Data Science CoursesA data science course can go a long way to boost employability. Whether you are a student or a professional. 

Demand for professional data science courses in academia is higher than ever. Data science courses prepare applicants for industry challenges by helping them acquire the knowledge and high-end skills needed to tackle real-world challenges. Whether you want a basic overview of a data science course or are looking forward to a promising career in this area, a specialized course will provide you with the exposure you need.

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 2022

1. MicroMasters Program in Statistics and Data Science

This program is a series of five courses designed to strengthen the foundations of machine learning, data science, and statistics. An ideal data science course for students who want to learn big data analysis. You can also use stochastic modeling and statistical inference to get a good understanding of data-driven forecasting. In this course, you can dig deeper into concepts such as statistics, data analysis methods, probabilities, and machine learning algorithms.

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
2. Data Science Specialization Course

This course contains all the tools and concepts you need for your Data Science course journey. They start by asking the right questions to draw conclusions and publish the final results. The skills acquired by creating data products using real-world data will be demonstrated in the final dissertation project. Upon completion of this course, students will be proud of an excellent portfolio that demonstrates 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
3. Machine Learning, Data Science, and Deep Learning with Python

The Data Science Specialized Course covers all major topics related to machine learning, including artificial neural networks and K-means clustering. In addition, you will learn the technical details of data visualization using Seaborn and MatPlotLib, as well as the actual implementation of machine learning. Generally, use MLLib Apache Spark scaling.

Major topics featured in this course:

  • Neural Networks and Deep Learning with Keras and TensorFlow
  • Transfer Learning
  • Image classification and recognition
  • Sentiment analysis
  • Multilevel 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.
4. Machine Learning with Javascript

Designed for Javascript developers, this data science course is designed for advanced memory profiling, building Tensorflow JS library-based apps, writing ML code, and other topics needed to fully understand related topics. In addition, you'll learn how to write programs that are compatible with both NodeJS and your browser. The program also teaches tricks and techniques for accelerating matrix-based code using the basics of linear algebra.

The primary topics featured in this course:

  • Identifying Relevant Data
  • Recording Observation Data
  • Algorithms Overview
  • Tensor Concatenation
  • Applications of Tensorflow
  • Linear Regression
  • Matrix Multiplication
  • Vectorized Solutions for increasing performance
  • Plotting MSE Values with Javascript
  • Logistic Regression
  • Stochastic and Batch Gradient Descent
5. The Complete Machine Learning Course with Python

If you are looking for a course that will help you build a solid foundation for your machine learning course, stop searching with this program. Learn how to distinguish machine learning from traditional programming, deep learning, and machine learning. You will also learn about neural networks, tensor operations, and advanced topics such as validation, dropout, testing, regularization, overfitting, 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
6. Data Science: Machine Learning

This discipline is provided by Harvard University and is designed to help applicants learn machine learning and related technical issues. Unlike other courses, this tutorial will help you dig deeper into ML data science techniques.

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
7. Intro to Machine Learning with PyTorch

Available in Udacity, this Nanodegree program is an ideal option for improving your skills and knowledge of data science courses, monitored models, data cleaning, and machine learning algorithms. In addition, candidates can explore other important topics such as unsupervised learning and deep learning. The course is divided into different steps, each step providing the learner with a hands-on experience in which 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
8. HarvardX's Data Science Professional Certificate

The HarvardX Data Science Course provides candidates with access to basic skills and knowledge to address the real-world challenges associated with the Data Analysis Course. The discipline covers several core concepts such as reasoning, machine learning, and regression. You will also learn how to develop basic skills such as data visualization using ggplot2, R programming, data wrangling using dplyr, and organizing Linux files.

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
9. Introduction to Machine Learning Course

This machine learning program helps you master the core areas of the subject, such as statistics and computer science, and use your predictive power efficiently. This is an ideal course for aspiring data scientists, data analysts, and others who want to build a career in a relevant field. Learn the details of the data exploration process through machine learning lenses.

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
10. ColumbiaX's Artificial Intelligence MicroMasters Program

If you are looking for a program that will help you gain a deeper understanding and expertise in AI and machine learning, stop your search in this course. These online data science courses cover all the core topics you need in this area. You can also gain hands-on experience in applying concepts through hands-on examples.

Core topics featured in this certification -

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

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 data science courses specializations listed above to start your journey. These courses are not only cost-effective but also offer the flexibility of learning anywhere, anytime.