Data science is about best online data science courses 2022 using statistics to draw insights from data to generate action and strengthen company effectiveness.
You don’t need to spend $1000s to learn Data Science courses online. Stanford University, Harvard University & Massachusetts Institute of Technology is providing free courses.
Data science is the process by which we analyze and put to use the vast amount of information produced in the contemporary world. Data scientists are professionals at analyzing, presenting, and sharing information using scientific methodologies to support strategic, data-driven decisions.
In accordance with Coursera, 30% of certificate holders commenced a new job immediately after finishing this specialization.
Learn Data Science Courses Online for Free
Here’s 8 free Courses that’ll teach you Data Science better than the paid ones.
Select a data science course that permits you to complete initiatives while you understand. Then, showcase your know-how with digital portfolios that businesses can access when considering your software
1. CS50’s Introduction to Artificial Intelligence with Python (Harvard)
In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
Method of Application
Interested students should:
2. Data Science: Machine Learning (Harvard)
In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
Method of Application
Interested students should:
Click Here to view details
3. Artificial Intelligence (MIT)
This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
Method of Application
Interested students should:
4. Introduction to Computational Thinking and Data Science (MIT)
This course aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.
Method of Application
Interested students should:
5. Machine Learning (MIT)
This course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.
Method of Application
Interested students should:
6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)
Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.
Method of Application
Interested students should:
r
7. Statistical Learning (Stanford)
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
In case you’re rusty with statistics, consider the Statistics with Python Specialization very first. You’ll study most of The main statistical competencies needed for data science.
Method of Application
Interested students should:
8. Mining Massive Data Sets (Stanford)
We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. The rest of the course is devoted to algorithms for extracting models and information from large datasets. Participants will learn how Google’s PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes.
Method of Application
Interested students should:
Conclusion
Subject areas protected include instruction and check sets; characteristic extraction; rules of equipment Studying techniques; regression; pattern recognition strategies; unsupervised Understanding tactics; assessment and diagnostics (overfitting, mistake costs, residual Examination, design assumptions checking and feature collection); ethical difficulties in data science; and communicating conclusions to stakeholders in created, oral, visual and electronic form.
And, Despite the fact that The share is increased as compared to 2019 (12%), Data Science remains to be very new to be a self-discipline. That’s why it’s not greatly presented in universities across the globe nonetheless.
Furthermore, just about 12% on the data scientists around the globe had only completed an undergraduate prior to coming into the sector. Although in a few countries, like India, the number goes nearly 31%.
The professionals in the field educate the students and supply them with total guidance. Via their very own encounters, the instructors offered simple undertaking know-how. They emphasise simple expertise generally.
