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The 12 Most Popular Data Science Courses on Coursera

1. Machine Learning by Andrew Ng at Stanford  University

In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

2. What is Data Science? by IBM

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

3. Neural Networks and Deep Learning by Andrew Ng at deeplearning.ai

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new “superpower” that will let you build AI systems that just weren’t possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: – Understand the major technology trends driving Deep Learning – Be able to build, train and apply fully connected deep neural networks – Know how to implement efficient (vectorized) neural networks – Understand the key parameters in a neural network’s architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.

4. Introduction to Data Science in Python by University of Michigan

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

5. The Data Scientist’s Toolbox by Johns Hopkins University

In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

6. SQL for Data Science by UC Davis

This course is designed to give you a primer in the fundamentals of SQL and working with data so that you can begin analyzing it for data science purposes. You will begin to ask the right questions and come up with good answers to deliver valuable insights for your organization. This course starts with the basics and assumes you do not have any knowledge or skills in SQL. It will build on that foundation and gradually have you write both simple and complex queries to help you select data from tables. You’ll start to work with different types of data like strings and numbers and discuss methods to filter and pare down your results. You will create new tables and be able to move data into them. You will learn common operators and how to combine the data. You will use case statements and concepts like data governance and profiling. You will discuss topics on data, and practice using real-world programming assignments. You will interpret the structure, meaning, and relationships in source data and use SQL as a professional to shape your data for targeted analysis purposes.

7. Mathematics for Machine Learning: Linear Algebra by Imperial College London

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we’re aiming at data-driven applications, we’ll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you’ll write code blocks and encounter Jupyter notebooks in Python, but don’t worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

8.  Google Cloud Platform Big Data and Machine Learning Fundamentals

This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account

9. Introduction to Probability and Data by Duke University

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes’ rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.

10. How Google does Machine Learning by Google Cloud

What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently — of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.

11. Introduction to Big Data by University of California San Diego

Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible — increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop!

12. Data-driven Decision Making by PwC

Welcome to Data-driven Decision Making. In this course you’ll get an introduction to Data Analytics and its role in business decisions. You’ll learn why data is important and how it has evolved. You’ll be introduced to “Big Data” and how it is used. You’ll also be introduced to a framework for conducting Data Analysis and what tools and techniques are commonly used. Finally, you’ll have a chance to put your knowledge to work in a simulated business setting. This course was created by PricewaterhouseCoopers LLP with an address at 300 Madison Avenue, New York, New York, 10017.

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Best Coursera Courses for Deep Learning

Here is a list of best coursera courses for deep learning.

1. Deep Learning Specialization

This deep learning specialization provided by deeplearning.ai and taught by Professor Andrew Ng, which is the best deep learning online course for everyone who want to learn deep learning. The DL specialization include 5 sub related courses:

1) Neural Networks and Deep Learning

This is the first course of the Deep learning specialization, which will tell you what is neural networks and deep learning. It includes 4 part: Introduction to deep learning; Neural Networks Basics; Shallow neural networks; Deep Neural Networks. For people who know or don’t know deep learning, this is the best beginning course for AI.

2) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

This is the second course of the Deep Learning Specialization, which focus on the “trick” to make the deep learning to work well:

  • Understand industry best-practices for building deep learning applications;
  • Be able to effectively use the common neural network “tricks”, including initialization, L2 and dropout regularization, Batch normalization, gradient checking;
  • Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence;
  • Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance;
  • Be able to implement a neural network in TensorFlow.

3) Structuring Machine Learning Projects

This is the third course in the Deep Learning Specialization. Professor Andrew Ng will teach you how to build a successful machine learning project. Most of the course content are drawn from Professor Andrew Ng’s experience from Stanford, Google , Baidu and the industry:

  • Understand how to diagnose errors in a machine learning system;
  • Be able to prioritize the most promising directions for reducing error;
  • Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance;
  • Know how to apply end-to-end learning, transfer learning, and multi-task learning

4) Convolutional Neural Networks

This is the fourth course of the Deep Learning Specialization, which will teach you how to build convolutional neural networks and apply it to image processing:

  • Understand how to build a convolutional neural network, including recent variations such as residual networks.
  • Know how to apply convolutional networks to visual detection and recognition tasks.
  • Know to use neural style transfer to generate art.
  • Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

5) Sequence Models

This is the fifth course of the Deep Learning Specialization, which will tell you how to build models for natural language, audio, and other sequence data:

  • Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
  • Be able to apply sequence models to natural language problems, including text synthesis.
  • Be able to apply sequence models to audio applications, including speech recognition and music synthesis.

2. Neural Networks for Machine Learning

This deep learning course provided by University of Toronto and taught by Geoffrey Hinton, which is a classical deep learning course. It provides both the basic algorithms and the practical tricks related with deep learning and neural networks, and put them to be used for machine learning.

3. Introduction to Deep Learning

This entry-level deep learning course belongs to the “Advanced Machine Learning Specialization“, which provided by Higher School of Economics and Yandex. The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.

4. Deep Learning in Computer Vision

This deep learning application course belongs to the “Advanced Machine Learning Specialization“, which provided by Higher School of Economics and Yandex. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. The course will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and oftenly demonstrated in movies and TV-shows example of computer vision and AI.

5. An Introduction to Practical Deep Learning

This deep learning course provided by Intel and taught by Intel Engineers, which provides an introduction to Deep Learning. Students will explore important concepts in Deep Learning, train deep networks using Intel Nervana Neon, apply Deep Learning to various applications and explore new and emerging Deep Learning topics.

6. Deep Learning for Business

This deep learning business course is provided by Yonsei University and it has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers. The second part focuses on the core technologies of DL and ML systems, which include NN (Neural Network), CNN (Convolutional NN), and RNN (Recurrent NN) systems. The third part focuses on four TensorFlow Playground projects, where experience on designing DL NNs can be gained using an easy and fun yet very powerful application called the TensorFlow Playground. This course was designed to help you build business strategies and enable you to conduct technical planning on new DL and ML services and products.

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