Data Science Immersive Training

offered by Logit Data Science



Our immersive program runs in-class for 12 weeks,
Monday through Friday, 9:30 am – 6:30 pm. Class
time includes lectures, labs as well as guest speakers.
Our classroom is located 1645 Vine Street, Los
Angeles, California at the legendary corner of
Hollywood and Vine.
Mornings will generally cover theory, while
afternoon labs will give students hands on experience
with real data sets. Students will have the opportunity
to work together as well as receive individualized
attention from our experienced instructors and
teacher’s assistants (TA’s). Students are invited to
audit our part time course “Introduction to Data
Science: Methods and Application ” if they wish to
refresh themselves on the foundations of statistics
and programing.
To ensure our students find job placements, we
have partnered up with a recruiting firm with
specialization in data science. Students will get to
meet with a recruiter 1 on 1, as well as a resume

Week 1: Introduction to data science and its
applications. Python and SQL to manage and
manipulate data.
Week 2: Basics of statistics, probability, and
linear algebra. These are the mathematical
foundations of machine learning.
Weeks 3-4: Supervised learning. Fit models
to labeled data, including regression,
regularization, and classification methods.
Week 5: Unsupervised learning. We apply
dimensionality reduction and clustering to
unlabeled data.
Week 6: Time series. ARIMA models and
other methods are used on time-dependent
Week 7: Advanced visualization. We show
you how d3.js and Flask can be used to
create compelling infographics.
Week 8: NLP, web scraping, and topic
modeling. This is data science as applied to
the natural-language text and
recommendation engines.
Week 9: Deep Learning. We cover the
emerging world of artificial neural networks.
Week 10: Big Data. Hands-on experience
with tools like Hadoop, Hive, and Spark for
managing extremely large data sets in a
parallel computing environment.
Weeks 11-12: Capstone Project.
Demonstrate learned skills and build
portfolios in a final project.
• Students are expected to have basic
programming experience.
• Understanding of basic probability
and statistics.
• A degree in quantities field is