hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python libraries Advanced knowledge of A.I. prediction models and automatic model creation Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources Option: To use the Anaconda Distribution (for Windows, Mac, Linux) Master the Python 3 programming language for Data Handling Master Pandas 2 and 3 for Advanced Data Handling This course is ideal for individuals who are This course is for you, regardless if you are a beginner or an experienced Data Scientist or This course is for you, regardless if you have a Ph.D. or no education or experience at all It is particularly useful for This course is for you, regardless if you are a beginner or an experienced Data Scientist or This course is for you, regardless if you have a Ph.D. or no education or experience at all.
Enroll now: Data Science Methods and Algorithms [2024]
Summary
Title: Data Science Methods and Algorithms [2024]
Price: $54.99
Average Rating: 5
Number of Lectures: 85
Number of Published Lectures: 85
Number of Curriculum Items: 85
Number of Published Curriculum Objects: 85
Original Price: $174.99
Quality Status: approved
Status: Live
What You Will Learn
- Knowledge about Data Science methods, algorithms, theory, best practices, and tasks
- Deep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidence
- Detailed and deep Master knowledge of Regression, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised Learning
- Hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python libraries
- Advanced knowledge of A.I. prediction models and automatic model creation
- Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
- Option: To use the Anaconda Distribution (for Windows, Mac, Linux)
- Master the Python 3 programming language for Data Handling
- Master Pandas 2 and 3 for Advanced Data Handling
Who Should Attend
- This course is for you, regardless if you are a beginner or an experienced Data Scientist
- This course is for you, regardless if you have a Ph.D. or no education or experience at all
Target Audiences
- This course is for you, regardless if you are a beginner or an experienced Data Scientist
- This course is for you, regardless if you have a Ph.D. or no education or experience at all
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
-
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!
Course Curriculum
Chapter 1: Introduction to Data Science Methods and Algorithms
Lecture 1: Introduction
Lecture 2: Setup of the Anaconda Cloud Notebook
Lecture 3: Download and installation of the Anaconda Distribution (optional)
Lecture 4: The Conda Package Management System (optional)
Chapter 2: Master Python for Data Handling
Lecture 1: Overview of Python for Data Handling
Lecture 2: Python Integer
Lecture 3: Python Float
Lecture 4: Python Strings I
Lecture 5: Python Strings II: Intermediate String Methods
Lecture 6: Python Strings III: DateTime Objects and Strings
Lecture 7: Overview of Python Native Data Storage Structures
Lecture 8: Python Set
Lecture 9: Python Tuple
Lecture 10: Python Dictionary
Lecture 11: Python List
Lecture 12: Overview of Python Data Transformers and Functions
Lecture 13: Python While-loop
Lecture 14: Python For-loop
Lecture 15: Python Logic Operators and conditional code branching
Lecture 16: Python Functions I: Some theory
Lecture 17: Python Functions II: create your own functions
Lecture 18: Python Object Oriented Programming I: Some theory
Lecture 19: Python Object Oriented Programming II: create your own custom objects
Lecture 20: Python Object Oriented Programming III: Files and Tables
Lecture 21: Python Object Oriented Programming IV: Recap and More
Chapter 3: Master Pandas for Data Handling
Lecture 1: Master Pandas for Data Handling: Overview
Lecture 2: Pandas theory and terminology
Lecture 3: Creating a Pandas DataFrame from scratch
Lecture 4: Pandas File Handling: Overview
Lecture 5: Pandas File Handling: The .csv file format
Lecture 6: Pandas File Handling: The .xlsx file format
Lecture 7: Pandas File Handling: SQL-database files and Pandas DataFrame
Lecture 8: Pandas Operations & Techniques: Overview
Lecture 9: Pandas Operations & Techniques: Object Inspection
Lecture 10: Pandas Operations & Techniques: DataFrame Inspection
Lecture 11: Pandas Operations & Techniques: Column Selections
Lecture 12: Pandas Operations & Techniques: Row Selections
Lecture 13: Pandas Operations & Techniques: Conditional Selections
Lecture 14: Pandas Operations & Techniques: Scalers and Standardization
Lecture 15: Pandas Operations & Techniques: Concatenate DataFrames
Lecture 16: Pandas Operations & Techniques: Joining DataFrames
Lecture 17: Pandas Operations & Techniques: Merging DataFrames
Lecture 18: Pandas Operations & Techniques: Transpose & Pivot Functions
Lecture 19: Pandas Data Preparation I: Overview & workflow
Lecture 20: Pandas Data Preparation II: Edit DataFrame labels
Lecture 21: Pandas Data Preparation III: Duplicates
Lecture 22: Pandas Data Preparation IV: Missing Data & Imputation
Lecture 23: Pandas Data Preparation V: Data Binnings [Extra Video]
Lecture 24: Pandas Data Preparation VI: Indicator Features [Extra Video]
Lecture 25: Pandas Data Description I: Overview
Lecture 26: Pandas Data Description II: Sorting and Ranking
Lecture 27: Pandas Data Description III: Descriptive Statistics
Lecture 28: Pandas Data Description IV: Crosstabulations & Groupings
Lecture 29: Pandas Data Visualization I: Overview
Lecture 30: Pandas Data Visualization II: Histograms
Lecture 31: Pandas Data Visualization III: Boxplots
Lecture 32: Pandas Data Visualization IV: Scatterplots
Lecture 33: Pandas Data Visualization V: Pie Charts
Lecture 34: Pandas Data Visualization VI: Line plots
Chapter 4: Regression, Prediction & Supervised Learning
Lecture 1: Regression, Prediction, and Supervised Learning. Section Overview (I)
Lecture 2: The Traditional Simple Regression Model (II)
Lecture 3: The Traditional Simple Regression Model (III)
Lecture 4: Some practical and useful modelling concepts (IV)
Lecture 5: Some practical and useful modelling concepts (V)
Lecture 6: Linear Multiple Regression model (VI)
Lecture 7: Linear Multiple Regression model (VII)
Lecture 8: Multivariate Polynomial Multiple Regression models (VIII)
Lecture 9: Multivariate Polynomial Multiple Regression models (VIIII)
Lecture 10: Regression Regularization, Lasso and Ridge models (X)
Lecture 11: Decision Tree Regression models (XI)
Lecture 12: Random Forest Regression (XII)
Lecture 13: Voting Regression (XIII)
Chapter 5: Classification & Supervised Learning
Lecture 1: Classification and Supervised Learning, overview
Lecture 2: Logistic Regression Classifier
Lecture 3: The Naive Bayes Classifier
Lecture 4: K-Nearest Neighbor Classifier (KNN) [Extra Video]
Lecture 5: The Decision Tree Classifier
Lecture 6: The Random Forest Classifier
Lecture 7: Linear Discriminant Analysis (LDA) [Extra Video]
Lecture 8: The Voting Classifier
Chapter 6: Cluster Analysis & Unsupervised Learning
Lecture 1: Cluster Analysis, an overview
Lecture 2: K-Means Cluster Analysis
Lecture 3: K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms
Lecture 4: Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
Lecture 5: Four Hierarchical Clustering algorithms
Instructors
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Henrik Johansson
Instructor at Udemy
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 0 votes
- 5 stars: 43 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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