Introduction to Data Science: Python
Learn about the principles and techniques of computer programming using Python, a widely used general purpose language that is an ideal combination of power and simplicity, and an introduction to a variety of numerical, computational, and computer science problems. Topics include importing websites, generating random numbers, the method of inverse transformations, and acceptance/rejection sampling. You will learn to use Python in a hands-on way through tutorials and weekly homework that challenges you to break down problems into manageable units. The emphasis is on practical implementation, not on computational aesthetics.
Data Modeling & Optimization
Learn about the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, advanced network optimization, integer programming, and simulation. You will explore modeling methodology (linear, network, integer, and heuristics), modeling tools (sensitivity analysis), software, and applications in finance, marketing, and operations management. You will study the mathematics underlying the optimization methods, and at the same time, considerable attention will also be paid to the business and applicability aspect of the subject.
Explore the core theory and application of classification and clustering techniques, feature selection, and performance evaluation. You will be introduced to machine learning, data mining, and statistical pattern recognition. Topics include supervised and unsupervised learning as well as best practices in machine learning. You will also draw from numerous case studies and applications to gain experience with application of the theory to key predictive and descriptive analytics problems in business intelligence.
Learn about the modern organizational information landscape built around big data and big data analytics. Rooted in the notion of evidence-based management, you will enhance your understanding of how successful data-driven businesses leverage the available data to enhance their competitive advantage by translating large volumes of multisource data into decision-guiding insights. You will receive an overview of key data type and source differences including structured vs. unstructured, numeric vs. text, transactional vs. descriptive, and graph vs. symbolic. You will also explore the key data analytical approaches and methodologies, including hypothesis testing, descriptive, prescriptive, and predictive analytics, supervised and unsupervised machine learning, text mining, and deep learning.
Build on the skills and models introduced during immersion and specifically focus on growth mindset, interpersonal influence, and team collaboration. Through interactive and collaborative learning, you’ll build skills in communication, relationship building, persuasion, negotiation, and conflict resolution. The workshops are a companion to Hult’s peer feedback process and can provide input on your development priorities.