Data Science Bootcamp (openHPI)

Data Science Bootcamp (openHPI)

The ultimate goal of the bootcamp is to cultivate strong data science skills with an emphasis on machine learning techniques to satisfactorily meet and exceed the requests of the Data science world. In the process, we will develop good habits for operating independently as data scientists and for operating as members of productive data science teams.

Why is the topic relevant? Why is it on everyone's mind?
Having data science and machine learning skills nowadays can potentially increase your success chances, whether that be as an individual or a business. Many industries offer their employees the opportunity to enroll in upskilling programs. In that way, domain experts can leverage the knowledge in their given field and seek higher roles in their company. As the demand for data science skills rises higher and higher, having a rounded understanding of data science and applying that knowledge practically can help widen your scope of knowledge.

Who should take this course?

  • People with basic python knowledge. That includes variables, conditional statements, while loops, and data structures.
  • People with domain knowledge that need to apply modern data analysis in their daily workload.

What will be taught in the course?

  • What is data science? why is it relevant?
  • Data Analysis and making sense of the data you have.
  • The use of libraries such as NumPy, Pandas, and Matplotlib for interesting data visualizations
  • Leveraging the power of ML

How will it be taught?

  • Videos
  • h5p
  • Quizzes
  • Two live streams

What needs to be accomplished?

  • Jupyter notebook Exercises
  • Weekly challenges
  • Exposure to real-life scenarios and datasets (no easy data)

How much time is expected to be spent?
The workload for the course is approximately 5 - 7 hours per week, depending on prior knowledge.

What you'll learn

  • What is Jupyter Notebooks and how to use it for Data Science
  • Work with real-life datasets and apply Numpy, Pandas and Matplotlib
  • Use scikit-learn to create powerful ML models

Who this course is for
High School and College Students
Domain Experts

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Quantum Machine Learning (with IBM Quantum Research) (openHPI) OpenHPI
Hasso-Plattner-Institut

Quantum Machine Learning (with IBM Quantum Research) (openHPI)

Whether we stream our favorite series, develop new drugs or have us being chauffeured by a self-driving car -- machine learning is an essential part of our modern life, and of our future. But the growing amount of data and our increasing demands pose difficulties for today's classical computers. Can quantum computing overcome these challenges? What potentials does the emerging field of quantum machine learning have? In this course, we will not only learn about quantum machine learning and its prospects, but we will also solve concrete tasks with both classical and quantum models.

Jan 11th 2023
2 Weeks
Data Engineering und Data Science – Klarheit in den Schlagwort-Dschungel (openHPI) OpenHPI
Hasso-Plattner-Institut

Data Engineering und Data Science – Klarheit in den Schlagwort-Dschungel (openHPI)

Die Schlagwörter Künstliche Intelligenz, Data Science, Data Engineering, und Big Data dominieren seit einigen Jahren nicht nur die IT-Schlagzeilen. In unserem Kurs wollen wir diese Wörter mit grundlegendem Inhalt füllen und die typischen Arbeitsschritte eines Data Scientists nachvollziehen. Insbesondere schauen wir hinter die Kulissen und betrachten den oft mühsamen Weg der Daten bis sie endlich genutzt werden können um z.B. mittels maschinellem Lernen Modelle trainieren zu können. Dazu gehören die Datenbeschaffung, die Datenreinigung, und die Datenintegration. Anschließend lernen wir, wie man aus diesen Daten und auch aus Texten neue Erkenntnisse mittels Data Mining und maschinellem Lernen gewinnt. Der Abschluss bildet eine Diskussion über Ethik und Fairness bei der automatisierten Datenanalyse.

Self Paced
Self-Paced
Understanding China, 1700-2000: A Data Analytic Approach, Part 1 (Coursera) Coursera
The Hong Kong University of Science and Technology - HKUST

Understanding China, 1700-2000: A Data Analytic Approach, Part 1 (Coursera)

The purpose of this course is to summarize new directions in Chinese history and social science produced by the creation and analysis of big historical datasets based on newly opened Chinese archival holdings, and to organize this knowledge in a framework that encourages learning about China in comparative perspective. Our course demonstrates how a new scholarship of discovery is redefining what is singular about modern China and modern Chinese history.

Nov 23rd 2026
5-12 Weeks
Bioinformatic Methods II (Coursera) Coursera
University of Toronto

Bioinformatic Methods II (Coursera)

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.

Nov 23rd 2026
5-12 Weeks
Visualización de Datos con Python (Coursera) Coursera
IBM

Visualización de Datos con Python (Coursera)

"Una imagen vale mas que mil palabras". Todos estamos familiarizados con esta expresión. Se aplica especialmente cuando se trata de explicar la información obtenida del análisis de conjuntos de datos cada vez más grandes. La visualización de datos juega un papel esencial en la representación de datos tanto a pequeña como a gran escala. Una de las habilidades clave de un científico de datos es la capacidad de contar una historia convincente, visualizando datos y hallazgos de una manera accesible y estimulante

Nov 23rd 2026
3 Weeks
Population Health: Predictive Analytics (Coursera) Coursera
Leiden University

Population Health: Predictive Analytics (Coursera)

Predictive analytics has a longstanding tradition in medicine. Developing better prediction models is a critical step in the pursuit of improved health care: we need these tools to guide our decision-making on preventive measures, and individualized treatments. In order to effectively use and develop these models, we must understand them better. In this course, you will learn how to make accurate prediction tools, and how to assess their validity. First, we will discuss the role of predictive analytics for prevention, diagnosis, and effectiveness. Then, we look at key concepts such as study design, sample size and overfitting.

Nov 23rd 2026
4 Weeks
Business Intelligence and Competitive Analysis (Coursera) Coursera
Rutgers University

Business Intelligence and Competitive Analysis (Coursera)

By the end of 2019, it is clear that American Airlines (AAL), the world’s largest airline group, is in trouble. With the growth rate of its stock price ranked at the bottom of all major US airlines and going in the opposite direction from the SP500 index, AAL needs to find out what is going on, and how to turn the company and its stock price around.

Nov 23rd 2026
4 Weeks
Ferramentas para Ciência de Dados: Introdução ao R (Coursera) Coursera
FIA Business School

Ferramentas para Ciência de Dados: Introdução ao R (Coursera)

Nossas boas-vindas ao Curso Ferramentas para Ciência de Dados: Introdução ao R. Neste curso, você aprenderá que o mundo evoluiu muito quando o assunto é tomada de decisão baseada em dados e já não é possível comparar a quantidade de informações a que temos acesso atualmente com o que tínhamos disponíveis décadas atrás.

Nov 30th 2026
4 Weeks
Data Processing Using Python (Coursera) Coursera
Nanjing University

Data Processing Using Python (Coursera)

This course is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level.

Nov 23rd 2026
5-12 Weeks
Computer Vision with Embedded Machine Learning (Coursera) Coursera
Edge Impulse

Computer Vision with Embedded Machine Learning (Coursera)

Computer vision (CV) is a fascinating field of study that attempts to automate the process of assigning meaning to digital images or videos. In other words, we are helping computers see and understand the world around us! A number of machine learning (ML) algorithms and techniques can be used to accomplish CV tasks, and as ML becomes faster and more efficient, we can deploy these techniques to embedded systems.

Nov 23rd 2026
3 Weeks
The Unix Workbench (Coursera) Coursera
Johns Hopkins University

The Unix Workbench (Coursera)

Unix forms a foundation that is often very helpful for accomplishing other goals you might have for you and your computer, whether that goal is running a business, writing a book, curing disease, or creating the next great app. The means to these goals are sometimes carried out by writing software. Software can’t be mined out of the ground, nor can software seeds be planted in spring to harvest by autumn. Software isn’t produced in factories on an assembly line. Software is a hand-made, often bespoke good. If a software developer is an artisan, then Unix is their workbench.

Nov 23rd 2026
4 Weeks