Spark, Ray, and Python for Scalable Data Science

Colleagues, according to Salary.com the average Data Scientist salary in the United States is $136,309. The Spark, Ray, and Python for Scalable Data Science program equips you to scale machine learning and artificial intelligence projects using Python, Spark, and Ray. Learn to integrate Python and distributed computing, scale data processing with Spark, conduct exploratory data analysis with PySpark, utilize parallel computing with Ray and scale machine learning and artificial intelligence applications with Ray. Skill-based training modules include: 1) Introduction to Distributed Computing in Python - you get some experience with one of Spark's primary data structures, the resilient distributed dataset (RDD). Next is key-value pairs and how Spark does operations on them similar to MapReduce. The lesson finishes up with a bit of Spark internals and the overall Spark application lifecycle, 2) Exploratory Data Analysis with PySpark - large data science workflow centered around natural language processing (NLP). He starts off with a general introduction to exploratory data analysis (EDA), followed by a quick tour of Jupyter notebooks. Next he discusses how to do EDA with Spark at scale, and then he shows you how to create statistics and data visualizations to summarize data sets. Finally, he tackles the NLP example, showing you how to transform a large corpus of text into numerical representation suitable for machine learning, 3) Parallel Computing with Ray - Ray programming API, with Jonathan comparing the similarities and differences between the Ray and Spark APIs. You learn how you can distribute functions with Ray, and 4) Scaling AI Applications with Ray - scale up machine learning and artificial intelligence applications with Python. The lesson starts with the general model training and evaluation process in Python. Then it turns to how Ray enables you to scale both the evaluation and tuning of our models. You see how Ray makes possible very efficient hyperparameter tuning. You also see how, once you have a trained model, Ray can serve predictions from your machine learning model.

Enroll today (teams & execs welcome): https://tinyurl.com/4pydnt23 

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Data Science for Business Leaders

Colleagues, the Data Science for Business Leaders program equips you to master the strategic decision-making skills for the people, platforms, and processes required to leverage the power of Data Science in your business. This course provides business leaders and managers with strategies and guidelines for how best to solve the human capital, technological, and management challenges of building data science into the business. Students will gain skills in identifying opportunities for data science across many functional areas of the business, as well as learn the tools to prioritize and execute on those opportunities as part of a data science initiative. Enrollees should have exposure to statistics and probability, and business decision-making in an IT or technical environment. Training modules include: 1) Introduction to Data Science - learn exactly what Data Science is, who Data Scientists are, and what's possible through Data Science, 2) Business Case for Data Science - create a data science strategy isn’t a standalone activity; it must be driven by a business's overarching operations and strategy. This course will cover how to articulate a business’s strategic objectives and identify opportunities for data science-based transformation, a critical starting point for any data strategy, 3) Human Capital of Data Science - the human capital component of Data Science is critical to delivering on a data science strategy. Learn how to recruit, hire, and train for a Data Science organization, and how to structure that organization in order to deliver value to the business. Asses ways to leverage data and data science to foster a data-driven culture throughout the business, 4) Data and Machine Learning Infrastructure Strategy - depend on the types of data to be leveraged for Data Science, the form and magnitude of that data, the types of data science models that a business plans to create, and the overall scale of operations represented by those data science models. This lesson investigates the parameters that must be considered both in creating a Data Architecture Strategy and in building a Machine Learning Architecture to support Data Science initiatives. The Capstone Project is “Building a 100-Day Data Plan” - create a Data Science strategy that drives transformation in the business during your first 100 days.

Enroll today (execs & teams welcome): https://fxo.co/E6Cx 

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Data Science with Python

Colleagues, Data Science professionals earn an average $136,309 per year according to Salary.com. This Data Science with Python program prepares you for a data science career by learning the fundamental data programming tools: Python, SQL, command line, and Git. Training modules - each with a hands-on project - include: 1) Introduction to SQL - learn SQL fundamentals such as JOINs, Aggregations, and Subqueries. Learn how to use SQL to answer complex business problems (Project: Investigate a Database), 2) Introduction to Python Programming - learn data structures, variables, loops, and functions. Learn to work with data using libraries like NumPy and Pandas (Project: Explore US Bikeshare Data), and 3) Introduction to Version Control - use version control and share your work with other people in the data. This program also includes real-world projects from industry experts - immersive content built in partnership with top tier companies, you’ll master the tech skills companies want, technical mentor support - mentors guide your learning and are focused on answering your questions, motivating you and keeping you on track, and 3) career services - access Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.

Enroll today (teams & execs welcome): https://tinyurl.com/2p8v72jr 

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Data Science Certification

Colleagues, the Global Knowledge 2021 IT Salary Survey ranks Google Certified Professional Data Engineer #1 out of all certifications with an average salary of $171,749. The program prepares you to become a Certified Data Scientist. Learn data science from industry experts at Harvard, Columbia, Cisco, Apple and Google. Training modules include: 1) Probability and Statistics for Data Science with R: Harvard faculty teaches you how to apply statistical methods to explore, summarize, make inferences from complex data and develop quantitative models to assist business decision making - instructional component, R tutorial videos, and exercises to reinforce concepts and give you an opportunity to see statistics in action, Michael Parzen, faculty member at Harvard and teaches one of the most popular classes. Kaitlin Hagan is a post-doctoral fellow at Brigham and Women's Hospital and has won numerous teaching awards and citations for her work; 2) Data Wrangling in R: Real-world data preparation for further analysis using R - get your data into R efficiently and polish it up so that it is as good as it can be, the instructor is the founder of Analytics Incubation Center at Cisco and has 15 years of analytics development experience; 3) Econometric Analysis: Methods and Applications: Quantitative and econometric analysis focused on practical applications that are relevant in fields such as economics, finance, public policy, business, and marketing, Alan Yang, is a faculty member at the Department of International and Public Affairs at Columbia University where he teaches courses in Introductory Statistics, Econometrics, and Quantitative Analysis in Program Evaluation and Causal Inference; 4) Classification Models: Online self-paced course with capstone project  - nstructor is a lead data scientist at one of the largest software companies in the world, author of a best-seller and an adjunct professor at University of Toronto; and 5) Clustering and Association Rule Mining: Learn Clustering methods and Association Rule Mining Techniques - Cluster Analysis and study most popular set of Clustering algorithms with end-to-end examples in R, the instructor is a Machine Learning Scientist with 10+ years of hands-on experience in predictive analytics and data science research at leading consulting, captive and R&D organizations.

Enroll today (teams & execs welcome): https://tinyurl.com/4dv98nsa 

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