Join us! This coming October 27 & 28th, we welcome to join our upcoming cohort for a hands-on developer bootcamp where you will solve machine learning problems from beginning-to-end using Logistic Regression. This is a FREE 12-hour bootcamp [9:00 am - 4:00 pm] spread over two days. Professional trainers will teach you how to use python effectively. This workshop explores Python's place in the scientific ecosystem, and how the language, with several readily available open-source libraries, can serve as a powerful tool for data analysis. Why Python for Data Science is Important? Python is a general-purpose programming language that is becoming more and more popular for doing data science. It is often the choice for developers who need to apply statistical techniques or data analysis in their work, or for data scientists whose tasks need to be integrated with web apps or production environments. In particular, Python really shines in the field of machine learning. Its combination of machine learning libraries and flexibility makes Python uniquely well-suited to developing sophisticated models and prediction engines that plug directly into production systems. One of Python’s greatest assets is its extensive set of libraries. Libraries are sets of routines and functions that are written in a given language. A robust set of libraries can make it easier for developers to perform complex tasks without rewriting many lines of code. What’s the Job Market for Data Scientists Like? With millions of worldwide job openings in Big Data, the role of a data scientist has become the hottest job of the decade. In today’s data-based world, companies are using the insights that data scientists provide to stay one step ahead of their competition while keeping overhead costs low. Big names like Oracle, Apple, Microsoft, Booz Allen Hamilton, State Farm, Walmart, and more all regularly have job postings for data scientists. According to Forbes, for most of 2016, there were an average of 2,900 unique job postings for data scientists each month. According to a McKinsey Global Institute study, it’s predicted that by 2018, there will be almost 200,000 open positions. What are the Topics Covered? Basics: Variables and Elementary Types, Operations, Console and Functions Data Structures: Tuples, Lists, Sets, Dictionaries/Maps Control flow statements: if, for, break, continue and else statements, while loops File handling: File I/O and context managers Exception handling: try, except and finally statements, handling and raising exceptions. Who Can Attend? If you have a desire to learn new things, have a programming background/ Who's Teaching? For the last 25 years, Venkatesh has been working in various domains and various technologies with DATA as a common theme. Starting with Data Warehouses, proceeding on to Data Mining, Business Intelligence and now Machine Learning, Deep Learning & AI. He successfully co-founded and exited a couple of startups so far. One of which is of Business Intelligence for Enterprises and the other is an Insurance sector product. Currently, he is invested in a few startups in the ML area and also sits on the boards of a few more. Venkatesh has a Masters in Computers Science and an MBA. He brings his formal education and experience, combined with his passion for DATA to develop Predictive Analytics capabilities to his enterprise clients in pharmaceutical and insurance verticals. Venkatesh in his spare time also follows his passion for teaching by conducting workshops in Machine Learning where he coaches aspiring students in the joy of DATA.
