Coursera
Coursera Logo

Practical Data Science with MATLAB Specialization 

  • Offered byCoursera

Practical Data Science with MATLAB Specialization
 at 
Coursera 
Overview

Duration

5 months

Start from

Start Now

Mode of learning

Online

Schedule type

Self paced

Difficulty level

Beginner

Official Website

Go to Website External Link Icon

Credential

Certificate

Practical Data Science with MATLAB Specialization
 at 
Coursera 
Highlights

  • Shareable Certificate
  • Earn a Certificate upon completion
  • 100% online courses
  • Start instantly and learn at your own schedule
  • Set and maintain flexible deadlines
  • Beginner Level
  • No prior experience required
Read more
Details Icon

Practical Data Science with MATLAB Specialization
 at 
Coursera 
Course details

More about this course
  • 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.
  • You'll apply your new skills on several real-world examples including: analyzing costs associated with severe weather events, predicting flight delays, and building machine learning models. The final capstone project will provide you the opportunity to apply concepts from all the courses to gain insight from raw data and to build predictive models.
Read more

Practical Data Science with MATLAB Specialization
 at 
Coursera 
Curriculum

COURSE 1- Exploratory Data Analysis with MATLAB

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.

COURSE 2 - Data Processing and Feature Engineering with MATLAB

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.

COURSE 3 - Predictive Modeling and Machine Learning with MATLAB

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.

COURSE 4 - Data Science Project: MATLAB for the Real World

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.

Practical Data Science with MATLAB Specialization
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

    Other courses offered by Coursera

    – / –
    3 months
    Beginner
    – / –
    20 hours
    Beginner
    – / –
    2 months
    Beginner
    – / –
    3 months
    Beginner
    View Other 6715 CoursesRight Arrow Icon

    Practical Data Science with MATLAB Specialization
     at 
    Coursera 
    Students Ratings & Reviews

    5/5
    Verified Icon1 Rating
    A
    ASHISH MISHRA
    Practical Data Science with MATLAB Specialization
    Offered by Coursera
    5
    Other: The course was taught in such a manner that even a novice can learn and grab the concepts easily.
    Reviewed on 16 May 2021Read More
    Thumbs Up IconThumbs Down Icon
    View 1 ReviewRight Arrow Icon
    qna

    Practical Data Science with MATLAB Specialization
     at 
    Coursera 

    Student Forum

    chatAnything you would want to ask experts?
    Write here...