Data Science Bootcamp
9-month, part-time online program for all levels
Data Science Bootcamp
9-month, part-time online program for all levels
Skillset you’ll get in our bootcamp
Python
+Pymystem
+NTLK
LightGBM
Pandas
XGBoost
Matplotlib
Jupyter Notebook
CatBoost
Sci-kit learn
Keras
Numpy
PySpark
Probability theory and statistics
Algorithms and numerical methods
Linear algebra
Preprocessing Data
Exploratory Data Analysis (EDA)
Data Storage
Time series analysis
Data labelling
Machine learning
A/B testing
Product funnel and conversion
Average data scientist
oosalary: $102,370
You’re ready to learn data science if you are:
IT-inclined
You liked math in high school and aren’t scared of code.
STEM-curious
You’re happy solving problems with logic and numbers.
Driven
You have the grit and at least 20 hours a week to commit.
Simulated work environment
Interactive platform
Learn on the user-friendliest coding platform. Concepts, data sets, and tasks all in one workspace.
Move as fast as you want and break things. Instantly run your code to visualize what you’ve made. Unlimited time and space to play.
Line-by-line code reviews
Level up with feedback from real data scientists. Practicing professionals will dive into your code and make sure you’re learning what you need to grow and develop.
⭐ It’s important to look not just at the amount of missing values in every column, but at the percentage of missing values in column, this can be done by slightly changing your code, for example like this:

data.insull().sum()*100/len(data)
Mia Murray
Code reviewer
16 portfolio projects
Work on real-world projects with real data from day one. Learn business analytics, product science, and machine learning. Apply those skills to business-relevant problems in banking, retail, ride-share, adtech, telecom, and insurance.
Take the big step — with our help
Moving up into data science is a challenge, so we put in the support to make sure you get there. We’ll cheer you on, provide advice, and help with coding tasks.
Numerical Methods
Data Preprocessing
Supervised Learning
Machine Learning in Business
Computer vision
Linear algebra
We have your back
Experienced Tutors
Teach you skills
Code Reviewers
Give you feedback
Tech
Support
Solve technical issues
Career Coaches
Get you employed
Senior Students
Provide you guidance
Community Managers
Make group learning fun
Let's get you a job
you will enjoy
Externships
All students will take on externships at actual companies with real life projects, we call them “Apiary projects”. Boost your LinkedIn profile and impress your future employers with relevant projects, and reviews from a real company.
Allcorrrect Games company wanted a way to sort user reviews. Result: Our student created a review-classifying Python script running off a pre-trained model.
Scentbird company wanted to predict customer LTV from user behavior on their website. Result: A running ML model that predicts the customer's LTV.
Bibliosphere company wanted to get info about people and books in 1000+ cities around the world. Result: It got a fully-documented cross-validation model.
Build a professional brand and find a great job>>>with weekly one-on-one career coaching
Free career coaching included
valued at $4000
Support beyond graduation
We stay in touch during the first two months of employment, to make sure you are confident in your new position.
Build a portfolio that will attract recruiters and impress at interviews. Learn how to smash through test tasks. We’ll help you get your worth in the industry.
Career Development Course
4+ free hours of mock interviews will give you the confidence to nail a real one
Join Practicum open house webinar
Prepare for an engaging learning experience. Our platform combines theory, real scenario tasks, and quizzes all in one place.
4.8 out of 5 rating based on 330+ reviews
The projects are modeled from real-world scenarios, which made them quite interesting to work on and solve.
Makenzie Wells
Sr Director of Technical Operations at Remind
Former Bis Dev at a diabetes startup
At Practicum I was glad to have recourse to the career program, where students review each other's resumes and provide moral support.
Jackie Lu
Data Analyst at Tesla
Former B.S., University of Florida
My favorite part of the program is the project and code review. At the end when your project is approved, there is that satisfaction that comes from achieving a feat.
Chukwuemeka Okoli
ML engineer at Leidos
Former Petroleum engineer
Somehow I feel like we are part of a group trying to achieve something together despite working individually, helping each other when needed and celebrating each other’s win.
Rachelle Perez
Practicum is a very good way for students to comprehend and practice our skills. That helps a lot for a person with no prior coding skills. I could apply my new skills to projects I was working on at that time for the research institution. That helped me learn faster than I expected.
Thao-Vy Vuong
Clinical Data Analyst | Population Health Research at ResMed
Worked in International Development
This no non-sense course touched upon theory and dropped you in the deep end to practice and learn. It’s hands-on, and there are multiple projects and case studies that ensure you learn by doing.
Yash Dubey
From theory to practice, even before finishing I was passing interesting interviews. I improved my portfolio significantly and feel I’m way more confident in my hard skills but also soft skills…
Hibatallah Kabbaj
The amount of job support you get is good for the price of the course, and it’s great when you consider they don’t have any ISAs or anything like that. In conclusion, if you’re looking for a detailed boot camp with great support from a big name in data science you can’t do much better than Practicum.
Jaylen Gentry
Associate Data Scientist at Spotify
Worked in Tourism Sales
Read all reviews on Career Karma, Course Report and Switchup
Our graduates are doing great at their awesome jobs
Get paid or get a refund
If you don’t get a job within six months of graduating Practicum, we will refund 100% of your tuition.
Read more in Terms of Use
Program structure
Basic Python
3 weeks
Your introduction to the world of data science! Key concepts and basic syntax in Python. Loops, conditions, and functions. The pandas library for data analysis. Your first analytical case study, followed by your first project.
Data Preprocessing
3 weeks
Compensating for less-than-perfect data. Handling missing and duplicate values. Changing data types. Systems thinking for analysts.
Exploratory Data Analysis (EDA)
3 weeks
Performing initial scans to detect patterns in data. Building basic graphs and generating your first hypotheses.
Integrated Project 1
1 week
Identify patterns to help you determine whether a given video game will be commercially successful or not.

Data Collection and Storage (SQL)
2 weeks
How databases are structured and how to pull data from them using SQL queries. Finding data online.

Supervised Learning
Diving into the most in-demand area of machine learning. How to tune machine learning models, improve metrics, and work with imbalanced data.

Chapter 1. Introduction to Supervised Learning
Chapter 2. Feature Preparation
Chapter 3. Classification Metrics
Chapter 4. Imbalanced Classification
Chapter 5. Regression Metrics
Chapter 6. Soft Skills


Introduction to Machine Learning
2 weeks
Mastering the basics of machine learning. How the scikit-learn library works and how to apply it in your very first machine learning project.

Supervised Learning
2 weeks
Diving into the most in-demand area of machine learning. How to tune machine learning models, improve metrics, and work with imbalanced data.
Machine Learning in Business
2 weeks
Applying what you’ve learned to business tasks. Discover business metrics, A/B testing, the bootstrapping technique, and data labeling.
Integrated Project 2
1 week
Prepare a prototype of a machine learning model to help a mining company develop efficient solutions.
Linear Algebra
2 weeks
Taking a deeper look at some algorithms you’ve already studied and understanding how to apply them. Key concepts in linear algebra: vectors, matrices, and linear regression.
Numerical Methods
2 weeks
Analyzing a number of algorithms that use numerical methods and applying them to practical tasks. Gradient descent, gradient boosting, and neural networks.
Тime Series
2 weeks
Exploring the time series. Understanding trends, seasonality, and feature creation.

Computer Vision
How to handle simple computer vision tasks using premade neural networks and the Keras library. A quick look at deep learning.

Chapter 1. Course Introduction
Chapter 2. Fully Connected Networks
Chapter 3. Convolutional Neural Networks
Chapter 4. Soft Skills

Unsupervised Learning
Figuring out what to do when you have no target features. Handling clustering tasks and looking for anomalies.

Chapter 1. Course Introduction
Chapter 2. Clustering
Chapter 3. Search for Anomalies

Final Project
Apply everything you’ve learned in a two-week bootcamp that simulates the experience of working as a junior data scientist.
Machine Learning for Texts
2 weeks
Applying machine learning to text data. Finding out how to convert text into numbers and how to use bag-of-words, TF-IDF, as well as embeddings and BERT.
Computer Vision
2 weeks
How to handle simple computer vision tasks using premade neural networks and the Keras library. A quick look at deep learning.

Final Project
Apply everything you’ve learned in a two-week bootcamp that simulates the experience of working as a junior data scientist.
Unsupervised Learning
2 weeks
Figuring out what to do when you have no target features. Handling clustering tasks and looking for anomalies.
Final Project
2 weeks
Apply everything you’ve learned in a two-week bootcamp that simulates the experience of working as a junior data scientist.
Final Project
2 weeks
Apply everything you’ve learned in a two-week bootcamp that simulates the experience of working as a junior data scientist.
Career Prep Course
2 weeks
This is a course devoted to preparing for life after Practicum. During this course, you will learn how to create a resume, a LinkedIn profile, and a GitHub account, along with improving networking and interviewing skills. This course is self-paced and ends with a final task. We’ll also perform a review of your career artifacts.
Career Acceleration Program
Up to 6 months after graduation
Prepare for real-world interviews and gain experience through authentic practice. This program is designed to help you find a real job and also provides some work with technical skills.
Apiary Projects
5-6 weeks
You'll gain confidence solving work tasks that use a real company's data to provide them with valuable insights. Learn to communicate with clients, meet their expectations, exchange peer reviews with colleagues, and present results to the company. The Apiary projects become available for participants sometime between the 8-10th Sprint, depending on the project. They are also available after graduation.
Basic Python
3 weeks
Your introduction to the world of data science! Key concepts and basic syntax in Python. Loops, conditions, and functions. The pandas library for data analysis. Your first analytical case study, followed by your first project.
Data Preprocessing
3 weeks
Compensating for less-than-perfect data. Handling missing and duplicate values. Changing data types. Systems thinking for analysts.
Exploratory Data Analysis (EDA)
3 weeks
Performing initial scans to detect patterns in data. Building basic graphs and generating your first hypotheses.
Integrated Project 1
1 week
Identify patterns to help you determine whether a given video game will be commercially successful or not.

Data Collection and Storage (SQL)
2 weeks
How databases are structured and how to pull data from them using SQL queries. Finding data online.

Supervised Learning
Diving into the most in-demand area of machine learning. How to tune machine learning models, improve metrics, and work with imbalanced data.

Chapter 1. Introduction to Supervised Learning
Chapter 2. Feature Preparation
Chapter 3. Classification Metrics
Chapter 4. Imbalanced Classification
Chapter 5. Regression Metrics
Chapter 6. Soft Skills


Introduction to Machine Learning
2 weeks
Mastering the basics of machine learning. How the scikit-learn library works and how to apply it in your very first machine learning project.

Supervised Learning
2 weeks
Diving into the most in-demand area of machine learning. How to tune machine learning models, improve metrics, and work with imbalanced data.
Machine Learning in Business
2 weeks
Applying what you’ve learned to business tasks. Discover business metrics, A/B testing, the bootstrapping technique, and data labeling.
Integrated Project 2
1 week
Prepare a prototype of a machine learning model to help a mining company develop efficient solutions.
Linear Algebra
2 weeks
Taking a deeper look at some algorithms you’ve already studied and understanding how to apply them. Key concepts in linear algebra: vectors, matrices, and linear regression.
Numerical Methods
2 weeks
Analyzing a number of algorithms that use numerical methods and applying them to practical tasks. Gradient descent, gradient boosting, and neural networks.
Тime Series
2 weeks
Exploring the time series. Understanding trends, seasonality, and feature creation.

Computer Vision
How to handle simple computer vision tasks using premade neural networks and the Keras library. A quick look at deep learning.

Chapter 1. Course Introduction
Chapter 2. Fully Connected Networks
Chapter 3. Convolutional Neural Networks
Chapter 4. Soft Skills

Unsupervised Learning
Figuring out what to do when you have no target features. Handling clustering tasks and looking for anomalies.

Chapter 1. Course Introduction
Chapter 2. Clustering
Chapter 3. Search for Anomalies

Final Project
Apply everything you’ve learned in a two-week bootcamp that simulates the experience of working as a junior data scientist.
Machine Learning for Texts
2 weeks
Applying machine learning to text data. Finding out how to convert text into numbers and how to use bag-of-words, TF-IDF, as well as embeddings and BERT.
Computer Vision
2 weeks
How to handle simple computer vision tasks using premade neural networks and the Keras library. A quick look at deep learning.

Final Project
Apply everything you’ve learned in a two-week bootcamp that simulates the experience of working as a junior data scientist.
Unsupervised Learning
2 weeks
Figuring out what to do when you have no target features. Handling clustering tasks and looking for anomalies.
Final Project
2 weeks
Apply everything you’ve learned in a two-week bootcamp that simulates the experience of working as a junior data scientist.
Final Project
2 weeks
Apply everything you’ve learned in a two-week bootcamp that simulates the experience of working as a junior data scientist.
Career Prep Course
2 weeks
This is a course devoted to preparing for life after Practicum. During this course, you will learn how to create a resume, a LinkedIn profile, and a GitHub account, along with improving networking and interviewing skills. This course is self-paced and ends with a final task. We’ll also perform a review of your career artifacts.
Career Acceleration Program
Up to 6 months after graduation
Prepare for real-world interviews and gain experience through authentic practice. This program is designed to help you find a real job and also provides some work with technical skills.
Apiary Projects
5-6 weeks
You'll gain confidence solving work tasks that use a real company's data to provide them with valuable insights. Learn to communicate with clients, meet their expectations, exchange peer reviews with colleagues, and present results to the company. The Apiary projects become available for participants sometime between the 8-10th Sprint, depending on the project. They are also available after graduation.
Take advantage of our
limited-time __ Back to School offer!
Kickstart your new career with 30% off
Tuition
Join a 9-month bootcamp. Learn online with a flexible schedule and a convenient payment scheme.