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Students
Tuition Fee
USD 432
Start Date
Medium of studying
Fully Online
Duration
Program Facts
Program Details
Degree
Bachelors
Major
Data Analytics | Data Management | Data Science
Area of study
Information and Communication Technologies
Education type
Fully Online
Timing
Full time
Course Language
English
Tuition Fee
Average International Tuition Fee
USD 432
Intakes
Program start dateApplication deadline
2024-10-01-
2024-09-12-
2024-11-01-
2024-12-01-
About Program

Program Overview


The online Bachelor of Science (B.Sc.) in Data Science equips students with skills and knowledge in data analytics, machine learning, and more. It prepares them for data-driven innovation and rewarding careers in data engineering, data science, and related fields. The program emphasizes ethical considerations, hands-on projects, and international recognition.

Program Outline


Degree Overview:

The Bachelor of Science (B.Sc.) in Data Science is an online degree program designed to equip students with the skills and knowledge necessary to thrive in the dynamic world of data. The program focuses on data analytics, machine learning, and other relevant fields, preparing students for data-driven innovation and a rewarding career in the field.


Objectives:

  • To provide students with a comprehensive understanding of data science principles and practices.
  • To develop students' skills in data preparation, visualization, and analysis.
  • To equip students with the ability to solve complex data science problems using a methodical and logical approach.
  • To prepare students for professional challenges in the field of data science.

Outline:


Program Structure:

  • The program is delivered entirely online, allowing students to study at their own pace and from anywhere in the world.
  • The program is divided into six semesters, with each semester consisting of several modules.
  • Students can choose to study full-time or part-time, with flexible study options available to accommodate individual needs.

Course Schedule:

  • Semester 1:
  • Introduction to Data Science (5 ECTS credits)
  • Introduction to Academic Work (5 ECTS credits)
  • Introduction to Programming with Python (5 ECTS credits)
  • Mathematics: Analysis (5 ECTS credits)
  • Collaborative Work (5 ECTS credits)
  • Statistics - Probability and Descriptive Statistics (5 ECTS credits)
  • Semester 2:
  • Object-Oriented and Functional Programming with Python (5 ECTS credits)
  • Mathematics: Linear Algebra (5 ECTS credits)
  • Intercultural and Ethical Decision-Making (5 ECTS credits)
  • Statistics - Inferential Statistics (5 ECTS credits)
  • Database Modeling and Database Systems (5 ECTS credits)
  • Project: Build a Data Mart in SQL (5 ECTS credits)
  • Semester 3:
  • Business Intelligence (5 ECTS credits)
  • Project: Business Intelligence (5 ECTS credits)
  • Machine Learning - Supervised Learning (5 ECTS credits)
  • Machine Learning - Unsupervised Learning and Feature Engineering (5 ECTS credits)
  • Data Science Software Engineering (5 ECTS credits)
  • Project: From Model to Production (5 ECTS credits)
  • Semester 4:
  • Agile Project Management (5 ECTS credits)
  • Big Data Technologies (5 ECTS credits)
  • Data Quality and Data Wrangling (5 ECTS credits)
  • Explorative Data Analysis and Visualization (5 ECTS credits)
  • Cloud Computing (5 ECTS credits)
  • Seminar: Ethical Considerations in Data Science (5 ECTS credits)
  • Semester 5:
  • Time Series Analysis (5 ECTS credits)
  • Neural Nets and Deep Learning (5 ECTS credits)
  • Electives A (10 ECTS credits)
  • Electives B (10 ECTS credits)
  • Semester 6:
  • Electives C (10 ECTS credits)
  • Introduction to Data Protection and IT Security (5 ECTS credits)
  • Model Engineering (5 ECTS credits)
  • Introduction to Academic Work: This module introduces students to the academic environment and equips them with essential skills for successful academic study.
  • Introduction to Programming with Python: This module teaches students the fundamentals of Python programming, a widely used language in data science.
  • Mathematics: Analysis: This module covers essential mathematical concepts and techniques relevant to data science.
  • Collaborative Work: This module focuses on developing students' teamwork and communication skills, essential for collaborative projects in data science.
  • Mathematics: Linear Algebra: This module covers linear algebra concepts and techniques, essential for machine learning and other data science applications.
  • Intercultural and Ethical Decision-Making: This module explores ethical considerations in data science, promoting responsible and ethical data practices.
  • Project: Build a Data Mart in SQL: This project-based module allows students to apply their knowledge of SQL to build a data mart, a specialized database for business intelligence.
  • Project: Business Intelligence: This project-based module allows students to apply their knowledge of business intelligence to solve real-world problems.
  • Machine Learning - Supervised Learning: This module introduces students to supervised learning techniques, where algorithms learn from labeled data to make predictions.
  • Machine Learning - Unsupervised Learning and Feature Engineering: This module covers unsupervised learning techniques, where algorithms discover patterns in unlabeled data, and explores feature engineering, the process of selecting and transforming data for better model performance.
  • Data Science Software Engineering: This module introduces students to software engineering principles and practices, ensuring that data science projects are built on solid foundations.
  • Project: From Model to Production: This project-based module allows students to apply their knowledge of machine learning and software engineering to build and deploy a data science model.
  • Agile Project Management: This module introduces students to agile project management methodologies, enabling them to manage data science projects effectively.
  • Seminar: Ethical Considerations in Data Science: This seminar provides a deeper exploration of ethical issues in data science, promoting responsible and ethical data practices.
  • Time Series Analysis: This module covers techniques for analyzing time-series data, which is data collected over time, such as stock prices or weather patterns.
  • Neural Nets and Deep Learning: This module introduces students to neural networks and deep learning, advanced machine learning techniques for complex tasks.
  • Electives A, B, and C: These elective modules allow students to specialize in areas of interest within data science, such as data engineering, data analysis, AI, FinTech, international marketing, supply chain management, or foreign languages.
  • Bachelor Thesis: This final project allows students to apply their knowledge and skills to a research question or real-world problem in data science.

Assessment:

  • The program utilizes a variety of assessment methods, including:
  • End-of-module exams:
  • These exams assess students' understanding of the key concepts and principles covered in each module.
  • Projects: These projects allow students to apply their knowledge and skills to real-world problems, demonstrating their ability to solve complex data science challenges.
  • Assignments: These assignments provide students with opportunities to practice their skills and demonstrate their understanding of specific concepts.
  • Presentations: These presentations allow students to communicate their findings and insights to their peers and instructors.
  • Machine learning: Students are assessed on their understanding of machine learning algorithms and their ability to apply them to solve real-world problems.
  • Software engineering: Students are assessed on their ability to design, develop, and deploy data science solutions using sound software engineering principles.
  • Communication: Students are assessed on their ability to communicate their findings and insights effectively, both orally and in writing.

Teaching:

  • The program utilizes a blended learning approach, combining online learning with live tutorials and individual study coaching.
  • Students have access to a variety of learning resources, including:
  • Online learning platform:
  • This platform provides access to course materials, interactive exercises, and assessments.
  • Live tutorials: These tutorials provide students with opportunities to interact with instructors and ask questions.
  • Individual study coaching: This coaching provides students with personalized support and guidance to help them succeed in their studies.
  • The faculty consists of experienced data science professionals and academics who are passionate about teaching and research.
  • The program incorporates innovative teaching methods, such as:
  • Interactive learning activities: The program incorporates interactive learning activities, such as simulations, case studies, and group projects, to enhance student engagement and learning.

Careers:

  • The Bachelor of Science in Data Science opens up a wide range of career opportunities in various industries, including:
  • Digital Analytics Consultant: Provides expertise in digital analytics, helping organizations understand customer behavior and optimize online marketing campaigns.
  • Machine Learning Engineer: Develops and deploys machine learning models for various applications.
  • Business Analyst: Uses data to analyze business processes, identify opportunities, and recommend improvements.
  • Research Scientist: Conducts research in data science, developing new algorithms and techniques.
  • The program provides students with the skills and knowledge necessary to succeed in these roles, including:
  • Data analysis and interpretation:
  • Students learn to analyze data, identify patterns, and draw meaningful conclusions.
  • Machine learning and predictive modeling: Students gain expertise in machine learning algorithms and their application to real-world problems.
  • Data visualization and communication: Students develop skills in creating effective data visualizations and communicating insights clearly and concisely.
  • Software engineering and development: Students learn to design, develop, and deploy data science solutions using sound software engineering principles.

Other:

  • The program includes an AI prompt engineering course, teaching students how to effectively use tools like ChatGPT in their daily lives, work, and studies.
  • Students are encouraged to build a portfolio of their work during their studies, showcasing their skills and experience to potential employers.
  • The program emphasizes the importance of ethical considerations in data science, promoting responsible and ethical data practices.
  • The program is accredited by the German Accreditation Council, ensuring that it meets high internal and external quality standards.
  • Students receive an official Europass Diploma Supplement upon graduation, ensuring that their degree is recognized internationally.

Tuition Fees Starting from € 239 (US$ 259) per month

  • based on the exchange rate from 31.07.2024 12:00 AM (CEST)
  • Online application No application fees Additional charges Graduation fees The total price includes a one-off graduation fee in the amount of €699 for Bachelor’s degree courses and €799 for Master’s degree courses. This fee is payable at the end of your studies. (The price mentioned above is before discounts) Separate charges are payable for exams taken outside Germany, Austria and Switzerland. These are to be paid to the exam centre on site and currently amount to (where necessary in the local currency): For exams taken this way, 120 euros is payable for single or multiple exams taken within an exam slot lasting up to max. 120 minutes. For exams lasting more than 120 minutes up to 360 minutes, 170 euros is payable to the respective institute per exam slot. Career coaching: this offer is available to Bachelor’s degree students (with at least 120 ECTS credits) and Master’s degree students. Cloud-based open source tools: in order to make use of cloud-based open source tools during your studies, you will need a desktop PC or laptop. Neither is included in the package of services we provide.
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About University

IU Internationale Hochschule


Overview:

IU Internationale Hochschule is an online university offering accredited Bachelor's, Master's, and MBA degrees. It boasts a large student body of over 100,000 individuals and has a high recommendation rate of 96% from its students.


Services Offered:

IU provides a comprehensive range of services to its students, including:

    Flexible study options:

    Students can choose to study online or on campus in Germany.

    AI-enhanced learning:

    Students benefit from AI-powered tutors to accelerate their learning.

    State-approved and accredited education:

    IU offers top-level education programs recognized by various international bodies.

    Global network:

    Students can connect with peers from over 140 countries.

    Career support:

    IU offers career offices and resources to help students find jobs after graduation.

    Study abroad opportunities:

    Students can study abroad at partner institutions like UCLA Extension and BCIT.

    High school diploma program:

    Students can earn a high school diploma and a Bachelor's degree simultaneously.

    Free webinars:

    IU hosts regular webinars to provide information about its programs and application process.

    Personal counseling:

    Students can access personalized support from study advisors.

Student Life and Campus Experience:


Key Reasons to Study There:

    Flexibility:

    IU offers flexible study options, allowing students to learn at their own pace and from anywhere in the world.

    Global network:

    Studying at IU provides opportunities to connect with students from diverse backgrounds and cultures.

    AI-powered learning:

    IU's AI-enhanced learning platform helps students learn faster and more effectively.

    Career-focused education:

    IU's programs are designed to equip students with the skills and knowledge needed for successful careers.

    International recognition:

    IU's degrees are recognized by various international bodies, opening doors to global career opportunities.

Academic Programs:

IU offers a wide range of academic programs across various disciplines, including:

    Bachelor's degrees:

    Data & IT, Business & Management, Health & Social Care, Psychology

    Master's degrees:

    Data & IT, Business & Management, Marketing & Communication, Health & Social Care, Psychology

    MBA degrees:

    General MBAs, Specialized MBAs in areas like Artificial Intelligence, Big Data Management, Finance & Accounting, Healthcare Management, and more.

Other:

  • IU has established partnerships with leading global companies, offering students internship and project opportunities.
  • IU's advisory board includes prominent figures like Jimmy Wales (Wikipedia) and Raffaela Rein (Career Foundry).
  • IU is recognized by WES Canada and U.S., allowing graduates to work or study in these countries.
  • IU is a member of UNESCO's Global Education Coalition, committed to providing accessible quality education to students in crisis.

Total programs
29
Admission Requirements

Entry Requirements:

  • General academic requirements:
  • Higher Secondary School Leaving Certificate such as A-Levels or IB Diploma and your transcript of records.
  • A subject-related higher education entrance qualification.

Language Proficiency Requirements:

  • Depending on your personal circumstances, you might be required to provide proof of your English language proficiency.
  • Your skills would need to match the B2 level of the Common European Framework (CEF).
  • We accept the following English language skills certificates*:
  • TOEFL (minimum 80 points)* or IELTS (minimum Level 6)* or Duolingo English Test (minimum 95 points)*
  • PTE Academics (minimum 59 points)* or Cambridge Certificate (minimum Grade B)*
  • Is English your native language, or have you graduated from an English-speaking school or university?
  • *Proof must not be older than five years.
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