Math for Data Science
Learn the essential mathematics concepts to understand how machine learning algorithms work
Upcoming Session
July 11 – 15
Live instructor-led training: 9am – noon PDT
Live office hours and support daily.
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HANDS-ON LEARNING
Essential Math Concepts for Data Science with Hands-On Practice
Probability theory, statistics, vector operations, matrix algebra, calculus and more with theoretical concepts and practical exercises in R and Python. The most comprehensive short-duration mathematics curriculum for data scientists, data engineers, analysts, and researchers.
START FROM GROUND UP
Designed for Both Practitioners and Beginners
Before you arrive for the in-person learning experience, our carefully designed self-paced learning modules will help you get up to speed with R and Python fundamentals.
Beginner in R and Python or a little rusty with syntax? No problem! Our pre-training learning modules will get you ramped up before the live training.
COMPLETE LEARNING ECOSYSTEM
Instructor-Led Training, Office Hours, Mentoring, and More
- Instructor-led, live training
- Daily, live office hours for homework support
- Hundreds of code samples for practice
- Cloud-based compute tools, inline code editor, and code repositories
- Hundreds of Pandas, NumPy, Seaborn, matplotlib and scikit-learn code samples
- Mentoring for class project
- One-year access to all supplementary learning material
- Verified certificate from The University of New Mexico Continuing Education
Curriculum
Probability Theory
Probability is the foundation of data science and machine learning. Machine learning models are designed, trained, tuned, and evaluated using tools and techniques from probability.
The module includes hands-on practical exercises in R and Python to apply you learning on real data.
In this module, you will learn:
- Combinatorics
- Joint, Marginal, and Conditional Probability
- Bayes Theorem
- Discrete Random Variables and Binomial Distribution
- Continuous Random Variable and Normal Distribution
- Maximum Likelihood Estimation
- Every-day applications of probability
Statistics Essentials
Machine learning and statistics are closely related fields and sometimes machine learning is referred as “statistical learning”. Statistics is what helps in transforming raw data into information.
A clear understanding on machine learning concepts like bias-variance trade-off, model evaluation, model performance comparison, hypothesis testing, confidence interval, etc. requires a solid background in statistics.
In this module we will cover these essential statistics concepts and their hands-on practical exercises in R and Python.
In this module, you will learn:
- Descriptive versus inferential statistics
- Population, Sample, and Bias
- The measure of Central Tendency
- Measure of Spread
- Standard Normal Distribution
- Skewness and Kurtosis
- Central Limit Theorem
- Probability Density Function
- Cumulative Distributive Function
- Hypothesis Testing and Statistical Significance
Linear Algebra for Data Science
Linear algebra lies behind many of the machine learning jargons such as principal component analysis (PCA), ridge regression, lasso regression, support vector machines etc. and forms the mathematical basis of nearly all machine learning algorithms. This module teaches you what linear algebra is, its importance to machine learning, and its applications.
In this module we will cover these fundamental linear algebra concepts with hands-on practical exercises in R and Python.
In this module, you will learn:
- Understanding scalars, vectors, and matrices
- Vector arithmetic (addition, subtraction, multiplication, division)
- Vector dot product and vector-scalar multiplication
- Matrix arithmetic (various multiplications and transformations)
- Matrix independence, rank, and inverses
- Matrix decompositions (LU and QR)
- Eigendecomposition and singular value decomposition(SVD)
- Principal components analysis (PCA)
Calculus for Data Science
Calculus plays an integral role in many Machine Learning algorithms. If you want to know what is happening inside your Machine Learning model, you will need to have a solid grasp of the fundamentals of calculus. It will teach you how to calculate derivatives, which is the fundamental working theory of a gradient descent. Gradient descent minimizes the error function inside a Machine Learning model, this process is also referred to as ‘training’ the model.
This module will cover the fundamental concepts behind gradient descent, teaching you the theory behind training your model using concepts of calculus. Our Jupyter notebooks will cover how to produce these functions in R/Python and use them to train multiple Machine Learning models.
In this module, we will cover:
- Functions
- Derivatives,
- Gradient
- Hessian
- Jacobian
- Anti-Derivatives
Earn a Verified Certificate
Earn a data science certificate from the University of New Mexico, verifying your skills. Step into the market with a proven and trusted skillset.
Recommended by Working Professionals
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Taught by Practitioners
Our instructors are dedicated to helping you steer your career. With years of experience in the field, our instructors are professional data scientists and practitioners. They bring real-world stories and anecdotes to the class, adding immense value to your learning.
Learning Plans and Schedule
Hurry up! Only 1 seat remaining at this price.
40% OFF
Dojo
$599
$999
- Pre-training material
- 15 hours of live instructions
- In-class learning material
- Online Python Jupyter notebooks
40% OFF
Guru
$779
$1299
- Everything in Dojo plan
- Bonus Python Jupyter notebooks during training period
- Learning platform access during training period
- Collaboration tools access during training period
- Recordings of live sessions for later review during training period
-
Verified certificate from The University of New Mexico
20
Sensei
$1499
- Everything in Guru plan
- Learning platform access for one year
- Access to our collaboration forum for one year
- Access to recorded sessions for one year
- Office hours during training period
- Mentoring for end of course project
10% OFF
Dojo
$899
$999
- Pre-training material
- 15 hours of live instructions
- In-class learning material
- Online Python Jupyter notebooks
20% OFF
Guru
$1039
$1299
- Everything in Dojo plan
- Bonus Python Jupyter notebooks during training period
- Learning platform access during training period
- Collaboration tools access during training period
- Recordings of live sessions for later review during training period
-
Verified certificate from The University of New Mexico
20% OFF
Sensei
$1199
$1499
- Everything in Guru plan
- Learning platform access for one year
- Access to our collaboration forum for one year
- Access to recorded sessions for one year
- Office hours during training period
- Mentoring for end of course project
A plan of your choice
Select the plan that best meets your financial needs.
Flexible Payment
Choose the repayment duration based on your future plans.
Deferred Payment
Start paying only after you've completed the training program.
Finance Your Learning
Looking for financing options and student loans?
Explore the student-friendly plans and start learning data science without having to worry about the cost.
FAQs
Access to the program content depends on the plan you choose at the time of registration. Learn more about different plans here.
Math for Data Science program is 2 days, 4 hours per day, for a total of 8 hours of live training. There is additional practice if you would like to keep refining your mathematics knowledge after the program ends.
There are no prerequisites for this program, however our pre-course prep work will include tutorials on fundamental concepts of data science in R and Python programming to help you prepare for the training program.
Classes are live and instructor-led. Office hours will be available multiple times a week in case students need assistance. The program is not self-paced but we do assign some homework and practical exercises to further facilitate your learning. Lectures will also be recorded to give students the option to go back and review.
The cost will depend on the plan purchased by the students and the discounts available at the time.
Please contact us at [email protected] for updated information on discount availability and payment plans.
We’re offering three different plans for this program.
- Dojo. With the Dojo plan, you will get 8 hours of live training, pre-training material, program content, and restricted access to Jupyter notebooks.
- Guru. With the Guru plan, you will get everything in the Dojo plan including bonus Jupyter notebooks during the program, access to the learning platform during the program, access to collaboration forum, recorded live sessions, and a verified data science certificate from the University of New Mexico worth 1 Continuing Education Credits.
- Sensei. With the Sensei plan, you will get everything included in the Guru plan with the additions of one year of access to the learning platform, Jupyter notebooks, collaboration forums, recorded sessions and office hours, and live support during the program.
Yes, we are offering an early-bird discount on all three plans.
The class will be 4 hours per day, and you can expect 1-2 hours of homework every night. Our instructors and teaching assistants will be available during office hours Monday-Thursday for additional help.
To register for the program, simply view our packages and register for the upcoming cohort. The payment can be made online on our website, via invoice, or wire transfer.
Once you are registered for the program, you will receive a few emails from us. One of those emails will contain steps to create your learning portal account and access the program content.
Please follow the steps in the email to create your account. If you’re facing any difficulty please email us at [email protected] for assistance.