Everything you learn in a data science master’s program has value. You wouldn’t be learning it otherwise, right?
Some courses, however, stand out. That’s either because their content is central to every aspect of data science, or because they teach specific data science skills and principles critical to excelling in the field. Or both!
This article identifies those courses and explains what makes them especially useful. We’ll discuss:
A Master of Science in Data Science is a graduate program for students interested in delving deeper into the applications of data analytics. These students typically have a background in one or more of the following areas:
Any professional hoping to improve their data analytics and data science skills can benefit from the learning experience of working with real-world data in a master’s program in data science.
Data science is the practice of collecting, organizing, analyzing, and using data to drive strategic decisions. From big data to deep learning and TensorFlow, from predictive analytics to artificial intelligence and natural language processing, from Python programming to SQL searches and data mining, data science and machine learning are how we make sense of the vast and complex world of virtual information and algorithms.
Data science job postings appear under various titles, including applications architect, data analyst, data engineer, machine learning engineer, machine learning scientist, and others. Data scientists often occupy senior positions managing data mining and data interpretation processes at universities, hospitals, nonprofits, and private businesses. While data science careers are varied, a data scientist can be categorized as any professional who organizes and interprets data.
As technology advances, the demand for professionals who can mine and interpret data has increased dramatically. Data science positions vary in educational and professional qualification requirements. Those holding a bachelor’s degree or associate degree sometimes qualify for low-level positions in data analysis areas like systems analytics, data visualization, and computer programming; they can advance into management or network architect positions as they gain more experience working with datasets. That’s a difficult path, however, requiring superior skills and an employer willing to take risks. Earning a master’s is a surer route to leadership positions.
If you’re hoping to be the kind of data scientist who leads teams and conducts comprehensive organizational problem-solving efforts, you’ll likely need to attend a master’s program. In fact, a survey from Burtch Works Executive Recruiting found that 88 percent of data scientists have at least a master’s degree and often a doctorate in such disciplines as data science and data analysis(obviously), applied math, statistics, computer science, or engineering.
Data science professionals can use their knowledge and skills in many ways and in almost every industry. You might specialize in business intelligence or robotics or healthcare informatics. There are almost too many options.
90 percent of data scientists hold master’s degrees, and 47 percent hold doctoral degrees. ( )
The Bureau of Labor Statistics sets median data scientist annual pay at just over $100,000. Top-paying sectors include ( ):
- Computer and peripheral equipment manufacturing ($148,290)
- Semiconductor and other electronic equipment manufacturing ($142,150)
- Specialized information services ($139,600)
- Data processing, hosting, and related services ($126,160)
- Accounting, tax preparation, bookkeeping, payroll services ($124,440)
|University and Program Name
Data science master’s programs are designed to equip you with the skills and knowledge that today’s data analytics employers seek. Most programs cover most or all of the following topics:
The online master’s in data science program at University of Virginia, for one, consists of 14 graded online courses:
The University of Virginia School of Data Science automatically enrolls all students into all fourteen online courses. Students must earn a minimum of B- in each class and a cumulative GPA of 3.0 in order to complete their degree.
Because different programs cover different subjects at varying levels of depth, it’s important to research the coursework you’ll be expected to complete in your prospective programs. Programs geared toward students hoping to transition into data science and data analysis from unrelated careers usually focus on foundational data science techniques such as programming languages. In contrast, programs geared toward students with data science backgrounds assume that students have some level of proficiency in basic techniques like programming languages and statistical modeling.
The master’s program at Stevens Institute of Technology is one that works exceptionally well for both kinds of students by offering traditional and advanced tracks within one data science master’s program. The traditional-track course sequence includes foundational coursework designed to familiarize those new to data science with basic disciplines:
Stevens’ advanced track course sequence assumed that students have some professional or educational experience with data science:
Here’s an overview of the most essential master’s in data science courses and what makes each of them so useful. If you end up especially interested in one of these crucial topics, check out Coursera or Bootcamp for single-serving deep-learning opportunities.
This course should cover the basics of advanced convex optimization and computational linear algebra as applied to data science. You’ll study applications like Markov chains and PageRank, PCA and dimensionality reduction, spectral clustering, and linear regression.
This course is often proof-based, meaning that you’ll learn how to prove theorems and gain experience proving theorems independently through data science projects and exams (an essential constituent of the data scientist’s toolkit). You’ll also gain a comprehensive understanding of the more complex foundational elements of data science optimization, such as convex functions, optimality conditions, and gradient descent.
This course teaches students the data types and concepts of Spark, a fast, scalable, open-source, general-purpose computing framework. Students learn how to use Spark for large-scale analytics and machine learning while also mastering data storage/retrieval tools, such as AWS and the Hadoop ecosystem.
As technology advances, more and more data scientists and data engineers are working with datasets that exceed the memories of singular machines. This course will equip you with the skill set and paradigm of computing necessary to handle these complex novel datasets. Big data analytics is one of the most salient data science skills you’ll learn throughout your entire master’s program.
This course teaches you how to turn data into information using inductive approaches to discover appropriate models.
You’ll be equipped with a formal basis for machine learning and knowledge discovery, exploring both practical and theoretical aspects of data mining. You’ll also learn how to go beyond standard deductive strategies of building models using known principles and delve into using data mining to construct new empirical models through data science projects.
It’s pretty self-explanatory—you’ll learn about the ethical issues that arise as the field of big data expands and how to handle them.
This course equips you with the frameworks, concepts, and theories necessary to think through and manage ethical issues that big data scientists meet in their professional lives. Understanding ethics in big data is a crucial data science skill because it protects individuals and organizations from legal and moral repercussions.
This course is an introduction to linear statistical models through a data science lens.
You’ll gain a mastery of the software R, an essential component of any data scientist’s toolkit. You’ll also be equipped with a comprehensive understanding of such topics as multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and principal components.
This course teaches machine learning techniques, including Bayesian learning, evolutionary algorithms, instance-based learning, reinforcement learning, and neural networks.
You’ll learn how to apply myriad data science and machine learning techniques to systems engineering, and you’ll likely gain extensive experience completing comprehensive programming assignments.
You’ll learn the basics of text analytics and natural language processing, both critical data science skills. Using AI and machine learning methods, you’ll practice parsing text into numeric vectors and converting high-dimensional vectors into low-dimensional vectors that can be analyzed and modeled.
This course focuses on recent developments in computational linguistics and machine learning, equipping you with the tools you need to advance and adapt in our rapidly changing technological world.
This course provides an overview of probability, conditional probability and independence, and Bayes’ theorem.
You’ll unlock a deeper understanding of probability’s applications in data science and probability itself by diving into the nuances of topics like discrete and continuous random variables, key distributions, conditional expectation, central limit theorem, and parameter estimation.
You’ll learn basic data analysis techniques in the context of real-world domains like public health, marketing, bioinformatics, and more.
This course should explore foundational programming techniques in Python, an essential language for data science and big data manipulation. Mastery of Python programming is a quality coveted by employers. You’ll also delve into R programming, another useful tool in data science and machine learning.
This course provides a comprehensive foundational knowledge of inferential statistical methods for discrete and continuous random variables as applied to data structures and datasets.
You’ll learn critical statistical tests for differences in means and proportions and also gain an understanding of such statistical basics as linear and logistic regression, causation versus correlation, confounding, resampling methods, and study design.
Schools offering highly regarded master’s in data science program include:
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