Este documento es un certificado de completación de un curso en línea de habilidades de negociación y comunicación efectiva ofrecido a través de Coursera. Fue completado con éxito por Daniel Meade Monteverde y verificado en la página web de Coursera.
Miguel Ángel Rodríguez Anticona has been awarded certificate #339233 for successfully completing a 12-hour course on Image Processing with Python, which he finished on January 08, 2023.
Miguel Ángel Rodríguez Anticona has been awarded a certificate numbered 26,907,469 for successfully completing a 4-hour course on Image Processing with Keras in Python. He completed the course on December 08, 2022.
Miguel Ángel Rodríguez Anticona has been awarded certificate #26,899,204 for successfully completing a 4-hour course on Biomedical Image Analysis in Python. He completed the course on November 29, 2022.
Miguel Ángel Rodríguez Anticona has been awarded a certificate numbered 26,644,019 for successfully completing a 4-hour course on Image Processing in Python, which he finished on November 20, 2022.
Miguel Ángel Rodríguez Anticona has been awarded a certificate numbered 15,072,716 for successfully completing a 4-hour Introduction to Anomaly Detection in R course on October 18, 2022.
Miguel Ángel Rodríguez Anticona has been awarded certificate #25,906,695 for successfully completing the online course "Support Vector Machines in R" which was 4 hours long and completed on September 15, 2022.
Miguel Ángel Rodríguez Anticona has been awarded a certificate numbered 15,848,726 for successfully completing an online course titled "Unsupervised Learning in R" which was 4 hours long and completed on August 29, 2022.
Missing data is a common problem in data analysis. This document discusses how to handle missing data in R. It likely provides techniques for detecting, removing, or imputing missing values so that the data can still be analyzed without compromising the results.
Miguel Ángel Rodríguez has successfully completed an online non-credit course entitled "Leading transformations: Manage change" offered through Coursera and authorized by Macquarie University. The course was taught by Richard Badham, PhD, Professor in the Department of Management at Macquarie Business School in Sydney, Australia. Coursera has verified Rodríguez's identity and participation in the course.
LLM powered contract compliance application which uses Advanced RAG method Self-RAG and Knowledge Graph together for the first time.
It provides highest accuracy for contract compliance recorded so far for Oil and Gas Industry.
How We Added Replication to QuestDB - JonTheBeachjavier ramirez
Building a database that can beat industry benchmarks is hard work, and we had to use every trick in the book to keep as close to the hardware as possible. In doing so, we initially decided QuestDB would scale only vertically, on a single instance.
A few years later, data replication —for horizontally scaling reads and for high availability— became one of the most demanded features, especially for enterprise and cloud environments. So, we rolled up our sleeves and made it happen.
Today, QuestDB supports an unbounded number of geographically distributed read-replicas without slowing down reads on the primary node, which can ingest data at over 4 million rows per second.
In this talk, I will tell you about the technical decisions we made, and their trade offs. You'll learn how we had to revamp the whole ingestion layer, and how we actually made the primary faster than before when we added multi-threaded Write Ahead Logs to deal with data replication. I'll also discuss how we are leveraging object storage as a central part of the process. And of course, I'll show you a live demo of high-performance multi-region replication in action.
Cómo hemos implementado semántica de "Exactly Once" en nuestra base de datos ...javier ramirez
Los sistemas distribuidos son difíciles. Los sistemas distribuidos de alto rendimiento, más. Latencias de red, mensajes sin confirmación de recibo, reinicios de servidores, fallos de hardware, bugs en el software, releases problemáticas, timeouts... hay un montón de motivos por los que es muy difícil saber si un mensaje que has enviado se ha recibido y procesado correctamente en destino. Así que para asegurar mandas el mensaje otra vez.. y otra... y cruzas los dedos para que el sistema del otro lado tenga tolerancia a los duplicados.
QuestDB es una base de datos open source diseñada para alto rendimiento. Nos queríamos asegurar de poder ofrecer garantías de "exactly once", deduplicando mensajes en tiempo de ingestión. En esta charla, te cuento cómo diseñamos e implementamos la palabra clave DEDUP en QuestDB, permitiendo deduplicar y además permitiendo Upserts en datos en tiempo real, añadiendo solo un 8% de tiempo de proceso, incluso en flujos con millones de inserciones por segundo.
Además, explicaré nuestra arquitectura de log de escrituras (WAL) paralelo y multithread. Por supuesto, todo esto te lo cuento con demos, para que veas cómo funciona en la práctica.
### Data Description and Analysis Summary for Presentation
#### 1. **Importing Libraries**
Libraries used:
- `pandas`, `numpy`: Data manipulation
- `matplotlib`, `seaborn`: Data visualization
- `scikit-learn`: Machine learning utilities
- `statsmodels`, `pmdarima`: Statistical modeling
- `keras`: Deep learning models
#### 2. **Loading and Exploring the Dataset**
**Dataset Overview:**
- **Source:** CSV file (`mumbai-monthly-rains.csv`)
- **Columns:**
- `Year`: The year of the recorded data.
- `Jan` to `Dec`: Monthly rainfall data.
- `Total`: Total annual rainfall.
**Initial Data Checks:**
- Displayed first few rows.
- Summary statistics (mean, standard deviation, min, max).
- Checked for missing values.
- Verified data types.
**Visualizations:**
- **Annual Rainfall Time Series:** Trends in annual rainfall over the years.
- **Monthly Rainfall Over Years:** Patterns and variations in monthly rainfall.
- **Yearly Total Rainfall Distribution:** Distribution and frequency of annual rainfall.
- **Box Plots for Monthly Data:** Spread and outliers in monthly rainfall.
- **Correlation Matrix of Monthly Rainfall:** Relationships between different months' rainfall.
#### 3. **Data Transformation**
**Steps:**
- Ensured 'Year' column is of integer type.
- Created a datetime index.
- Converted monthly data to a time series format.
- Created lag features to capture past values.
- Generated rolling statistics (mean, standard deviation) for different window sizes.
- Added seasonal indicators (dummy variables for months).
- Dropped rows with NaN values.
**Result:**
- Transformed dataset with additional features ready for time series analysis.
#### 4. **Data Splitting**
**Procedure:**
- Split the data into features (`X`) and target (`y`).
- Further split into training (80%) and testing (20%) sets without shuffling to preserve time series order.
**Result:**
- Training set: `(X_train, y_train)`
- Testing set: `(X_test, y_test)`
#### 5. **Automated Hyperparameter Tuning**
**Tool Used:** `pmdarima`
- Automatically selected the best parameters for the SARIMA model.
- Evaluated using metrics such as AIC and BIC.
**Output:**
- Best SARIMA model parameters and statistical summary.
#### 6. **SARIMA Model**
**Steps:**
- Fit the SARIMA model using the training data.
- Evaluated on both training and testing sets using MAE and RMSE.
**Output:**
- **Train MAE:** Indicates accuracy on training data.
- **Test MAE:** Indicates accuracy on unseen data.
- **Train RMSE:** Measures average error magnitude on training data.
- **Test RMSE:** Measures average error magnitude on testing data.
#### 7. **LSTM Model**
**Preparation:**
- Reshaped data for LSTM input.
- Converted data to `float32`.
**Model Building and Training:**
- Built an LSTM model with one LSTM layer and one Dense layer.
- Trained the model on the training data.
**Evaluation:**
- Evaluated on both training and testing sets using MAE and RMSE.
**Output:**
- **Train MAE:** Accuracy on training data.
- **T
AWS Cloud Technology and Services by Miguel Ángel Rodríguez Anticona.pdf
1. #34,896,967
H A S B E E N AWA R D E D TO
Miguel Ángel Rodríguez Anticona
FO R S U C C E S S F U L LY C O M P L E T I N G
AWS Cloud Technology and Services
L E N G T H
3 HOURS
C O M P L E T E D O N
JUL 01, 2024
Jonathan Cornelissen
CEO, DataCamp