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© 2016 Your company
upgrade your career
Become a data scientist
in 9 months
Real cases portfolio
Employment assistance
Online & flexible hours
Start from scratch
Who is this course for?
You want to pivot into a different career
Pump up your resume by getting new skills
Simply try and see if you like it
You want to pivot into a different career
Pump up your resume by getting new skills
Simply try and see if you like it
The work of a data scientist is creative even by the standards of the tech industry. The salary is high even for the IT market. The entry requirements are low compared to fellow engineers. And the number of vacancies is growing explosively.
Why choose data science?
Let's take a look into your future
Prestigious position
Data Scientist
Data Analyst
Machine Learning Engineer
Business Analyst
Product Analyst
IT System Analyst
Data Analyst Consultant
Marketing Analyst
Interesting tasks
— Analyze big data
— Apply machine learning to make predictions, identify patterns and draw conclusions
— Help to create and improve products in business, industry and science
Skills relevant for today's job market
Python
Jupyter Notebook
GitHub
SQL
PostgreSQL
XGBoost
A/B tests
Scikit-learn
These average monthly salaries in Nigeria are taken from Salary Explorer. But you can do better.
High salary and clear prospects
NGN 290 000
Junior Data Scientist right after the course
NGN 450 000
Middle Data Scientist in a year
NGN 600 000
Senior Data Scientist in 2 years
This future is real with Practicum
We nurtured 5000 IT specialists in the US, Europe, Latin America and Middle East. Came to Nigeria to rock — and to help you find a job in one of the best tech companies.
And our 3-step employment plan
This stage normally takes from 1 to 3 months. We will not stop searching until you find a company that suits you. Because with the portfolio our graduates get, they are in a position to choose.
Get your dream job and upgrade your career
At this stage you will learn about the job market, discover companies that hire juniors, and get ready for technical interview. Together we will find the best options so that you make the best choice.
Learn self-presentation strategies to get the offers
You will work on case studies for real companies, receive recommendations for LinkedIn, and fill your GitHub with polished professional projects that stand out to employers.
Build your portfolio to create a competitive resume
Your upgrade begins in a top-rated American bootcamp
We will not leave you alone with code, platform and bugs. You will be led through every stage of the course by trained industry professionals
With a whole team to support you on the way
Always ready to answer questions, remind you of deadlines, send links to videos and lectures, or simply listen and support.
Community managers
Practicing data scientists check your code and projects, point out any errors, and provide feedback.
Code reviewers
Tutors
With over 3 years of experience at prominent tech companies. Their task is not to give you ready answers but to teach you how to resolve any issue yourself.
24/7 support
Support specialists help with all technical issues around the clock. In case you will be studying at night or in a different time zone.
Learning with us is not a peaceful retreat, but a hardcore restart. That’s why it works.
With a learning process designed to make you a professional
Students work in sprints lasting from 2 to 3 weeks. Each sprint includes a set of lessons and tasks on the platform, and a project. It's up to you to allocate your time during the sprint but you have to meet deadlines. Just like in a real work life!
Series of sharp sprints instead of exhausting marathon
Learn the topics illustrated by a lot of industry cases. Ask questions! We’re ready to help you find answers as many times as needed. That doesn’t mean you’re slow, that just means all this data science stuff is not that easy.
Theory based on real life examples
Try to do it yourself immediately after theoretical stage. Write code, get it wrong, receive quick feedback, and correct your mistakes. Repeat it twice and more. 500 of 640 hours of studying at Practicum is devoted to practice.
Practice in simulator and real projects
During the course you will deal with typical for a data scientist tasks from different business fields. You will solve them and add the best cases to your portfolio. Some of them may even be projects of real companies.
With a portfolio that speaks for itself
<
>
Study the data on computer games and find patterns that determine the success of the game.
Your first prediction
A quick look at deep learning
Learn the basics of computer vision and build a model that determines the approximate age of a person from a photo.
Your first machine learning project
Сreate a model that helps to identify a new mining site with the lowest risk of loss.
Study the data on computer games and find patterns that determine the success of the game.
Your first prediction
A quick look at deep learning
Learn the basics of computer vision and build a model that determines the approximate age of a person from a photo.
Your first machine learning project
Сreate a model that helps to identify a new mining site with the lowest risk of loss.
We always keep an eye on the market and adjust our program to fit the requirements of the best job postings worldwide.
This is your career upgrade program
We always keep an eye on the market and adjust our program to fit the requirements of the best job postings worldwide.
This is your career upgrade program
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
+1 project for your portfolio

Compensating for less-than-perfect data. Handling missing and duplicate values. Changing data types. Systems thinking for analysts.
Exploratory Data Analysis (EDA) — 3 weeks
+1 project for your portfolio

Performing initial scans to detect patterns in data. Building basic graphs and generating your first hypotheses.
Statistical Data Analysis — 3 weeks
+1 project for your portfolio

Probability theory, the most common distributions, and statistical methods in Python. Sampling and statistical significance. Identifying and handling anomalies.
Integrated Project 1 — 1 week
+1 project for your portfolio

Identify patterns to help you determine whether a given video game will be commercially successful or not.
Data Collection and Storage (SQL) — 2 weeks
+1 project for your portfolio

How databases are structured and how to pull data from them using SQL queries. Finding data online.
Introduction to Machine Learning — 2 weeks
+1 project for your portfolio

Mastering the basics of machine learning. How the scikitlearn library works and how to apply it in your very first machine learning project.
Supervised Learning — 2 weeks
+1 project for your portfolio

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
+1 project for your portfolio

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
+1 project for your portfolio

Prepare a prototype of a machine learning model to help a mining company develop efficient solutions.
Linear Algebra — 2 weeks
+1 project for your portfolio

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
+1 project for your portfolio

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
+1 project for your portfolio

Exploring the time series. Understanding trends, seasonality, and feature creation.
Machine Learning for Texts — 2 weeks
+1 project for your portfolio

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
+1 project for your portfolio

How to handle simple computer vision tasks using premade neural networks and the Keras library. A quick look at deep learning.
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
+1 project for your portfolio

Apply everything you’ve learned in a two-week bootcamp that simulates the experience of working as a junior data scientist.
Career Prep Course (Optional) — 5-12 weeks
If you want guidance on how to land your dream job after completing the main program, the Career Prep Course has all the information you need. You'll cover key groundwork needed before you can start applying for jobs. This includes learning how to write resumes and cover letters, building an online presence on LinkedIn and GitHub, and growing your professional network. Once that's done, you'll focus on the different stages of the job application process, from job search strategies, to interviews, all the way to negotiating an offer.
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
+1 project for your portfolio

Compensating for less-than-perfect data. Handling missing and duplicate values. Changing data types. Systems thinking for analysts.
Exploratory Data Analysis (EDA) — 3 weeks
+1 project for your portfolio

Performing initial scans to detect patterns in data. Building basic graphs and generating your first hypotheses.
Statistical Data Analysis — 3 weeks
+1 project for your portfolio

Probability theory, the most common distributions, and statistical methods in Python. Sampling and statistical significance. Identifying and handling anomalies.
Integrated Project 1 — 1 week
+1 project for your portfolio

Identify patterns to help you determine whether a given video game will be commercially successful or not.
Data Collection and Storage (SQL) — 2 weeks
+1 project for your portfolio

How databases are structured and how to pull data from them using SQL queries. Finding data online.
Introduction to Machine Learning — 2 weeks
+1 project for your portfolio

Mastering the basics of machine learning. How the scikitlearn library works and how to apply it in your very first machine learning project.
Supervised Learning — 2 weeks
+1 project for your portfolio

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
+1 project for your portfolio

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
+1 project for your portfolio

Prepare a prototype of a machine learning model to help a mining company develop efficient solutions.
Linear Algebra — 2 weeks
+1 project for your portfolio

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
+1 project for your portfolio

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
+1 project for your portfolio

Exploring the time series. Understanding trends, seasonality, and feature creation.
Machine Learning for Texts — 2 weeks
+1 project for your portfolio

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
+1 project for your portfolio

How to handle simple computer vision tasks using premade neural networks and the Keras library. A quick look at deep learning.
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
+1 project for your portfolio

Apply everything you’ve learned in a two-week bootcamp that simulates the experience of working as a junior data scientist.
Career Prep Course (Optional) — 5-12 weeks
If you want guidance on how to land your dream job after completing the main program, the Career Prep Course has all the information you need. You'll cover key groundwork needed before you can start applying for jobs. This includes learning how to write resumes and cover letters, building an online presence on LinkedIn and GitHub, and growing your professional network. Once that's done, you'll focus on the different stages of the job application process, from job search strategies, to interviews, all the way to negotiating an offer.
This is your career upgrade pack
Skills that employers need
Portfolio of at least 15 projects
9 months experience
New profession and diploma
Community of data scientists
Confidence you need to upgrade career
Price and payment options
Monthly
NGN 49 594
UP FRONT
NGN 415 199
In 9 instalments
Course start date
September 29
To learn more about the Data Scientist program, go through our short onboarding course or join a free introductory webinar.
And this is why you are making the right decision
You will not waste your time on irrelevant knowledge. Practicum bootcamp was designed by practicing engineers with over 20 years of industry experience. We understand the needs of the tech market and use our knowledge to train the next generation of developers.
Strong expertise in technology
You will not study alone, occasionally communicating with mentors and managers. All these 9 months and long after you will be surrounded by other students, graduates and industry professionals from all around the world, ready to share their experience and vacant job offers.
Huge international IT community
Reviews from
the best decision makers
<
>
Pedro Giestas Gomes
Data Scientist, graduate
Portugal
After graduation I caught the attention of Teleperformance, an international company that offers business process outsourcing and consulting. They were looking for data analysts when I sent them my CV and they asked me to do a test assignment. Luckily, I had already done a similar project for Practicum, so I aced the task quite easily. They liked it and offered me a position there. And I said, ‘Yeah, why not?' I saw it as a first step to get more skills.
Amit Alon
Data Scientist, graduate
Israel
I was looking for the best place to learn more techniques and widen my knowledge, especially in deep learning. I chose this course as it can mediate the gap between academia and industry. This was exactly what I was looking for. I don’t have professional experience and the course gave me great background so I can bring a lot to the table in addition to my research background.
Stefano Pedicino
Data Scientist, graduate
Germany
I joined Practicum, having a little experience in languages such as C, C++ and C#. The first thought I had on the course was that the working platform was great. I had already tried some other platforms, but I usually felt really bad with them. On the contrary with this one, I really had a good feeling. Theory was really clearly explained and exercises well structured. Without going long, only in the first month, I had learned the basics of Python and Pandas library. And just a bit later I found myself working on projects, being able to organize databases with thousand of records on my own. It was something absolutely amazing.

I went trough Data Preprocessing, Exploratory data analysis (this one super interesting, helped me to learn to draw stunning graphics with data), statistical data analysis and SQL in few months. After that, started the Data Science part. What I loved most about this one is the possibility to create machine learning algorithms and models. I had never thought, to get in such a short time to be able to do something like this.

Those were my experiences about the program, platform and lessons but Practicum is not only that, they have a platform to communicate among students where you can get help from tutors, support team and managers if you have any sort of problems. They are always there, ready to support you in continuing studying and helping you to be successful in the programm. A really big thanks to the entire team!
Alper Arslan
Data Scientist, graduate
Netherlands
As a student of Practicum Data Science course, I feel it made a lot of contribution, even though I am at the beginning of the way. It is exciting to have a large number of projects, to have an intense but flexible schedule, to see your own development by doing projects after learning with theory and practices, and to feel yourself in the real IT life cycle. Thanks to the whole team.
Jaylen Gentry
Data Scientist, graduate
USA
Before attending Practicum I did not come a technical back nor a math background. I had been looking at bootcamps for some time and came across Practicum. After researching each of the different curriculums I decided that Data science is the one I was most interested in. Short after I started the full program I discovered a passion for data science I didn’t know that I had.

The coursework was beyond expectations. They didn’t assume you had knowledge of all the math behind algorithms and they didn’t use terminology that only professional mathematicians would know. The only skills you need to bring to this course are problem solving and googling. If you can do that effectively then you will make it through this course.

During the program they offer coursework to prepare for applying for jobs. They do not put you in contact with employers directly but they do show you great way to contact them yourself. The amount of job support you get is good for the price of the course and great when you consider they don’t have any ISAs or anything like that. In conclusion, if you are looking for a detailed bootcamp with great support from a big name in data science, you should try Practicum.
Rachelle Perez
Data Scientist, graduate
USA
Frankly, I was not sure I would enjoy learning on my own but Practicum’s structure has allowed me to finally grasp things I have seen in other courses and Youtube videos but never really understood. I think there are 4 reasons why: the focus on hands-on practice, the content, support, and deadlines.

As a student, you have to constantly test your knowledge through small tasks and after each module, do projects that look holistically at what was learned so far. The content is non-intimidating (love the cute illustrations!) and simple to understand. No fancy words! The focus is on helping grasp the concept.

There is a lot of support from Practicum’s tutors and community manager but also from other students. 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.

Finally, having a plan of action is great. Practicum facilitates this through a mix of realistic soft and hard deadlines to help you stay on track. Now, I can’t see myself ever taking an in-person bootcamp as I remember how often things would go over my head and I didn’t have the space and time to grasp a concept before moving on.
FAQ
Can you learn a profession in 9 months?
We think so. It means spending an average of 20 hours a week studying: reading theory in the simulator, performing tasks, doing projects and talking with your tutor.
What do I need to join the course?
All you need to start with is the desire and ability to study for an average of 20 hours a week.
How and when will I be studying?
The course consists of three components: theory with reinforcement in the simulator; homework for independent practice; and working on code with a tutor. You can study in the simulator whenever it is convenient for you, while homework is tied to a 2-week cycle.
When does the course start?
The start date of the nearest group is September 29. However, sometimes we can postpone the start of the course in order to provide you with the best possible learning experience. No worries, we’ll let you know if this happens!
Will I be able to get a job after the course?
It will not be easy, but we believe you will. The important thing for employers is that you know how to do projects rather than simply possessing a skill set. We will teach you how to apply theory in practice and expect you to make every effort to find a job after graduating: you will actively apply for vacancies, have interviews, demonstrate your projects and perform test assignments where required.
Great, but can you help me find a job?
Yes, we will help you. Students have the option to complete a course on finding a job. During this course the Practicum team helps future graduates to put together a portfolio, conducts training interviews followed by debriefing, and teaches you how to write covering letters.
What if my background isn't in tech?
Not a problem! While experience in programming, math, or data would definitely be helpful, our programs are doable even if you're starting from scratch. If you need extra help mastering Python or wrapping your head around some math, our team will be there. If you do have experience with tech, the learning curve will be gentler, but you'll still learn a lot. Many of our students are not first-time coders.
What if I can't keep up with the workload?
There will always be someone to help you — we offer 24/7 support. Along with technical assistance, emotional support is always available, too. You can contact staff or fellow students directly via Slack or ask your question on our around-the-clock platform.
Who are the tutors?
Practicum's tutors are all professional data analysts with extensive experience. They are all currently employed in the data industry, which ensures that their knowledge is up to date.
Our tutors provide guidance with weekly video sessions for your group and make themselves available to chat via Slack. Tutors can also help clear an obstacle on a project or create a strategy for a student's educational and professional development goals.
Are you self-paced?
Practicum's courses are a blend of self-paced and instructor-led. Our goal is to immerse our students in a real-life working environment. Just as you'll face deadlines working at a real company, we also have deadlines for our projects.
There are no mandatory scheduled classes. All the coding sessions and webinars are recorded and posted for students who weren't able to attend.
Will I get a refund if I leave during the program?
Yes. If you've already gone through part of the program, your refund will only cover the part you haven't completed.
Can you learn a profession in 9 months?
We think so. It means spending an average of 20 hours a week studying: reading theory in the simulator, performing tasks, doing projects and talking with your tutor.
What do I need to join the course?
All you need to start with is the desire and ability to study for an average of 20 hours a week.
How and when will I be studying?
The course consists of three components: theory with reinforcement in the simulator; homework for independent practice; and working on code with a tutor. You can study in the simulator whenever it is convenient for you, while homework is tied to a 2-week cycle.
When does the course start?
The start date of the nearest group is September 29. However, sometimes we can postpone the start of the course in order to provide you with the best possible learning experience. No worries, we’ll let you know if this happens!
Will I be able to get a job after the course?
It will not be easy, but we believe you will. The important thing for employers is that you know how to do projects rather than simply possessing a skill set. We will teach you how to apply theory in practice and expect you to make every effort to find a job after graduating: you will actively apply for vacancies, have interviews, demonstrate your projects and perform test assignments where required.
Great, but can you help me find a job?
Yes, we will help you. Students have the option to complete a course on finding a job. During this course the Practicum team helps future graduates to put together a portfolio, conducts training interviews followed by debriefing, and teaches you how to write covering letters.
What if my background isn't in tech?
Not a problem! While experience in programming, math, or data would definitely be helpful, our programs are doable even if you're starting from scratch. If you need extra help mastering Python or wrapping your head around some math, our team will be there. If you do have experience with tech, the learning curve will be gentler, but you'll still learn a lot. Many of our students are not first-time coders.
What if I can't keep up with the workload?
There will always be someone to help you — we offer 24/7 support. Along with technical assistance, emotional support is always available, too. You can contact staff or fellow students directly via Slack or ask your question on our around-the-clock platform.
Who are the tutors?
Practicum's tutors are all professional data analysts with extensive experience. They are all currently employed in the data industry, which ensures that their knowledge is up to date.
Our tutors provide guidance with weekly video sessions for your group and make themselves available to chat via Slack. Tutors can also help clear an obstacle on a project or create a strategy for a student's educational and professional development goals.
Are you self-paced?
Practicum's courses are a blend of self-paced and instructor-led. Our goal is to immerse our students in a real-life working environment. Just as you'll face deadlines working at a real company, we also have deadlines for our projects.
There are no mandatory scheduled classes. All the coding sessions and webinars are recorded and posted for students who weren't able to attend.
Will I get a refund if I leave during the program?
Yes. If you've already gone through part of the program, your refund will only cover the part you haven't completed.