The term "data analytics" is often thought of as a combination of the following disciplines: 1. computer science 2. statistics 3. domain expertise

Courses of Study:

I: Computer Science

I: Computer Science

Introduction to Computer Science and Programming (using Python)

Computer Systems Engineering. p>

Introduction to Computer Science and Programming (using Python)

Computer Systems Engineering: this course covers topics related to the engineering of computer software and hardware systems; techniques for controlling complexity; powerful modularity using client-server design, virtual memory, and threading; networking; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and cryptography ; and the impact of computer systems on society.

Computational Architecture: an introduction to digital systems engineering. Beginning with the MOS transistor, the course develops a series of building blocks-logic gates, combinational and sequential circuits, finite state machines, computers, and finally complete systems (both hardware and software).

Introduction to Algorithms: It covers common algorithms used to solve computational problems, algorithmic examples and data structures.

Artificial Intelligence: this course introduces students to the basic representations of artificial intelligence, problem solving methods and learning approaches.

Object-Oriented Programming with C / C ++ / Java

II: Mathematical Statistics

Applied Mathematics: an introduction to discrete mathematics geared towards computer science and engineering.

Introduction to Probability and Statistics (Programming with R): this course provides a basic introduction to probability and statistics in applications. Topics include: random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.

Linear Algebra (using R programming or other mathematical tools): This course covers matrix theory and linear algebra

Statistics/Machine Learning (using R programming): An introduction to core algorithms for data analysis, such as types of linear and nonlinear regression, classification techniques, such as logistic regression, plain Bayes, SVMs, decision trees (Vanilla Decision Trees, Random Forests, Augmentation), unsupervised supervised learning methods (e.g. clustering, introduction to neural networks)

Advanced Machine Learning (Programming in Python): designed for students with a strong interest in artificial intelligence, focusing on neural networks for image/text processing.

Three: Domain Specializations

Ideally, these should be based on job interests/domains so that each student chooses an area of specialization (e.g., web development, mobile app development, data analytics, marketing analytics, supply chain, finance, manufacturing, etc.).

The core topics here in the Data Analytics Specialization course should be:

Data collection and cleansing: this should include using open source tools (e.g. Python / R) to grab data from the web, connecting to databases etc. Additionally, data cleansing and ETL concepts (e.g. deduplication, merging, lost data estimation techniques can't be created either) to analyze datasets.

Data Visualization and Reporting : Create BI dashboards using tools like SAS / SAP or R / Python to present insights and data analysis through visualization and data storytelling presentations.

Data Analytics Apps 1/2: Complete end-to-end data analytics projects with a business focus. This theme should be repeated twice in the final years. It should very importantly include connecting to actual databases and deploying models in production, not just ad hoc data analysis of static datasets.

Advanced Data Computing: students here should create projects with large-scale data analytics using open-source and proprietary tools (e.g., Hadoop / Spark, HANA, or other MPP databases)

Expanded Readings:

The following will also be covered:

1. Fundamentals of network engineering. Why: Graduates should understand computer networks so they can work with, manage, and improve an organization's network and data architecture when needed. Topics include: network engineering, databases, data warehousing.

2. Research Methodology: the ability to design projects in a systematic way using quantitative and qualitative methodologies from hypothesis generation to generating business recommendations.

3. Unstructured Data Analytics: students should have an understanding of text mining, natural language processing, social media mining, web mining and the basics of such applications. These can also be taken in the form of electives.

One thing to note is that good data analysts and business intelligence are not tool-focused. Ideally any tool (R / SAS / SAP / Python / other) is taught as a supplement to the theoretical concepts of data analysis. For example, R programming using statistics and probability. Python for neural networks and other machine learning tasks.SAS VA or SAP Lumira with data visualization and data reporting concepts.SQL with database concepts, etc. This is an area where many new data analytics programs are missing, so the result is to produce graduates who are just app developers or users and do not solve real-world problems.