Data Analyst Roadmap
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SQL
150+ SQL Interview Questions:
Here is the complete excel sheet of over 300+ questions for SQL which will help you ace any interview : Link.
Even practice one question a day will help you master your skills.
SQL Theory Questions
For SQL Theory Questions, I used this resource : Link which has 100+ SQL Theory questions and literally everything you need to prepare for your interview. Use this and you will be set for life in SQL Theory questions.
SQL Cheatsheet : Link
PYTHON
Python For Data Analyst : Link
The link contains everything you need to know for Python for Data Science.
LEARN PYTHON :
Python in 30 days : Link
Free Python Youtube : Link
Python 40 Coding Questions for Data Analyst :
Array
Interval
String
Matrix
MATHS AND STATISTICS :
120 Data Analyst Interview Questions : Link
99 Data Analyst Interview Questions : Link
DATA VISUALIZATION :
Free Data Visualization Courses : Link
DATA ANALYTICS PROJECTS :
Here is the link for Data Analytics projects
6-Month Data Analyst Roadmap
Month 1: Foundation Building
Week 1: Getting Started & Environment Setup
Focus: Python Fundamentals
Set up Python environment (Anaconda, Jupyter Notebook)
Begin "Python in 30 days" course - Days 1-7
Learn basic Python syntax, variables, data types
Install essential libraries: pandas, numpy, matplotlib
Daily Practice: 1 basic Python exercise
Goal: Write your first "Hello World" and basic calculations
Week 2: Python Data Structures & Control Flow
Focus: Core Python Programming
Continue "Python in 30 days" course - Days 8-14
Master lists, dictionaries, tuples, sets
Learn loops, conditionals, functions
Daily Practice: 1 Python coding question from the 40 coding questions resource
Goal: Build simple programs using control structures
Week 3: Introduction to SQL
Focus: SQL Basics
Install SQL environment (MySQL, PostgreSQL, or SQLite)
Learn SELECT, FROM, WHERE clauses
Understand database structure, tables, rows, columns
Practice basic queries on sample datasets
Daily Practice: 2-3 basic SQL queries
Goal: Write simple SELECT statements confidently
Week 4: SQL Fundamentals Continued
Focus: Essential SQL Operations
Learn JOINs (INNER, LEFT, RIGHT, FULL OUTER)
Master GROUP BY, HAVING, ORDER BY
Understand aggregate functions (COUNT, SUM, AVG, MIN, MAX)
Daily Practice: 3-4 SQL questions from the 150+ SQL interview questions
Goal: Perform basic data analysis using SQL
Month 2: Intermediate Skills Development
Week 5: Advanced Python for Data Analysis
Focus: Pandas & NumPy
Learn pandas fundamentals: DataFrames, Series
Data loading, cleaning, and basic manipulation
NumPy for numerical computations
Daily Practice: 1 pandas exercise + 1 SQL question
Goal: Load and manipulate datasets using pandas
Week 6: Data Cleaning & Preprocessing
Focus: Data Preparation
Handle missing values, duplicates, outliers
Data type conversions and formatting
String manipulation in both Python and SQL
Daily Practice: Clean one messy dataset + 2 SQL questions
Goal: Transform raw data into analysis-ready format
Week 7: Advanced SQL Queries
Focus: Complex SQL Operations
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Subqueries and CTEs (Common Table Expressions)
CASE statements and conditional logic
Daily Practice: 3-4 intermediate SQL questions from interview resources
Goal: Write complex analytical queries
Week 8: SQL Performance & Optimization
Focus: Efficient Query Writing
Understanding indexes and query optimization
SQL best practices and performance tuning
Review SQL theory questions (start with 20 questions)
Daily Practice: Optimize 2 slow queries + theory review
Goal: Write efficient, optimized SQL code
Month 3: Statistics & Advanced Analysis
Week 9: Mathematics & Statistics Foundations
Focus: Statistical Concepts
Descriptive statistics (mean, median, mode, variance)
Probability distributions and hypothesis testing
Correlation and regression basics
Start reviewing 120 Data Analyst Interview Questions
Daily Practice: 2 statistics problems + 1 Python exercise
Goal: Understand core statistical concepts
Week 10: Python Statistical Analysis
Focus: Statistics with Python
Use scipy and statsmodels libraries
Implement statistical tests in Python
Data distribution analysis and visualization
Daily Practice: 1 statistical analysis in Python + 2 SQL questions
Goal: Perform statistical analysis using Python
Week 11: Introduction to Data Visualization
Focus: Basic Visualization
Learn matplotlib and seaborn
Create basic charts: bar, line, scatter, histograms
Start free data visualization courses
Daily Practice: Create 2 different chart types + 1 SQL question
Goal: Build clear, informative visualizations
Week 12: Advanced Data Visualization
Focus: Interactive & Advanced Charts
Learn plotly for interactive visualizations
Dashboard concepts and best practices
Color theory and design principles
Daily Practice: Create 1 interactive chart + review 3 theory questions
Goal: Design professional-looking visualizations
Month 4: Business Intelligence Tools
Week 13: Introduction to BI Tools
Focus: BI Fundamentals
Choose a BI tool (Power BI, Tableau, or Looker)
Learn interface and basic functionality
Connect to data sources
Daily Practice: Build 1 simple dashboard + 2 SQL questions
Goal: Create your first BI dashboard
Week 14: Advanced BI Development
Focus: Dashboard Creation
Advanced chart types and customizations
Filters, parameters, and interactivity
Best practices for dashboard design
Daily Practice: Enhance existing dashboard + Python practice
Goal: Build an interactive, professional dashboard
Week 15: Data Modeling & ETL
Focus: Data Architecture
Understand data warehousing concepts
Learn ETL processes and data pipelines
Practice data modeling techniques
Daily Practice: Design 1 data model + SQL optimization
Goal: Design efficient data structures
Week 16: BI Project Integration
Focus: End-to-End BI Solution
Combine SQL, Python, and BI tools
Build a complete analytics project
Documentation and presentation skills
Daily Practice: Work on integrated project + theory review
Goal: Complete one full BI project
Month 5: Advanced Topics & Specialization
Week 17: Advanced Python Libraries
Focus: Specialized Tools
Learn scikit-learn for machine learning basics
Time series analysis with pandas
API integration and web scraping
Daily Practice: 2 advanced Python exercises + SQL questions
Goal: Expand Python toolkit for analysis
Week 18: Database Administration Basics
Focus: SQL Mastery
Learn stored procedures and functions
Database design and normalization
Advanced SQL interview question practice
Daily Practice: 5 advanced SQL questions + theory review
Goal: Become SQL expert-level
Week 19: Advanced Analytics Techniques
Focus: Analytical Methods
Cohort analysis and customer segmentation
A/B testing methodologies
Advanced statistical modeling
Daily Practice: 1 advanced analysis + visualization practice
Goal: Master advanced analytical techniques
Week 20: Business Acumen Development
Focus: Business Understanding
Learn business metrics and KPIs
Industry-specific analytics approaches
Stakeholder communication skills
Daily Practice: Analyze business case + interview question review
Goal: Think like a business-focused analyst
Month 6: Interview Preparation & Portfolio Building
Week 21: Portfolio Project 1
Focus: SQL-Heavy Project
Choose a complex dataset
Perform comprehensive SQL analysis
Document findings and insights
Daily Practice: Project work + 3 interview questions daily
Goal: Complete SQL showcase project
Week 22: Portfolio Project 2
Focus: Python Analytics Project
End-to-end Python analysis project
Include data cleaning, analysis, and visualization
Create compelling presentation
Daily Practice: Project development + interview prep
Goal: Complete Python showcase project
Week 23: Portfolio Project 3
Focus: BI Dashboard Project
Build comprehensive business dashboard
Include multiple data sources
Focus on business impact and insights
Daily Practice: Dashboard refinement + mock interviews
Goal: Complete BI showcase project
Week 24: Interview Mastery
Focus: Final Preparation
Complete remaining interview questions from all resources
Practice explaining projects and technical concepts
Mock interviews and technical assessments
Daily Practice: 10 interview questions + project presentation practice
Goal: Be interview-ready with strong portfolio
Daily Practice Schedule Throughout 6 Months
Resources to Use Daily:
SQL: 150+ SQL Interview Questions Excel sheet
Python: 40 Coding Questions for Data Analyst
Theory: 120 + 99 Data Analyst Interview Questions
SQL Theory: 100+ SQL Theory Questions resource
Visualization: Free Data Visualization Courses
Weekly Commitments:
Monday-Wednesday: Focus on week's primary topic
Thursday: Integration day (combine skills)
Friday: Review and practice interview questions
Saturday: Project work or portfolio development
Sunday: Review week's learning and plan ahead
Success Metrics by Month:
Month 1: Basic Python and SQL proficiency
Month 2: Data manipulation and intermediate SQL
Month 3: Statistical analysis and visualization
Month 4: BI tool proficiency and dashboard creation
Month 5: Advanced skills and specialization
Month 6: Interview readiness and strong portfolio
Additional Tips:
Consistency: Practice daily, even if just 30 minutes
Documentation: Keep a learning journal and code repository
Community: Join data analyst communities and forums
Real Data: Work with real datasets whenever possible
Feedback: Seek reviews of your work from experienced analysts
Essential Tools to Master:
Python: pandas, numpy, matplotlib, seaborn, plotly
SQL: Any major database system (MySQL, PostgreSQL, SQL Server)
BI Tools: Power BI, Tableau, or similar
Others: Excel, Git, Jupyter Notebook
Remember: This roadmap is intensive but achievable. Adjust the pace based on your available time and prior experience. The key is consistent daily practice and building real projects that demonstrate your skills.