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Python Immersive One Week Bootcamp

Python Immersive One Week Bootcamp


Math maters
37-07 74th st
Jackson heights
Queens, NY 11372
Register for Course
Monday, October 15, 2018 -
12:45pm to 1:45pm


FREE RETAKES Python Immersive $499 (Fee Adjusted from 2 Day bootcamp if attended) 35 hours in total, 5 sessions/days of 7 hours each Monday to Friday 9 am to 5 pm prepay to reserve your seat: The course is developed for non-programmers and non-statt audience. It consist of games, graphics, and examples to sensitize you to the terms used in Data Science. Day 1 / 2 (This course is prerequisite for Part 2) Notes for 1st Session: Group size is max 3. Topics: Introduction to Python Foundations of programming: Python built-in Data types Concept of mutability and theory of different Data structures Control flow statements: If, Elif and Else Definite and Indefinite loops: For and While loops Writing user-defined functions in Python Classes in Python Read and write Text and CSV files with python List comprehensions and Lambda. Classes and inheritance. Print Hello World Azure Notebooks & Anaconda Book and Content Functions (Arguments and Return) Loops (For While) If else List/Dictionary Nested Loops with if else List/Dictionary (JSON) Class Lambda Functions List Comprehension File Handling Web Scraping Exception handling SQLite Python Capstone Project for Github Portfolio Matplotlib Numpy Pandas Scipy Python Lambdas Python Regular Expressions Collection of powerful, open-source, tools needed to analyze data and to conduct data science. Working with jupyter anaconda notebooks pandas numpy matplotlib git and many other tools. Data Loading, Storage, and File Formats Data Cleaning and Preparation Data Wrangling: Join, Combine, and Reshape Plotting and Visualization Data Aggregation and Group Operations Time Series Reference Github: Day 2/2 PPT: Python Data Analytics We’ll cover the machine learning and data mining techniques are used for in a simple example in Python. Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multivariate Regression Multi-Level Models Support Vector Machines K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Reference Github: (Portfolio Building for your project) Day 3 Select your project, download data, clean wrangle and massage your data and make it ready for anaysis Day4 Run Machine Learning Models and select the best model Tweak Model parameters Day 5 Fine tune and publish your portfolio #Instructor: Shivgan Joshi [email protected] 929 356 5046 ** Payment Policy: We only accept payment at door and before the class. We accept payment through event leap, cash, Venmo & Paypal(+5). **

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