Data Analyst Roadmap

Introduction to Data Analytics

What is data analytics, roles, and industry use cases.

Types of Analytics

Descriptive, diagnostic, predictive, prescriptive analytics.

Checkpoint — Analytics Basics

Key Data Concepts

Data collection, cleanup, exploration, visualization.

Statistical Foundations

Central tendency, dispersion, distributions.

Checkpoint — Data Foundations

Excel for Analysis

Excel Functions

IF, VLOOKUP, HLOOKUP, CONCAT, TRIM, AVERAGE, SUM.

Reporting & Charts

Pivot tables, charting, dashboards.

Checkpoint — Excel Mastery

Programming Skills

Learn SQL

Queries, joins, aggregations, filtering data.

Python or R

Python, R fundamentals for data analysis.

Checkpoint — Programming Basics

Data Collection

Databases, CSV files, APIs, web scraping.

Data Cleanup

Handling missing data, duplicates, outliers.

Checkpoint — Data Preparation

Data Analysis Techniques

Descriptive Analysis

Mean, median, mode, variance, standard deviation.

Statistical Analysis

Hypothesis testing, correlation, regression.

Checkpoint — Statistical Analysis

Data Visualization

Visualization Tools

Tableau, Power BI, Matplotlib, Seaborn, ggplot2.

Charting Techniques

Bar, line, scatter, histogram, heatmap, pie charts.

Checkpoint — Data Storytelling

Advanced Topics

Machine Learning Basics

Supervised & unsupervised learning, evaluation.

Big Data Concepts

Hadoop, Spark, MapReduce, parallel processing.

Checkpoint — Advanced Analytics

Deep Learning (Optional)

Neural networks, CNNs, RNNs, TensorFlow, PyTorch.

Practice & Portfolio

Projects, Kaggle competitions, real-world datasets.

Checkpoint — Industry Ready Data Analyst