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Data Science + Machine Learning + AI Career Track enroll

Become a machine learning and data science engineer. Learn to build end-to-end machine learning models that solve decision making problems in areas of your interest. This career track starts with Python, and takes you through building a portfolio of projects using advanced data science, machine learning and deep learning techniques.

  • Level

    Beginner to professional. This track starts at the beginning and continues up to entry-level professional.

  • Duration

    790 Hours. Or, ~9 months of dedicated learning @ 15-20 hours per week

  • What's included

    394 lessons. 185 videos. 51 quizzes. 100's code samples. Dozens of labs. 10+ mini projects. 3 portfolio projects.

  • Certificate

    Python certificate, SQL & DBs certificate, Data Science certificate, and a Deep Learning certificate

There are 6 courses in this track. Start from the top and work your way down.

  1. Python 101 - Introduction to Python icon

    Python 101 - Introduction to Python enroll

    In this first module of the Python course, you'll learn how to code using the Python programming language. You'll get to know some of the fundamental concepts of programming, write procedural scripts, and build interesting projects that can show you the value of knowing how to code.

    • Course
    • Beginner
    • Python
  2. Python 201 - Procedural Python icon

    Python 201 - Procedural Python enroll

    In this second module of the Python course, you'll learn how to code using the paradigm of procedural programming. You'll get to know additional data types and control structures in Python, learn how to work with functions, and how to use APIs to interact with sources on the Internet.

    • Course
    • Intermediate
    • Python
  3. SQL & Databases (MySQL) icon

    SQL & Databases (MySQL) enroll

    Learn the fundamentals relational databases and the Structured Query Language (SQL) using MySQL.

    • Course
    • Beginner
    • SQL & Databases
  4. Python 301 - Object-Oriented Python icon

    Python 301 - Object-Oriented Python enroll

    In this module of the Python course, you'll learn how to write programs using the object-oriented approach to programming. You'll get to know how to model your code around objects and classes and apply this way of programming by building a game. You'll also learn about web scraping, exception handling, and writing tests for your programs.

    • Course
    • Intermediate
    • Python
  5. Data Science & Machine Learning with Python icon

    Data Science & Machine Learning with Python enroll

    Learn the foundations of data science and machine learning using Python. Learn how to think like a data scientist. Understand what it means to learn from data using ML tools and algorithms. In this course you'll use Jupyter Lab, Numpy, Matplotlib, Seaborn, Pandas, Scikit Learn, and much more to dive into ever more advanced analysis and predictive modeling using data and code.

    • Course
    • Advanced
    • Python
    • Deep Learning
    • Data Science & ML
  6. Deep Learning & Neural Networks with Python icon

    Deep Learning & Neural Networks with Python enroll

    Learn the fundamentals of Deep Learning applications by building, training and deploying PyTorch models from scratch. You’ll work with transfer learning using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as well as learn how to deploy your models.

    • Course
    • Advanced
    • Python
    • Deep Learning
    • Data Science & ML

Track your progress in the Data Science + Machine Learning + AI Career Track

Enroll for free
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Secure your future by learning the world’s most valuable skills.

Python tops the charts of the most in-demand programming languages, thanks to its use in AI, machine learning and data science.

Earn an advanced Python, machine learning and data science certification, and invest in your career.

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Get a better learning experience for a better value

With our affordable tuition, flexible schedule, and best-in-class curriculum and mentorship, CodingNomads ranks as one of the best data science programs by Fortune and Forbes.

See our student reviews.

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Learn faster with a data science mentor

Most data science bootcamps are either too expensive, or don’t provide enough support to get you across the finish line.

By joining CodingNomads mentorship program, work 1:1 with technical and career mentors to get guidance and support whenever you need it.

Ben B.

Ben B.

DSML Career Track

"CodingNomads not only made me proficient in Python in record time (4 weeks), but their mentoring service made my data science experience in the program incredibly practical. I had begun interviewing for Sr. data scientist roles within weeks of starting the program based on precise career advice and coaching. These interviews are backed up by practical, real-world projects that showcase my understanding of data science applicable to product management, marketing, and other business-oriented roles. "

Read more reviews on SwitchUp
Elia B.

Elia B.

DS/ML Career Track

"The community and my mentor helped me prepare for a technical job interview only a few days after I enrolled to the course. I got the job. Now having started my new job, I am still in touch with my mentor who makes sure I am succeeding in my new role and regularly offers help and guidance. "

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Anonymous

Anonymous

DS/ML Career Track

"This was the only course I found that met all my needs! ...The learning experience was amazing - in big part due to my outstanding mentor who would be available to answer all my questions or - even better - help me answer them myself. The community was another big asset. With the support of my mentor and the community I made it from close to zero coding experience to a challenging medical data science capstone project in about 7 months. And the support continues.. I am very grateful for this experience. "

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Anonymous

Anonymous

DS/ML Career Track

"Where CodingNomads really excels is in their excellent mentoring program. I have had the pleasure of meeting (online) a few of them, and it is clear, in my view, that they are chosen not just for their knowledge and experience but also for their capacity to provide encouragement and support to those who really want to learn this material...the value of having a person who knows the trade to help you get your head around Machine Learning concepts and problem solving techniques cannot be overstated. "

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Frequently Asked Questions

What will I learn in the Python Machine Learning and Data Science career track?

The Machine Learning and Data Science career track includes the following courses in sequential order:

  • Python 101
  • Git & GitHub
  • Python 201
  • SQL & Databases
  • APIs & Databases
  • Python 301
  • Data Science + Machine Learning
  • Deep Learning

Throughout each section of the Python Machine Learning and Data Science bootcamp, you’ll learn:

Python

  • Python basics and programming fundamentals
  • Git & GitHub, the CLI, PyCharm IDE, virtual environments
  • Object-Oriented Programming and Functional Programming
  • Relational Databases: design, build, deploy and maintain modern databases
  • RESTful APIs
  • Projects: Learn to build complex, scalable software applications from scratch. Build a Python portfolio project that consumes, analyzes and stores data using APIs and SQL. Start the Python course.

Data Science and Machine Learning

  • Python Data Science Ecosystem: Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook
  • Dealing with Data: wrangling, cleaning, preprocessing, normalization & standardization, feature extraction
  • Fundamentals of Machine Learning
  • Model Evaluation & Validation: Train and test data sets using validation schemes and curves, baseline models, appropriate metrics, confusion matrices
  • Supervised Learning: Extract insights from labeled data with Linear Regression, Gradient Descent, Decision Trees, Bagging, Boosting
  • Unsupervised Learning: Find patterns in unlabeled datasets with Dimensionality Reduction using PCA, Clustering with K-means and DBScan.
  • Projects: Implement DSML methodologies into multiple projects using real-world datasets. Build an end-to-end machine learning model capstone project that solves a decision making problem in an area of your interest.
  • Start the Data Science and Machine Learning course.

Deep Learning

  • Deep Learning fundamentals
  • Machine Learning + Deep Learning toolkit: PyTorch, Matplotlib, Pandas, Numpy
  • How to build, train and deploy PyTorch models from scratch.
  • Computer vision and image classification with Convolutional Neural Networks (CNNs)
  • Natural Language Processing (NLP) with Recurrent Neural Nets (RNNs)
  • Deployment and the production environment
  • Projects: Build multiple projects that demonstrate different machine learning competencies. Apply deep learning principles in a capstone that highlights your domain knowledge and interests.
  • Start the Deep Learning course.

What’s the difference between the Data Science + Machine Learning course and the Machine Learning and Data Science career track?

Career tracks contain multiple courses that go from beginner to professional.

The Data Science + Machine Learning course is one of the courses included in the Python Data Science + Machine Learning career track.

How much does CodingNomads data science bootcamp cost?

We have three enrollment options with varying levels of access and support.

  1. Enroll for free: Track your progress and access thousands of pages of documentation.
  2. Premium Membership: Access all the videos, interactive content, apply for certificates, and join our Discord forum.
  3. Mentorship: get dedicated support from technical and career mentors - our version of an online coding bootcamp.

See pricing and enrollment options.

How does the online mentorship program work?

Mentorship programs help you learn faster and keep a strong pace, and also give you professional insight and a community to learn with.

In the mentorship program, you’ll be paired 1:1 with a technical mentor for weekly meetings, and 24/7 access to reach out to the entire community for guidance and support when you need it. After you complete the curriculum with technical mentorship, you can enroll in career mentorship to help you land your next job.

Read about our online coding bootcamp mentorship programs.

Are there live lectures on a set schedule?

No. All lectures are pre-recorded, so you can complete the curriculum on the schedule that works for you.

The only live requirements are in our coding bootcamp mentorship programs, where you meet each week with your mentor.

Does this program offer a data science certification?

Yes. The data science bootcamp offers a Python certification, data science certification, and a deep machine learning certification.

Certificates are available if you are enrolled in Premium Membership or Mentorship. Here’s how to receive your certification:

  1. If you are enrolled in Premium Membership or Mentorship, you can request a certificate at the end of each course. If you enroll in a career track program, most students request one comprehensive certification at the end, but you can request a certificate for each individual course if you choose.
  2. We review your work and provide feedback for any remaining elements needed to issue your certificate.
  3. Once you meet the graduation criteria, we'll issue your certificate to the email associated with your account.

Is there a data science certification exam?

We do not have one singular, comprehensive data science certification exam. Instead, each of our courses includes quizzes, lab assignments, journal entries, and projects that all must be completed in order to receive a data science certification / machine learning certification.

How long does the Python machine learning and data science bootcamp take?

Learning to code is a process, and everyone learns at a different pace. In total, on average, students need 6-12 months studying 15 hours / week to go from beginner to professional, plus the time it takes to find a job. This timeframe includes deploying complete capstone projects, which is required for graduation.

Here’s how it breaks down:

  • 2-4 months: Learn Python basics, SQL, APIs, Git & GitHub
  • 2-4 months: Learn Data Science + Machine Learning
  • 2-4 months: Deep Learning
  • 1-6 months: Job search. In addition to learning, you’ll want to consider the time it will take you to get a job. This can sometimes be the toughest part. Don’t worry though, because we can help you out with career mentorship.

What if I plan to study full-time?

If you plan to study full-time, great! You can expect to learn faster than the average times mentioned above.

That said, learning to code is not just about reading curriculum and watching videos. To become a professional, you must do the work. Writing code and building projects from scratch takes time, no matter how many hours per week you study.

To go from beginner to professional studying full-time, you should still expect a minimum of 4-6 months to gain the proficiency to build a job-worthy portfolio project, pass technical interviews, and succeed on the job.

Are the courses project-based?

Yes. We teach the real-world tasks of software engineers, so you get plenty of practice building projects using professional developer tools and IDEs. In order to receive a certificate of completion, you must complete the project assignments within each course.

Course structure

Our curriculum generally follows a “read something, watch something, do something” format:

  • First, you read documentation that introduces a concept.
  • Next, you watch a video that demonstrates the concept.
  • Finally, you practice writing the concept in your IDE through lab exercises, assignments, quizzes, journal entries, project assignments and more.

Our curriculum may not include a video or assignment for every concept, but at the end of each course you’ll have the opportunity to implement all learned concepts into your capstone project.

What is data science? What is machine learning? What is deep learning? What’s the difference?

What is data science?

Data science is the act of uncovering patterns and meaningful information from large data sets to inform decisions. When data sets are too massive to be analyzed by human-driven techniques, data scientists can use Python libraries for data science to analyze hypotheses programmatically. Data science is a subdiscipline of Artificial Intelligence (AI). For more information see our blog: What does a data scientist do?

What is machine learning?

Machine learning uses algorithms and statistical models to analyze data, and find patterns to generalize the trends of the data. As the name implies, machine learning enables computers to learn from and make predictions or decisions based on the data collected, without human assistance. Machine learning involves training models on data to learn patterns and relationships, so that models can generalize their learning to new, unseen data. Machine learning is also a subdiscipline of Artificial Intelligence (AI).

Data science vs machine learning

Data science focuses on the entire process of extracting insights from data, including data collection, cleaning, analysis, and visualization, while machine learning is a subset of data science that specifically deals with creating algorithms that enable computers to learn from data and make predictions or decisions. Data science often incorporates machine learning techniques as a tool to analyze and extract insights from data. This is why our Data Science + Machine Learning course teaches the foundations of both.

What is deep learning?

Deep learning is a branch of machine learning and AI that was originally inspired by models of biological neurons. Deep learning deals with artificial neural networks, which are designed to mimic the human brain's structure and function. In deep learning, neural networks are “deeply” layered upon each other, and are composed of interconnected nodes (neurons) that process and transform data. This deep layering effect allows the neural networks to automatically learn complex patterns from raw data with less need for human training and programming. Deep learning enables functions like image recognition, speech synthesis, natural language processing, playing complex games, and more.

Deep learning vs machine learning

Some main differences between deep learning vs. machine learning include:

  • Human interaction: Deep learning is a specialized subset of machine learning that can automatically learn and extract features from raw data, while machine learning requires more manual programming from humans.
  • Complexity: Deep learning can handle larger amounts of complex data than traditional machine learning models, and better captures intricate patterns and relationships in data.
  • Amount of Data: because of deep learning’s multiple layers and complex architectures, deep learning models need a substantial amount of diverse examples to generalize effectively. Whereas traditional machine learning models can perform reasonably well with smaller datasets.
  • Computation and Training: Deep learning models often require more computational resources, such as GPUs, as well as more time to train the models than machine learning models.

What are the prerequisites for data science?

Below are the general prerequisites for data science:

  • Familiar with Python programming, or the willingness to learn
    • If / else statements
    • For loops
    • Writing functions
    • Understand objects attribute vs. objects method
    • Comfortable reading documentation
  • Basic algebra
  • Knowledge of statistics and calculus is beneficial, but not required
  • Genuine interest and curiosity in engineering, logic, problem solving, math and data analysis
  • Motivation to work hard and try new things repeatedly until you get your desired result

All of the technical data science skills you need to know are taught in CodingNomads’ Python Data Science + Machine Learning Career Track. For the soft skills, well that’s on you!