Insights, tutorials, and career guidance to help you succeed in data analytics
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 comprehensive 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 comprehensive 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 for mentor review.
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.
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