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The Data Buzz series brings you a regular roundup of what’s trending in data science.
Looking for your next data science course on Coursera? With almost 200 data science courses available on our platform, all created and taught by the world’s best universities, it can be hard to know where to start. Here are ten of the most popular options, from introductory to advanced, along with some tips to help you choose the one that’s right for you.
Learn the basics
Basic Statistics, University of Amsterdam
You’ll learn to:
Is it right for you?
This course is the perfect place to start if you have some basic math background, but no previous background in statistics or data science. The instructors focus specifically on applications in the behavioral and social sciences, so you might find the material especially valuable if you work in a social science field.
Introduction to Big Data, University of California, San Diego
You’ll learn to:
Is it right for you?
If you’re new to data science and interested in understanding the landscape of Big Data, this course is right for you. You don’t need any prior programming experience – but the course does include some basic hands-on programming assignments, and you should feel comfortable downloading and installing open-source software tools and applications.
Master the tools
Python Programming: A Concise Introduction, Wesleyan University
You’ll learn to:
Is it right for you?
This course is intended for learners who have little or no prior programming background – but if you have some programming experience and want to learn Python specifically, you’ll be able to work through the material at your own (likely quicker) pace. Hands-on exercises and projects are central to the syllabus, so if you prefer hands-on learning, you’ll definitely love this course.
Mastering Data Analysis in Excel, Duke University
You’ll learn to:
Is it right for you?
This course is right for you if you’re interested in data analysis and business decision-making. Beginners are welcome – you don’t need any background in analytics or programming to get started. A basic math background will be helpful, however, and you may also want to complete the first course in the Excel to MySQL Specialization (Business Metrics for Data-Driven Companies) before enrolling in this one.
Hadoop Platform and Application Framework, University of California, San Diego
You’ll learn to:
Is it right for you?
This course is designed for beginning programmers and business people who would like to understand the core tools used to wrangle and analyze Big Data – if you want to feel empowered to have in-depth, informative conversations with Big Data specialists in your field, you’ll benefit from the content covered here. No prior data science or Hadoop experience is necessary, but some basic programming experience may be helpful.
Start your first Specialization
Data Science Specialization, Johns Hopkins University: Courses 1-3, The Data Scientist’s Toolbox, R Programming and Getting and Cleaning Data
You’ll learn to:
Is it right for you?
Are you an enthusiastic data science novice who’s committed to learning the essentials of the field from the ground up? If so, these courses – and the Data Science Specialization – are for you. Three outstanding Johns Hopkins instructors will step you through the basics at a manageable pace, and provide plenty of guidance and real-world examples as you move into advanced topics like inference, regression, and machine learning.
Take a machine learning deep dive
Machine Learning
You’ll learn to:
Is it right for you?
If you have a strong statistics and programming background, and you’re ready to immerse yourself in the cutting-edge science and technology of machine learning, this course is for you. You’ll have the opportunity to learn from Coursera Co-Founder and machine learning pioneer Dr. Andrew Ng – and you’ll to join a global community of machine learning enthusiasts that’s been growing steadily since this course was first offered online in 2011.
Neural Networks for Machine Learning
You’ll learn to:
Is it right for you?
To succeed in this course, you’ll need a background in Calculus and a solid foundation of Python programming skills. If you’ve already dipped your toes in data science and machine learning, and you’re ready for the challenge of mastering advanced content, this course will provide the perfect opportunity to do so.
The post The 10 most popular data science courses on Coursera appeared first on Coursera Blog.
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Looking for your next data science course on Coursera? With almost 200 data science courses available on our platform, all created and taught by the world’s best universities, it can be hard to know where to start. Here are ten of the most popular options, from introductory to advanced, along with some tips to help you choose the one that’s right for you.
Learn the basics
Basic Statistics, University of Amsterdam
You’ll learn to:
- Calculate descriptive statistics: Mean, median, mode, standard deviation, and variance
- Assess relationships between variables: Correlation and regression
- Calculate probabilities, probability distributions, and sampling distributions
- Understand fundamentals of inferential statistics: Confidence intervals and significance tests
Is it right for you?
This course is the perfect place to start if you have some basic math background, but no previous background in statistics or data science. The instructors focus specifically on applications in the behavioral and social sciences, so you might find the material especially valuable if you work in a social science field.
Introduction to Big Data, University of California, San Diego
You’ll learn to:
- Define Big Data and explain where it comes from
- Describe some of the problems that Big Data can be used to solve
- Use a five-step process to structure a Big Data analysis
- Summarize key features of Hadoop, including the MapReduce model
- Install Hadoop and run your first Hadoop program
Is it right for you?
If you’re new to data science and interested in understanding the landscape of Big Data, this course is right for you. You don’t need any prior programming experience – but the course does include some basic hands-on programming assignments, and you should feel comfortable downloading and installing open-source software tools and applications.
Master the tools
Python Programming: A Concise Introduction, Wesleyan University
You’ll learn to:
- Use the Spyder development environment
- Write basic functions in Python
- Work with lists, libraries, dictionaries, tuples, and data files
- Use Python’s statistics library
- Build and managing databases
Is it right for you?
This course is intended for learners who have little or no prior programming background – but if you have some programming experience and want to learn Python specifically, you’ll be able to work through the material at your own (likely quicker) pace. Hands-on exercises and projects are central to the syllabus, so if you prefer hands-on learning, you’ll definitely love this course.
Mastering Data Analysis in Excel, Duke University
You’ll learn to:
- Build data models in Excel
- Apply basic Excel formulas and mathematical functions
- Use Excel to implement data analysis methods, including binary classification, information theory and entropy measures, and linear regression
Is it right for you?
This course is right for you if you’re interested in data analysis and business decision-making. Beginners are welcome – you don’t need any background in analytics or programming to get started. A basic math background will be helpful, however, and you may also want to complete the first course in the Excel to MySQL Specialization (Business Metrics for Data-Driven Companies) before enrolling in this one.
Hadoop Platform and Application Framework, University of California, San Diego
You’ll learn to:
- Explain the Hadoop architecture, software stack, and execution environment
- Apply concepts and techniques like MapReduce to solve problems in Big Data
- Use the Apache Spark cluster computing framework
Is it right for you?
This course is designed for beginning programmers and business people who would like to understand the core tools used to wrangle and analyze Big Data – if you want to feel empowered to have in-depth, informative conversations with Big Data specialists in your field, you’ll benefit from the content covered here. No prior data science or Hadoop experience is necessary, but some basic programming experience may be helpful.
Start your first Specialization
Data Science Specialization, Johns Hopkins University: Courses 1-3, The Data Scientist’s Toolbox, R Programming and Getting and Cleaning Data
You’ll learn to:
- Get set up with GitHub, R, and RStudio
- Use R for data analysis: Programming, reading data, and accessing R packages
- Obtain data from the web, APIs, databases, and colleagues in various formats
- Make data “tidy” to streamline downstream analysis
Is it right for you?
Are you an enthusiastic data science novice who’s committed to learning the essentials of the field from the ground up? If so, these courses – and the Data Science Specialization – are for you. Three outstanding Johns Hopkins instructors will step you through the basics at a manageable pace, and provide plenty of guidance and real-world examples as you move into advanced topics like inference, regression, and machine learning.
Take a machine learning deep dive
Machine Learning
You’ll learn to:
- Implement supervised learning techniques: Parametric/non-parametric algorithms, support vector machines, kernels, neural networks
- Implement unsupervised learning techniques: Clustering, dimensionality reduction, recommender systems, deep learning
- Articulate and apply best practices in machine learning, including bias/variance theory and innovation processes
- Apply machine learning techniques to robotics, text processing, computer vision, data mining, and other important areas
Is it right for you?
If you have a strong statistics and programming background, and you’re ready to immerse yourself in the cutting-edge science and technology of machine learning, this course is for you. You’ll have the opportunity to learn from Coursera Co-Founder and machine learning pioneer Dr. Andrew Ng – and you’ll to join a global community of machine learning enthusiasts that’s been growing steadily since this course was first offered online in 2011.
Neural Networks for Machine Learning
You’ll learn to:
- Apply artificial neural networks to speech and object recognition, image segmentation, language processing, and more
- Optimize and generalize neural networks
- Recognize and discuss case studies that involve neural networks applications
Is it right for you?
To succeed in this course, you’ll need a background in Calculus and a solid foundation of Python programming skills. If you’ve already dipped your toes in data science and machine learning, and you’re ready for the challenge of mastering advanced content, this course will provide the perfect opportunity to do so.
The post The 10 most popular data science courses on Coursera appeared first on Coursera Blog.
Continue reading...
