Texas Christian University (TCU) offers several data science programs and courses for students interested in this exciting and rapidly growing field. Data science combines concepts and skills from computer science, mathematics, and statistics to extract powerful insights from complex data sets. While specific prerequisites vary across TCU’s undergraduate and graduate data science offerings, there are certain foundational courses and knowledge all students need to be prepared for upper-level data science coursework and careers.
Computer Science Requirements
A strong computer science background provides the computational skills and techniques required for collecting, processing, analyzing and interpreting large volumes of data. TCU’s data science programs require an introductory computer science sequence that covers programming basics, data structures, databases and computational thinking.
Introductory Programming Courses
Courses like Introduction to Computer Science I and II teach essential programming skills in languages like Python and R. Students learn data types, variables, conditional logic, loops, functions, arrays, algorithms and object-oriented programming concepts. These courses also introduce data visualization using programming libraries like Matplotlib.
The Data Structures course builds on intro programming with advanced data types like stacks, queues, trees, graphs and hash tables. Students implement complex data structures and algorithms vital for organizing and optimizing data operations.
Database Management Systems
In the Database Systems course, students learn to model and implement database solutions using systems like SQL Server, MongoDB and MySQL. Topics include data modeling, query languages, transactions, data warehousing, mining and analytics. Relational databases and NoSQL systems are both covered.
Data science leverages statistical and mathematical models to derive actionable insights from massive, multi-dimensional data sets. TCU data science students need a strong quantitative foundation across calculus, linear algebra, probability and statistics.
Calculus I and II teach the essential concepts of derivatives, integrals, limits, series and differential equations. Understanding rates of change and making predictions is central to data science work.
Linear Algebra provides mathematical tools like vectors, matrices, vector spaces and linear transformations for analyzing complex multidimensional data. Matrix methods are used extensively in data science algorithms.
Probability and Statistics
Probability evaluates likelihood and uncertainty in random events – critical for sampling, estimating population parameters and hypothesis testing. Statistics describes how to properly collect, summarize, analyze, interpret and present data insights. Courses like Probability & Statistics I and II are required.
Data Science Major/Degree Options
TCU offers both undergraduate and graduate degree programs in data science aligned to industry needs in this rapidly evolving field.
Undergraduate Data Science Degrees
Typical undergraduate degree options that provide core knowledge for data science include:
- Computer Science Major
- Information Systems Major
- Mathematics Major
- Statistics Major
- Computer Science Minor + Analytics Minor
These programs combine coursework across programming, databases, algorithms, data mining, data visualization, statistical modeling and big data analytics. Some interdisciplinary options are emerging as well.
Graduate Data Science Programs
For students interested in advanced data science skills and career specialization, TCU offers a Master of Science (MS) in Data Science. This 30-credit STEM master’s degree dives deeper into areas like:
- Machine Learning
- Data Mining
- Statistical Learning
- Big Data Management
- Data Visualization
- Programming for Data Science
- Ethics in Data Science
Hands-on lab work, real-world projects and faculty research opportunities enable students to gain highly marketable data science skills.
Developing Core Data Science Competencies
In addition to completing formal course requirements, TCU data science students need to actively develop key competencies for success in the field:
Learn Python and R programming for data tasks like scraping, cleaning, analyzing, machine learning and visualizations. Master tools like NumPy, Pandas, Matplotlib, Scikit-Learn, TensorFlow, Spark, SQL, MongoDB and more.
Practice fitting and assessing models like linear/logistic regression, time series, decision trees, clustering algorithms, neural networks and other advanced techniques. Balance under/overfitting.
Create insightful graphs, charts, plots and dashboards to effectively communicate data insights to others. Master data viz tools like Tableau, Power BI and D3.js.
Develop mathematical maturity across calculus, linear algebra, probability and statistics required for data science algorithms and models.
Apply the data science methodology – ask questions, collect/clean data, analyze, visualize and communicate results – to solve real-world problems. Think critically.
Tools & Technology
Learn popular data science programming languages, libraries, database systems and cloud platforms like Python, R, SQL, Hadoop, Spark, AWS, etc. Leverage open-source tools.
Gaining real-world experience via data science internships and projects is also highly recommended to complement coursework.
In summary, core prerequisites for TCU’s data science programs include computer science, mathematics, statistics, programming skills and an analytical mindset. Students who master these foundational concepts will be prepared for advanced coursework and career success in this high-demand field.