jplavorr

aws-data-wrangler

Pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

Big-Data-with-PySpark

Advance your data skills by mastering Apache Spark. Using the Spark Python API, PySpark, you will leverage parallel computation with large datasets, and get ready for high-performance machine learning. From cleaning data to creating features and implementing machine learning models, you'll execute end-to-end workflows with Spark. The track ends with building a recommendation engine using the popular MovieLens dataset and the Million Songs dataset.

Data-Science

Meu portfólio para projetos de Data Science

Data-Scientist-with-Python

In this track, you'll learn how this versatile language allows you to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher. Through interactive exercises, you'll get hands-on with some of the most popular Python libraries, including pandas, NumPy, Matplotlib, and many more. You'll then work with real-world datasets to learn the statistical and machine learning techniques you need to train decision trees and use natural language processing (NLP). Start this track, grow your Python skills, and begin your journey to becoming a confident data scientist.

DataAnalysisProjects

This contains data analysis projects

Finance-with-Python

Analyze the financial market and set up strategies to improve investments with Data Science & Machine Learning

Insights-de-Vendas-In-Power-BI

Dashboard otimizado provendo rapidamente insights das vendas.

Marketing-Analytics

Gain the Python skills you need to make better data-driven marketing decisions. In this track, you’ll learn how to analyze campaign performance, measure customer engagement, and predict customer churn. Working with real-world data, including retail transactions, you'll discover how to analyze social media data, extract insights from text data, and gain market basket analysis skills that will help you better understand your customers. You’ll also use statistical models and machine learning to forecast customer lifetime value. Through hands-on activities, you’ll use popular packages such as pandas, Matplotlib, tweepy, NLTK, seaborn, NumPy, SciPy, and scikit-learn to help you improve your company’s marketing strategy. By the end of the track, you'll be ready to navigate the world of marketing using Python.

Math-Behind-Moneyball-with-Python

In this repository I will start a series, based on Coursera's 'Math Behind Moneyball' course (which is about Data Science in sports). I will reproduce the content seen in the course using Python and drawing conclusions from the data.

Microsoft-Data_Scientist.

Learn new skills and discover the power of Microsoft products with step-by-step guidance. Start my journey by exploring the learning paths and modules to prepare for microsoft certification exam.

jplavorr/Data-Science

Meu portfólio para projetos de Data Science

jplavorr/Math-Behind-Moneyball-with-Python

In this repository I will start a series, based on Coursera's 'Math Behind Moneyball' course (which is about Data Science in sports). I will reproduce the content seen in the course using Python and drawing conclusions from the data.

jplavorr/Finance-with-Python

Analyze the financial market and set up strategies to improve investments with Data Science & Machine Learning

jplavorr/Marketing-Analytics

Gain the Python skills you need to make better data-driven marketing decisions. In this track, you’ll learn how to analyze campaign performance, measure customer engagement, and predict customer churn. Working with real-world data, including retail transactions, you'll discover how to analyze social media data, extract insights from text data, and gain market basket analysis skills that will help you better understand your customers. You’ll also use statistical models and machine learning to forecast customer lifetime value. Through hands-on activities, you’ll use popular packages such as pandas, Matplotlib, tweepy, NLTK, seaborn, NumPy, SciPy, and scikit-learn to help you improve your company’s marketing strategy. By the end of the track, you'll be ready to navigate the world of marketing using Python.

jplavorr/Microsoft-Data_Scientist.

Learn new skills and discover the power of Microsoft products with step-by-step guidance. Start my journey by exploring the learning paths and modules to prepare for microsoft certification exam.

jplavorr/aws-data-wrangler

Pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

jplavorr/Big-Data-with-PySpark

Advance your data skills by mastering Apache Spark. Using the Spark Python API, PySpark, you will leverage parallel computation with large datasets, and get ready for high-performance machine learning. From cleaning data to creating features and implementing machine learning models, you'll execute end-to-end workflows with Spark. The track ends with building a recommendation engine using the popular MovieLens dataset and the Million Songs dataset.

jplavorr/Data-Scientist-with-Python

In this track, you'll learn how this versatile language allows you to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher. Through interactive exercises, you'll get hands-on with some of the most popular Python libraries, including pandas, NumPy, Matplotlib, and many more. You'll then work with real-world datasets to learn the statistical and machine learning techniques you need to train decision trees and use natural language processing (NLP). Start this track, grow your Python skills, and begin your journey to becoming a confident data scientist.

jplavorr/Insights-de-Vendas-In-Power-BI

Dashboard otimizado provendo rapidamente insights das vendas.

jplavorr/jplavorr.github.io

This platform is dedicated to showcasing my projects in the fields of Machine Learning and Data Science. Browse through my portfolio to discover a variety of projects, from exploratory data analysis to predictive modeling. Stay tuned for regular updates and new additions to my collection.

jplavorr/Machine-Learning-with-Pyspark

Here you can find all my study on pyspark which I use for my work with machine learning models and big data

jplavorr/Math-projects

As my background is in mathematics, I have a huge interest in applications of programming in the field. Here are some projects where I solve math problems with programming techniques.

jplavorr/Recommendation-Systems-

Here you can find all my study on recommender systems, which I used to build a recommendation model in a work with big data applied in spark and tensorflow.

jplavorr/Time-Series-with-Python

Time series data is one of the most common data types and understanding how to work with it is a critical data science skill if you want to make predictions and report on trends. In this track, you'll learn how to manipulate time series data using pandas, work with statistical libraries including NumPy and statsmodels to analyze data, and develop your visualization skills using Matplotlib, SciPy, and seaborn. You'll then apply your time series skills using real-world data, including financial stock data, UFO sightings, CO2 levels in Maui, monthly candy production in the US, and heartbeat sounds. By the end of this track, you'll know how to forecast the future using ARIMA class models and generate predictions and insights using machine learning models.

jplavorr/Amostragem

Para os trabalhos do mestrado na área de amostragem

jplavorr/beAnalytic_case

Um case para vaga de engenheiro de dados Jr.

jplavorr/ComputerVision

Welcome to my Computer Vision Projects repository! This is a comprehensive collection of various projects that I've undertaken in the field of computer vision, exploring a range of technologies, tools, and techniques.

jplavorr/Estimador-via-Jackknife

Seminário para disciplina de Inferência estatística da UFPE.

jplavorr/fashion-mnist

A MNIST-like fashion product database. Benchmark :point_down:

jplavorr/Introduction-to-Tableau

An introductory project using Tableau to analyze data from an Audio Books company

jplavorr/jplavorr

README.md

jplavorr/kedro_tutorial

Starting my studies with the kedro framework for orchestrating machine learning pipelines.

jplavorr/M-todo-de-Suaviza-o-Direta

Nesse trabalho se encontra o trabalho realizado para cadeira de séries temporais sobre o método da suavização direta.

jplavorr/MLOps

This project is an open-source initiative that aims to provide a hands-on learning experience for anyone interested in the emerging field of Machine Learning Operations, or MLOps.

jplavorr/mlopsproject

Project for mlops end to end project with CI/CD pipelines

jplavorr/persontracking

jplavorr/R_project_Time_Series

Projeto dedicado para a disciplina de Séries Temporais (Mestrado) do Departamento de Estatística da UFPE

jplavorr/seedz

jplavorr/ufc_project

Project for UFC fights prediciton

jplavorr/Wavelets

Trabalho de conclusão de mestrado para disciplina de Séries Temporais do departamento de estatística da UFPE.