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10 Best Data Science Courses [Recommended by Data Scientist]

Posted in Data Science, Courses

Ramya Shankar

Last Updated 27 Oct, 2021

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Here is the list of best data science courses that will help you build your knowledge base in this subject.

Top 10 Data Science Courses in 2021

1. MicroMasters Program in Statistics and Data Science

This program features a five-course series formulated to strengthen their foundation in machine learning, data science, and statistics. It is an ideal course for students who wish to learn big data analysis. Plus, you’ll also acquire a good understanding of making data-driven predictions using probabilistic modeling and statistical inference. With this course, you can dive deeper into the concepts of statistics, data analysis techniques, probability, machine learning algorithms, and more.

The course covers a wide range of topics:

 

 

 

• Probabilistic Models Introduction
• Data Analysis In Social Science
• Statistics Fundamentals
• Machine Learning With Python
• Big Data Analysis
• Deep Neural Networks
• Clustering Methodologies

On completing this specialization, students can apply for various job roles such as system analyst, data analyst, data scientist, etc. Additionally, you’ll also acquire practical skills in unsupervised learning techniques and supervised techniques.

Prerequisites: Proficiency in python programming, mathematical reasoning, and college-level calculus is required.

Level: Intermediate
Duration: 14 months 10-14 hours weekly (approximately)

You can signup here.

2. Data Science Specialization Course

This course includes all the tools and concepts that you will require in your data science journey. They start by asking the right questions to draw inferences and, lastly, publishing the achieved results. The skills you learn by using the real-world data to build a data product are exhibited in the final capstone project. When this course gets completed, students can boast of a great portfolio that will display their mastery of the subject.

It is a fun and exciting program to learn the following topics:

 

• Github
• R Programming
• Machine Learning
• Data Science
• Regression Analysis
• Rstudio
• Debugging
• Data Analysis
• Cluster Analysis
• Regular Expression
• Data Manipulation
• Data Cleansing

You will learn the data scientist’s toolbox’s main ideas and tools by opting for this course. You will get an overview of the tools, questions, and data that data scientists and data analysts require to work. This course includes two components, including the know-how of how to turn data into actionable knowledge.

Prerequisites: Experience in programming (in any language). We also recommend that the learner knows algebra (linear and calculus algebra are not required).

Level: Beginner
Duration: 11 Months 7 Hours Weekly (approximately)

You can signup here.

3. Machine Learning, Data Science, and Deep Learning with Python

The specialization covers all the major topics related to machine learning, including artificial neural networks, K-means clustering, etc. Additionally, you'll learn the technicalities of data visualization with Seaborn and MatPlotLib and the practical implementation of machine learning at a large scale with MLLib Apache Spark.

Major topics featured in this course:

• Neural Networks and Deep Learning with Keras and TensorFlow
• Transfer Learning
• Image classification and recognition
• Sentiment analysis
• Multi-Level Models
• Regression analysis
• Multiple Regression
• Random Forests and Decision Trees
• A/B Tests and Experimental Design
• Collaborative Filtering
• Reinforcement Learning
• Support Vector Machines
• Feature Engineering
• Hyperparameter Tuning, and more.

With this course, you'll also learn to classify sentiments, images, and data using deep learning concepts. It is an ideal learning program for professional programmers and data analysts intending to switch their careers. You can opt for this specialization even if you are new to Python as it features a crash course for a better understanding of the subject.

Prerequisites:

• Linux, Mac, or Windows computer that can run new versions like Anaconda 3.
• Prior experience in scripting or programming is mandatory.
• It would help if you were skilled in high school level mathematics.

Level: Intermediate
Duration:14 hours (approximately)

You can signup here.

 

5. The Complete Machine Learning Course with Python

If you are looking for a course that can help you build a strong foundation in Machine Learning, then end your search with this program. You'll learn to differentiate between machine learning and classical programming, deep learning, and machine learning. Plus, you'll also acquire knowledge about neural networks, tensor operations, and advanced topics such as validation, dropout, testing, regularization, under and overfitting.

This learning program provides detailed insight into the following topics:

• Linear Regression with Scikit-Learn
• Robust Regression
• Cross-validation
• Logistic Regression
• Confusion Matrix
• Concepts of Support Vector Machine
• Radial Basis Function
• Linear SVM Classification
• Visualizing Boundary
• Ensemble Machine Learning Methods
• Gradient Boosting Machine
• kNN introduction
• Dimensionality Reduction Concept
• Clustering

You'll acquire a good understanding of machine learning tools used for tackling real-world issues. It's a perfect course to learn about ML performance metrics, including recall, R-squared, confusion matrix, MSE, prevision, accuracy, etc.

 

 

Prerequisites:

• Fundamental knowledge of Python programming is required.
• Experience in linear algebra.

Level: Beginner-Intermediate
Duration: 17.5 Hours (approximately)

You can signup here.

6. Data Science: Machine Learning

Offered by Harvard University, this specialization is created to help the aspirants learn machine learning and the technical problems associated with it. Unlike other courses, this learning program will help you dig deeper into ML's data science methodologies.

The following are the core topics featured in this course:

 

 

 

• Machine Learning Basics
• Principal Component Analysis
• Machine Learning Algorithms
• Building Recommendation System
• Regularization and its uses
• Cross-Validation

The program also offers knowledge of training data and efficient ways of using data set for discovering predictive relationships. On signing up for this course, you'll even know about implementing machine learning in various products such as speech recognition, postal service, spam detectors, etc.

Prerequisites: None
Level: Beginner
Duration: 8 weeks – 2-4 hours per week (approximately)

You can signup here.

7. Intro to Machine Learning with PyTorch

Available at Udacity, this Nanodegree program is an ideal option to enhance your skills and knowledge in supervised models, data cleaning, and machine learning algorithms. Additionally, candidates can also explore other important topics like unsupervised and deep learning. The course is divided into different steps, with each one offering practical experience to the learners where they can test their skills through code projects and exercises.

The course is inclusive of the following topics:

• Model Construction
• Neural Network Design
• Pytorch Training
• Unsupervised Learning Method Implementation
• Deep Learning

The specialization offers the experience of handling real projects where candidates learn to create immersive content for top tier organizations. Plus, you'll also attain a major in the relevant tech skills. The learners are also guided for various training sessions, interview preparation, professional profile maintenance, and other crucial career growth areas.

Prerequisites: Fundamental knowledge of Python programming is needed.
Level: Intermediate
Duration: 3 months / 10 hours per week (approximately) (3 months access)

You can signup here.

8. HarvardX's Data Science Professional Certificate

The HarvardX Data Science program allows the candidates to access essential skills and knowledge for handling real-world challenges related to data analysis. The specialization features various core concepts, including inference, machine learning, and regression. You'll also learn to develop basic skill sets, including data visualization using ggplot2, R programming, data wrangling using dplyr, Linux file organization, etc.

Following are the topics covered in this program -

• Data Science Basics
• Data Science Visualization and Probability
• Inference and Modeling
• Productivity Tools
• Wrangling
• Linear Regression
• Machine Learning
• Capstone

Besides, you'll also learn the tricks and techniques of implementing machine learning algorithms using advanced tools. The program will allow you to gain a deeper knowledge of data science concepts through business case studies. This specialization offers a unique opportunity for learning with the global partners of the university.

 

 

 

Prerequisites: None
Level: Beginner
Duration: 1 year five months – 3 hours/week (approximately)

You can signup here.

9. Introduction to Machine Learning Course

This Machine learning program will help you master the subject's core areas, including statistics and computer science, to efficiently leverage the predictive power. It's an ideal course for aspiring data scientists, data analysts, and others who want to build their career in the relevant field. You'll learn about the details of data investigating processes through the lens of machine learning.

The course is created to help candidates learn the following concepts:

• Use of Naïve Bayes
• Posterior Probability Calculation
• Support Vector Machines
• Coding Decision tree using Python
• Choosing a Machine Learning Algorithm
• Enron Email Dataset Patterns
• Regressions and Outliners
• Clustering and Scaling

In addition to the above, you'll also learn how to extract and identify useful Machine Learning features for the best representation of the data. The course offers a rich learning experience to the candidates through its professionally designed syllabus. Plus, there are interactive quizzes for the students to test their skills and knowledge in the subject.

Prerequisites – Background in Machine Learning or relevant experience is needed.
Level: Intermediate
Duration: 10 weeks (approximately)

You can signup here.

10. ColumbiaX's Artificial Intelligence MicroMasters Program

If you are looking for a program that can help you gain an in-depth understanding and expertise in AI and Machine Learning, then end your search with this course. This online learning course covers all the core topics needed in this field. Plus, you'll also gain hands-on experience in applying the concepts through real-life examples.

Core topics featured in this certification -

• Principles of Artificial Intelligence
• Machine Learning Essentials and Algorithms
• Robotics
• Animation and CGI Motion
• Neural Networks Designing

It is an ideal course for students pursuing Computer Science graduate courses who want to enhance their knowledge of the subject. The specialization allows the candidates to develop automated computer systems to improve their overall working experience through bioinformatics, robotic control, autonomous navigation, data mining, and other advanced systems.

Prerequisites:

 

 

 

• Candidates must be good with calculus, statistics, and advanced algebra.
• Fundamental knowledge of programming languages is needed.

Level: Beginner
Cost: $894
Duration: 1 year 8-10 hours/week (approximately)

You can signup here.

Conclusion

Machine learning is a fun and interesting subject to learn that allows individuals to boundlessly experiment with their skills and knowledge. To build a career in this field, start with gaining complete knowledge of ML and the related concepts. Pick any of the specializations listed above to start your journey. These courses are not only cost-effective but also offer the flexibility of learning anywhere, anytime.