François Chung, Ph.D.

Tag: data science

Google Cloud: big data and machine learning

Google Cloud: big data and machine learning

|

Coursera training, MOOC (2020). This online training introduces the big data and machine learning (ML) capabilities of Google Cloud Platform (CGP). Through a combination of presentations, demos and hands-on labs, the training gives an overview of Google Cloud and a detailed view of the data processing and ML solutions, such as BigQuery, Cloud SQL, Dataproc, Pub/Sub, Dataflow and Data Studio.

Week 1: Big data and ML fundamentals

Main topics:

  • Exploring a BigQuery public dataset;
  • Choosing the right solution approach;
  • Recommending products using Cloud SQL and Spark;
  • Predicting visitor purchases using BigQuery ML.

Week 2: Modern data pipeline challenges

Main topics:

  • Real-time IoT dashboards;
  • Creating a streaming data pipeline;
  • ML on unstructured datasets;
  • Classifying images with pre-built ML models.

References

Spark fundamentals

Spark fundamentals

|

Cognitive Class training, MOOC (2020). This learning path addresses the fundamentals of Apache Spark, an open source engine for large scale data processing that is revolutionizing the analytics and big data world. This training is an opportunity to learn from industry leaders about Spark, which is built around speed, ease of use and analytics, and provides hands-on opportunities and projects to build confidence with the Spark toolset.

Course 1: Spark fundamentals I

Main topics:

  • Introduction to Spark;
  • Resilient Distributed Dataset (RDD) and DataFrames;
  • Spark application programming;
  • Introduction to Spark libraries;
  • Spark configuration, monitoring and tuning.

Course 2: Spark fundamentals II

Main topics:

  • Introduction to notebooks;
  • RDD architecture;
  • Optimizing transformations and actions;
  • Caching and serialization;
  • Developing and testing.

Course 3: Spark MLlib

Main topics:

  • Spark MLlib data types;
  • Review of algorithms;
  • Decision trees and random forests;
  • Spark MLlib clustering.

Course 4: Exploring GraphX

Main topics:

  • Introduction to Graph-Parallel;
  • Exploring graph operators;
  • Visualizing and modifying GraphX;
  • Aggregation and caching.

Course 5: Big data in R using Spark

Main topics:

  • Introduction to SparkR;
  • Data manipulation in SparkR;
  • Machine learning in SparkR.

References

Training

Spark fundamentals I (course certificate)
Spark – Level 1 (certification badge)
Spark fundamentals II (course certificate)
Spark MLlib (course certificate)
Exploring GraphX (course certificate)
Big data in R using Spark (course certificate)
Spark - Level 2 (certification badge)

Related articles

Hadoop fundamentals (Cognitive Class training)
Data science specialization (Coursera training)

Learn more

Cognitive Class

Hadoop fundamentals

Hadoop fundamentals

|

Cognitive Class training, MOOC (2020). This learning path presents Hadoop, which is an open source framework for distributed storage and processing of big data. The training covers content that is critical to anyone's success in this realm by explaining the Hadoop conceptual design, introducing MapReduce, YARN (Yet Another Resource Negotiator) and Hive, then explaining how to use Hadoop and manipulate data without the use of complex coding.

Course 1: Hadoop 101

Main topics:

  • Introduction to Hadoop;
  • Hadoop architecture and HDFS;
  • Hadoop administration;
  • Hadoop components.

Course 2: MapReduce and YARN

Main topics:

  • Introduction to MapReduce and YARN;
  • Limitations of Hadoop v1 and MapReduce v1;
  • YARN architecture.

Course 3: Moving data into Hadoop

Main topics:

  • Load scenarios;
  • Using Sqoop;
  • Flume overview;
  • Using Data Click.

Course 4: Accessing Hadoop data using Hive

Main topics:

  • Introduction to Hive;
  • Hive DDL - Data Definition Language;
  • Hive DML - Data Manipulation Language;
  • Hive operators and functions.

References

Training

Hadoop 101 (course certificate)
Hadoop Foundations – Level 1 (certification badge)
MapReduce and YARN (course certificate)
Hadoop Programming – Level 1 (certification badge)
Moving data into Hadoop (course certificate)
Hadoop Administration – Level 1 (certification badge)
Accessing Hadoop data using Hive (course certificate)
Hadoop Data Access – Level 1 (certification badge)
Hadoop Foundations – Level 2 (certification badge)

Related articles

Spark fundamentals (Cognitive Class training)
Data science specialization (Coursera training)

Learn more

Cognitive Class

Azure: fundamentals, machine learning and Power BI

Azure: fundamentals, machine learning and Power BI

|

Microsoft Docs training, MOOC (2020). These 3 online courses present Microsoft Azure and Power BI. The training teaches the basic cloud concepts, along with hands-on exercises, and provides an overview of Azure services, such as Azure Machine Learning (ML), which is a cloud platform for training, deploying, managing and monitoring ML models. Furthermore, the training explains how to use Power BI and build business intelligence reports.

Course 1: Azure fundamentals

Main topics:

  • Principles of cloud computing;
  • Azure architecture and service guarantees;
  • Compute, data storage and networking;
  • Security, responsibility and trust;
  • Infrastructure standards with Azure Policy;
  • Azure resources with Azure Resource Manager.

Course 2: Azure machine learning

Main topics:

  • Working with data;
  • Orchestrate ML with pipelines;
  • Deploy ML models;
  • Automate model selection;
  • Tune hyperparameters;
  • Monitor models and data drift.

Course 3: Power BI

Main topics:

  • Get started building with Power BI;
  • Get data with Power BI Desktop;
  • Model and explore data;
  • Use visuals, publish and share.

References

Training

Microsoft Docs (badges and trophies)

Related articles

Google Cloud: big data and machine learning (Coursera training)
AWS: foundations and machine learning (AWS training)
Neural networks and deep learning (Coursera training)

Learn more

Microsoft Docs (Azure fundamentals)
Microsoft Docs (Azure machine learning)
Microsoft Docs (Power BI)

Data science specialization

Data science specialization

|

Coursera training, MOOC (2020). This specialization covers the concepts and tools needed throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. Topics covered include using R to clean, analyze, and visualize data, navigating the entire data science pipeline from data acquisition to publication, using GitHub to manage data science projects, and performing regression analysis, least squares and inference using regression models.

Course 1: Data scientist’s toolbox

Main topics:

  • Data science fundamentals;
  • R and Rstudio;
  • Version control and GitHub;
  • R Markdown, scientific thinking and big data.

Course 2: R programming

Main topics:

  • Background and getting started;
  • Programming with R;
  • Loop functions and debugging;
  • Simulation and code profiling.

Course 3: Getting and cleaning data

Main topics:

  • Finding data and reading different file types;
  • Data storage systems;
  • Organizing, merging and managing data;
  • Text and data manipulation in R.

Course 4: Exploratory data analysis

Main topics:

  • Analytic graphics and base plotting in R;
  • Lattice and ggplot2;
  • Data dimension reduction;
  • Cluster analysis techniques.

Course 5: Reproducible research

Main topics:

  • Concepts, ideas and structure;
  • Markdown and knitr;
  • Reproducible research checklist;
  • Evidence-based data analysis.

Course 6: Statistical inference

Main topics:

  • Probability and expected values;
  • Variability, distribution and asymptote;
  • Intervals, testing and p-value;
  • Power, bootstrapping and permutation tests.

Course 7: Regression models

Main topics:

  • Least squares and linear regression;
  • Linear and multivariate regression;
  • Residuals and diagnostics;
  • Logistic and Poisson regression.

Course 8: Practical machine learning

Main topics:

  • Prediction, errors and cross validation;
  • Caret package;
  • Decision trees and random forests;
  • Regularized regression and combining predictors.

Course 9: Developing data products

Main topics:

  • Shiny, GoogleVis and Plotly;
  • R Markdown and Leaflet;
  • R Pakages and Swirl.

References

Related articles

Spark fundamentals (Cognitive Class training)
Hadoop fundamentals (Cognitive Class training)
AWS: foundations and machine learning (AWS training)

Learn more

Coursera

Languages

Recent articles

Categories

News

Recent (Twitter) & Popular (Reddit)

Search