Data Science Part-Time (Includes Deep Learning)
Solve real-world problems in public open data and private industry specific domains
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 based on Statistics, Machine Learning and Neural Networks/Deep 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
Key skills & technologies
- Acquire, clean, and parse large sets of data using Python (Pandas and PySpark)
- Gain knowledge on choosing the appropriate modeling technique to apply to your data (Statistical, Machine Learning, Deep Learning)
- Apply probability and statistics concepts to create and validate predictions about your data
- Programmatically create predictive data models using machine learning techniques (Sklearn, Spark mllib, Keras, Tensorflow)
- Communicate your results to an appropriate audience with compelling visualizations.
Quick info
- Duration: 6-weeks (60 hours)
- Schedule: Saturday & Sunday; 9:30am-2:30pm
- Level: Basic to Intermediate.
- Pricing: $3,500
Course plan
There are six modules in the Data Science. Each module introduces one or two core Machine Learning techniques, and Big Data Certification 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 compelling visualizations
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 Regression and Classification models using Statistical and Neural Network techniques. You will learn Model Evaluation and Model Interpretation techniques to help decide whether to use Statistical approach Vs Neural Networks.
WEEK 4: NATURAL LANGUAGE PROCESSING
Extract features from text (convert text into numbers & vectors) and build Sentiment Analysis using Naïve Bayes Classifiers and more advanced Neural Network techniques including LSTMs. Social Media Data Collection & Storage.
WEEK 5: DECISION TREES AND ENSEMBLES, CLUSTERING
Supervised Learning beyond classical models and Unsupervised learning with K-means, Word2Vec Neural Network techniques.
WEEK 6: BIG DATA ANALYTICS
Scaling data analysis with large datasets on Spark(ML) (Hadoop eco system) on AWS/Azure Cloud
Is it for me?
Do I need any programming experience to attend?
What will I be doing?
In 6-weeks you will learn the foundations of data science, Supervised and Unsupervised learning techniques applied to Structured, Semi Structured and Unstructured data (Text, Images) and Time-series data both locally and on big data clusters.
What age is this for?
This workshop is suitable for working professionals.
Do I need a computer?
It is best if you bring your own laptop with 8GB RAM and 100-150GB free space on your hard drive. We moved all our learning to the Cloud (Azure, AWS and Google)
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.
Register
Call us at (214)-997-6100 if you have special circumstances or looking for dedicated corporate training