Data Engineering with AWS (Nanodegree Program)

Colleagues, in the Data Engineering with AWS - Nanodegree Program you will learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets. Skill-based courses include: 1) Data Modeling - create relational and NoSQL data models to fit the diverse needs of data consumers. Use ETL to build databases in PostgreSQL and Apache Cassandra, Introduction to Data Modeling - understand the purpose of data modeling, the strengths and weaknesses of relational databases, and create schemas and tables in Postgres, 3) NoSQL Data Models - when to use non-relational databases based on the data business needs, their strengths and weaknesses, and how to creates tables in Apache Cassandra (Project: Data Modeling with Apache Cassandra); 4) Cloud Data Warehouses - create cloud-based data warehouses. You’ll sharpen your data warehousing skills, deepen your understanding of data infrastructure, and be introduced to data engineering on the cloud using Amazon Web Services (AWS) - Introduction to Cloud Data Warehouses, Introduction to Data Warehouses, you'll be introduced to the business case for data warehouses as well as architecture, extracting, transforming, and loading data, data modeling, and data warehouse technologies, 5) ELT and Data Warehouse Technology in the Cloud - learn about ELT, the differences between ETL and ELT, and general cloud data warehouse technologies, 6) AWS Data Warehouse Technologies - to set up Amazon S3, IAM, VPC, EC2, and RDS. You'll build a Redshift data warehouse cluster and learn how to interact with it, 6) Implementing a Data Warehouse on AWS - implement a data warehouse on AWS (Project: Data Warehouse. You will build an ETL pipeline that extracts data from S3, stages data in Redshift, and transforms data into a set of dimensional tables for an analytics team); 7) Spark and Data Lakes - learn about the big data ecosystem and how to use Spark to work with massive datasets. You’ll also learn about how to store big data in a data lake and query it with Spark. Introduction to Spark and Data Lakes - learn how Spark evaluates code and uses distributed computing to process and transform data. You'll work in the big data ecosystem to build data lakes and data lake houses, 8) Big Data Ecosystem, Data Lakes, and Spark - learn about the problems that Apache Spark is designed to solve. You'll also learn about the greater Big Data ecosystem and how Spark fits into it, 9) Spark Essentials - use Spark for wrangling, filtering, and transforming distributed data with PySpark and Spark SQL - Using Spark in AWS, learn to use Spark and work with data lakes with Amazon Web Services using S3, AWS Glue, and AWS Glue Studio, 10) Ingesting and Organizing Data in a Lakehouse. In this lesson you'll work with Lakehouse zones. You will build and configure these zones in AWS (Project: STEDI Human Balance Analytics - work with sensor data that trains a machine learning model. You'll load S3 JSON data from a data lake into Athena tables using Spark and AWS Glue, 11) Automate Data Pipelines. In this course, you'll build pipelines leveraging Airflow DAGs to organize your tasks along with AWS resources such as S3 and Redshift, 12) Automating Data Pipelines - build data pipelines, 13) Data Pipelines. In this lesson, you'll learn about the components of a data pipeline including Directed Acyclic Graphs (DAGs). You'll practice creating data pipelines with DAGs and Apache Airflow, 14) Airflow and AWS - create connections between Airflow and AWS first by creating credentials, then copying S3 data, leveraging connections and hooks, and building S3 data to the Redshift DAG, 15) Data Quality - track data lineage and set up data pipeline schedules, partition data to optimize pipelines, investigating Data Quality issues, and write tests to ensure data quality, 16) Production Data Pipelines - build Pipelines with maintainability, reusability and monitoring,  in mind. They will also learn about pipeline monitoring (Project: Data Pipelines - work on a music streaming company’s data infrastructure by creating and automating a set of data pipelines with Airflow, monitoring and debugging production pipelines. 

Enroll today (teams & executives are welcome): https://tinyurl.com/4pxyw9vt  

Download your free Data Science  - Career Transformation Guide.

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Much career success, Lawrence E. Wilson - AI Academy (share with your team) https://tinyurl.com/hh7bf4m9 

Data Analyst (Nanodegree Program)

Colleagues, in the Data Analyst (Nanodegree Program) you will learn to clean up messy data, uncover patterns and insights, make predictions using machine learning, and clearly communicate your findings. Skill-based training modules include: 1) Introduction to Data Analysis with Pandas and NumPy - Pandas • Exploratory data analysis • Basic data visualizations • Jupyter notebooks • Data storytelling • Data analysis process • NumPy • Data manipulation, 2) Advanced Data Wrangling - Data cleaning • Data storage • Data Tidiness Assessment • Data gathering • Pandas • Data quality assessment • File i/o, and 3) Data Visualization with Matplotlib and Seaborn - Latent variables • Data visualization design • Data fluency • Exploratory data analysis • Professional presentations • Data limitations and biases • Data storytelling • Jupyter notebooks • Quantitative data visualization. 

Enroll today (teams & executives are welcome): https://tinyurl.com/mr9ec5rj  

Download your free Data Science  - Career Transformation Guide.

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Much career success, Lawrence E. Wilson - AI Academy (share with your team) https://tinyurl.com/hh7bf4m9 

Data Science with R Programming Certification

Colleagues, the Data Science with R Certification training will help you gain expertise in Machine Learning Algorithms such as K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R programming. This course is well suited for professionals and beginners. Throughout this course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. This Data Science with R training encompasses a conceptual understanding of Statistics, Time Series, Text Mining, introduction to Deep Learning, and other relevant concepts to kickstart your Data Science career. Skill-based training lessons include: 1) Introduction to Data Science with R, 2) Statistical Inference, 3) Data Extraction, Wrangling and Exploration, 4) Introduction to Machine Learning, 5) Classification Techniques, 6) Data Visualization in R, 7) Recommender Engines, 8) Text Mining, 9) Time Series, and 10) Deep Learning.

Enroll today (teams & executives are welcome): https://fxo.co/EFzP 

Download your free Data Science  - Career Transformation Guide.

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Much career success, Lawrence E. Wilson - AI Academy (share with your team) https://tinyurl.com/hh7bf4m9 

Machine Learning Specialization

Colleagues, the Machine Learning Specialization taught by Andrew Ng is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program. Gain high demand skills in Logistic Regression, Artificial Neural Network, Linear Regression, Decision Trees and Recommender Systems. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.\n\nThis Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression. Build and train a neural network with TensorFlow to perform multi-class classification. Apply best practices for machine learning development so that your models generalize to data and tasks in the real world. Build and use decision trees and tree ensemble methods, including random forests and boosted trees. Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. Build recommender systems with a collaborative filtering approach and a content-based deep learning method. Build a deep reinforcement learning model. Skill-based lessons include: 1) Supervised Machine Learning: Regression and Classification, 2) Advanced Learning Algorithms and 3) Unsupervised Learning, Recommenders, Reinforcement Learning.

Enroll today (teams & execs welcome): https://tinyurl.com/yc5c8snp 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

IBM Data Science Professional Certificate

Colleagues, the IBM Data Science Professional Certification program will prepare for a career in the high-growth field of data science. In this program, you’ll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist in as little as 5 months. No prior knowledge of computer science or programming languages is required. Data science involves gathering, cleaning, organizing, and analyzing data with the goal of extracting helpful insights and predicting expected outcomes. The demand for skilled data scientists who can use data to tell compelling stories to inform business decisions has never been greater.\n\nYou’ll learn in-demand skills used by professional data scientists including databases, data visualization, statistical analysis, predictive modeling, machine learning algorithms, and data mining. You’ll also work with the latest languages, tools,and libraries including Python, SQL, Jupyter notebooks, Github, Rstudio, Pandas, Numpy, ScikitLearn, Matplotlib, and more. Upon completing the full program, you will have built a portfolio of data science projects to provide you with the confidence to excel in your interviews. You will also receive access to join IBM’s Talent Network where you’ll see job opportunities as soon as they are posted, recommendations matched to your skills and interests, and tips and tricks to help you stand apart from the crowd. This program is ACE® and FIBAA recommended —when you complete, you can earn up to 12 college credits and 6 ECTS credits. This 10 course program include: 1) What is Data Science?, 2) Tools for Data Science, 3) Data Science Methodology, 4) Python for Data Science, AI & Development, 5) Python Project for Data Science, 6) Databases and SQL for Data Science with Python, 7) Data Analysis with Python, 8) Data Visualization with Python, 9) Machine Learning with Python, and 10) Applied Data Science Capstone.

Enroll today (teams & executives are welcome): https://tinyurl.com/fv79vc3e 

Download your free Data Science  - Career Transformation Guide.

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Much career success, Lawrence E. Wilson - AI Academy (share with your team) https://tinyurl.com/hh7bf4m9 

“ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (new book on Amazon Audible & Kindle)

Friends, the new “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle) book is the latest entry to the “Transformative Innovation” series. AI in all of its manifestations represents a bona fide “generational change” of human society as we know it. AI, like many of the historic innovations of the past, has the unsurpassed potential to impact the human race for both good and evil.  All humanity has a vested interest in ensuring the impact of AI is positive. The alternative is unfathomable: A dystopian environment leading to the destruction of mankind in a Noahic antediluvian episode of human culture by the hands of man himself or the divine, omnipotent hand of our sovereign Creator.

This book has a “5 Star” rating on Amazon. Reader comment “If you're curious about how AI works, how it's changing the world, and what might happen next, this book is great. It's a bit like a guide that helps you understand this complex topic. It's a journey that could open your mind to new ideas and change how you see the world.”

Table of Contents:

I - Introduction

II - The Birth of Artificial Intelligence (AI)

III - AI Research and Development

IV - The AI Quantum Multiplier

V - Technological Synergy

VI - The Ethics of Artificial Intelligence

VII - The Global Race for AI Supremacy

VIII - Understanding Artificial General Intelligence (AGI)

IX - Approaches to AGI Development

X - The Era of Artificial Superintelligence (ASI)

XI - Ensuring Safety in AGI and ASI

XII  - AGI and ASI Impact on Society and the Economy

XIII - AI Singularity (“Technological Singularity”)

XIV - Looking Beyond the Singularity

XV - Conclusions

Our journey will examine the birth of AI, the pending transition to Artificial General Intelligence, the era of Artificial Super Intelligence and thoughts on the possibility of AI (or “Technological”) Singularity. Although some of these topics have verifiable, concrete answers, overall, the pendulum rapidly swings from the domain of the known to the domain of the unknown and speculative in the concluding chapters. Our commitment is to amplify the known (factual) elements of Artificial Intelligence and only when necessary delve into the realm of the unknown (subjective) aspects of AI Singularity … and beyond.

Join us for a journey that will transform your thinking and possibly your life!

Order today:

Audible (https://tinyurl.com/3pswj5sx) 

Kindle (https://www.amazon.com/dp/B0CG536WDN)   

3 Book Series: “Transformative Innovation”: 

 

1 - ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity (Audible) (Kindle)

2 - ChatGPT - The Era of Generative Conversational AI Has Begun (Audible) (Kindle) 

3 - The Race for Quantum Computing  (Audible) (Kindle) 

Thank you for dropping a brief review on this book’s Audible or Kindle page.

Regards, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9