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Teaching tomorrow’s Data Scientists
BICARD gives you the tools, techniques, and fundamental concepts needed to make an impact as a data scientist. In just 12 weeks, you’ll apply problem-solving and creative thinking to real-world data sets, gaining experience across the data science stack: data munging, exploration, modeling, validation, visualization, and communication.
Grounded in Python, our program covers the necessary tools and concepts used by data scientists in industry, including machine learning, statistical inference, and working with data at scale. As you learn more advanced techniques, you’ll use tools like SQL and NoSQL. When you graduate, you’ll have a solid grasp of machine learning, statistics, and will have built numerous data science applications.
Week 1 – Exploratory Data Analysis and Software Engineering Best Practices
Week 2 – Statistical Inference, Bayesian Methods, A/B Testing, Multi-Armed Bandit
Week 3 – Regression, Regularization, Gradient Descent
Week 4 – Supervised Machine Learning: Classification, Validation, Ensemble Methods
Week 5 – Clustering, Topic Modeling (NMF, LDA), NLP
Week 6 – Network Analysis, Matrix Factorization, and Time Series
Week 7 – Hadoop, Hive, and MapReduce
Week 8 – Data Visualization with D3.js, Data Products, and Fraud Detection Case Study
Weeks 9-10 – Capstone Projects
Week 12 – Interview Preparation
We’ll give you a take home assignment to assess your quantitative and programming skills, then conduct two technical interviews. The first evaluates your proficiency with programming in Python while the second covers probability, statistics, experiment design, and basic modeling. We look for students who are familiar with data analysis tools and practices and a background in a quantitative disciplines like foundational statistics, probability, linear algebra, or mathematics.
What is changing in the realm of big data?
Big data is changing the way people within organizations work together. It is creating a culture in which business and IT leaders must join forces to realize value from all data. Insights from big data can enable all employees to make better decisions—deepening customer engagement, optimizing operations, preventing threats and fraud, and capitalizing on new sources of revenue. But escalating demand for insights requires a fundamentally new approach to architecture, tools and practices.