Data Science with Python - IT Academy

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Objectives

In the Data Science itinerary with Python we will learn how to extract value from data by ingesting, transforming, storing, analyzing and visualizing data.

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Final Project

- Project Crypto Punks

Technical Test

- Barcelona Rainfall Analysis

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COURSE SYLLABUS

S01 Installation of the work environment

S02 Introduction to Python

S03 Data Structure and Control Structures

• S03 T01: Data structure

• S03 T02: Structure of a Matrix

• S03 T03: Control structure

• S03 T04: Practice with numerical programming

• S03 T05: Data exploration

S04 Data visualization

• S04 T01: Graphic display of a dataset

• S04 T02: Graphic display of Multiple variables

S05 Other basic Python modules

• S05 T01: Log Transformation with Regular Expressions

S06 Sampling methods

• S06 T01: Sampling Models

S07 Data, Probabilities and Statistics

• S07 T01: Data, probabilities and statistics

S08 Hypothesis Testing

• S08 T01: Hypothesis test

S09 Correlation, Feature Scaling & Feature Engineering

• S09 T01: Feature Engineering

S10 Introduction Machine Learning

S11 Train and Test

• S11 T01: Practicing with training and test sets

S12 Supervised Learning - Regressions

• S12 T01: Supervised Learning - Regressions

S13 Supervised Learning - Classification

• S13 T01: Supervised Learning - Classification

S14 Imbalance Classification Dataset

S15 Unsupervised Learning - Clustering

• S15 T01: Unsupervised Learning - Clustering

S16 Advanced Machine Learning

• S16 T01: Pipelines, grid search i text mining

• S16 T02: Web Scraping

S17 Relational databases

• S17 T01: Relational databases

• S17 T02: MySQL database

S18 NoSQL databases

• S18 T01: NoSQL database

S19 Data science Toolbox

• S19 T01: Interactive display with ElasticSearch Stack Kibana