Online 6 Months Full Stack Data Science Bootcamp
No previous knowledge in programming required!
6 months fully online with:
– Live courses
– 1-on-1 mentorship
– Instant technical support in-office hours
– Graded homework and exercises
– Several hands-on projects including group collaboration
Bootcamp is designed to be intensive as well as flexible.
Lessons take place in Monday, Wednesday and Friday evenings between 19:00 – 22:00 CET*.
Subjects are taught in a balanced way so that you can digest the topics by practicing.
Several hands-on project are awaiting you to build up your portfolio.
Group projects make you accustomed to work in an agile team environment.
*: CET: Central European Timezone – Berlin
In Tuesdays and Thursdays, you meet with your mentor, get guidance and help.
In the weekday mornings, our experts wait for your questions in the office hours between 10:00 – 17:00 CET*
All homeworks, exercises and projects are graded with feedback.
Our career support team will be with your throughout your job search.
How Bootcamp Works?
You get access to the curriculum by registering for our online platform. You will have unlimited access to the curriculum content with updates.
Online live lessons start
You attend the lessons in the comfort of your home. The lessons take place on Mondays, Wednesdays and Saturdays. Your instructor teaches the subjects of that week using the online curriculum you access through our system. You follow the lessons, ask your questions in real-time
With the beginning of the lessons, we assign a mentor to you. Your mentor is a seasoned data science expert who will guide you through the bootcamp. You and your mentor meet twice a week on Tuesdays and Thursdays. You can ask your mentor the points that you got stuck or you can get guidance on any issue related to data science. Your mentor also supports you so that you are keeping on pace with the course.
We also support you via our office hours. During these times, you can ask your questions on Slack and get the answers right away. This ensures that you don’t get stuck on any technical problem and continue enhancing your data science skills during the bootcamp.
At the end of each main section, you develop projects using a real world data. The aim of these projects are two-fold. First, you’ll gain hands-on project experience by working on these projects. Second, you’ll build your portfolio which will help a lot in your job search after graduation.
Once you successfully graduate from the bootcamp, our career support team will start working with you to find your dream job in data science. We support you with our network of partner companies.
The Technologies You’ll Master
The comprehensive full-stack data science program that is designed for everyone. No previous programming knowledge is required! With live online lessons augmented by one-on-one mentorship and office hours, this program is curated to make you a data scientist.
For whom: For those who have no previous programming knowledge
Introduction to Python
- Python Basics
- Basic Data Types
- What are Variables?
- Tuples and Sets
Further into Python
- Conditional Statements
- Errors and Exception Handling
- File Operations
Python Data Science Libraries
- Introduction to NumPy
- Basics of NumPy Array
- Indexing and Slicing of NumPy Arrays
- Mathematical Operations on NumPy Arrays
- Introduction to Pandas
- DataFrame Basics
- Filtering DataFrames
- Grouping and Aggregation
- Operations on DataFrames
- Combining DataFrames
Data Visualization with Python
- Basic Chart Types
- Introduction to Matplotlib
- Visualization with Matplotlib
- Plotting Basic Charts With Matplotlib
- Introduction to Seaborn
- Visualization with Seaborn
- Introduction to Plotly
- Visualization with Plotly Express
Introduction to Statistics
- Main Statistical Concepts
- Statistical Distributions
- Population, Sampling and Related Theorem
Explorotary Data Analysis
- What is Exploratory Data Analysis?
- Data Cleaning – Variable Types
- Data Cleaning – Missing Values
- Data Cleaning – Outliers
- Data Exploration – Univariate Analysis
- Data Exploration – Multivariate Analysis
- Feature Engineering – Part 1
- Feature Engineering – Part 2
- The Concept of Statistical Modeling
Project 1 (Explorotary Data Analysis)
- Project Details
Introduction to Databases and SQL
- Introduction to Databases
- SELECT – FROM – WHERE
- Grouping and Aggregating Data
- CASE Statements
- Joining Data
- Operators and Functions
- SQL Queries in Python (SQLAlchemy)
- MySQL Installation and Configuration
Introduction to Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- What is Regression
- Simple Linear Regression Models
- Assumptions of Linear Regression
- Understanding the Relationship
- Evaluating Goodness of Fit
- Making Predictions
- Overfitting and Regularization
Project 2 (Regression)
- What is Classification
- Logistic Regression
- Performance Metrics
- Imbalanced Data
- Cross Validation
Project 3 (Classification)
Supervised ML Algorithms
- K-nearest Neighbors
- Decision Trees
- Random Forest
- Support Vector Machine
- Boosting Methods
- Evaluating Clusters
- Hierarchical Clustering
Dimension Reduction Problems
- What is Dimension Reduction
Project 4 (Unsupervised Machine Learning)
Introduction to Deep Learning
- What Is Deep Learning
- Artificial Neural Network
- Introduction to Keras and TensorFlow
- Convolutional Neural Networks
- Recurrent Neural Network