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April 15, 2026

Roadmap 2026

Learn Data Analytics

Master the process of collecting, organizing, and analyzing data to uncover insights that support better decision making. This path covers everything from fundamental statistics to advanced predictive analytics.

✔ Understand trends, optimize performance, and predict future outcomes.
✔ Fundamental concepts like data preprocessing and statistical modeling.
✔ Mastery of industry-standard tools: Excel, Tableau, Python, and SQL.
Data Analytics

01. Data Foundations

Introduction to Data Analytics

The foundation of how data flows from raw input to strategic business intelligence.

Workflow of Data Analytics Life Cycle Phases of Analytics Model Planning for Data Analytics Real-time Analytics Platforms What is a GAN?

02. Statistics & Probability

Statistics in Data Analytics

The backbone of interpreting data to identify patterns, trends, and relationships.

Intro to Statistics in Analytics Descriptive vs. Inferential Types of Statistical Data Levels of Measurement Descriptive Statistics Guide Common Probability Distributions Discrete Distributions Continuous Distributions Measures of Central Tendency Measures of Variability Basic Bivariate Analysis Central Limit Theorem Skewness & Kurtosis Hypothesis Testing Z tests and T Tests ANOVA & Chi-Square Test Correlation & Covariance Bias, Variance, Tradeoff How to Learn Statistics

03. Business Intelligence

Tableau in Data Analytics

Advanced data visualization tool used for interactive dashboards and reports.

Various Data Sources Interface & Navigation Dimension vs. Measure Calculated Fields & Parameters LOD Expressions Predictive Analysis in Tableau Dashboard Layout Best Practices COVID-19 Data Viz How to Learn Tableau

04. Predictive Modeling

Mastering Machine Learning

Using algorithms to find patterns, automate decisions, and make smarter predictions.

ML with Python Intro Basics of Machine Learning Regression Analysis in ML Classification in ML Clustering Algorithms Data Preprocessing Feature Scaling & Norm Split Data (Train/Test) Regression Theory Linear Regression Variants Decision Tree Regression Logistic Regression Naive Bayes Classifiers Decision Tree Classifiers Reinforcement Learning How to Learn ML

05. Data Analysis with Excel

Spreadsheet Mastery

Core process of cleaning, transforming, and summarizing data using Microsoft Excel.

Excel Interface & Functions PivotTables & PivotCharts Data Cleaning Implementation Conditional Formatting VLOOKUP / XLOOKUP Logic Advanced Charting Techniques

06. Future Tech Bonus

AI & Generative AI Insights

Strengthen your overall understanding of key concepts in AI, Machine Learning, and LLMs.

AI vs ML vs DL AI vs Machine Learning AI Concepts Overview Chat GPT vs Gemini Generative AI Tools Generative AI Models Gen AI vs Predictive AI

Expert Knowledge Base

What is the difference between AI and Data Analytics?

Data Analytics examines history for insights; AI builds systems that learn and act autonomously.

Is coding required for Data Analytics?

Yes, Python and SQL are standards for data manipulation and building machine learning models.

What are the 4 types of Data Analytics?

Descriptive (what happened), Diagnostic (why), Predictive (future), and Prescriptive (how to respond).

How does Gen AI help Analysts?

It automates code, summarizes technical reports, and generates synthetic data for testing.