Master Data Science In 1 Month, 1 Day and 7 Hours. This course is designed to teach students who have no experience with data science that are working in another field and want to know what the buzz is all about. The course focuses on building a solid foundation of data science through core concepts such as Statistics, Machine Learning, Data Visualization and others. Beyond just theory – it clearly demonstrates how all of these concepts are applied in practice by focusing on real world examples with detailed discussion using industry standard tools

Do you want to learn data science, but don’t know where to start? Master Data Science in One Month is the ideal course for you. Whether you are just starting out or just looking to learn more about data science, this course is for you. This course will take you from knowing absolutely nothing about data science to building your own data science portfolio.

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How to Master Data Science

Master Data Science In 1 Month

If you are a Beginner and want to learn Data science with a great passion towards it, then trust me, this article would definitely help you to build your plan to learn Machine Learnign and Data Science.

1. Learn a Programming Language (R or Python).
2. Get familiar with applied mathematics (LA, Stats, Prob).
3. Start reading blogs on ML/AI and listen to podcasts.
4. Read some amazing books to build up foundations.
5. Learning Machine Learning and Deep Learning.
6. Work on experimenting with the skills and do some hands-on.
7. Building some end-to-end projects with competitive Data Science.
8. Apply and crack interviews.

What Can I Become with a Master’s Degree in Data Science and Big Data?

Data Science and Big Data is a very hot topic nowadays among Information Technology (IT) specialists, but also among media outlets around the world. Directly connected to data analysis and business intelligence, the knowledge and skills learned during a Bachelor’s or Master’s degree in Big Data are incredibly valuable for today’s job market.

In a world where every type of job involves working with data (of any kind), almost everybody needs a skilled data scientist to filter the red flags, interpret data, and find solutions. A data scientist is like an analyst who can predict and calculate the potential results of the business by looking at streams of data.

In a world where every type of job involves working with data (of any kind), almost everybody needs a skilled data scientist to filter red flags, interpret data, discover patterns and trends and use them to find solutions. A data scientist is like an analyst who can predict and calculate the potential results of the business by looking at streams of data.

All fine and dandy, but what are some specific jobs for a Data Science graduate? We’ll get to that in a minute!

First, let’s check some of the best countries where you can study Data Science and have the best jobs prospects:

• Data Science degrees in the US 
• Data Science degrees in the UK 
• Data Science degrees in Germany 
• Data Science degrees in Italy 
• Data Science degrees in France 
• Data Science degrees in Finland 

Make sure you don’t overlook online Data Science courses that allow you to study from the comfort of your home, while also managing other responsibilities, like a full-time job or taking care of your family.

Now, let’s take a look at some of the most popular Data Science and Big Data jobs. As a bonus, we’ve added some of the most common responsibilities and the average salaries in the US, based on information from PayScale.

1. Statistician – 72,100 USD/year

Work for: national governments, local authorities, consulting and reporting companies, market research companies and research institutes.

Responsibilities:

• Collect, organise and interpret data, providing useful insights, patterns, and solutions
• Review the quality and accuracy of the data
• Design and test new data collection methods and strategies

2. Business Intelligence (BI) Analyst – 67,600 USD/year

Work for: tech companies, financial companies, consulting and reporting companies.

Tasks:

• Help the organisation improve the collaboration between different departments
• Deliver presentations on organizational data, company trends, and future directions
• Organise seminars and trainings to help managers better understand business intelligence

3. Data Analyst – 59,700 USD/year

Work for: telecommunications companies, finance companies, manufacturing companies, construction and utility companies and other large companies.

Responsibilities:

• Collect and analyse data relevant for the client’s needs
• Test and evaluate new data sources and data collection methods
• Interpret data and present findings as charts or reports

4. Big Data Engineer – 114,800 USD/year

Work for: tech, entertainment, retail, and trade companies.

Tasks:

• Create, test, and manage Big Data infrastructure and tools
• Monitor and evaluate performance and suggest changes to the infrastructure
• Define policies regarding data retention

5. Database Administrator (DBA) – 72,800 USD/year

Work for: financial & medical institutions, social media companies, research institutes, law firms, etc.

Responsibilities:

• Install, configure, and monitor database systems
• Monitor and maintain the efficiency of the company’s servers
• Collaborate with Cybersecurity experts to ensure data safety

6. Data Architect – 115,800 USD/year

Work for: public or private organisations, like education institutions, healthcare facilities, and others

Responsibilities:

• Design, structure, and maintain data and databases
• Ensure the validity and accuracy of the collected data
• Create a data strategy in collaboration with other departments in the company

7. Machine Learning Engineer – 111,600 USD/year

Work for: large international organisations, like Amazon, Apple, Microsoft, Accenture, Autodesk, etc.

Tasks:

• Design, create, and test self-running & self-learning software
• Works with the Data Science team and helps them become more efficient
• Makes sure the code is easy to scale, maintain, and debug

udemy data science course review

Udemy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you.

For every topic, the instructor first teaches the theory then helps you implement it from scratch. This taught me both the “why” and the “how”, and I believe that this is an effective way to learn. Thank you for a great course!

Ready to start your Data Science & Big Data journey?

Harvard Business Review called data scientist “the sexiest job” of the 21st century. And who can blame them? With great salaries and exciting work, it’s no surprise Data Science jobs are growing in popularity.According to the US Bureau of Labor Statistics, jobs from the Computer & Information Research as well as Statistics are expect to grow 16-30% by 2028.

If you have a keen eye for patterns and puzzles, the focus and dedication of an analyst, as well as programming prowess, you should definitely start unlocking the challenges of data with a Bachelor’s or Master’s degree in Big data and Data Science.

Was it Worth Studying a Data Science Masters?

Since completing my Masters in Data Science, I have had a number of people contact me asking for my experience with the course and whether it is worth recommending. Therefore, I thought it best to summarise my decision for starting the course, what I have achieved during my studies, and the outcome in the years following.

data science course udemy free

Udemy is one of the most popular MOOC-based e-learning platforms in the world. Udemy has a wide variety of Data Science courses. That’s why in this article, I have listed 75 FREE Data Science Courses on Udemy.

Course Name	Rating	Time to Complete	Prerequisites
1.	Introduction to Data Science using Python	4.5/5	2hr 32min	None
2.	What is Data Science?	4.3/5	40min	None
3.	Learn NumPy Fundamentals (Python Library for Data Science)	4.7/5	1hr 49min	None
4.	Essentials of Data Science	4.3/5	1hr 41min	Basic statistics and math
5.	Data Science, Machine Learning, Data Analysis, Python & R	4.1/5	8hr 7min	None
6.	Python For Data Science	4.4/5	3hr 55min	None
7.	Python for Data Science – Great Learning	4.3/5	1hr 55min	None
8.	Python Crash Course for Data Science and Machine Learning	4.6/5	1hr 39min	None
9.	Introduction to Data Science for Complete Beginners	4.4/5	1hr 56min	None
10.	Intro to Data for Data Science	4.5/5	1hr 1min	None
11.	Introduction to Python For Data Science 2021	4.5/5	57min	None
12.	Data Science with Analogies, Algorithms and Solved Problems	4.0/5	1hr 19min	Basic mathematics
13.	Learn Data Science With R Part 1 of 10	3.9/5	8hr 42min	Basic Maths,Stats and Programing
14.	Maths for Data Science by DataTrained	3.6/5	55min	10th class Level math
15.	Data Science for Business Leaders: Machine Learning Defined	4.4/5	1hr 58min	None
16.	Data Science: Intro To Deep Learning With Python In 2021	4.3/5	1hr 54min	Python Basics
17.	How To Build a Career in Data Analytics and Data Science	4.2/5	1hr 39min	None
18.	NumPy for Data Science Beginners: 2021	4.2/5	1hr 51min	Python Basics
19.	50 Must-Know Concepts, Algorithms in Machine Learning	4.0/5	54min	Machine Learning basics
20.	SQL Crash Course for Aspiring Data Scientist	4.5/5	1hr 24min	None
21.	Intro To Machine Learning Course With Python: Internship	4.2/5	40min	None
22.	Explore, Track, Predict the ISS in Realtime With Python	4.9/5	1hr 13min	Python basics
23.	Implementation of ML Algorithm Using Python	4.4/5	48min	Python basics
24.	SQL for Data Analysis: Solving real-world problems with data	4.5/5	1hr 57min	None
25.	Bootcamp for KNIME Analytics Platform	4.5/5	4hr 13min	None
26.	Learn Python: Python in 80 Minutes for Beginners	4.5/5	1hr 20min	None
27.	Introduction to Spacy for Natural Language Processing	4.5/5	1hr 34min	Python and Machine Learning basics
28.	Learn Data Analysis using Pandas and Python	4.2/5	1hr 39min	None
29.	AI foundations for business professionals	4.4/5	2hr	None
30.	WEKA – Data Mining with Open Source Machine Learning Tool	3.5/5	3hr 30min	Basic maths
31.	Introduction to Data Analysis for Government	4.4/5	1hr 12min	You should be a government employee
32.	R Basics – R Programming Language Introduction	4.5/5	4hr 6min	Basics of statistics and data structure
33.	Find Actionable Insights using Machine Learning and XGBoost	4.5/5	37min	Python basics
34.	Association Mining for Machine Learning	4.6/5	1hr 48min	None
35.	Master Data Analysis with Python – Intro to Pandas	4.6/5	5hr 9min	Python Basics
36.	Logistic Regression Practical Case Study	4.7/5	1hr 4min	The basic theory of Logistic Regression
37.	Acumen Presents: Prasad Setty of Google on People Analytics	4.5/5	35min	None
38.	Planning for Data Collection	4.3/5	30min	None
39.	Augmented Data Visualization with Machine Learning	4.6/5	3hr 15min	Basic Understanding of Statistics & Business Analysis
40.	Python for Data Analysis	4.4/5	1hr 10min	Python basics
41.	Polynomial Regression, R, and ggplot	4.8/5	1hr 5min	Basic R programming
42.	Deep Learning Prerequisites: The Numpy Stack in Python V2	4.6/5	1hr 59min	Linear Algebra, Probability, and Python Programming
43.	Artificial Neural Network for Regression	4.7/5	1hr 11min	Deep Learning Basics
44.	Modern Data Visualization with Oracle Analytics Cloud	4.4/5	3hr 54min	Basic Understanding of Reports and Analysis
45.	R, ggplot, and Simple Linear Regression	4.6/5	2hr 14min	None
46.	Fundamentals Data Analysis & Decision Making Models – Theory	4.0/5	31min	None
47.	Baseball Data Wrangling with Vagrant, R, and Retrosheet	4.7/5	2hr 10min	R Programming basics
48.	Data Visualization in Python Masterclass™ for Data Scientist	4.4/5	1hr 48min	Python basics
49.	Big Data Analysis With Pandas Data Frame	4.6/5	1hr 43min	Python basics
50.	Learn Data Cleaning with Python	4.1/5	50min	Python basics
51.	Tableau Fundamentals for Aspiring Data Scientists	4.2/5	1hr 58min	None
52.	Statistics literacy for non-statisticians	4.7/5	1hr 36min	None
53.	Python for linear algebra (for absolute beginners)	4.8/5	1hr 49min	None
54.	Microsoft Excel – Basic Data Visualization in Excel	4.4/5	1hr 1min	Basic understanding of Excel formulas
55.	Practical Deep Learning Projects	4.9/5	1hr 54min	Machine Learning basics
56.	Big Data and Hadoop Essentials	4.2/5	43min	None
57.	Python AI and Machine Learning for Production & Development	4.7/5	1hr 43min	Python Basics
58.	The Ultimate Python and Pandas Data Analysis Course	4.2/5	1hr 1min	Python Basics
59.	Data Visualization with Excel – Crash Course	4.4/5	33min	None
60.	Learn Statistical Data Analysis with Python	4.4/5	1hr 2min	Python basics
61.	Executive Data Storytelling	4.4/5	30min	None
62.	Data Visualization	4.5/5	1hr 10min	None
63.	Excel Data Visualization for Business Analysts	4.3/5	1hr 12min	Excel basics
64.	Introduction to R	4.4/5	10hr 4min	Basic understanding of maths and statistics
65.	Data Warehouse basics for absolute beginners in 30 mins	4.5/5	34min	Basic Database Understanding
66.	Excel Data Validation For beginners	4.6/5	1hr 23min	Microsoft Excel basics
67.	R and RStudio for Beginners – A Quick Introduction	4.5/5	1hr 2min	None
68.	Training Sets, Test Sets, R, and ggplot	4.7/5	1hr 30min	Basic understanding of linear and polynomial regression.
69.	Introduction to Bayesian Statistics	4.7/5	1hr 19min	Understanding of probability basics.
70.	Microsoft Excel Pivot Tables – The Beginner Course	4.6/6	50min	Excel basics
71.	Learn Keras: Build 4 Deep Learning Applications	4.3/5	1hr 29min	Python basics
72.	Learn R for Business Analytics from Basics	3.8/5	1hr 43min	None
73.	Baseball Database Queries with SQL and dplyr	4.5/5	3hr 2min	R Basics
74.	CatBoost vs XGBoost – Quick Intro and Modeling Basics	4.6/5	50min	Some Python and Modeling experience
75.	Genetic Algorithm for Machine Learning

Why did I choose to study Data Science?

It was the spring of 2016 and I was coming towards the end of a 6 month internship at one of the largest consulting firms in the City of London. I had taken this role to gain experience and figure out whether becoming an Actuary was the correct route for my career. I quickly found passion in the data analytics of the role as I was being pulled into meetings to discuss numbers I had crunched or was able hack together a tool to automate previously manual tasks. However, I also found that the traditional track I would be heading on if I moved onto the graduate scheme no longer interested me due to years of standardised exams and little to no creativity. Furthermore, most of my work at this point was within Excel and I was gaining little to no coding experience.

It was at this point I also started to explore a magical term that kept coming up in my job searches: Data Science. I had come from a background in mathematics and, due to the nature of the job market in the UK, had been nudged towards the well-founded traditional roles such as accountancy and actuarial consulting. And yet, here was a new role that defied all the expectations I had set for myself of my future career. Where becoming an Actuary I would be solving problems by learning regulatory standards, Data Scientists are encouraged to creatively find solutions that fit within the commercial environment. Furthermore, the role opportunities were no longer fixed into a few select firms but almost all companies were looking for some variation of a Data Scientist and the idea that I could move into a completely new industry, from fashion to finance, greatly appealed to my interests.

However, as I started to apply for Data Science roles it quickly became apparent that I was lacking two key skills: applying Machine Learning and coding.

What were my options?

The first solution was to simply teach these myself, I had the statistical experience to learn machine learning and had done enough coding in MatLab to feel confident that I could learn Python or R. However, if I was to do these I was unsure if this was enough and how I would clearly demonstrate my newly acquired skills on a CV to employers.

I considered online boot camps but they were often fixed in their content and I was unsure of how repeating someone else’s work would be received by employers. Furthermore, there was no guarantee that these were credible to employers and were expensive to self fund. Today, only three years later, the list of courses supported by universities makes this much more of a viable option and is something that is definitely worth considering but at the time these were lacking. Unfortunately, boot camps were a costly risk that I was uncertain would pay off.

Therefore, I decided to look for the options available at universities. At the time there were two types of courses that fit within my goals; business analysts courses and computer science machine learning. The former focused on applying analytics within commercial environments but, as this was run through business schools, was far more expensive at over £25,000 for one year of studying. The latter provided the teaching through academic research and focused more on teaching the underlying theory than the application. Furthermore, as this was an academic course run through the Computer Science department, the cost was considerably less for the year at £9,000 (for UK citizen).

What did I choose?

In the end, I decided to play it safe and commit a full year to the academic masters in the hope that I would gain the applied knowledge in machine learning and develop my coding skills through project work.

Therefore, I joined the 2016/17 cohort of the Data Science MSc at City, University of London’s Computer Science department. This was the second year the course was offered at this university and, at the time, was the only university in London that offered a Data Science Masters (though others had some variations of this).

The first term consisted of the three main topics of Data Science: fundamentals of data science, machine learning and visualisations. Each module consisted of a coursework component that we were given a choice of any publicly available dataset to apply our newly learned methods on. With these, I was quickly able to improving my coding skills and even built the confidence to start sharing these projects publicly.

In the second term we had two core modules, Big Data and Neural Computing, and were given the choice of two optional modules. The list of options was comprehensive and enabled us to pick specialisms from computer vision to data architectures. I chose Data Visualisations (a continuation of the first term’s module) and Software Agents (the basics of AI by applying Reinforcement Learning). Again, these modules included coursework and with the fundamentals from the first term, I was really able to expand my applications and think creatively. Big Data also introduced text data and natural language processing.

Over the two terms, I had been given a broad overview of most of the Data Science topics and had a deep knowledge of Machine Learning and Neural Networks from the core modules. As we moved into the first component of the course, the dissertation, we were given the choice to complete this whilst in an internship role (and be provided an extension on the deadline to account for balancing whilst working). I found a suitable role, defined my research topic and over the following months applied all the skills I had gained so far towards applying AI within business.

Did I gain the skills I needed from the course?

I had two goals to achieve; to demonstrate that I understand machine learning and apply these with coding. The course not only provided a clear ‘box tick’ against these on my CV but enabled me to continue to expand my skills after with more and more interesting projects. Part of any job application is to get past the initial checks and I was now doing this much more consistently. Furthermore, as I moved into interview stages, I had all these project to discuss and truly demonstrate confidence in my understanding far more than I would have achieved on my own.

The Masters opened up all the doors that I had previously been knocking on and even had recruiters contacting me directly following the projects I had posted publicly. In the end, I found that I enjoyed the research aspect of my dissertation and the freedom to pursue the field and have since move onto a PhD in Artificial Intelligence. Ironically, this is the last thing I would have considered back in 2016 but as the field is constantly expanding it is incredible to be at the forefront of this, particularly because many problems require an applied mindset and fit within commercial problems and are not simply theoretical.

Best 23 Schools with Data Science Master’s Programs

Looking to freshen your résumé and improve your earning potential? You’re in exactly the right place at exactly the right time. As society places an increased emphasis on technology and the importance of accurate data, universities have been looking to improve their existing degree data and analytical degree programs and to create entirely new degrees offerings, both online and on-campus. We’ve listed 23 of these programs below.

Choose the Best Masters in Data Science Program For You

Does the data science program provide the skills you need?

Some data science programs target fields such as:

• Business Analytics
• Artificial Intelligence
• Information Systems

• Engineering
• Applied Statistics
• Programming Languages: R or Python

Can you work a data science program into your schedule?

Although plenty of universities offer a blend of evening, weekend and online classes, some of the programs mentioned here still require you to be present on-campus to complete your data science master’s degree. If seeking a local university, decide if you would like to attend an on-campus, hybrid, or online data science programs.

Are there internships or real-world practicums for data science students?

Unless you’re pursuing a doctorate, field experience is going to be one of the most important investments of your career. Look for data science programs that connect you to different industries, offer a variety of opportunities and numerous networking events.

Will a master’s degree in data science help to start my career?

The proof is in the numbers. If you can’t find it on the program’s website, ask about the percentage of data science, or related program, students who have received employment offers or promotions after they completed their program.

How did we choose the list of schools with data science graduate programs?

We interviewed 13 faculty members of universities and schools that offer data science programs. We asked:

• What are some crucial courses that data science graduate programs should have?
• What are some pros and cons to online data science programs?
• Is there value in capstone projects?
• Should students place high value on U.S. News & Reports top schools listings?
• What are some additional resources that your data science program offers to its students?

As a result of their answers, we listed programs that had the following:

• Machine Learning Course
• Database Course
• Capstone Requirement
• Student Organization or Association for Data Science Students
• Career Resources for Data Science Students
• Programs Offering Related Coursework

data science coursera

Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills and experience.

• IBM Data Science by IBM
• IBM Data Science
• IBM
• Professional Certificate
• Rated 4.6 out of five stars. 80007 reviews
• 4.6
• (80,007)
• 920k students
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• IBM Data Analyst by IBM

IBM Data Analyst
IBM
Professional Certificate
Rated 4.6 out of five stars. 39824 reviews
4.6
(39,824)
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• Machine Learning by Stanford University

Machine Learning
Stanford University
Course
Rated 4.9 out of five stars. 163798 reviews
4.9
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• Data Science Math Skills by Duke University

Data Science Math Skills
Duke University
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• Introduction to Web Development by University of California, Davis

Introduction to Web Development
University of California, Davis
Course
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• Data Processing Using Python by Nanjing University

Data Processing Using Python
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• Data Visualization in Google Slides by Coursera Project Network

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• Data Science Fundamentals with Python and SQL by IBM

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• Applied Data Science with Python by University of Michigan

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University of Michigan
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4.5
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760k students
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• AI For Everyone by DeepLearning.AI

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4.8
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• Data Analysis in R with RStudio & Tidyverse by Codio

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• HTML, CSS, and Javascript for Web Developers by Johns Hopkins University

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Rated 4.7 out of five stars. 12315 reviews
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• Introduction to Business Analysis Using Spreadsheets: Basics by Coursera Project Network

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• Computer Science: Programming with a Purpose by Princeton University

Computer Science: Programming with a Purpose
Princeton University
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• SQL for Data Science by University of California, Davis

SQL for Data Science
University of California, Davis
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Rated 4.6 out of five stars. 10574 reviews
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• Python and Statistics for Financial Analysis by The Hong Kong University of Science and Technology

Python and Statistics for Financial Analysis
The Hong Kong University of Science and Technology
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Rated 4.4 out of five stars. 2773 reviews
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• Cybersecurity and Its Ten Domains by University System of Georgia

Cybersecurity and Its Ten Domains
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• Google IT Support by Google

Google IT Support
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Rated 4.8 out of five stars. 110670 reviews
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1m students
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• Data Science for Business Innovation by EIT Digital , Politecnico di Milano

Data Science for Business Innovation
EIT Digital
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Rated 4.3 out of five stars. 166 reviews
4.3
(166)
7.6k students
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• Introduction to Business Analytics: Communicating with Data by University of Illinois at Urbana-Champaign

Introduction to Business Analytics: Communicating with Data
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Rated 4.6 out of five stars. 464 reviews
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The List of Master’s in Data Science Programs

University of California, Berkeley

Online Master of Information and Data Science (MIDS)

• School: School of Information
• Location: Berkeley, CA
• Full-Time Program: Yes – 12 to 20 months
• Part-Time Program: Yes
• Online Option: Yes

The UC Berkeley School of Information’s Master of Information and Data Science is a new degree intended for professionals who want to learn how to solve real-world problems in the data science realm. Students emerge with a solid understanding of the data lifecycle using the latest tools and methods for gaining insights from data.

Although all coursework is delivered online, students are expected to attend a 4-5 day immersion session on the UC Berkeley campus.

Drexel University

Master of Science in Data Science

• School: College of Computing and Informatics
• Location: Philadelphia, PA
• Full-Time Program: Yes
• Part-Time Program: Yes
• Online Option: Yes
• Website: Drexel University

Drexel University’s program is housed within the universities College of Computing and Informatics. The program provides three elective tracks: Analytics, Mining and Algorithms, Visualization and Communication, and Management and Accountability. Students cover topics in each track including Machine Learning, Human-Computer Interaction, and Privacy. Students finish their program with a capstone project where they can apply their learning to a practical, real-world project with a total of 45 credits.

Illinois Institute of Technology

Master of Data Science

• School: College of Science
• Location: Chicago, IL
• Full-time Program: Yes – 12 to 16 months
• Part-Time Program: Yes
• Online Option: Yes
• Website: Illinois Institute of Technology

The Master of Data Science program, in keeping with IIT’s status as a leading technical university (it’s nationally ranked by US News & World Report for its Engineering, Computer Science, Math, and Physics programs), prepares students from a variety of backgrounds for data science careers with rigorous theoretical and practical training. In addition to technical material, courses include other important subjects such as communications, project management, and professional ethics.

The curriculum includes a capstone practicum project working with a variety of Chicago-area companies in a variety of industries. The program is available online as well as part-time, making it a good fit for students who need to keep working while they earn the degree.

University of Missouri – Columbia

Data Science and Analytics Master’s Program

• School: Graduate School
• Location: Columbia, MO
• Full-Time Program: Yes – two years
• Part-Time Program: No
• Online Option: Online Only
• Website: University of Missouri – Columbia

The University of Missouri’s Data Science and Analytics Master’s Program is a cohort-based program that emphasizes student community through annual on-campus executive sessions and weekly cooperative tasks. Courses are taken 2 at a time in 8-week online modules designed to build off one another up to 34 credits total. The Industry Advisory Board is a partnership with industry leaders such as Boeing, IBM and The New York Times to provide students with networking opportunities.

Columbia University

Master of Science in Data Science

• School: Data Science Institute
• Location: New York, NY
• Full-Time Program: Yes – 30 credits
• Part-Time Program: Yes
• Online Option: No
• Website: Columbia University

To accommodate professionals, Columbia’s MSDS degree can be completed part-time or full-time. The curriculum includes 21 credits of required/core courses, including a capstone. The required courses cover the usual stomping grounds (e.g. machine learning). The capstone consists of a semester-length data science project sponsored by a faculty member or local organization. Students can choose a track focused on entrepreneurship or explore a subject area in Columbia’s Data Science Institute. This diverse group of research centers is conducting work on everything from health analytics and cybersecurity to smart cities and new media. There are also working groups devoted to frontiers in computing and materials discovery analytics.

Indiana University, Bloomington

Master of Science in Data Science

• School: School of Informatics & Computing
• Location: Bloomington, IN
• Full-time Program: Yes – 30 credits
• Part-Time Program: Yes – up to 5 years to complete the program
• Online Option: Yes
• Website: Indiana University, Bloomington

This MSDS distinguishes itself partly by offering two distinct paths for students: a technical path and a decision-maker path. The technical curriculum includes courses in topics such as analysis of algorithms, cloud computing, and security for networked systems. The decision-maker track covers project management and driving organizational change using data. The degree is available in a campus, online, or hybrid version. In the blended format, 12 credits are completed online and 18 credits are finished on campus.

Bloomington has a sturdy reputation in quantitative analysis and computer science, and there are plenty of research opportunities open to students. The School of Informatics & Computing runs 15 research centers, including those that deal with bioinformatics, cybersecurity, social informatics, and much more.

Johns Hopkins University

Master of Data Science

• School: Whiting School of Engineering
• Location: Baltimore, MD
• Full-Time Program: Yes – 18 months
• Part-Time Program: Yes – two to three years
• Online Option: Yes
• Website: Johns Hopkins University

Johns Hopkins data science master’s degree is a rigorous program that provides students with an education that covers topics in both mathematics and computer science. The majority of the 10 total courses are available online, allowing students to complete the degree entirely off-site.  Students will come out of this program with an understanding of both the theory and application of data science tools and techniques.

Northeastern University

Master of Data Science

• School: Khoury College of Computer Sciences
• Location: Boston, MA
• Full-Time Program: Yes – two to three years
• Part-Time Program: Yes
• Online Option: No
• Website: Northeastern University

Northeastern University’s data science master’s degree is comprised of 32 semester hours and  provides the skills needed to work in data science or begin a doctoral degree. Their core curriculum is supplemented by a large number of elective courses from various Northeastern departments as well as partner colleges. Master’s students at Northeastern have the opportunity to participate in the co-op program. This program gives students with up to 12 months of professional experience to apply their learning outside the classroom.

University of California, San Diego

Master of Advanced Study in Data Science and Engineering

• School: Departments of Computer Science and Engineering
• Location: San Diego, CA
• Full-Time Program: No
• Part-Time Program: Yes – two years
• Online Option: No
• Website: University of California, San Diego

The MAS is aimed at early- to mid-career data professionals working with massive data sets. That might mean folks who are currently on a technical leadership track or those who are looking at a career shift to data science. Courses are delivered on alternating weekends, on a Friday/Saturday schedule, with instructional materials available online. Most students are experienced engineers with 2+ years of experience.

As you would expect from a career-focused degree, the core curriculum is built on data science fundamentals—areas like programming, analysis, systems, and machine learning. In the second year, students are expected to work in teams to design a data science project, write a final report, and explain their findings in an oral presentation. There might even be a demo of the working prototype.

University of California – Santa Barbara

Master of Arts in Statistics – Data Science Track

• School: Department of Statistics and Applied Probability
• Location: Santa Barbara, CA
• Full-Time Program: Yes – two years
• Part-Time Program: No
• Online Option: No
• Website: University of California – Santa Barbara

UCSB’s Data Science 42 credit master’s degree is a track within the Master of Arts in Statistics. This degree can be a initial step towards pursuing a doctorate, or to a career in data science. Students are required to complete a Data Science Project, either assigned or selected by the student. This program provides a strong foundation in statistics paired with the computational tools needed to apply these statistic to real-world data.

University of Michigan – Ann Arbor

Master’s in Data Science

• School: College of Literature, Science and the Arts
• Location: Ann Arbor, MI
• Full-Time Program: Yes – two years
• Part-Time Program: No
• Online Option: No
• Website: University of Michigan – Ann Arbor

The University of Michigan’s master’s in data science prepares students to identify relevant datasets and apply statistical and computational methods to answer questions and derive insight. Students will take 43 credits in topics including machine learning, database management, and applied regression. At the end of the program students will select a capstone course in one of 8 practical topics from epidemiology to statistical consulting.

University of Virginia

Master of Science in Data Science (MSDS)

• School: Data Science Institute
• Location: Charlottesville, VA
• Full-time Program: 11 months
• Part-Time Program: No
• Online Option: No
• Website: University of Virginia

This professional master’s program emphasizes an integrated, team-based curriculum. Initial semesters begin with courses in languages, computation, and linear modeling. Once students have the basics, they can proceed to practice, application and the all-important capstone project.

UVA’s Data Science Institute has opportunities for graduate students to work on interdisciplinary big data projects, take advantage of training programs and high-speed computational resources, get involved with research related to data integration, systems biology, ethics, law and more. Every year, UVA also hosts Datapalooza, a University-wide showcase of data-driven research, resources and outreach.

University of Wisconsin

Master of Science in Statistics: Data Science

• School: Department of Statistics
• Location: Madison, WI
• Full-time Program: Yes – one year
• Part-Time Program: No
• Online Option: No
• Website: UW – MS in Statistics

UW’s accelerated MS in Statistics is targeted at the working Wisconsin professional—especially folks who want to move up the leadership/career ladder. The curriculum covers stats courses, complex data analysis, models, data visualization and communication skills. In the summer semester, students tackle a practicum focused on change management in a complex organization. This kind of degree packs a lot into a short span.

Online Master of Science in Data Science

• School: UW Extension
• Location: Online
• Full-time Program: Yes – 36 credits
• Part-Time Program: Yes
• Online Option: Yes
• Website: UW – MS in Data Science

Alternatively, you can explore the more flexible online MSDS. Tuition is held to a flat fee, whether you live in Wisconsin or out of state, and courses have no set meeting times. The program targets all the usual data science hot points – statistical methods, data mining, prescriptive analytics, etc. What’s really nice is that its Virtual Lab offers free access to software tools and programming languages such as R, Python, SQL Server and Tableau.

University of Denver

Online MS in Data Science program

• School: The Daniel Felix Ritchie School of Engineering and Computer Science
• Location: Denver, CO
• Program Length: 18 to 24 months
• Online Option: Yes

The University of Denver Ritchie School offers an online MS in Data Science that prepares students to develop data collection tools and use data insights to help their organization make important strategic decisions. The curriculum covers data science topics including data mining, database management, and machine learning and can be completed in as little as one year. A bachelor’s degree is required to apply.

Syracuse University

Online Master’s in Data Science

• School: School of Information Studies and Whitman School of Management
• Location: Syracuse, NY
• Full-Time Program: 18+ months
• Part-Time Program: No
• Online Option: Yes

Syracuse University’s online Master’s in Applied Data Science is designed for professionals to gain the specialization skills necessary to excel in their evolving field. The program features live online classes, interactive coursework and opportunities to network. Through the online learning format, students can advance their current skillset while staying on track to meet their professional and personal goals.

Southern Methodist University

Master of Science in Data Science

• School: Dedman College of Humanities and Sciences, Lyle School of Engineering and Meadows School of the Arts
• Location: Dallas, Texas
• Full-Time Program: Yes – 20 months
• Part-Time Program: Yes
• Online Option: Online Only

DataScience@SMU is an online Master of Science in Data Science program from Southern Methodist University that is designed for working professionals. The program features a project-based, interdisciplinary curriculum that helps students build the in-demand technical, analytical and communications skills needed to manage large data sets.

Students attend an in-person immersion, participate in live, weekly online classes, and compete interactive coursework that provides a comprehensive understanding of computer science, statistics, strategic behavior, business analytics, machine learning and data visualization. GRE waivers are available for applications with three or more years of work experience.

Bay Path University

Master of Science in Applied Data Science

• School: Graduate School
• Location: Longmeadow, MA
• Full-Time Program: Yes
• Part-Time Program: Yes – one to two years
• Online Option: Online Only
• Website: Bay Path University

Bay Path University’s program provides two tracks to fill the needs of all students. The generalist track provides a well-rounded education in data science that provides you with all the tools you need to be a valuable member of an applied data science team. The specialist track dives deep into theory and the applications of specific algorithms. Both tracks provide a comprehensive curriculum including a capstone project where students apply what they have learned, earning 36 credit hours.

Cabrini University

Master of Science in Data Science

• School: Graduate School
• Location: Radnor, PA
• Full-Time Program: Yes
• Part-Time Program: Yes – two years
• Online Option: Online Only
• Website: Cabrini University

Cabrini University’s program covers the four essential areas of data science. Software and programming, business and management, data warehousing and data visualization. Required courses include diverse topics such as natural language processing, machine learning, project management and data visualization. The program finishes with a internship or capstone project giving students an opportunity to apply their experience. 36 credit hours.

Carleton College

Collaborative Master’s in Data Science

• School: Collaborative
• Location: Ottawa, ON
• Full-Time Program: Yes – two years or 16 months for MBA path
• Part-Time Program: No
• Online Option: No
• Website: Carleton University

Carleton College’s Data Science Master’s program allows graduate students to add a data science focus to one of the 13 participating master’s programs. This multi-department collaboration provides data science training to students in many disciplines. This competitive program accepts 40 students each fall from various departments. Students accepted into this specialization will complete a thesis, coursework or a final project depending upon their Master’s program. Internships are also required for the Business Analytics MBA program.

Carnegie Mellon University

Master of Computational Data Science (MCDS)

• School: School of Computer Science
• Location: Pittsburgh, PA
• Full-time Program: Yes – 16 months
• Part-Time Program: No
• Online Option: No
• Website: Carnegie Mellon University

The MCDS has been around for over 10 years and its program explores the design, engineering and deployment of very large information systems. Hands-on experience is important here. CMU has scores of research projects on the boil, and students are expected to contribute. For the capstone project, folks can choose to work alone or as a team. There’s also a summer internship with opportunities from companies like Apple, Amazon, eBay, Google, UBS and more. Job experience is not required for admission, but relevant work is highly valued.

Cornell University

Master of Professional Studies (MPS) in Applied Statistics (Option II: Data Science)

• School: Department of Statistical Science
• Location: Ithaca, NY
• Full-time Program: Yes – two to four semesters
• Part-Time Program: No
• Online Option: No
• Website: Cornell University

Built on a solid base of statistics, Cornell’s MPS is a Professional Science Master’s (PSM) – an accreditation awarded to programs that combine advanced training in science or math with highly-valued business skills. Everyone is required to take common core courses (e.g. applied stats), but Option II students focus much more on data science.

A big pillar of Cornell’s program is the two-semester-long MPS project with an industry partner. Past large-scale data analytics projects have included collaborating with a health company to create a system that offered personalized article recommendations for anonymous users and

Working with comScore to analyze browsing activities in order to infer users’ age and gender. This is the kind of degree meant to prepare graduates for stats careers in government or industry.

Duke University

Master of Science in Data Science

• School: Graduate School
• Location: Durham, NC
• Full-Time Program: Yes – two years
• Part-Time Program: No
• Online Option: No
• Website: Duke University

Duke’s program provides students with an interdisciplinary education in data science in 42 credits. The core courses such as machine learning and data visualization are paired with electives from Duke’s many graduate programs. The degree also includes a practical, year-long capstone where students work with a non-academic stakeholder and a Duke professor to complete a project.

Harvard University

Master of Science in Data Science

• School: John A. Paulson School of Engineering and Applied Sciences
• Location: Cambridge, MA
• Full-Time Program: Yes – three semesters
• Part-Time Program: No
• Online Option: No
• Website: Harvard University

Harvard’s three semester, 12 course program provides all the skills needed to apply data science to any field. Students learn the essential skills including building and understanding statistical models, visualizing and communicating data, designing computational pipelines using common tools, and collaborating with a team. The degree includes a minimum of one research project or semester long independent study, allowing students to apply their learning.