Data Science (HarvardX)

Colleagues, the “Data Science” program from  HarvardX prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The program covers concepts such as probability, inference, regression, and machine learning and helps you develop an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document preparation with RStudio. In each course, we use motivating case studies, ask specific questions, and learn by answering these through data analysis. Case studies include: Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, Building a Baseball Team (inspired by Moneyball), and Movie Recommendation Systems. You will learn Fundamental R programming skills, Statistical concepts such as probability, inference, and modeling and how to apply them in practice, Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr, Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio, Implement machine learning algorithms, and In-depth knowledge of fundamental data science concepts through motivating real-world case studies. The 9 courses that comprise this program include: 1) R Basics, 2) Visualization, 3) Probability, 4) Inference and Modeling, 5) Productivity Tools, 6) Wrangling, 7) Linear Regression, 8) Building Machine Learning Models, and 9) Capstone Project.

Enroll today!: Teams and executives are welcome: https://edx.sjv.io/9LVMxy

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)

ChatGPT + Excel: AI-Enhanced Data Analysis & Insight Specialization

Colleagues, in the “ChatGPT + Excel: AI-Enhanced Data Analysis & Insight Specialization” program you will learn to: 1) Turn Text into Data: Extract structured datasets from unformatted text or images with ChatGPT.Example: Convert a messy list of LinkedIn posts into a clean table with post titles, dates, and engagement stats, 2) Create Formulas: Generate complex formulas tailored to solve specific challenges, even if you don’t know how to write them from scratch, 3) Design Stunning Visualizations: Build engaging charts and graphs directly from your prompts or replicate a chart from a photo. Example: Transform a hand-drawn sales graph into a professional Excel chart in seconds, 4) Merge and Unify Datasets: Seamlessly combine data from multiple Excel sheets or CSV files into one cohesive table. Example: Integrate customer data with sales records to track performance by demographic or region, 5) Automate with VBA Scripts: Create powerful macros with ChatGPT to eliminate repetitive tasks and enhance productivity, 6) Turn Images Into Excel Solutions: Use ChatGPT to turn a photo of a whiteboard into an Excel-based project plan or inventory tracker. Example: Snap a photo of a brainstorming session and convert it into a structured Excel sheet, and 7) Craft Data-Driven Stories: Build narratives that make your data compelling and actionable for presentations. Skill-based training modules include: ChatGPT + Excel: Master Data, Make Decisions, Tell Stories, ChatGPT + Excel: Master AI-driven Formulas & Visualizations, and Prompt Engineering for ChatGPT. You will also gain highly marketable skills with: Plot (Graphics), Data Visualization, Prompt Patterns, Spreadsheet Software, Data Presentation, LLM Application, Data Analysis, AI Literacy, Excel Formulas, Consolidation, Data Storytelling, Excel Macros, Data Synthesis, Storytelling, AI powered creativity, and AI Enablement. And you will use tools: Prompt Engineering, Microsoft Excel, Generative AI, and ChatGPT. 

Enroll today!: Teams and executives are welcome: https://imp.i384100.net/VOK7aJ 

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)

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)