# 10 Best Data Science Courses and Certifications Online

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There are thousands of online courses and classes that will help you enhance your Data Science skills and earn your Data Science certificate.

In this post, our specialists have actually assembled a curated list of the 10 Best of the Best Data Science courses, tutorials, training programs, classes and certifications that are available online right now.

We have included just those courses that satisfy our top quality requirements. We have put a great deal of effort and time into gathering these all for you. These courses are suitable for all levels, beginners, intermediate learners, and experts.

Here’s a look at these courses and what they have to offer for you!

## 10 Best Data Science Courses and Certifications Online

### 1. Machine Learning A-Z™: Hands-On Python & R In Data Science by “Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team” Udemy Course Our Best Pick

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

As of right now, more than 888481+ people have enrolled in this course and there are over 161303+ reviews.

Course Content
Welcome to the course! Here we will help you get started in the best conditions.
——————– Part 1: Data Preprocessing ——————–
Data Preprocessing in Python
Data Preprocessing in R
——————– Part 2: Regression ——————–
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
Regression Model Selection in Python
Regression Model Selection in R
——————– Part 3: Classification ——————–
Logistic Regression
K-Nearest Neighbors (K-NN)
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Classification Model Selection in Python
Evaluating Classification Models Performance
——————– Part 4: Clustering ——————–
K-Means Clustering
Hierarchical Clustering
——————– Part 5: Association Rule Learning ——————–
Apriori
Eclat
——————– Part 6: Reinforcement Learning ——————–
Upper Confidence Bound (UCB)
Thompson Sampling
——————– Part 7: Natural Language Processing ——————–
——————– Part 8: Deep Learning ——————–
Artificial Neural Networks
Convolutional Neural Networks
——————– Part 9: Dimensionality Reduction ——————–
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Kernel PCA
——————– Part 10: Model Selection & Boosting ——————–
Model Selection
XGBoost
Bonus Lectures

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### 2. Python for Data Science and Machine Learning Bootcamp by Jose Portilla Udemy Course

“Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!”

As of right now, more than 568581+ people have enrolled in this course and there are over 119090+ reviews.

Course Content
Course Introduction
Environment Set-Up
Jupyter Overview
Python Crash Course
Python for Data Analysis – NumPy
Python for Data Analysis – Pandas
Python for Data Analysis – Pandas Exercises
Python for Data Visualization – Matplotlib
Python for Data Visualization – Seaborn
Python for Data Visualization – Pandas Built-in Data Visualization
Python for Data Visualization – Plotly and Cufflinks
Python for Data Visualization – Geographical Plotting
Data Capstone Project
Introduction to Machine Learning
Linear Regression
Logistic Regression
K Nearest Neighbors
Decision Trees and Random Forests
Support Vector Machines
K Means Clustering
Principal Component Analysis
Recommender Systems
Natural Language Processing
Neural Nets and Deep Learning
Big Data and Spark with Python
BONUS SECTION: THANK YOU!

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### 3. The Data Science Course 2022: Complete Data Science Bootcamp by “365 Careers, 365 Careers Team” Udemy Course

“Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning”

As of right now, more than 518631+ people have enrolled in this course and there are over 111252+ reviews.

Course Content
Part 1: Introduction
The Field of Data Science – The Various Data Science Disciplines
The Field of Data Science – Connecting the Data Science Disciplines
The Field of Data Science – The Benefits of Each Discipline
The Field of Data Science – Popular Data Science Techniques
The Field of Data Science – Popular Data Science Tools
The Field of Data Science – Careers in Data Science
The Field of Data Science – Debunking Common Misconceptions
Part 2: Probability
Probability – Combinatorics
Probability – Bayesian Inference
Probability – Distributions
Probability – Probability in Other Fields
Part 3: Statistics
Statistics – Descriptive Statistics
Statistics – Practical Example: Descriptive Statistics
Statistics – Inferential Statistics Fundamentals
Statistics – Inferential Statistics: Confidence Intervals
Statistics – Practical Example: Inferential Statistics
Statistics – Hypothesis Testing
Statistics – Practical Example: Hypothesis Testing
Part 4: Introduction to Python
Python – Variables and Data Types
Python – Basic Python Syntax
Python – Other Python Operators
Python – Conditional Statements
Python – Python Functions
Python – Sequences
Python – Iterations
Part 5: Advanced Statistical Methods in Python
Advanced Statistical Methods – Linear Regression with StatsModels
Advanced Statistical Methods – Multiple Linear Regression with StatsModels
Advanced Statistical Methods – Linear Regression with sklearn
Advanced Statistical Methods – Practical Example: Linear Regression
Advanced Statistical Methods – Logistic Regression
Advanced Statistical Methods – Cluster Analysis
Advanced Statistical Methods – K-Means Clustering
Advanced Statistical Methods – Other Types of Clustering
Part 6: Mathematics
Part 7: Deep Learning
Deep Learning – Introduction to Neural Networks
Deep Learning – How to Build a Neural Network from Scratch with NumPy
Deep Learning – TensorFlow 2.0: Introduction
Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
Deep Learning – Overfitting
Deep Learning – Initialization
Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
Deep Learning – Preprocessing
Deep Learning – Classifying on the MNIST Dataset
Deep Learning – Business Case Example
Deep Learning – Conclusion
Appendix: Deep Learning – TensorFlow 1: Introduction
Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Dataset
Appendix: Deep Learning – TensorFlow 1: Business Case
Software Integration
Case Study – What’s Next in the Course?
Case Study – Preprocessing the ‘Absenteeism_data’
Case Study – Applying Machine Learning to Create the ‘absenteeism_module’
Case Study – Analyzing the Predicted Outputs in Tableau
Appendix – pandas Fundamentals
Appendix – Working with Text Files in Python
Bonus Lecture

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### 4. R Programming A-Z™: R For Data Science With Real Exercises! by “Kirill Eremenko, Ligency I Team, Ligency Team” Udemy Course

“Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2”

As of right now, more than 237716+ people have enrolled in this course and there are over 47229+ reviews.

Course Content
Hit The Ground Running
Core Programming Principles
Fundamentals Of R
Matrices
Data Frames
Homework Solutions
Bonus Tutorials

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### 5. Data Science A-Z™: Real-Life Data Science Exercises Included by “Kirill Eremenko, Ligency I Team, Ligency Team” Udemy Course

“Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!”

As of right now, more than 204905+ people have enrolled in this course and there are over 32095+ reviews.

Course Content
Get Excited
What is Data Science?
————————— Part 1: Visualisation —————————
Introduction to Tableau
How to use Tableau for Data Mining
————————— Part 2: Modelling —————————
Stats Refresher
Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Building a robust geodemographic segmentation model
Model maintenance
————————— Part 3: Data Preparation —————————
ETL Phase 1: Data Wrangling before the Load
Handling errors during ETL (Phases 1 & 2)
SQL Programming for Data Science
ETL Phase 3: Data Wrangling after the load
Handling errors during ETL (Phase 3)
————————— Part 4: Communication —————————
Working with people
Presenting for Data Scientists
Homework Solutions
Bonus Lectures

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### 6. “Machine Learning, Data Science and Deep Learning with Python” by “Sundog Education by Frank Kane, Frank Kane, Sundog Education Team” Udemy Course

“Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks”

As of right now, more than 166859+ people have enrolled in this course and there are over 27669+ reviews.

Course Content
“Getting Started
Statistics and Probability Refresher, and Python Practice
Predictive Models
Machine Learning with Python
Recommender Systems
More Data Mining and Machine Learning Techniques
Dealing with Real-World Data
Apache Spark: Machine Learning on Big Data
Experimental Design / ML in the Real World
Deep Learning and Neural Networks
Generative Models
Final Project

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### 7. Statistics for Data Science and Business Analysis by “365 Careers, 365 Careers Team” Udemy Course

“Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis”

As of right now, more than 150018+ people have enrolled in this course and there are over 32673+ reviews.

Course Content
“Introduction
Sample or population data?
The fundamentals of descriptive statistics
Measures of central tendency, asymmetry, and variability
Practical example: descriptive statistics
Distributions
Estimators and estimates
Practical example: inferential statistics
Hypothesis testing: Introduction
Hypothesis testing: Let’s start testing!
Practical example: hypothesis testing
The fundamentals of regression analysis
Subtleties of regression analysis
Assumptions for linear regression analysis
Dealing with categorical data
Practical example: regression analysis
Bonus lecture”

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### 8. Python A-Z™: Python For Data Science With Real Exercises! by “Kirill Eremenko, Ligency I Team, Ligency Team” Udemy Course

“Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization”

As of right now, more than 146076+ people have enrolled in this course and there are over 24563+ reviews.

Course Content
Welcome To The Course
Core Programming Principles
Fundamentals Of Python
Matrices
Data Frames
Homework Solutions
Bonus Lectures

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### 9. Data Science and Machine Learning Bootcamp with R by Jose Portilla Udemy Course

Learn how to use the R programming language for data science and machine learning and data visualization!

As of right now, more than 83152+ people have enrolled in this course and there are over 15164+ reviews.

Course Content
Course Introduction
Course Best Practices
Windows Installation Set-Up
Mac OS Installation Set-Up
Linux Installation
Development Environment Overview
Introduction to R Basics
R Matrices
R Data Frames
R Lists
Data Input and Output with R
R Programming Basics
Data Manipulation with R
Data Visualization with R
Data Visualization Project
Interactive Visualizations with Plotly
Capstone Data Project
Introduction to Machine Learning with R
Machine Learning with R – Linear Regression
Machine Learning Project – Linear Regression
Machine Learning with R – Logistic Regression
Machine Learning Project – Logistic Regression
Machine Learning with R – K Nearest Neighbors
Machine Learning Project – K Nearest Neighbors
Machine Learning with R – Decision Trees and Random Forests
Machine Learning Project – Decision Trees and Random Forests
Machine Learning with R – Support Vector Machines
Machine Learning Project – Support Vector Machines
Machine Learning with R – K-means Clustering
Machine Learning Project – K-means Clustering
Machine Learning with R – Natural Language Processing
Machine Learning with R – Neural Nets
Machine Learning Project – Neural Nets
Bonus Section

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### 10. Complete Machine Learning & Data Science Bootcamp 2022 by “Andrei Neagoie, Daniel Bourke, Zero To Mastery” Udemy Course

“Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!”

As of right now, more than 72562+ people have enrolled in this course and there are over 11987+ reviews.

Course Content
“Introduction
Machine Learning 101
Machine Learning and Data Science Framework
The 2 Paths
Data Science Environment Setup
Pandas: Data Analysis
NumPy
Matplotlib: Plotting and Data Visualization
Scikit-learn: Creating Machine Learning Models
Supervised Learning: Classification + Regression
Milestone Project 1: Supervised Learning (Classification)
Milestone Project 2: Supervised Learning (Time Series Data)
Data Engineering
Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2
Storytelling + Communication: How To Present Your Work
Learn Python
Learn Python Part 2
Where To Go From Here?
BONUS SECTION”

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### How Long Does It Take to Learn Data Science?

The answer to the question “How long does it ttake to learn Data Science” is … it depends. Everyone has different needs, and everybody is operating in different scenarios, so the answer for one person may be totally different than for another person.

Consider these questions: What are you trying to Learn Data Science for? Where is your beginning point? Are you a newbie or do you have experience with Data Science? How much can you practice? 1 hour per day? 40 hours each week? Check out this course about Data Science.

### Is Data Science Easy Or Hard to Learn?

No, learning Data Science isn’t hard for many people. Check this course on how to Learn Data Science in no time!

### How to Learn Data Science Fast?

The fastest method to Learn Data Science is to first get this Data Science course, then practice whatever you learn whenever you can. Even if its simply 15 minutes a day of practice. Consistency is essential.

### Where to Learn Data Science?

If you want to explore and learn Data Science, then Udemy provides you the best platform to learn the Data Science. Check this course on how to Learn Data Science in no time!