Python Data Science 1-Day (2.5 hrs) (Beginners Course Bootcamp NYC) $49

dim. 4 novembre à 20:15 — informations

dim. 11 novembre à 20:15 — informations

dim. 18 novembre à 20:15 — informations

Fuseau horaire : Paris (GMT+02:00)

Mathmatter Tutoring
Jackson Heights

Chaque semaine le dimanche

Sunday Python Part 1/2 & Part 2/2 (3-5 optional Project Portfolio for Github)

FREE RETAKES & PAYMENT ADJUSTED FOR 5 DAY COURSE The course is developed for non programmers and non stat audience.

It consist of games, graphics, and examples to sensitize you to the terms used in Data Science. Notes for 1st Session:

Group size is max 3. Day 1 / 2 (This course is prerequisite for Part 2)

Two day intensive boot camp for Python Data Science Enthusiast.

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


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


[masked] Payment Policy: We only accept payment at door and before the class. We accept payment through event leap, cash, Venmo & Paypal(+5).


Mathmatter Tutoring
Jackson Heights

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