Category: College Passout Series
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Data Analyst Portfolio and Landing Your First Job (Data Analyst Series, Part 22)
A hiring panel gives your application about two minutes. Here is how to build three portfolio projects that survive that read, and how to run the job hunt itself like a pipeline you can diagnose.
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Ethics, Privacy and Bias in Data Analysis (Data Analyst Series, Part 21)
Removing names does not make a dataset anonymous, and a technically correct query can still produce a badly biased answer. Here is what an analyst has to check before publishing a number.
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Data Modeling Basics: Fact Tables, Dimensions and Grain (Data Analyst Series, Part 20)
Three people quoted three different revenue numbers for the same month, and none of them had made a mistake. This part covers fact tables, dimensions, grain and slowly changing dimensions, so your numbers stay consistent from one query to the next.
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Presenting Data Analysis to Stakeholders Without Losing the Room (Data Analyst Series, Part 19)
An analyst spent eleven of her fifteen minutes explaining how she cleaned the table, and her recommendation died in the room. This part covers the order, the one sentence and the appendix that get analysis acted on.
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A/B Testing and Experiments Explained Simply (Data Analyst Series, Part 18)
A team shipped a new checkout button, saw a 9 percent lift on day two, and rolled it out. Six weeks later the number was worse than before. This part shows how experiments actually work, and how to stop reading noise as a win.
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Metrics and KPIs: How to Define What Actually Matters (Data Analyst Series, Part 17)
Three dashboards, three different numbers for active users, and all three were right. This part shows you how to define a metric so it means one thing, and how to tell a real KPI from a number that just looks busy.
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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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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.
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