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Data Analysis with R Workshops by Azzurro.io

Overview
Provider

Azzurro.io

Dates

Wednesday 30th of March (and then every Wednesday)

Duration

Eight days

Location

Kharkiv

Price_Front_Page

Free

Price

Free

Type

Workshop

Language

Russian, English

Requirements

It is necessary to have a laptop with installed R language and IDE R-Studio

Image Credits

Proftrack

Azzurro.io company invites active learners to join “Data Analysis with R” eight Workshops that will start on Wednesday 30th of March (and then every Wednesday at 19:00) at Kharkiv, Fabrika.space (Blagoveshchenskaya, 1). Course language: Russian.

Workshop instructor: Mykola Pavlov.

If you had visited this workshop, please leave the review below.

—–

З 30 березня у Харкові на території Fabrika.space розпочнеться серія воркшопів від Data Scientist із Azzurro.io Миколи Павлова – “Data Analysis with R”. Курс включає в себе 8 занять тривалістю по 2 години, присвячених Data Science.

План занять:

Introduction to data

  • R programming language
  • Observations and variables
  • Relationship between variables
  • Population and sample
  • Dependent and independent variables
  • Experimental design and sampling methods

Data exploration, visualization and cleaning

  • Data import, cleaning and manipulations
  • Scatter plot
  • Histogram, mean, variance and standard deviation
  • Box plots, quartiles, median and outliers
  • Data transformations
  • Categorical data, contingency tables and bar plot

Probability

  • Outcome, random process and Law of Large numbers
  • Disjoint/joint outcomes, addition rule
  • Independence
  • Conditional, marginal and joint probabilities
  • Multiplication Rule
  • Bayes theorem
  • Random variables, Expected Value, Variance
  • Probability distributions: PDF, CDF
  • Normal distribution
  • Geometric distribution
  • Binomial distribution

Statistical Inference

  • Point estimates
  • Confidence interval
  • Hypothesis testing
  • Type I, type II errors, power
  • Paired data, different of two means
  • T-distribution
  • Inference for categorical data

Regression analysis

  • Linear regression and least squares (LS)
  • Conditions for fitting regression line
  • Residuals analysis, R^2
  • Interpretation and inference
  • Multiple regression
  • Model selection
  • Logistic regression

Predictive Analytics

  • Machine learning and Supervised learning
  • Regression / Classification
  • Error functions
  • Linear model
  • Gradient descent, SGD, mini-batches
  • Decision Trees, Random Forest, Neural Networks, SVM
  • Bias-Variance tradeoff, regularization L1/L2
  • Cross-validation
  • Hyperparameters tuning

BigData, R and Apache Spark

  • Resilient Distributed Datasets (RDD)
  • Map-Reduce
  • SparkR, Data Frame operations
  • Machine Learning in Spark

Для участі у воркшопі потрібно попередньо зареєструватись тут, та необхідно мати при собі ноутбук з попередньо встановленою мовою R і IDE R-Studio.

Якщо ви брали участь у цьому воркшопі, будь ласка, залиште ваш відгук нижче.

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