top of page

Data Science

The art of data analysis! Learn all the tools you need for a successful start to your first data science project. In addition to the basics of programming, names such as Pandas or NumPy will no longer be foreign to you. Analysing data sets will be easy for you and you will be able to apply your first machine learning methods.

TechLabs_Düsseldorf_2.jpg

Prior Requirements - 

  • Basic knowledge of programming

  • Must know basics of Python

img_data_science_icon.png

How to join the track

You can choose this track as part of the local Digital Shaper Program or the remote Code At Home Bootcamp.

While you need to attend in-person for our Digital Shaper Program, the Code at Home Bootcamp provides a convenient option for those unable to access physical locations or seeking a quicker completion pace.

Time

6 months, 5 hours per week or

3 months, 8 hours per week

Certificate

Receive a graduation certificate
by presenting your project

Participants

Over 900 graduates

About

Basics of Python

Use of Python for Data Science and Machine Learning

Learn to use Numpy for Numerical Data

Learn to use Pandas for Data Analysis

Data visualization Techniques

Use of Scikit-Learn for Machine Learning tasks

Outcome

Receive a graduation certificate by presenting your project

Access to the course material for free

Support from expert mentors

What is Data Science?

Learn data science - theory and hands-on-learning with - R or Python

It doesn't matter if you are a beginner or already have some experience. The program is designed so that you can choose which level of experience you want to start with, whether you want to do hands-on or theory courses and whether you prefer to program in Python or R. You decide. Both - the hands-on as well as theoretical - tracks include basic programming exercises at first to get to know the chosen programming language. The hands-on track is made for people who want to learn how to quickly apply machine learning algorithms to solve problems without a dive into the mathematical background. The theory track includes materials and exercises which covers the theoretical (mathematical) background of several algorithms like linear and logistic regression, neural networks or dimensionality reduction. The tracks also include TechLabs Notebooks where you can deepen the knowlegde and practice the skills you learned during the online courses.

  • Setup & Introduction
    Part A: Setup - Chapter 01: Course Explanation - Chapter 02: Setup your programming Environment - Chapter 03: Coursera Audit Guide Part B: Introduction - Chapter 01: What is Data Science?
  • Introduction to Python Programming
    Part A - Setup - Chapter 01: Variables, Expressions and Statements - Chapter 02: Conditional Execution - Chapter 03: Functions - Chapter 04: Loops and Iterations - Chapter 05: Strings - Chapter 06: Lists - Chapter 07: Dictionaries - Chapter 08: Tuples - Chapter 09: Sets - Chapter 10: Sequence Functions - Chapter 11: Object Oriented Programming
  • Data Manipulation
    Part A: Regular Expressions - Chapter 01: Regular Expressions in Python Part B: Numpy - Chapter 01: List Vs Array in Python - Chapter 02: How to create Arrays and Functions in Python - Chapter 03: Useful Array Attributes of Numpy - Chapter 04: Numpy Array Index to Access Single Element - Chapter 05: Numpy Slicing and Accessing Subarrays - Chapter 06: Split, Reshape and Concatenate Numpy Arrays - Chapter 07: Arithmatic Operations on Arrays Using Numpy - Chapter 08: Summary Statistics in Numpy - Chapter 09: A Boolean Mask on Numpy Arrays Part C: Pandas - Chapter 01: Getting Started with Data Analysis - Chapter 02: DataFrame and Series Basics - Chapter 03: Indexes - Chapter 04: Filtering - Chapter 05: Updating Rows and Columns - Chapter 06: Add or Remove Rows and Columns From DataFrames - Chapter 07: Sorting Data - Chapter 08: Grouping and Aggregating - Chapter 09: Cleaning Data - Chapter 10: Working with Dates and Time Series in Data - Chapter 11: Reading and Writing Data to Different Sources
  • Statistics & Data Visualization
    Part A: Introduction to Data - Chapter 01: Introductions and Theory - Chapter 02: Introduction to Jupyter Notebooks - Chapter 03: Data Types in Python - Chapter 04: Introduction to Libraries and Data Management - Chapter 05: Continued to Data Basics Part B: Univariate Data - Chapter 01: Introductions and Theory - Univariate Data - Chapter 02: Important Python Libraries - Chapter 03: Tables, Histograms, Boxplots in Python - Chapter 04: Univariate Data Analysis Using NHANES Data Part C: Multivariate Data - Chapter 01: Introductions and Theory - Multivariate Data - Chapter 02: Multivariate Data Selection - Chapter 03: Multivariate Distributions - Chapter 04: Multivariate Data Analysis Using NHANES Data
  • Machine Learning
    - Chapter 01: Fundamentals of Machine Learning and Introduction to Scikit Learn - Chapter 02: Supervised Machine Learning - Chapter 03: Evaluation

Why Data Science?

Data Scientist - The most in demand job in the 21st century!

“Data is the new oil” - a catchy phrase already said in 2006, which becomes more important over time. What makes companies like Google, Baidu or Amazon so powerful? It’s the amount of data they possess and the employees that are able to analyze it. The data volume that is created, as well as the number of companies that want to use this data, grow exponentially every year. There are beautiful and interesting insights hidden in this sea of ever increasing data, waiting to be discovered - and Data Science is the key to decipher all these hidden insights. Therefore the demand for data scientists is increasing. Data Science is a pretty versatile field. Data Scientists are demanded in almost every imaginable field, such as healthcare, banking, logistics, e-commerce and many more. Hence, as a data scientist you are able to solve a lot of different problems, gain important knowledge and deliver value to other people and your business.

DS

DS

DS

DS

DS

DS

DS

DS

DS

What will you learn?

Data Science is a multidisciplinary domain. In it’s core it is the task of finding valuable insights from data.

How to apply machine learning models to solve problems like churn prediction or customer segmentation

Exploration of raw data to gain useful insights

General programming knowledge in R or Python

How to present your results in a structured and visually appealing way

Michael P., Machine Learning Engineer

"The mentorship and support from the Techlabs team were invaluable. The instructors were highly knowledgeable and always available to answer questions and provide guidance."

You can´t wait to acquire the most in demand tech skills?

Are you ready to learn?

Projects from previous participants

Have a look at these completed projects and get inspired!

Image-empty-state.png

Asset Analyzer — Analyzing Financial Products

DS

Image-empty-state.png

Cinemalytics

DS

Choose your Journey.

Locally in your city

Discover the most effective way to build tech domain knowledge with our free hybrid program! The blended-learning concept combines remote learning with in-person community events in your city.

Digital Shaper Program

#codeathome Bootcamp

Remote

Learn to code from anywhere with our free bootcamp! The fully remote program offers online learning tracks, project work and connects you with a supportive community and mentors from around the globe.

Not what you're looking for?

How about:

bottom of page