WHAT YOU WILL LEARN
The 6-week intermediate level data science course is a practical introduction to the interdisciplinary field of data science and machine learning, which is at the intersection of computer science, statistics, and business. You will learn to use the programming languages, tools, and technologies to help you acquire, clean, parse, and filter your data. A significant portion of the course will be a hands-on approach to the fundamental modeling techniques and machine learning algorithms that enable you to build robust predictive models about real-world data and test their validity. You will also gain practice communicating your results and insights about how to build systems that are more intelligent using the data that you have gathered.
Recommender systems are used to predict the best products to offer to customers. These systems have become extremely popular in virtually every single industry, helping customers find products they’ll like. Most people are familiar with the idea, but nearly everyone is exposed to several forms of personalized offers and recommendations each day (Google search ads being among the biggest source). Building recommendation systems is part science, part art, and many have become extremely sophisticated. Such a system might seem daunting for those uninitiated, but it’s actually fairly straight forward to get started if you’re using the right tools and techniques
- Duration: 6-weeks (60 hours)
- Schedule: Saturday & Sunday; 9:30am-2:30pm
- Level: Basic to Intermediate.
- Pricing: $3,500
KEY SKILLS & TECHNOLOGIES
- Acquire, clean, and parse large sets of data using Python
- Gain knowledge on choosing the appropriate modeling technique to apply to your data
- Apply probability and statistics concepts to create and validate predictions about your data
- Programmatically create predictive data models using machine learning techniques
- Communicate your results to an appropriate audience
There are six modules in the Data Science. Each module introduces one or two core Machine Learning techniques, and Big Data framework concepts and tools while working through the practical implementation.
WEEK 1: DATA SCIENCE FOUNDATIONS – STATISTICS, PYTHON AND SQL; EXPLORATORY DATA ANALYSIS
Build on Descriptive Statistics, Probability Theory, and explore distributions using charts
WEEK 2: MACHINE LEARNING, BIAS-VARIANCE AND MODEL EVALUATION
Model Selection and Diagnostics
WEEK 3: WEB SCRAPING, REGRESSION AND CLASSIFICATION
Gather data from Internet Sources, and start with building classical Regression and Classification models
WEEK 4: NAÏVE BAYES, NATURAL LANGUAGE PROCESSING
Modeling with Naïve Bayes Classifiers, Social Media Data Collection & Storage, Sentiment Analysis
WEEK 5: DECISION TREES AND ENSEMBLES, CLUSTERING
Supervised Learning beyond classical models and Unsupervised learning with K-means
WEEK 6: BIG DATA ANALYTICS
Scaling data analysis with large datasets on Spark and Hadoop Map-Reduce
IS IT FOR ME?
Do I need any programming experience to attend?Yes – you will need previous experience in Python language.
What will I be doing?In 24-weeks you will learn the foundations of data science, Supervised and Unsupervised learning techniques applied to Unstructured Text and Time-series data both locally and on big data clusters.
What age is this for?This workshop is suitable for university students and professionals.
Do I need a computer?It is best if you bring your own laptop with 16GB RAM and 100-150GB free space on your hard drive.
What’s the experience like?The course is 70% hands-on in-class modeling exercises combined with 30% lectures to explain the concepts. There are four homework assignments to reinforce the learning in the class and a final project presentation to be presented in front of a Divergence Datascience meetup audience. Our instructors are always there to help you if you get stuck.
Call us at (214)-997-6100 if you have special circumstances or looking for dedicated corporate training