If accountability, a set schedule, and dedicated classmates match your learning style, we suggest learning full-time. But if you have a packed schedule and flexibility is what matters most to you, we recommend choosing our Flex program.
No matter which pace you pick, you'll learn our tried-and-true curriculum proven to help students land jobs and start promising data science careers.
Curriculum & Program Experience
Phase 1: Data Analysis and Engineering
You’ll learn basic Python programming, how to use Jupyter Notebooks, and will be familiarized with popular Python libraries that are used in data science. To organize your data, you’ll learn about data structures, relational databases, ways to retrieve data, and the fundamentals of SQL for data querying structured databases.
Items covered:
- Variables
- Booleans and Conditionals
- Lists
- Dictionaries
- Looping
- Functions
- Data Structures
- Data Cleaning
- Pandas
- NumPy
- Matlotlib/Seaborn for Data Visualization
- Git/Github
- SQL
- Accessing Data through APIs
- Web Scraping
Phase 2: Scientific Computing and Quantitative Methods
You’ll learn about the fundamentals of probability theory like combinations and permutations. You'll also learn about statistical distributions and how to create samples, then apply this knowledge by running A/B experiments. Lastly, you'll build your first data science model using linear regression.
Items covered:
- Combinatorics
- Probability Theory
- Statistical Distributions
- Bayes Theorem
- Sampling Methods
- Hypothesis Testing
- A/B Testing
- Linear Regression
- Model Evaluation
Phase 3: Machine Learning Fundamentals
You'll learn about regression analysis and a new form of regression: logistic regression. You'll also learn about penalization terms, preventing overfitting through regularization, and using cross-validation to validate regression models.
Items covered:
- Linear Algebra
- Logistic regression
- Maximum Likelihood Estimation
- Optimization Cost Function
- Pipeline Building
- Hyperparameter Tuning
- Grid Search
- Scikit-Learn
- Gradient Descent
- K-Nearest Neighbors
- Decision Trees
- Ensemble Methods
Phase 4: Advanced Machine Learning
You'll be introduced to threading and multiprocessing to be able to work with big data. In doing so, you’ll learn about PySpark and AWS, and how to use those tools to build a recommendation system. You'll also learn about densely connected neural networks and sentiment analysis.
Items covered:
- Dimensionality Reduction
- Clustering
- Times Series Analysis
- Neural Networks
- Big Data
- Natural Language Processing
- Text Vectorization
- Natural Language Toolkit
- Regular Expressions
- Word2Vec
- Text Classification
- Recommendation Systems
Phase 5: Data Science Project
In your final project, you’ll work individually to create a large-scale data science and machine learning project. This final project provides an in-depth opportunity for you to demonstrate your learning accomplishments and get a feel for what working on a large-scale data science project is really like.
Pick a start date that fits your schedule
In our full-time courses, you'll have access to a virtual classroom where you’ll interact with your instructors and fellow students on a fixed, full-time schedule: 9 hours a day, Monday through Friday, for 15 weeks.
This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.
Upon registration, the instructor will send along additional information about how to log-on and participate in the class.