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UQU Programs
Data Science

Introduction to Data Science

DS1102
3 hours English

Introduction to Data Science

Introduction to Data Science
1 Introduction to Data Science : Data-Analytic Thinking / Importance of Data Science / Need for Data Science / What Is Data Science? / Data Science Process / Business Intelligence and Data Science / Prerequisites for a Data Scientis / Components of Data Science / Tools and Skills Needed
2 Con-Introduction to Data Science / The Ubiquity of Data Opportunities / Data Science, Engineering, and Data-Driven Decision Making / Data Processing and “Big Data” / Data and Data Science Capability as a Strategic Asset / Data-Analytic Thinking
3 Data Engineering for Data Science / What is Data Engineering? / Data Engineering Skills and Activities / Data Engineering Life Cycle / Data Engineering vs. Data Science / The Major Undercurrents Across the Data engineering Lifecycle
4 Designing Good Data Architecture / What is Data Architecture? / Major Architecture Concepts and Components / Examples & Types of Data Architecture
5 Data Modeling and Analytics Techniques and Technologies / Analytics for Data Science / Data Analytics Examples / Statistics / Databases Querying for Data Sciences / Data Warehousing / Regression Analysis / Data Science Methods and Machine Learning / Data Analytics and Text Mining / Answering Business Questions with These Techniques
6 Data Science Tasks and Techniques / Co-occurrences and Associations: Finding Items That Go Together / Profiling: Finding Typical Behavior / Link Prediction and Social Recommendation / Data Reduction, Latent Information, and Movie Recommendation / Bias, Variance, and Ensemble Methods / Data-Driven Causal Explanation and a Viral Marketing Example
7 Platforms for Data Science (Data Science Tool: Python) / Basics of Python for Data Science / Python Libraries: DataFrame Manipulation with pandas and NumPy / Exploration Data Analysis with Python / Python for Machine ML
8 Con- Platforms for Data Science (Data Science Tool: R) / Reading and Getting Data into R / Ordered and Unordered Factors / Arrays and Matrices / Matrices / Lists and Data Frames / Statistical Models in R / Manipulating Objects / Data Distribution
9 Con- Platforms for Data Science (Data Science Tool: MATLAB) / Data Science Workflow with MATLAB / Importing Data / How Data is Stored . / How MATLAB Represents Data . / MATLAB Data Types / Automating the Import Process
10 Con- Platforms for Data Science / Data Science Visualizations Tools / Data Science and Cloud Tools / Data Science and Big Data Tools
11 Data Science and Business Strategy / Thinking Data-Analytically / Achieving and Sustaining Competitive Advantage with Data Science / Attracting and Nurturing Data Scientists and Their Teams / Be Ready to Accept Creative Ideas from Any Source / Be Ready to Evaluate Proposals for Data Science Projects / Examine Data Science Case Studies / Case Study: Applying the Fundamental Concepts of Data Science to a
12 Human Factors and Data Science / What Data Can’t Do: Humans in the Loop / Privacy, Ethics, and Mining Data About Individuals / Is There More to Data Science?
14 Review, quizzes and midterm
1.1 Mapped to: K1

Identify and describe the methods and techniques commonly used in data science.

Teaching Strategy Lectures Discussions
Assessment Methods Participation Exams
1.2 Mapped to: K1

Demonstrate proficiency with the methods and techniques for obtaining, organizing, exploring, and analyzing data.

Teaching Strategy Tutorials Discussions
Assessment Methods Participation Project Exams
2.1 Mapped to: S3

Recognize how data preparation, analysis, inferential statistics, modeling, and machine learning can be utilized in an integrated capacity.

Teaching Strategy Lectures Tutorials Discussions
Assessment Methods Participation Project
3.1 Mapped to: V3

Work in groups to discuss data science case studies problems and solutions.

Teaching Strategy Tutorials Discussions
Assessment Methods Participation Project
3.2 Mapped to: V3

Demonstrate own learning and professional development.

Teaching Strategy Tutorials Discussions
Assessment Methods Participation Project Exams
Assessment
Weight
Participation and discussions
10%
Project
25%
Midterm Exam
25%
Final exam
40%
Total
100%