Professional Certificate in Applied Data Science

Get the inside track to an in-demand career in data science

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Course Dates

STARTS ON

October 14, 2021

Course Duration

DURATION

6 months, online
10-15 hours per week

Course Duration

Limited Time Offer!

US$12,500 US$7,500

Pay Nothing While You Learn. Financing Options Starting At US$51/Month.

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Demand for data scientists crosses all industries as employers scramble to harness the power of data analytics to personalize their products, minimize risk, and expand into new markets. The Professional Certificate in Applied Data Science from the Thayer School of Engineering at Dartmouth positions professionals to take full advantage of this trend. Upon completing this program, you will be ready to take advantage of new opportunities and face new challenges in the field of Data Science.

"Data scientists are able to think of ways to use data to solve problems that otherwise would have been unsolved, or solved using only intuition."

–Peter Skomoroch, former principal data scientist at LinkedIn

3X

Data Science jobs are expected to grow almost 3X as fast as other job categories in the coming years.

SOURCE: BUREAU OF LABOR STATISTICS

$120,000

Average salary of a data scientist.

SOURCE: ITSTAFFING.COM

94%

Of data science graduates have found jobs since 2011.

SOURCE: ITSTAFFING.COM

Who Is This Program For?

  • Career Launchers, wanting to enter an exciting, evolving field with a foundation that ensures flexibility as new platforms and technologies emerge. Titles may include business analyst, financial analyst, software engineer, data analyst and associates.
  • Career Builders, aiming to build on their existing skills, whether in a technical or non-technical capacity. Titles may include technical and non-technical roles across fields and industries.
  • Career Switchers, seeking new ways to expand opportunities, engage in continuous learning, and increase their earning potential in a high-demand field. Titles may include non-technical professionals at the junior management or early executive level in a business function such as general management, sales, marketing, administration or human resources banking & financial services, consulting, operations and information technology.

Prerequisites: This course requires mandatory moderate knowledge of Calculus, Linear Algebra, Statistics, and Probabilities.

Program Highlights: Prepare to Enter the Data Science Job Market

  • One-to-one career coaching*
  • World-class faculty and thought leadership
  • Regular live webinars*
  • Rigorous, graded assignments (Professional-level certificate)
  • Assistance with career planning
  • Real-world application of knowledge
  • Small group mentoring sessions
  • Learn from and network with your colleagues through peer discussions
  • Ivy League education
Additionally, you will develop high-demand skills in today’s job market, including: Data Visualization, Machine Learning, Risk Management, and Predictive Capabilities. You will understand the basics and potential of Python coding through live coding sessions and application-based assignments. Create visualizations, build linear and logistic regression models, and apply common Machine Learning algorithms such as K-means clustering and random forest. Access the personalized career guidance you need — beyond resumes and LinkedIn profiles — to prepare for a future career in data science. Lastly, you will also develop a data science portfolio to share with prospective employers.

"My goal is to demystify data science and help you unlock your potential as a data scientist. Together we will explore this landscape and develop your toolkit so that, using data science, you can provide valuable solutions to your future organization. My hope is that you will learn the key concepts to accelerate your journey of becoming a successful data scientist, whatever your industry or field of work may be."

–Geoffrey Parker, Professor of Engineering at Dartmouth College, Director of the Master of Engineering Management Program at Thayer School of Engineering

*Services provided by Emeritus, a learning partner for this program.

A decorative image related to Personal Career Success Team explaining the key features of 1:1 Career Coaching, small group mentoring sessions, and Live Webinars

The Triumvirate: Your Personal Career Success Team

Career Coaching + Mentorship from Industry Experts + Webinars = Job-Ready Confidence

Where do you fit in the expanding universe of Data science? What is your unique skill set?

  • One-to-one sessions with your Career Coach will help you develop your value proposition to employers.
  • Mentors will help you navigate the challenges specific to data science careers in small group sessions.
  • Regular Webinars will improve your networking and job search skills. You’ll use these Webinars to craft your Linkedin profile, develop and practice delivering your elevator pitch for different audiences, identify interview goals, and practice developing a rapport with prospective employers.

Program Modules

In just six months, you will become skilled in the fundamentals of data science, mastering critical concepts using Python. By receiving one-on-one support from a mentor who is an expert in the industry, you will be prepared to accelerate your career as a data scientist.

Pre-Module: 4-Week Python Coding Camp
All participants complete a month long data camp to learn or reinforce foundational Python coding skills. Live office hours and moderated discussion boards offer personalized support.

Module 1:

Practical Applications of Analytics

Learn to implement Python concepts; evaluate how companies leverage data to find opportunities; use a problem-solving framework; create functions and 'while' loops in Python; work with values, tuples, and lists; and explore Markdown in Jupyter Notebook.

Module 2:

Data Structures and Plotting

Identify structured and unstructured data; understand continuous and categorical data; explore data structures; determine what kind of data can be expressed in vectors vs. matrices; format and analyze data; and create simple plot points in Python.

Module 3:

Introduction to Statistics and Probability

To understand confidence intervals and p-values, gain a solid base of knowledge in statistics and probability, including means and distributions; and use Python to create quantile-quantile plots and perform hypothesis tests.

Module 4:

Linear Models - Ordinary Least Squares

Understand how to build and visualize OLS regression models, apply the problem-solving framework to create univariate and multivariate regression models; create user-friendly model outputs; and apply metrics to regression models.

Module 5:

Linear Regression: Interactions and Transformations

Learn transformation of variables into your model; fit variable interactions; perform the Shapiro-Wilks test to assess normality and execute transformations; create multivariate models with interaction variables; evaluate model fit and complexity; share results.

Module 6:

Logistic Regression and Applying GLMs

Explore modeling for binary outcomes; apply the problem-solving framework to create logistic regression models that determine the odds and probability of a given outcome; clearly communicate the output of models; and apply metrics to classification models.

Module 7:

Data Visualization Strategies

To effectively communicate to non-technical stakeholders, create interactive dashboards in Python; employ best practices for creating effective data visualizations; and plot different types of data using Seaborn in Python.

Module 8:

Experimental Design, Causal Research, and Targeting Analysis

Use statistical tests to determine statistical significance while using visualization techniques to fully illustrate the differences between test and control; apply experimental design and targeting analysis; and perform A/B testing in Python.

Module 9:

Machine Learning

Focus on modeling techniques to solve business challenges; apply machine learning techniques, such as K-means clustering, classification, and regression trees, and resampling through k-fold cross-validation; learning within the industry.

Module 10:

Final Project to Your Job-Ready Portfolio

Synthesize all of the concepts, skills, and applications you have learned, develop a project that demonstrates and explains various tools and techniques explored in the course; creating a portfolio presentation to share with prospective employers.

Module 1:

Practical Applications of Analytics

Learn to implement Python concepts; evaluate how companies leverage data to find opportunities; use a problem-solving framework; create functions and 'while' loops in Python; work with values, tuples, and lists; and explore Markdown in Jupyter Notebook.

Module 6:

Logistic Regression and Applying GLMs

Explore modeling for binary outcomes; apply the problem-solving framework to create logistic regression models that determine the odds and probability of a given outcome; clearly communicate the output of models; and apply metrics to classification models.

Module 2:

Data Structures and Plotting

Identify structured and unstructured data; understand continuous and categorical data; explore data structures; determine what kind of data can be expressed in vectors vs. matrices; format and analyze data; and create simple plot points in Python.

Module 7:

Data Visualization Strategies

To effectively communicate to non-technical stakeholders, create interactive dashboards in Python; employ best practices for creating effective data visualizations; and plot different types of data using Seaborn in Python.

Module 3:

Introduction to Statistics and Probability

To understand confidence intervals and p-values, gain a solid base of knowledge in statistics and probability, including means and distributions; and use Python to create quantile-quantile plots and perform hypothesis tests.

Module 8:

Experimental Design, Causal Research, and Targeting Analysis

Use statistical tests to determine statistical significance while using visualization techniques to fully illustrate the differences between test and control; apply experimental design and targeting analysis; and perform A/B testing in Python.

Module 4:

Linear Models - Ordinary Least Squares

Understand how to build and visualize OLS regression models, apply the problem-solving framework to create univariate and multivariate regression models; create user-friendly model outputs; and apply metrics to regression models.

Module 9:

Machine Learning

Focus on modeling techniques to solve business challenges; apply machine learning techniques, such as K-means clustering, classification, and regression trees, and resampling through k-fold cross-validation; learning within the industry.

Module 5:

Linear Regression: Interactions and Transformations

Learn transformation of variables into your model; fit variable interactions; perform the Shapiro-Wilks test to assess normality and execute transformations; create multivariate models with interaction variables; evaluate model fit and complexity; share results.

Module 10:

Final Project to Your Job-Ready Portfolio

Synthesize all of the concepts, skills, and applications you have learned, develop a project that demonstrates and explains various tools and techniques explored in the course; creating a portfolio presentation to share with prospective employers.

For full details of the curriculum, please download the brochure.

Download Brochure

Featured Student Projects

An image of a broken headlight of a car

Predicting accident outcomes in New York City:

The goal of this project was to accurately predict whether a traffic accident could result in injury or death and assist in the deployment of officers.

An image of a man's hand holding a mobile screen with Twitter displayed on the screen

How do social networks affect stocks?

Machine learning algorithms and NLP techniques were used to understand and determine the influence of Twitter and Facebook conversations on stocks. Together with financial data, the student calculated rules for investing and predicting the price of various stocks.

An image of a paper and pen with a signature

To be renewed, or not to be renewed:

In this project, a model was created to accurately and consistently predict whether a contract will be renewed in an energy company, identifying variables important to creating better plans for the future to enable more efficiency and productivity.

An image of a satellite in outerspace

Icarus - when satellites fall:

The United States Air Force, which includes the Space Force, tracks the telemetry of thousands of near-Earth objects, also known as satellites. As additional satellites are launched and traffic above Earth's atmosphere increases, older satellites have a greater chance of falling from orbit. Students used machine learning to help predict satellite decay, or the point at which a satellite loses orbit and re-enters the atmosphere. The final model, deployed live, is currently in use by the Space Force.

Faculty

Faculty Member Geoffrey Parker

Geoffrey Parker

Professor of Engineering at Dartmouth College, Director of the Master of Engineering Management Program at Thayer School of Engineering

Geoffrey Parker is Professor of Engineering at Dartmouth College, where he also serves as Director of the Master of Engineering Management Program. Parker also teaches regularly in the Dartmouth Tuck School of Business executive education program. In addition, he is a research fellow at MIT’s Initiative for the Digital Economy where he leads platform industry research studies and co-chairs the annual MIT Platform Strategy Summit. Prior to joining the Dartmouth faculty, he was Professor of Management Science at the Tulane University A. B. Freeman School of Business. He received a B.S.E. from Princeton and M.S. and Ph.D. from MIT. More info

Testimonials

“The design of the program was well-thought out and really demonstrated how to learn and develop skills within the field of data science. The learning instructor was amazing, and it was extremely smart to have a career coach and mentor. I really feel that beyond the structure, the people made this program great.”

— Drew Miller, Associate Director of Analytics, Red House Communications

“The project gave me the opportunity to apply the skills I learned to solve a real-world problem.”

— Chukwudi Umezinne, Retail Associate (Fuel Center), Kroger

“I really enjoyed the final project, especially putting down the textbook and working through an unstructured problem that I was passionate about. And the fact that the course gave me the skills to do that was also great.”

— Samuel Schmidt, Associate Scientist, Engineering, Merck

“The live instruction sessions and office hours were extremely helpful and engaging. And the interactions with the career counselor and mentor were enlightening about the industry. ”

— James B.Carter, Math/Science Teacher

“I learned a lot from the course – from not being able to write a machine learning code to being able to do one. ”

— Bobby Reyes, Data Analyst, Optymyze

“Alternating independent coding homework with live code sessions gave me an ideal balance of guided and unguided learning.”

— Adriana Maria Surmak, Data Science Fellow, Correlation One

“I like the format – starting with Datacamp to build foundational coding skills (providing confidence) and then moving into math and Python for ML.”

— Tim Noordewier, Sr. Traffic and Civil Engineer, Sam Schwartz Engineering

Professional Certificate

Example image of certificate that will be awarded after successful completion of this program

Professional Certificate

This is a rigorous, fully graded, skill-based program. The Professional Certificate in Applied Data Science from Thayer School of Engineering at Dartmouth shows prospective employers that you are ready to take on a challenging role in data science, from concept to code.

Download Brochure

Your verified digital certificate will be issued in your legal name and emailed to you, at no additional cost, upon completion of the program, as per the stipulated requirements. All certificate images are for illustrative purposes only and may be subject to change at the discretion of the Thayer School of Engineering.

Pay Nothing While You Learn. Financing Options Starting At US$51/Month.

We offer flexible and transparent payment options through our partner Ascent Funding

US Residents (Deferred payment option available)

Rest of the World

Still Have Questions?

  • How do I know if this program is right for me?

    Demand for individuals with skills in data science has grown faster than supply, creating opportunities for those with quantitative abilities to launch into this career field. This online Professional Certificate in Applied Data Science is based on portions of the Master of Engineering Management program curriculum taught at the Thayer School of Engineering at Dartmouth by Professor Geoffrey Parker. It is organized around the skills that technology giants such as Amazon, Google, Facebook, Apple, and McKinsey value in data science professionals. Numerous participants in the on-campus version of the program have leveraged the skills acquired at Dartmouth to obtain positions in data analytics-oriented roles across a variety of industries.

    Are there any prerequisites for this program?

    You must have a knowledge of math and basic coding experience to enroll in this program.

    • Mathematics requirement: This course requires mandatory moderate knowledge of Calculus, Linear Algebra, Statistics, and Probabilities.
    • Assessment: Students can take a sample assessment to test their math skills prior to the start of the program. You can view the sample assessment while submitting the application for the program.

    What is the typical class profile?

    A global and experienced peer group is one of the key elements of the program. The typical class will have early stage, mid-level, as well as senior professionals looking to create a new career pathway in data science. A large majority of participants come from the US but we attract many participants from the wider English-speaking markets like Australia, UK etc. Finally, your class will have professionals from various sectors of the economy, with high representation from Banking and Financial Services, Healthcare, E-commerce, Energy and IT Services.

    Why is there such a strong emphasis on math?

    In order to fully understand the real business problems that data science can help solve, you need to understand how the algorithms work. Take for example machine learning, an approach that is being applied across every industry. Math is a fundamental aspect of understanding what machine learning is about. As you proceed through your career and develop new skills, you will need to leverage this mathematical understanding to engage with and comprehend new literature and peer-reviewed journals on Machine Learning approaches.

    What are the requirements to earn the certificate?

    This is a graded program. You will be required to complete a combination of individual assignments, quizzes, and a final project. Each component carries a certain number of points and a cumulative score of 75% is required to pass and obtain your Professional certificate.

    Will I be guaranteed a job in data science?

    The program will give you the skills you need to be prepared for a job in this field, however a job placement is not guaranteed. Our program team includes data science mentors and career coaches to help you prepare for your job search. Employment offers will depend on many factors, including your prior experience, education, and target job market.

  • How much time is required each week?

    This is a rigorous program that will require 10-15 hours per week. Python Coding Camp, which takes place during the first four weeks of the program, may be more time intensive.

    How will I spend my time?

    This program offers a mix of individual learning, small group sessions, and larger group webinars on topics to accelerate your career. You will divide your time among recorded video lectures, coding video demos, assignments, attending office hours, group webinars, and meetings with your dedicated career coach and mentor.

    What is it like to learn online with Dartmouth and their learning collaborator, Emeritus?

    It’s interactive, flexible, rigorous, and designed to include a lot of personal attention so that you can reach your learning goals more effectively. You will join a community of learners who are excited to expand and share their knowledge. Here are a few elements that set this program apart:

    • Program Director, Geoffrey Parker, is a Professor of Engineering at Dartmouth and Director of the Master of Engineering Management Program at Thayer School of Engineering. He received a B.S.E. from Princeton and M.S. and Ph.D. from MIT and has received multiple awards for his teaching and research.
    • 1:1 career coaching will help you craft an elevator pitch, cover letter, resume, LinkedIn profile, networking, interview skills, and negotiation skills.
    • Your data science mentor, an experienced professional currently practicing in the field, will make presentations and answer questions about the assignments and the final project.
    • Live webinars will focus on topics to accelerate your career in data science.
    • Live office hours held weekly throughout the program will address your questions about what you are learning.
    • A final graded assignment will serve as part of your job-ready portfolio.

    Who is Emeritus and where are they located?

    The Thayer School of Engineering at Dartmouth College is collaborating with Emeritus to create and deliver this program. The certificate is issued by the Thayer School of Engineering at Dartmouth College. Emeritus provides technology and student support throughout the experience.

    Emeritus is based in Singapore, with offices located in Boston, Mexico, Dubai, and Mumbai. More than 30,000 professionals from over 150 countries have taken programs with Emeritus to date.

    When are the live sessions scheduled?

    Because participants join from all over the world, we strive to schedule the live sessions at times when the majority of participants can join. Typically, that will be between 12 pm and 4 pm UTC. All sessions are recorded with recordings available 24 hours after the session. Moderated discussion boards supplement the live sessions.

    How do I interact with other program participants?

    Peer learning adds substantially to the overall learning experience and is an important part of the program. You can connect and communicate with other participants through our learning platform. You will also be part of a small learning group who you will work with throughout the entire duration of the program, along with your dedicated mentor.

  • What do I receive upon successful completion of the program?

    Upon successful completion of the program, you will receive a certificate from The Thayer School of Engineering at Dartmouth College. The digital certificate will be sent approximately two weeks after the program, once grading is complete.

    Can I get the hard copy of the certificate?

    No, only verified digital certificates will be issued upon successful completion. This allows you to share your credentials on social platforms such as LinkedIn, Facebook, Twitter, etc.

    Do I receive alumni status after completing this program?

    No, there is no alumni status granted for this program.

    How long will I have access to the learning materials?

    You will have access to the online learning platform and all the videos and program materials for 1 full year following the program start date. Access to the learning platform is restricted to registered participants per the terms of agreement.

  • What equipment or technical requirements are there for this program?

    Participants will need these minimum system requirements to run Anaconda (Python and R programming languages)

    • Processors: 1 GHz
    • RAM: 1 GB of RAM
    • Disk space: 1 GB
    • Operating systems: Windows 7 or later, MacOS and Linux
    • Compatible tools: Any text editor, Command prompt

    Recommended System Requirements

    • Processors: 2.60 GHz
    • RAM: 8 GB of RAM
    • Operating systems: Windows 10, MacOS and Linux
    • Compatible tools: Any text editor, Command prompt

    Participants would also need the latest version of their preferred browser to access the learning platform. In addition, Microsoft Office and a PDF viewer are required to access documents, spreadsheets, presentations, PDF files, and transcripts.

    Do I need to be online to access the program content?

    Yes, the learning platform is accessed via the internet and video content is not available for download. You can download files of video transcripts, assignment templates, readings, etc. Video lectures must be streamed via the internet and webinars and small group sessions will require an internet connection.

  • What is the program fee for the Professional Certificate in Applied Data Science from Dartmouth College and what forms of payment do you accept?

    • The program fee is shown at the top of this page and payment is accepted only in US dollars.
    • Flexible payment options are available
    • Tuition assistance is available for participants who qualify. Please contact your program advisor to discuss.

    What if I don’t have a credit card – is there another mode of payment accepted?

    Yes, you can do the bank remittance in USD via wire transfer. Please contact your Program Advisor for more details.

    Is there an option to make flexible payments for this program?

    We offer flexible payment options through our partner Ascent Funding. We partner with Ascent Funding to offer you flexible and transparent loan options.

    Click here to learn more

    There are also installment payment options available - you can find them at the top of this page

    Does the program fee include taxes? Are there any additional fees?

    Yes, the program fee is inclusive of any taxes with the exception of GST for Singapore residents

  • What is your refund policy?

    Participants are eligible for a full refund if they cancel their enrollment before or during their trial period. The trial period for all programs is 21 calendar days (excluding course holidays) from and including the first day of class, which is the participant’s scheduled start date.

    Participants may request a prorated refund after the trial period has passed up until they have completed 60% of their time in the program. Refunds are determined through proration of tuition, based on the number of days that have elapsed from the scheduled start date through the date that the Withdrawal Request Form is completed. Refunds will not be granted once 60% of a participant’s scheduled program time has passed, regardless of the amount of work a participant has submitted or sessions a participant has attended.

    After a participant is withdrawn, they will no longer be able to attend office hour or 1:1 sessions with Course Leaders, Mentors, or Career Coaches, submit work for review, or access their program dashboard or curriculum.

    How do I request a refund?

    Participants who would like to cancel their enrollment should email ProgramSupport@Emeritus.org or use the “Support” tab in their learning platform in order to receive the Withdrawal Request Form. This form must be submitted by midnight UTC of the last day of the trial period to be eligible for a full refund. No cancellations will be processed unless this form is received. The payment of refunds will be completed within 30 days after the effective date of withdrawal or dismissal.

    Can I defer to a future cohort?

    Participants have the option to request a deferral to a future cohort within the first 30 days from their Program Start Date. Cohort changes may be made only once per enrollment and are subject to the availability of cohorts scheduled at the discretion of Emeritus. Participants requesting a deferral must be in good academic standing. Participants who would like to defer their enrollment should email ProgramSupport@Emeritus.org or use the “Support” tab in their learning platform in order to receive the Deferral Request Form.

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Flexible payment options available. Learn more.