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Plotting in Python with Matplotlib for Data Analysts (Data Analyst Series, Part 16)
Turn a pandas summary into a chart. Draw a bar, a line and a scatter with Matplotlib, label them, and save them to share, using the same sales data from Part 15.
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Pandas Essentials for Data Analysts: DataFrames, Filtering and Group By (Data Analyst Series, Part 15)
The Python library that reads a file too big for a spreadsheet and answers in a line. A beginner walkthrough of DataFrames, filtering, groupby and missing data in pandas 3.0.
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Your First Dashboard in Power BI or Looker Studio (Data Analyst Series, Part 14)
A plain, beginner walkthrough of building your first dashboard in Looker Studio and Power BI, from connecting data to wiring filters, plus which tool to start with and why.
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Choosing the Right Chart and Avoiding the Ones That Mislead (Data Analyst Series, Part 13)
The chart you pick decides what people believe. Here is how to match bar, line, pie and scatter to your question, and how to spot the truncated axis and dual-axis tricks before they reach your slide.
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Correlation vs Causation: The Traps Between Two Columns (Data Analyst Series, Part 12)
Two columns move together on a chart and someone declares one drives the other. Here is how to tell correlation from causation, spot the confounder, and avoid Simpson’s paradox before it reaches your slide deck.
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Statistics a Data Analyst Actually Uses (Data Analyst Series, Part 11)
The working statistics an analyst uses daily: mean versus median, the standard deviation, the normal curve and its 68, 95, 99.7 rule, percentiles, and the sampling ideas behind margins of error and significance.
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Exploratory Data Analysis: Finding the Story in Your Data (Data Analyst Series, Part 10)
Exploratory data analysis is the poke-around phase where a clean table turns into a story. Learn to summarise, read distributions, spot outliers and find relationships before you report anything.
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Cleaning Messy Data: Missing Values, Duplicates and Inconsistent Labels (Data Analyst Series, Part 9)
Real exports arrive broken. Learn to profile a dataset and fix the five recurring problems, missing values, duplicates, inconsistent text, wrong types and outliers, in a repeatable order.
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SQL GROUP BY, Aggregations and Window Functions Explained (Data Analyst Series, Part 8)
Collapse many rows into one number with GROUP BY and the aggregate functions, filter groups with HAVING, then keep every row while ranking and running totals with window functions.
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SQL Joins: Combining Tables with INNER and LEFT JOIN (Data Analyst Series, Part 7)
Learn SQL joins the practical way. INNER JOIN keeps matches, LEFT JOIN keeps every row, and the ON versus WHERE trap that quietly drops the rows you need.
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SQL SELECT, WHERE and ORDER BY: Your First Queries (Data Analyst Series, Part 6)
Write your first real SQL queries. SELECT to choose columns, WHERE to filter rows, ORDER BY to sort and LIMIT to cap, all against a five-row sample table.
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How Data Is Stored: Tables, Types, CSV Files and Databases (Data Analyst Series, Part 5)
Tables, data types, CSV files and databases explained for beginners, so the SQL in the next parts reads like plain sentences instead of a puzzle.
Architect’s Toolkit
PJ’s Tools
VMware Cloud Foundation
- VCF Documentation
- VCF 9 Planning & Preparation Workbook
- VCF Bill of Materials (BoM)
- VMware Compatibility Guide
- VMware Interoperability Matrix
- VMware Configuration Maximums
- VMware Ports & Protocols
- VMware Hands-on Labs
- RVTools Download
Nutanix
AI & Cloud-Native Platform
- NVIDIA Build (Model Catalog)
- NVIDIA AI Enterprise Reference Architecture
- NVIDIA NIM Performance Benchmarking
- NVIDIA NGC Catalog
- NeMo Microservices Helm Chart
- Helm Charts Repository
- Hugging Face Models
Architecture & Design
About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
Recommendations & Endorsements
Pranay has very deep technical knowledge and exceptional capabilities. He has strong ability to well-manage complex situations and deliver the customer expectations with high standards of satisfaction. We worked together in one of most complex migration projects and Pranay delivered flawlessly.
Pranay was my VMware Architect on one of our financial accounts at IBM. He was instrumental in documenting and recommending our reference architecture. As a Chief Architect, you are only as good as your team that supports you. Pranay was top notch.
Pranay is proactive, result oriented, responsible and technically sound. Always ready to put all his energy to get the job done. Exceptional troubleshooting skills in Windows Server, virtualization and VMware — a great asset to any company.
Pranay is one of the most talented and dedicated persons I have ever worked with. His vast knowledge from different domains enables him to discuss and help on almost every topic in IT. Needless to say, he is expert of experts in VMware.
DrJha