18 Best Online Courses on Maths, Algorithms, Data Structures for Data Science and Machine Learning

As the buzz around data science and machine learning grows every day, there are a lot of self-taught folks who have kick-started the machine learning journey with online courses on data science and machine learning & AI. Those who are not from Computer Science background and want to pursue a career in data science or machine learning, they need to be familiar with mathematics, linear algebra, calculus, algorithms, statistics, and data structures. In this post, we have aggregated a curated list of most popular and best online courses on Mathematics, Algorithms, Data Structures & Statistics for Data Science and Machine Learning.

It’s tough to learn much of anything by just reading a Data Science or ML/AI book. Well, you gotta be pretty good at math to understand everything. So, my suggestion is to enroll in some Maths/stats, Algorithms, Data Structure course, and start working on basics. In this article, you can get to know data structures and algorithms for data science, the difference between data structures and data science, data structures and algorithms in python for data science, data structures for data science course etc.

MOOCs and online courses are useful to jump start. After you do a MOOC though you have to continue with doing real work as in exercises, Kaggle competitions and just playing with things.

Best Online Courses on Mathematics, Algorithms, Data Structures & Statistics

Mathematics for Machine Learning & Data Science – Imperial College London

Ratings: 4.6

Students Enrolled: 160K+

For higher-level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics.

This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

In the first course on Linear Algebra, you will look at what linear algebra is and how it relates to data.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data.

This course is of intermediate difficulty and will require Python and NumPy knowledge.

If you do not wish to opt for the whole specialization in one go, you can also opt for individual courses:

At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

Through the assignments of this specialization, you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real-world problems.

Sign up for the Whole Specialization.

Graph Search, Shortest Paths, and Data Structures – Stanford University

Ratings: 4.8

Students Enrolled: 48K+

The primary topics in this part of the specialization are data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

Sign up for this course.

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Programming for Everybody (Getting Started with Python) – University of Michigan

Ratings: 4.8

Students Enrolled: 1.4M+

It’s one of the most popular courses on Coursera – more than a million students have enrolled for this course.

This course aims to teach everyone the basics of programming computers using Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course.

Sign up for this course.

Python for Everybody Specialization – University of Michigan

Ratings: 4.8

Students Enrolled: 1.6M+

This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language.

In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.

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Python Data Structures – University of Michigan

Ratings: 4.9

Students Enrolled: 450K+

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”. This course covers Python 3.

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Algorithms (Part 1) – Princeton University

Ratings: 4.9

Students Enrolled: 630K+

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

All the features of this course are available for free. It does not offer a certificate upon completion.

Enroll for this course.

Algorithms – Stanford University

Ratings: 4.8

Students Enrolled: 150K+

Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. 

This specialization is an introduction to algorithms for learners with at least a little programming experience – so suitable for intermediate learners.

The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.

Learners will practice and master the fundamentals of algorithms through several types of assessments. Each course concludes with a multiple-choice final exam.

After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.

When you finish every course and complete the hands-on project, you’ll earn a Certificate that you can share with prospective employers and your professional network.

Sign up for this course

Data Structures and Algorithms – UC San Diego and NRUHSE

Ratings: 4.6

Students Enrolled: 310K+

Data structures play a central role in computer science and are the cornerstones of efficient algorithms. 

This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice.

For each algorithm you develop and implement; you will have to debug your programs without even knowing what these tests are!

The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine.

Sign up for this course

Related Course: Data Structures and Algorithms Specialization by Tsinghua University

Data Science Math Skills – Duke University

Ratings: 4.5

Students Enrolled: 150K+

Data science courses contain math – no avoiding that!

This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus.

Topics include:

  • Set theory, including Venn diagrams
  • Properties of the real number line
  • Interval notation and algebra with inequalities
  • Uses for summation and Sigma notation
  • Math on the Cartesian (x,y) plane, slope and distance formulas
  • Graphing and describing functions and their inverses on the x-y plane
  • The concept of instantaneous rate of change and tangent lines to a curve
  • Exponents, logarithms, and the natural log function
  • Probability theory, including Bayes’ theorem

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best online courses for maths, linear algebra, algorithms, data structures & statistics for Machine Learning and Data Science

Java Programming: Solving Problems with Software – Duke University

Ratings: 4.6

Students Enrolled: 170K+

This online course teaches you to code in Java and helps improve your algorithm, programming, and problem-solving skills.

You will learn to design algorithms as well as develop and debug programs. Using custom open-source classes, you will write programs that access and transform images, websites, and other types of data.

Sign up for the Course

This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

Option-1: Java Programming and Software Engineering Fundamentals Specialization

Ratings: 4.6 | Students Enrolled: 390K+

Centered around projects, this Specialization will help you create a portfolio of work to demonstrate your new programming skills. In the capstone you will create a recommender engine similar to those used by Netflix or Amazon. Additional projects in your portfolio will include an interactive webpage that applies filters to images, an analysis of CSV data files, an encryption program, and a predictive text generator.

Sign up for this Specialization.

Option-2: Object Oriented Programming in Java Specialization

R Programming – Johns Hopkins University

Ratings: 4.6

Students Enrolled: 490K+

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Sign up for this course.

This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:

Statistics with R Specialization – Duke University

Ratings: 4.7

Students Enrolled: 230K+

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports.

Additionally, you will learn how to demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.

You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.

Sign up for this course.

Web Design for Everybody: Web Development & Coding – University of Michigan

Ratings: 4.7

Students Enrolled: 290K+

This is an excellent course for beginners and can be taken by those without any prior knowledge or background and would help you master the technology to develop high-quality websites. 

This Specialization covers how to write syntactically correct HTML5 and CSS3, and how to create interactive web experiences with JavaScript.

The capstone would give you an opportunity to develop a professional-quality web portfolio, and design a responsive site that uses tools such that it can be accessed by those with visual, audial, physical, and cognitive impairments.

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Accelerated Computer Science Fundamentals for Data Science – University of Illinois

Ratings: 4.7

Students Enrolled: 24K+

Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing, and shortest paths.

Students will learn to design and implement an object-oriented program in the C++ language, including defining classes that encapsulate data structures and algorithms.

Additionally, students will learn to analyze the running time and space needs of an algorithm, asymptotically to ensure it is appropriate at scale, including for big data.

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HTML, CSS & JavaScript for Web Developers – Johns Hopkins University

Ratings: 4.8

Students Enrolled: 250K+

This highly popular course covers all the tools every web page coder should know. Topics covered would include designing modern web pages using HTML and CSS, coding in a manner that the components would rearrange themselves depending on the size of the user’s screen (tablet, mobile, desktop). 

Finally, you will get a thorough introduction to the most ubiquitous, popular, and incredibly powerful language of the web – Javascript. Using Javascript, you will be able to build a fully functional web application that utilizes Ajax to expose server-side functionality and data to the end-user.

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Divide & Conquer, Sorting & Searching, and Randomized Algorithms – Stanford University

Ratings: 4.8

Students Enrolled: 130K+

The primary topics in this part of the specialization are asymptotic (“Big-oh”) notation, sorting and searching, divide and conquer (master method, integer, and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).

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Ruby on Rails – Johns Hopkins University

Ratings: 4.5

Students Enrolled: 71K+

As Machine Learning is a part of Data Science it is a composition of various mathematical computations. It means that an application using the technology needs to provide complex calculations, fast. 

Like any other web application framework, Ruby-on-Rails (ROR) is also a server-side web application framework written under the MIT license for developing robust web applications. 

Ruby on Rails has been a preferred choice for the developers because it assists with task automation, which is a blessing in the technology world.

The famous Ruby on Rails Web Services used worldwide are: Basecamp, Kickstarter, Airbnb, GitHub, ASKfm, Couchsurfing, SlideShare, etc. to name a few. These are all successful web application frameworks that are used by millions of people worldwide.

In this course, we will explore how to build web applications with the Ruby on Rails web application framework, which is geared towards rapid prototyping. Yes, that means building quickly! At the conclusion of this course, you will be able to build a meaningful web application and deploy it to the “cloud” using a Heroku PaaS (Platform as a Service).

Sign up for this course.

Note: Ruby is not an ideal choice for machine learning processes. But, Ruby is actually a good choice for user interaction and API functions.

As with the help of the ROR framework, the developers can create MVPs in a better and faster way; the framework is highly recommended for creating beautiful web frameworks and services. But when it comes to Machine Learning with Ruby on Rails is not justified enough.

The best alternative for ML would be Python.

best online courses on Mathsmatics, Linear Algebra, Programming, Statistics, Data Structures, Algorithms for Machine Learning
Source: Bacancy Technology

Full Stack Web and Multiplatform Mobile App Development – HKUST

Ratings: 4.7

Students Enrolled: 160K+

Learn front-end and hybrid mobile development, with server-side support, for implementing a multi-platform solution.

The first two courses in this Specialization cover front-end frameworks: Bootstrap 4 and Angular.

You’ll also learn to create hybrid mobile applications, using the Ionic framework, Cordova and NativeScript.

On the server-side, you’ll learn to implement NoSQL databases using MongoDB, work within a Node.js environment and Express framework, and communicate to the client side through a RESTful API. Learners enrolling in this Specialization are expected to have prior working knowledge of HTML, CSS, and JavaScript.

Ideally, learners should complete the courses in the specified sequence. It is strongly recommended that the Angular course be completed before proceeding with the Ionic and Cordova and/or the NativeScript course.

Sign up for this course.

Related Course: Full Stack Development with React

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