Exploratory Data Analysis

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Tatiana Petrache
Exploratory Data Analysis

Acest curs este predat în limba română, iar materialele sunt în limba engleză şi/sau în limba română, după caz.

La cerere, cursul poate fi personalizat.

Without knowing what your data is all about, it’s hard to estimate potential value. Exploring new datasets using visual tools like Dataiku (https://www.dataiku.com/dss/editions/) solves this problem, by shifting the focus from data wrangling to data analytics and insights. In this 2 day course you will learn how to visually explore unseen datsets, create insightful visualizations and uncover patterns in the data using clustering techniques. We will also provide you with an intuitive framework to connect all the dots between your existing datasets, machine learning and business objectives.

All the concepts below will be combined with concrete exercises based on real datasets and use cases which will enable you to have a clear and gradual understanding of Exploratory Data Analysis and Effective Visualizations. We will walk you through an end to end scenario, from ingesting data to telling a story and eventually making decisons with that data.

Caracteristici curs

  • Capitole 14
  • Durata 2 zile
  • Nivel de cunostinte Orice nivel
  • Limba Romana
  • Studenti 12
  • Day 1: Intro to Data Exploratory Analysis

    • Capitol 1.1 Basic terminology and concepts Locked 0m
    • Capitol 1.2 Dataiku Intro: Install and get yourself familiar with the tool Locked 0m
    • Capitol 1.3 Load and Explore: Your first encounter with the data Locked 0m
    • Capitol 1.4 Clean and Enrich: Intuitive Data Wrangling Locked 0m
    • Capitol 1.5 Visualize: Get more insights Locked 0m
    • Capitol 1.6 Visualization tips – What can go wrong: Misleading Axes and Histogram Bins, Style against Substance, Principle of Proportional Ink Locked 0m
    • Capitol 1.7 Key take-aways for the day Locked 0m
  • Day 2: Bring in new datasets:

    • Capitol 2.1 Types of Joins Locked 0m
    • Capitol 2.2 More Visualizations: Complete the Data Story Locked 0m
    • Capitol 2.3 Intro to Clustering: Uncover hidden patterns in your data Locked 0m
    • Capitol 2.4 Interpret and Integrate Clustering Results: Build your intuition Locked 0m
    • Capitol 2.5 Data Storytelling: Present your findings Locked 0m
    • Capitol 2.6 Is this data Machine Learning material? – The Machine Learning Canvas Locked 0m
    • Capitol 2.7 Key take-aways for the day Locked 0m
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Tatiana Petrache