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)
- Classes: March [11-12, 18-19, 25-26], April [1-2, 8-9, 15-16]
- Schedule: Saturday & Sunday; 9:30am-2:30pm
- Level: Intermediate.
WHAT YOU WILL LEARN
- 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
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