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.
| 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
| 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
| 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
| 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
In your 5th semester, you'll need to choose an elective module, which you can choose from one of the following modules:
-
Data Engineer
-
Data Analyst
-
AI Specialist
-
FinTech
-
Derivatives and Risk Management
In your 5th semester, you'll need to choose an elective module, which you can choose from one of the following modules:
-
International Marketing and Branding
-
Applied Sales
-
Supply Chain Management
-
Smart Factory
-
Automation and Robotics
In your 5th semester, you'll need to choose an elective module, which you can choose from one of the following modules:
-
International Marketing and Branding
-
Applied Sales
-
Supply Chain Management
-
Smart Factory
-
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.
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:
| 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 |
| 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 |
| 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.