Insights, tutorials, and career guidance to help you succeed in data analytics
A step-by-step path to becoming a data analyst in 2026 — spreadsheets, SQL, statistics, Python and BI tools, with a realistic timeline.
How many projects you need, choosing datasets that show business impact, structuring a case study, and the mistakes that get portfolios ignored.
Quantisation made inference 2.2 times slower. Pruning to 90 percent sparsity saved nothing. Distillation lost at every alpha across two seeds. ONNX export was 4.4 times faster. All four timed on one machine.
TF-IDF bigrams fit in 0.07 seconds and landed within 0.7 points of a GRU that took 10.1. A frozen transformer lost to bag of words. An SVM beat the convolutional network. Rules got 87 percent with no training.
Six classifiers scoring up to 0.9870 on held-out digits collapsed to 0.5074 when the images moved one pixel, and to 0.1667 at two. Augmentation cost 17 points on clean data and bought nothing.
On imbalanced data, accuracy is the metric that hides failure. Read a confusion matrix properly, choose between precision and recall, and set a threshold your business can live with.
Scored 0.94 in the notebook and 0.61 in production? That is usually a splitting problem, not a modelling one. Leakage, temporal order and grouped data, with working code.
Join our challenge to build deep learning models from scratch and beat SOTA on CIFAR-10/100. Part 1: Understanding datasets, benchmarks, and building our baseline.
Master data augmentation: Cutout, AutoAugment, RandAugment, Mixup, and CutMix. Boost accuracy from 84% to 93% with complete PyTorch implementations.
Build ResNet, WideResNet, PyramidNet, and DenseNet from scratch. Complete implementations with skip connections and dense blocks.
Advanced training techniques: label smoothing, knowledge distillation, stochastic depth, SWA, and EMA. Push to 97.5% accuracy.
Combine multiple models for maximum accuracy: model averaging, snapshot ensembles, TTA, and diverse architectures. Reach 98.5%.
The ultimate push to 99%+: PyramidNet-272, ShakeDrop, extended training, and final ensemble. Complete SOTA implementation.
A full guide to choosing the right tools for your data analytics journey. Learn when to use each tool and how to build your skills progressively.
Learn the 10 most common SQL mistakes that data analysts make and how to avoid them. Includes practical examples and best practices.
Master practical SQL queries for e-commerce analysis. Learn to build sales reports, customer segmentation, and product analytics.
Build a strong foundation in data concepts, business thinking, and Excel-based analytics. Master data types, KPIs, and Excel skills.
Master SQL from basics to advanced queries. Learn SELECT, JOINs, aggregations, subqueries, and work with real databases.
Take SQL to the next level with window functions, CTEs, complex JOINs, and performance optimization techniques.
Master descriptive statistics, probability, distributions, sampling, and confidence intervals. The mathematical backbone of data analysis.
Master hypothesis testing, correlation analysis, A/B testing, and EDA techniques. Learn to tell stories with data.
Learn R from scratch - RStudio, data types, vectors and data frames, the tidyverse, dplyr verbs and a complete data wrangling pipeline.
Master ggplot2, stringr, lubridate and advanced dplyr. Build publication-quality charts and write production-grade R code.
The USP skill - build and deploy live, interactive dashboards. UI components, reactivity, shinydashboard, plotly, DT and shinyapps.io deployment.
Microsoft Power BI Desktop, Power Query, star schema modelling and DAX measures. Plus when to pick Power BI vs R Shiny.
Tableau Desktop and Public - charts, calculated fields, parameters, dashboards, filter actions and publishing to Tableau Public.
End-to-end capstone project. Scope, source data, build SQL + R pipelines and prototype your R Shiny dashboard to a standard somebody else could pick up and run.
Deploy your capstone, build a portfolio, write an ATS-friendly resume, optimise LinkedIn, and ace technical and behavioural interviews.
Understand the differences between SQL and NoSQL databases. Learn when to use each with real-world examples from Netflix, Uber, and Amazon.
Weeks 1 to 12 above follow the Data Analyst & BI curriculum in order. Read them in sequence, or see how the twelve weeks fit together.
Prefer the guided version? View the full programme
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