Raw Data to Insights: Data Ingestion & Statistical Analysis

Raw Data to Insights: Data Ingestion & Statistical Analysis

• Course Overview
• describe how we can use statistical analysis to add value to data
• recorgnize the concept of data correction along with the various essential approaches of implementing data correction which includes data detection localization, imputation and correction
• demonstrate how we can facilitate outlier detection using R
• describe the layered architecture of data from the perspective of data ingestion, prcoessing, and visualization
• list and compare the various essential data ingestion tools that we can use to ingest data
• set up Kafka and Apache NiFi to ingest data
• demonstrate the steps involved in ingesting data from databases to Hadoop clusters using Sqoop
• demonstrate how we can ingest data using WaveFront
• detect outliers using R and ingest data using Apache NiFi and WaveFront

Explore how statistical analysis can turn raw data into insights, and then examine how to use the data to improve business intelligence, in this 10-video course. Learn how to scrutinize and perform analytics on the collected data. The course explores several approaches for identifying values and insights from data by using various standard and intuitive principles, including data exploration and data ingestion, along with the practical implementation by using R. First, you will learn how to detect outliers by using R, and how to compare simple linear regression models, with and without outliers, to improve the quality of the data. Because today's data are available in diversified formats, with large volume and high velocity, this course next demonstrates how to use a variety of technologies: Apache Kafka, Apache NiFi, Apache Sqoop, and Wavefront (a program for simulating two-dimensional acoustic systems) to ingest data. Finally, you will learn how these tools can help users in data extraction, scalability, integration support, and security.

Raw Data to Insights: Data Management & Decision Making

Raw Data to Insights: Data Management & Decision Making

• Course Overview
• describe the capabilities and advantages provided with the application of data-driven decision making
• load data from databases using R
• demonstrate how to prepare data for analysis
• recall the concept of data correction using the essential approaches of simple transformation rules and deductive correction
• implement data correction using simple transformation rules
• implement data correction using deductive correction
• describe the various essential distributed data management frameworks used to handle big data
• describe the approach of implementing data analytics using Machine Learning
• recognize how to implement exploratory data analysis using R
• recognize how to implement predictive modelling using Machine Learning
• correct data using deductive correction, analyze data in R and facilitate predictive modelling with Machine Learning

To master data science, it is important to turn raw data into insights. In this 12-video course, you will learn to apply and implement various essential data correction techniques, transformation rules, deductive correction techniques, and predictive modeling using critical data analytical approaches by using R. The key concepts in this course include: the capabilities and advantages of the application of data-driven decision making; loading data from databases using R; preparing data for analysis; and the concept of data correction, using the essential approaches of simple transformation rules and deductive correction, Next, examine implementing data correction using simple transformation rules and deductive correction; the various essential distributed data management frameworks used to handle big data; and the approach of implementing data analytics using machine learning. Finally, learn how to implement exploratory data analysis by using R; to implement predictive modeling by using machine learning; how to correct data with deductive correction; and how to analyze data in R and facilitate predictive modeling with machine learning.