The Power of Statistics (Google)

Colleagues, in the “The Power of Statistics” from Google you will discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional. By the end of this course, you will: Describe the use of statistics in data science. Use descriptive statistics to summarize and explore data. Calculate probability using basic rules. Model data with probability distributions. Describe the applications of different sampling methods. Calculate sampling distributions. Construct and interpret confidence intervals. Conduct hypothesis tests. You will also gain hands-on skills with Probability Distribution, Statistics, Probability and Statistics, Sampling (Statistics), Descriptive Statistics, Probability, Statistical Methods, Statistical Hypothesis Testing, Analytics, Advanced Analytics, Statistical Programming, Statistical Inference, Statistical Analysis, A/B Testing, Data Analysis, and Data Science. And use skills in Python Programming, Introduction to Statistics with Python, Probability, Sampling, Confidence Intervals, Introduction to Hypothesis Testing, and Capstone Project.

Enroll today!: Teams and executives are welcome: https://imp.i384100.net/9VjY14

Recommended Readings: The “Data-Driven Organizations” series.

1 - The Promise of Data-Driven Decision-Making  (Audible) (Kindle)

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

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much career success from the Data Science Academy (please subscribe and share with your colleagues)

Data Analysis: Basic Probability and Statistics (HarvardX)

Colleagues, the “Data Analysis: Basic Probability and Statistics” training from HarvardX will increase your quantitative reasoning skills through a deeper understanding of probability and statistics, with the engaging "Fat Chance" approach that's made this course a favorite among learners worldwide.You will learn to: Solve combinatorial counting problems, Solve problems using basic and advanced probability, The normal distribution and its many statistical applications, and Recognize common fallacies in probability, as well as some of the ways in which statistics are abused or simply misunderstood. Skill-based lessons include: 1) Basic Counting - Counting Numbers, Large Numbers, The Multiplication Principle, More on the Multiplication Principle and Factorials, The Subtraction Principle, Advanced Counting; 2) Counting Collections - Binomial Coefficients, Applications of Collections, Multinomials, Collections with Repetition; 3) Basic Probability - Flipping Coins, Rolling Dice, Playing Poker, Distributions of Bridge Hands,; 4) Expected Value - Chuck-A-Luck, Slot Machines, Strategizing; 5) Conditional Probability - The Monty Hall Problem, Set-Up and Examples, Elections; 6) Bernoulli Trials, The Gambler's Ruin; 7) The Normal Distribution - Games, Games: Examples and Variance, Iterating Games, The Normal Distribution - Part 1 and 2.

Enroll today!: Teams and executives are welcome: https://edx.sjv.io/gReEY5

Listen today via Amazon Audible (https://tinyurl.com/ydbyh2t9) 

Or read now on Kindle (https://tinyurl.com/hptundzs) 

These books are part of the “Data-Driven Organizations” series.

1 - The Promise of Data-Driven Decision-Making  (Audible) (Kindle)

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

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Order today and much career success from the Data Science Academy (please subscribe and share with your colleagues)

Implementing Data Engineering Solutions Using Azure Databricks (Certification Exam DP-750)

Colleagues, the DP-750 exam validates the modern Azure Databricks toolkit: Lakeflow Spark Declarative Pipelines for low-code pipeline authoring, Lakeflow Connect for managed ingestion, Lakeflow Jobs for orchestration, Unity Catalog for fine-grained access control and lineage, AI/BI Genie for natural-language data discovery, Databricks Asset Bundles for CI/CD, and Photon for query acceleration. Newer features the exam emphasizes--liquid clustering, attribute-based access control, deletion vectors, and structured streaming with Auto Loader--reflect where production data engineering is heading. This certification matters because organizations are consolidating fragmented data stacks onto governed lakehouses, and Microsoft is signaling that Azure Databricks fluency is now a distinct, certifiable specialty alongside Fabric and Synapse skills. Learn How To: Provision and configure an Azure Databricks workspace, choose appropriate compute for the task at hand, and work fluently in notebooks across SQL and Python. Design and build the Unity Catalog object model--catalogs, schemas, volumes, tables, views, materialized views, foreign catalogs--with naming and isolation patterns that survive contact with production. Secure and govern data using the full Unity Catalog toolkit: privilege grants, row filters, column masks, attribute-based access control, service principals, managed identities, Key Vault-backed secrets, lineage tracking, audit logs, retention policies, and Delta Sharing. Reason about lakehouse data design--Delta Lake fundamentals, file formats, partitioning, liquid clustering, slowly changing dimensions, temporal tables, and the medallion architecture--as the conceptual backbone of every pipeline. Ingest data through every supported path: Lakeflow Connect, notebook-based ingestion, SQL methods, change data capture, Spark Structured Streaming, Azure Event Hubs, and Auto Loader. Cleanse, profile, and transform data using the full transformation toolkit, then enforce quality with validation checks, schema management, and pipeline expectations. Build and ship production pipelines using Lakeflow Spark Declarative Pipelines and Lakeflow Jobs, with proper Git workflow, a complete testing strategy, and Asset Bundles for deployment via CLI or REST API. Monitor, troubleshoot, and optimize workloads using the Spark UI, DAG analysis, OPTIMIZE/VACUUM, and Azure Monitor with Log Analytics. Approach the DP-750 exam with the conceptual reasoning skills its scenario-based question format demands. Lessons address: 1) Foundations: Workspaces, Compute, and Notebooks, 2) Unity Catalog: Structure, Security, and Governance, 3) Designing Data for the Lakehouse, 4) Ingesting and Transforming Data, and 5) Production Pipelines and Operations.

Enroll today!: Teams and executives are welcome: https://tinyurl.com/yyyrbzp3

Listen today via Amazon Audible (https://tinyurl.com/ydbyh2t9) 

Or read now on Kindle (https://tinyurl.com/hptundzs) 

This book is part of the “Data-Driven Organizations” series.

1 - The Promise of Data-Driven Decision-Making  (Audible) (Kindle)

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

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much career success from the Data Science Academy (please subscribe and share with your colleagues)

Discrete Math for Computer Science - Algorithms & Recursion

Colleagues, the “Discrete Math for Computer Science - Algorithms & Recursion” focuses on the mathematical foundations behind algorithms, efficiency, and recursive problem solving, building on the logic and counting techniques developed in earlier courses. It introduces key ideas from number theory and shows how they naturally lead to efficient algorithms used throughout computer science. The course begins with modular arithmetic, divisibility, and greatest common divisors, leading to classic algorithms such as the Euclidean algorithm and its extended form. These concepts are then applied to practical problems in cryptography, including modular exponentiation, key exchange, and public-key encryption, illustrating how abstract mathematics enables secure communication. You will learn to: Analyse algorithm efficiency using asymptotic growth and mathematical reasoning. Apply number theory concepts to algorithms and basic cryptographic systems. Design and reason about recursive algorithms using induction and recurrence relations. Gain high-demand skills: Computational Thinking, Key Management, Encryption, Applied Mathematics, Mathematical Theory and Analysis, Algorithms, Theoretical Computer Science, Combinatorics, Arithmetic, and Cryptography. Training lessons address: 1) Modular Arithmetic, 2) Greatest Common Divisor, 3) Cryptography, 4) Algorithms, 5) Induction, and 6) Recursion.

Enroll today!: Teams and executives are welcome: https://imp.i384100.net/k4PG0n 

Recommended Readings: The “Data-Driven Organizations” series.

1 - The Promise of Data-Driven Decision-Making  (Audible) (Kindle)

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

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much career success from the Data Science Academy (please subscribe and share with your colleagues)

Statistics and Data Science - Methods Track (MITx)

Colleagues, the “MicroMasters® program in Statistics and Data Science (SDS)” program was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS). It is a multidisciplinary approach consisting of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam. Skill-based training modules include: 1) Probability - The Science of Uncertainty and Data, 2) Machine Learning with Python: from Linear Models to Deep Learning, 3) Fundamentals of Statistics, 4) Learning Time Series with Interventions, and 5) Capstone Exam in Statistics and Data Science.

Enroll today!: Teams and executives are welcome: https://edx.sjv.io/6kDJRr 

Recommended Reading: “Data-Driven Organizations” series.

1 - The Promise of Data-Driven Decision-Making  (Audible) (Kindle)

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

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much career success from the Data Science Academy (please subscribe and share with your colleagues)

Google Data Analytics Professional Certificate

Colleagues, in the “Google Data Analytics Professional Certificate” certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required. Gain an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job. Learn key analytical skills (data cleaning, analysis, & visualization) and tools (spreadsheets, SQL, Python, Tableau). Understand how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and Python. Learn how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms. You will acquire high-demand skills in Interviewing, Data Structures, Data Presentation, Object Oriented Programming (OOP), Data Analysis, Data Literacy, Data Ethics, Data Visualization, Interactive Data Visualization, Data Validation, Web Presence, Sampling (Statistics), Data Cleansing, Data Storytelling, Spreadsheet Software, and Stakeholder Communications. Applied Learning Project: Includes over 180 hours of instruction and hundreds of practice-based assessments, which will help you simulate real-world data analytics scenarios that are critical for success in the workplace. The content is highly interactive and exclusively developed by Google employees with decades of experience in data analytics. Through a mix of videos, assessments, and hands-on labs, you’ll get introduced to analysis tools and platforms and key analytical skills required for an entry-level job. Training modules will cover: 1) Foundations: Data, Data, Everywhere, 2) Ask Questions to Make Data-Driven Decisions, 3) Prepare Data for Exploration, 4) Process Data from Dirty to Clean, 5) Analyze Data to Answer Questions, 6) Share Data Through the Art of Visualization, 7) Introduction to Data Analysis Using Python, 8) Google Data Analytics Capstone: Complete a Case Study, and 9) Accelerate Your Job Search with AI.

Enroll today!: Teams and executives are welcome: https://imp.i384100.net/nLaGo6 

Recommended Readings: The “Data-Driven Organizations” series.

1 - The Promise of Data-Driven Decision-Making  (Audible) (Kindle)

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

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much career success from the Data Science Academy (please subscribe and share with your colleagues)

Listen to or read the newest “Data-Driven Organizations” Amazon Audible & Kindle Book Series

Listen to or read the newest “Data-Driven Organizations” Amazon Audible & Kindle Book Series 

Friends, greetings. Here are the newest “Data-Driven Organizations” audio and ebooks for your reading-listening pleasure (https://tinyurl.com/hh7bf4m9).

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

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

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

Data Engineer - Discover Your Path to Career Success (2026)

Colleagues, did you know that according to Mordor Intelligence “The Big Data Engineering Services Market size is estimated at USD 91.54 billion in 2025, and is expected to reach USD 187.19 billion by 2030, at a CAGR of 15.38% during the forecast period (2025-2030)”? For more details see the Indeed article “Data Engineer Job Description: Top Duties and Qualifications” (March 2025).

Job Titles: Data Engineer, Senior Data Engineer, Cloud Data Engineer, Big Data Engineer, Data Architect, ETL (Extract, Transform, Load) Developer, Data Pipeline Engineer, Analytics Engineer, Machine Learning Engineer (with Data Engineering focus), DataOps Engineer

Salaries: BuiltIn, Glassdoor, Indeed, Levels.fyi, PayScale, ZipRecruiter (will vary by experience level & location)

Career Opportunities: Dice, Indeed, Kaggle, LinkedIn, Monster, Simply Hired, Zip Recruiter

Certification and Training programs - add an extra 5%-10% to your compensation (enroll now):

• Azure Databricks & Spark For Data Engineers (PySpark/SQL) (Udemy)

• Big Data Architect Masters Program (Udemy)

• Cloud Database Engineer Specialization (Google)

• Database Engineer Professional Certificate (Meta)

• IBM Data Engineering Professional Certificate (IBM)

• Introduction to Data Engineering (Datacamp)

• Introduction to Data Engineering (IBM)

Begin your path to success today, Lawrence E. Wilson - Data Science Academy (share with colleagues & friends)