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The Open Source Data Science University The CyberPolyglot

Start your online Bachelor degree in Data Science

The most important economic and social development of our times is digitalisation. Digital technology not only fills all areas of interpersonal communication, but also has an important influence on our economy. It represents existing forms of technology, while paving the way for new business models that were not previously possible. Its distinguishing feature is the consistent use of data - no matter where, no matter when. Data Science is therefore at the core of all digitalisation processes. Study data science to get started on a rewarding and highly in-demand career path!

The online Bachelor's degree in Data Science covers data preparation, visualisation, and analysis; everything you need to know in order to launch a career in the field. You’ll learn to take a methodical, focused, and logical approach to data science problems, and the focus on practical problem-solving skills will have you ready for your future professional challenges, with confidence.

Year 1

Module Courses Alternative Courses Links
Introduction to Data Science Python for Data Science (Coursera) Introduction to Data Science (edX)
Introduction to Academic Work Learning How to Learn (Coursera) Study Skills for Academic Success (Coursera)
Introduction to Programming with Python Python for Everybody (Coursera) Introduction to Python Programming (Udacity)
Mathematics: Analysis Khan Academy - Calculus Calculus 1A: Differentiation (edX)
Collaborative Work Teamwork Skills (Coursera) Collaborative Working in a Remote Team (FutureLearn)
Statistics - Probability and Descriptive Statistics Intro to Statistics (Udacity) Statistics and Probability (Khan Academy)

Recommended Books:

  • "Python for Data Analysis" by Wes McKinney
  • "Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
  • "Linear Algebra and Its Applications" by David C. Lay

Year 2

Module Courses Alternative Courses Links
Object-Oriented and Functional Programming with Python Advanced Python (Coursera) Python Programming: Advanced Topics (edX)
Mathematics: Linear Algebra Khan Academy - Linear Algebra Linear Algebra (MIT OpenCourseWare)
Intercultural and Ethical Decision-Making Ethics in Data Science (Coursera) Data Ethics (edX)
Statistics - Inferential Statistics Inferential Statistics (Coursera) Statistics for Data Science (Udacity)
Database Modeling and Database Systems SQL for Data Science (Coursera) Database Management Essentials (edX)

Recommended Books:

  • "Python Data Science Handbook" by Jake VanderPlas
  • "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
  • "Ethics of Big Data" by Kord Davis and Doug Patterson

Year 3

Module Courses Alternative Courses Links
Business Intelligence Business Intelligence (Coursera) Introduction to Business Intelligence (edX)
Project: Business Intelligence BI Project Management (Coursera) Business Intelligence Strategy (edX)
Machine Learning - Supervised Learning Machine Learning (Coursera) Machine Learning A-Z (Udemy)
Machine Learning - Unsupervised Learning and Feature Engineering Unsupervised Learning (Coursera) Feature Engineering for Machine Learning (edX)
Data Science Software Engineering Software Engineering for Data Science (Coursera) Data Science Design Patterns (edX)
Project: From Model to Production Deploying Machine Learning Models (Coursera) Machine Learning Deployment (edX)

Recommended Books:

  • "Pattern Recognition and Machine Learning" by Christopher M. Bishop
  • "Data Science for Business" by Foster Provost and Tom Fawcett
  • "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Extra Year

Module Courses Alternative Course Links
Agile Project Management Agile Project Management Principles (Coursera) Agile Scrum Mastery: Agile Scrum Training (Udemy)
Big Data Technologies Big Data Essentials: Hadoop, Spark, and Kafka (Coursera) Big Data Technologies: Spark, Hadoop, and MapReduce (Udemy)
Data Quality and Data Wrangling Data Cleaning and Preprocessing (Coursera) Data Wrangling with Python and Pandas (Udemy)
Explorative Data Analysis and Visualization Data Visualization with Python (Coursera) Data Analysis and Visualization Using Python (Udemy)
Cloud Computing Cloud Computing Basics (Coursera) Cloud Computing for Beginners (Udemy)
Seminar: Ethical Considerations in Data Science Ethics in Data Science (Coursera) Data Science Ethics (Udemy)

Recommended Books:

  • "Agile Project Management with Scrum" by Ken Schwaber
  • "Hadoop: The Definitive Guide" by Tom White
  • "Python for Data Analysis" by Wes McKinney
  • "Cloud Computing: Concepts, Technology & Architecture" by Thomas Erl
  • "Ethics of Big Data" by Kord Davis

Electives

Elective A Options:

In your 5th semester, you'll need to choose an elective module, which you can choose from one of the following modules:

  1. Data Engineer

  2. Data Analyst

  3. AI Specialist

  4. FinTech

  5. Derivatives and Risk Management

Elective B Options:

In your 5th semester, you'll need to choose an elective module, which you can choose from one of the following modules:

  1. International Marketing and Branding

  2. Applied Sales

  3. Supply Chain Management

  4. Smart Factory

  5. Automation and Robotics

Elective C Options:

In your 5th semester, you'll need to choose an elective module, which you can choose from one of the following modules:

  1. International Marketing and Branding

  2. Applied Sales

  3. Supply Chain Management

  4. Smart Factory

  5. Automation and Robotics

Bachelor Thesis & Capstone Project

  • Capstone Project: Applying Data Science to Business Problems

This structure provides a comprehensive path through foundational to advanced topics in Data Science, incorporating practical projects and electives to tailor the degree towards specific career goals.

Stan4fordXhar4vard

Let's expand the Data Science curriculum further with additional modules and content:

Course Code Course Title Duration Effort Content Link
DS-101 Introduction to Data Science 8 weeks 5-7 hrs/week Harvard - CS109 Data Science
DS-102 Data Wrangling and Visualization 6 weeks 6-8 hrs/week Stanford - CS224W Social and Information Network Analysis
DS-103 Machine Learning Foundations 10 weeks 7-10 hrs/week Harvard - CS181 Machine Learning
DS-104 Deep Learning and Neural Networks 8 weeks 8-12 hrs/week Stanford - CS231n Convolutional Neural Networks for Visual Recognition
DS-105 Statistical Analysis for Data Science 6 weeks 5-7 hrs/week Harvard - STAT110 Probability
DS-106 Big Data Technologies and Analytics 8 weeks 6-9 hrs/week Stanford - CS246 Mining Massive Data Sets
DS-107 Natural Language Processing 8 weeks 7-10 hrs/week Stanford - CS224N Natural Language Processing with Deep Learning
DS-108 Data Ethics and Privacy 4 weeks 3-5 hrs/week Harvard - DATA61 Ethics
Module Course Title Duration Content Link
DS-109 Introduction to Statistical Learning 4 weeks Stanford Introduction to Statistical Learning
DS-110 Data Wrangling and Cleaning 3 weeks Stanford Data Wrangling
DS-111 Exploratory Data Analysis 4 weeks Stanford EDA
DS-112 Machine Learning Fundamentals 5 weeks Stanford Machine Learning
DS-113 Big Data Technologies and Tools 4 weeks Stanford Big Data Technologies
DS-114 Deep Learning Foundations 6 weeks Stanford Deep Learning
DS-115 Natural Language Processing 4 weeks Stanford NLP
DS-116 Data Visualization 3 weeks Stanford Data Visualization
DS-117 Advanced Machine Learning 8 weeks Stanford Advanced ML
DS-118 Time Series Analysis 5 weeks Stanford Time Series Analysis
DS-119 Reinforcement Learning 6 weeks Stanford Reinforcement Learning
DS-120 Ethics in Data Science 4 weeks Stanford Ethics in Data Science
DS-121 Applied Data Science Projects 10 weeks Stanford Data Science Projects
DS-122 Cloud Computing for Data Science 6 weeks Stanford Cloud Computing
DS-123 Blockchain and Cryptocurrencies 4 weeks Stanford Blockchain
DS-124 Data Science for Internet of Things 5 weeks Stanford IoT
DS-125 Geospatial Data Analysis 4 weeks Stanford Geospatial

Certainly! Here's the expanded Data Science curriculum with additional modules and content from Harvard and Stanford:

Supervised Learning

Course Code Course Title Duration Effort Content Link
DS-201 Supervised Learning 5 weeks 5-7 hrs/week Stanford CS229 - Machine Learning
DS-202 Advanced Supervised Learning Techniques 6 weeks 6-8 hrs/week Stanford CS229 - Machine Learning Advanced Topics
DS-203 Applied Supervised Learning in Python 4 weeks 4-6 hrs/week Harvard CS50 - AI with Python
DS-204 Linear Regression and Model Evaluation 4 weeks 4-6 hrs/week Harvard STAT110 - Probability
DS-205 Classification and Support Vector Machines 5 weeks 5-7 hrs/week Stanford CS229 - Support Vector Machines

Unsupervised Learning

Course Code Course Title Duration Effort Content Link
DS-301 Unsupervised Learning 5 weeks 5-7 hrs/week Stanford CS229 - Machine Learning
DS-302 Clustering and Dimensionality Reduction 6 weeks 6-8 hrs/week Stanford CS229 - Unsupervised Learning
DS-303 Principal Component Analysis (PCA) 4 weeks 4-6 hrs/week Stanford STAT202 - Data Mining and Analysis
DS-304 Advanced Unsupervised Learning Techniques 5 weeks 5-7 hrs/week Harvard CS181 - Advanced Unsupervised Learning
DS-305 Applied Unsupervised Learning in Python 4 weeks 4-6 hrs/week Harvard CS50 - AI with Python

Extra

Data Science Curriculum

Course Code Course Title Duration Effort Content Links
DS-101 Data Science: Visualization 8 weeks 2-4 hrs/week Harvard - Visualization
Stanford - Data Visualization
DS-102 Databases: Self-Paced Self-paced Self-paced Harvard - Introduction to Computer Science
Stanford - Databases
DS-103 Introduction to Power BI 4 weeks 2-4 hrs/week Microsoft - Analyzing and Visualizing Data with Power BI (Free with audit)
Stanford - Introduction to Data Science
DS-104 CS50's Introduction to Computer Science 11 weeks 6-18 hrs/week Harvard - CS50
Stanford - Code in Place
DS-105 Data Science: Linear Regression 8 weeks 2-4 hrs/week Harvard - Linear Regression
Stanford - Statistical Learning
DS-106 CS224n Natural Language Processing with Deep Learning 10 weeks 10-15 hrs/week Harvard - Data Science: Machine Learning
Stanford - NLP
DS-107 CS50's Introduction to AI with Python 7 weeks 10-30 hrs/week Harvard - AI with Python
Stanford - Machine Learning
DS-108 CS229 Machine Learning (Unsupervised Learning section) Self-paced Self-paced Harvard - Unsupervised Learning
Stanford - Unsupervised Learning

This table now includes the appropriate free courses from both Harvard and Stanford, ensuring the content is accessible and relevant to the specified topics.

This comprehensive list includes a wide range of courses covering both supervised and unsupervised learning, alongside additional modules to provide a robust Data Science curriculum drawing from top-tier universities.

This expanded curriculum now includes modules on Applied Data Science Projects, Cloud Computing for Data Science, Blockchain and Cryptocurrencies, Data Science for Internet of Things, and Geospatial Data Analysis. Each module is linked to relevant Stanford resources or other reputable sources for further exploration and study in those specialized areas of Data Science.