Do you find yourself in an industry or field that increasingly uses data to answer questions? Are you working with an overwhelming amount of data and need to make sense of it? Do you want to avoid becoming a full-time software developer or statistician to do meaningful tasks with your data?
Completing this specialization will give you the skills and confidence you need to achieve practical results in Data Science quickly. Being able to visualize, analyze, and model data are some of the most in-demand career skills from fields ranging from healthcare, to the auto industry, to tech startups.
This specialization assumes you have domain expertise in a technical field and some exposure to computational tools, such as spreadsheets. To be successful in completing the courses, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation).
Throughout this specialization, you will be using MATLAB. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your data science tasks. You will be provided with free access to MATLAB for the duration of the specialization to complete your work.
Overview
Syllabus
Course 1: Exploratory Data Analysis with MATLAB
- Offered by MathWorks. In this course, you will learn to think like a data scientist and ask questions of your data. You will use ... Enroll for free.
Course 2: Data Processing and Feature Engineering with MATLAB
- Offered by MathWorks. In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation ... Enroll for free.
Course 3: Predictive Modeling and Machine Learning with MATLAB
- Offered by MathWorks. In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and ... Enroll for free.
Course 4: Data Science Project: MATLAB for the Real World
- Offered by MathWorks. Like most subjects, practice makes perfect in Data Science. In the capstone project, you will apply the skills ... Enroll for free.
- Offered by MathWorks. In this course, you will learn to think like a data scientist and ask questions of your data. You will use ... Enroll for free.
Course 2: Data Processing and Feature Engineering with MATLAB
- Offered by MathWorks. In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation ... Enroll for free.
Course 3: Predictive Modeling and Machine Learning with MATLAB
- Offered by MathWorks. In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and ... Enroll for free.
Course 4: Data Science Project: MATLAB for the Real World
- Offered by MathWorks. Like most subjects, practice makes perfect in Data Science. In the capstone project, you will apply the skills ... Enroll for free.
Courses
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In this course, you will learn to think like a data scientist and ask questions of your data. You will use interactive features in MATLAB to extract subsets of data and to compute statistics on groups of related data. You will learn to use MATLAB to automatically generate code so you can learn syntax as you explore. You will also use interactive documents, called live scripts, to capture the steps of your analysis, communicate the results, and provide interactive controls allowing others to experiment by selecting groups of data. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background is required. To be successful in this course, you should have some knowledge of basic statistics (e.g., histograms, averages, standard deviation, curve fitting, interpolation). By the end of this course, you will be able to load data into MATLAB, prepare it for analysis, visualize it, perform basic computations, and communicate your results to others. In your last assignment, you will combine these skills to assess damages following a severe weather event and communicate a polished recommendation based on your analysis of the data. You will be able to visualize the location of these events on a geographic map and create sliding controls allowing you to quickly visualize how a phenomenon changes over time.
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Like most subjects, practice makes perfect in Data Science. In the capstone project, you will apply the skills learned across courses in the Practical Data Science with MATLAB specialization to explore, process, analyze, and model data. You will choose your own pathway to answer key questions with the provided data. To complete the project, you must have mastery of the skills covered in other courses in the specialization. The project will test your ability to import and explore your data, prepare the data for analysis, train a predictive model, evaluate and improve your model, and communicate your results.
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In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling. This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed Exploratory Data Analysis with MATLAB. Throughout the course, you will merge data from different data sets and handle common scenarios, such as missing data. In the last module of the course, you will explore special techniques for handling textual, audio, and image data, which are common in data science and more advanced modeling. By the end of this course, you will learn how to visualize your data, clean it up and arrange it for analysis, and identify the qualities necessary to answer your questions. You will be able to visualize the distribution of your data and use visual inspection to address artifacts that affect accurate modeling.
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In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed courses 1 through 2 of this specialization. By the end of this course, you will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models.
Taught by
Adam Filion, Brandon Armstrong, Brian Buechel, Cris LaPierre, Erin Byrne, Heather Gorr, Isaac Bruss, Maria Gavilan-Alfonso, Matt Rich, Michael Reardon and Nikola Trica
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Reviews
4.8 rating, based on 31 Class Central reviews
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An excellent and must have specialization for all core engineering branch (mechanical, electrical, civil,etc.) students desirous of learning and applying machine learning and data science in their domain.
Instructors were awesome. Concepts explained in a very clear and concise manner. -
I have recently completed Practical Data Science with MATLAB specialization offered by MathWorks through Coursera.
I would like to thank MathWorks and Coursera for this well-prepared excellent experience. Looking forward to release of future courses. -
Just Gaussian Processes (GP) and Support Vector Machine (SVM) techniques were missing to learn for regression problems. Rest of specialization was amazing because gave me a lot of knowledges about Data Science. Everything was wonderful, thank you so much!
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Really enjoyed the course, the project had the right difficulty to be challenging while not impossible, and I got the chance to apply the entire machine learning workflow.
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This specialization/course is beginner friendly for anyone who wants to know the basics of data sciences. Both mathematical and practical (programming) explanations are given in summary, and emphasis is given more on 'how to use/deploy' than 'how it works'.
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An excellent specialization for those looking to develop a better understanding of MATLAB and how it applies to Data Science. The individual courses were very good but not without flaws. The learning curve was very steep and some of the instructional videos are fast paced (consider downloading them to watch when you have spare time). The FAQ's say no prerequisites are required but I would disagree. I've have a degree in Math and have taken the MATLAB Onramp and Fundamentals courses on MathWork's website. I have also taken courses in Statistics. As far as this specialization is concerned I recommend taking the free MATLAB Onramp course first and I also recommend having a good understanding of basic statistics.
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The course gives an overview of all machine learning techniques and help you to organize how to tackle a problem depending on the characteristics of your data. The power of the machine learning apps in Matlab allow multiple testing of different techniques and evaluation of results. The course is well-organized and easy to follow. Although some help when you get stuck in the code would be a plus
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Lot and useful information is provided along the four courses. Great instructors and videos. All the code provided in the livescripts is a great help, not only for the course but for future work. Fourth course is the more challenging one, based mostly in practicing the learned tools previously.
Matlab is great development environment, probably the best! It is my grownup Lego:) -
It is such a nice course, in the last one you can show your expertise learnt in the the subsequent course of this especiallization.
It is really nice how to practically, data science, investigation, featuring and modelling could be teached clear -
Matlab Specialization courses really awesome and high-standard courses. I really appreciate the Mathworks and it's all the instructors and all who behind the scene of the whole specialization courses. Thanks Mathworks, thanks to all.
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Really enjoyed this specialization course and learned data science from scratch. Thank you Mathworks and Coursera. I would recommend this specialization to all pioneers in the field of Matlab, Data Science, and Machine learning.
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Excellente specialization to learn to process data and visualization. I recomend this specialization to engineering and science students, becasue I cosider it is a efficient tool to apply in the courses and job tasks.
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You need to read the last section first to understand the goal of the project. If you do it step-by-step, be ready to spend more time - you will correct your solution several times only due to fuzzy instructions.
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very helpful and useful training. Last course -Capstone project was really difficult however the most important. I highly recommend MATLAB for data analysis. Its much more user friendly compare to e.g. python.
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Great course. I like the presentation skills applied and the quizes. The projects at the end of every course were challenging enough to help build competency. Best course I have taken on Coursera so far
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Love this course! It introduced me to the world of data science. Course is challenging at times but it's a good thing it drives you to explore the documentation. Videos and quizes are well made
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This was a well set up course, catering for people of various skill levels. It was however still at a sufficiently technical level to have high credibility for industry use.
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the course was really good easily understandable
totally I am satisfied with the course
now I am able to implement the concepts learned in Matlab
thanks, MathWorks -
really good, effective, step by step following the student, extremely useful for statistics general knowledge, machine learning and matlab programming
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Very useful and practical course. The depth of content is high enough and it's a good place to gain experience and knowledge about data science techniques.
