🔷 90-Day Data Analyst Roadmap (Python, SQL, AWS)
Master data analysis in just 3 months with our structured program. Learn Python programming, SQL databases, and AWS cloud services through hands-on projects. Perfect for complete beginners, this roadmap takes you from basic coding to building real-world dashboards with AWS QuickSight.
• 12 weeks of structured learning (10-15 hours/week)
• Built-in projects using real-world datasets
• Interview preparation & portfolio building included
🛠 Assumptions

1

Goal
Become job-ready for Junior Data Analyst roles in 90 days, targeting positions at tech companies and startups. With 4 hours of daily focused study, you'll build a portfolio of 3-4 projects demonstrating proficiency in data analysis and visualization.

2

Focus Areas
Master Python (Pandas, NumPy), SQL (from basic queries to window functions), and AWS cloud services. You'll learn to store data in S3, query with Athena, manage databases in RDS, and create interactive dashboards using QuickSight. By week 8, you'll be building full-stack data analysis solutions.

3

Background
Coming from an engineering degree and SAP consulting background, you bring strong analytical thinking and business process knowledge. While technical coding experience is minimal, your SAP expertise provides a solid foundation in data structures and business intelligence concepts.

4

Learning Style
Learn through hands-on projects using real-world datasets. Each week includes guided tutorials, practical exercises, and a mini-project. You'll analyze actual business data, create visualizations, and build a professional portfolio on GitHub.
Time Commitment: 4 hours/day (2 hours morning, 2 hours evening), including weekends, with additional time for projects and interview prep.
📌 Week 1-4: Python & SQL Foundations + AWS S3
🎯 Goal: Master the fundamentals of data analysis by learning Python programming, SQL database querying, and AWS S3 cloud storage. You'll start with basic coding and progress to handling real datasets in the cloud.

1

Week 1: Python Basics & Jupyter Notebook
Learn Python syntax, data types, and control structures. Master Jupyter Notebook for interactive coding, including variables, loops, functions, and basic data structures. Complete hands-on exercises using real datasets.

2

Week 2: SQL Basics + AWS S3
Start with SQL fundamentals (SELECT, WHERE, JOIN) while learning to store and organize data in AWS S3 buckets. Practice writing queries on sample business datasets and understand cloud storage best practices.

3

Week 3: Pandas, NumPy & Data Cleaning
Master data manipulation with Pandas DataFrames and NumPy arrays. Learn essential data cleaning techniques including handling missing values, removing duplicates, and standardizing formats.

4

Week 4: SQL Intermediate & AWS Athena
Advance to complex SQL queries including subqueries and window functions. Connect AWS Athena to analyze S3 data using SQL, preparing you for real-world data analysis scenarios.
🔹 Week 1: Python Basics & Jupyter Notebook
📌 Topics
  • Set up your development environment with Python & Jupyter Notebook through Anaconda (focus on Python 3.x)
  • Python fundamentals: variables, integers, floats, strings, and type conversion for data analysis
  • Data structures: lists for time series data, dictionaries for key-value pairs, sets for unique values
  • Control flow: if-else for data filtering, for/while loops for data processing
  • Write custom functions for business calculations and data transformation
🛠 Hands-on
Create a business analysis toolkit in Python that includes:
  • Sales data calculator (revenue, tax, discounts, growth rates)
  • Unit conversion tool (currencies, measurements, temperatures)
  • Data format converter (CSV to JSON, text cleaning)
Complete HackerRank Python fundamentals (focus on first 10 days): https://www.hackerrank.com/domains/tutorials/10-days-of-python
Aim to spend 2 hours on tutorials each morning and 2 hours practicing in the evening, following your planned schedule.
🔹 Week 2: SQL Basics + AWS S3 (Cloud Storage)
📌 Topics
  • SQL Basics: SELECT, WHERE, GROUP BY, ORDER BY using real business metrics (revenue, customer counts, product sales)
  • Aggregate Functions: SUM for total sales, AVG for average order value, COUNT for customer transactions
  • SQL Joins: Connect customer data (INNER JOIN), include all orders (LEFT JOIN), merge product catalogs (RIGHT JOIN)
  • AWS S3: Create buckets, organize data folders, set permissions, understand storage classes (Standard vs Glacier)
🛠 Hands-on
  1. Set up a FREE AWS Account with MFA security enabled
  1. Upload an e-commerce dataset from Kaggle to S3 (recommended: Brazilian E-commerce or Sales Transaction dataset)
  1. Practice SQL on DB Fiddle with sample retail database: calculate monthly sales, customer retention, and product performance
📚 Resources
  • AWS S3 Beginner Guide: aws.amazon.com/getting-started/hands-on/backup-files-to-amazon-s3
  • SQL for Data Science by UC Davis (Free Coursera Course)
  • Mode Analytics SQL Tutorial (focus on business metrics)
🔹 Week 3: Pandas, NumPy & Data Cleaning
1
📌 Topics
Pandas: Create and manipulate DataFrames from CSV/JSON files, filter data using .loc/.iloc, handle time series with resample(), merge datasets with concat/join, and learn groupby operations for business metrics calculation
2
📌 Topics
NumPy: Create arrays from business data, perform statistical calculations (mean, median, std), use boolean masking for data filtering, and optimize calculations with vectorized operations
3
📌 Topics
Data Cleaning & Transformation: Handle missing values with fillna/dropna, fix data types with astype(), remove duplicates with drop_duplicates(), standardize text data with string methods, and create derived features for analysis
🛠 Hands-on: Download the Brazilian E-commerce dataset from Week 2's S3 bucket, clean the data by handling missing order dates, standardize product categories, calculate customer-level metrics (total spend, average order value, purchase frequency), and save the cleaned dataset back to AWS S3 for next week's advanced SQL analysis.
🔹 Week 4: SQL Intermediate & AWS Athena

1

1

SQL Window Functions
Master ROW_NUMBER for customer purchase rankings, RANK for top-selling products, LEAD/LAG for month-over-month sales comparisons, and PARTITION BY for category-level analysis

2

2

AWS Athena
Run serverless SQL queries directly on S3 data, create custom tables with Glue Crawler, optimize costs with partitioning, and export results to QuickSight

3

3

Hands-on
Query Brazilian E-commerce data in Athena to analyze customer cohorts, calculate product category performance, and build sales trend reports using window functions

4

4

SQL Case Study
Analyze Kaggle's Brazilian E-commerce data to identify top customers, calculate customer lifetime value, and create executive dashboards using window functions
📌 Week 5-8: AWS RDS, Data Analysis & Visualization
🎯 Goal: Master advanced data infrastructure, analysis techniques, and visualization tools through hands-on practice with the Brazilian E-commerce dataset.

1

Week 5
Set up AWS RDS PostgreSQL instance, migrate cleaned S3 data, and write complex SQL queries using CTEs, window functions, and stored procedures for advanced business metrics.

2

Week 6
Conduct thorough EDA using Pandas: analyze customer behavior patterns, identify seasonal trends, calculate product affinity scores, and uncover key business insights.

3

Week 7
Create impactful visualizations with Matplotlib & Seaborn: customer cohort analysis plots, sales trend charts, geographical distribution maps, and interactive dashboards.

4

Week 8
Build professional AWS QuickSight dashboards: connect to RDS, design executive KPI summaries, create drill-down reports, and set up automated refresh schedules.
🔹 Week 5: AWS RDS & Advanced SQL
📌 Topics
  • Set up AWS RDS PostgreSQL with proper instance sizing, security groups, and backup configuration
  • Master user management, database security, and cost optimization in RDS
  • Connect Jupyter Notebook to RDS using SQLAlchemy and psycopg2
  • Advanced SQL: Common Table Expressions (CTEs), recursive queries, and materialized views
  • Optimize query performance with proper indexing and EXPLAIN ANALYZE
🛠 Hands-on
  • Create an AWS RDS PostgreSQL database with Multi-AZ deployment for high availability
  • Import the cleaned Brazilian E-commerce dataset from S3 to RDS using COPY commands
  • Write complex SQL queries using CTEs to analyze customer purchase patterns
  • Create materialized views for frequently accessed metrics like customer lifetime value
  • Build a query optimization report comparing different indexing strategies
🔹 Week 6: Exploratory Data Analysis (EDA)
📌 Topics
  • Feature Engineering: Create customer segments based on recency/frequency/monetary (RFM) analysis, calculate days between purchases, and derive seasonal indicators from order dates
  • Statistical Analysis: Calculate key metrics like average order value, customer lifetime value, and product category distribution across Brazilian regions
  • Outlier Detection: Identify and handle unusual patterns in shipping times, payment values, and review scores using statistical methods
🛠 Hands-on
Conduct comprehensive EDA on Brazilian E-commerce data from AWS RDS: analyze payment methods distribution, investigate delivery time patterns across regions, and examine the correlation between review scores and product categories.
Create insightful visualizations using Pandas & Matplotlib to show customer purchase patterns, seller performance metrics, and product category trends over time.
🔹 Week 7: Data Visualization with Matplotlib & Seaborn
📌 Topics: Master data visualization techniques essential for e-commerce analytics:
  • Create histograms to analyze order value distributions and review score patterns
  • Build scatter plots to explore relationships between delivery time and customer satisfaction
  • Design box plots to compare product prices across different categories
  • Develop heatmaps to visualize sales patterns across Brazilian regions and time periods
🛠 Hands-on: Apply these visualization techniques to the Brazilian E-commerce dataset to create compelling data stories about customer behavior, sales trends, and market dynamics. Learn to customize colors, fonts, and layouts to match corporate branding requirements.
📚 Resources: Matplotlib Documentation: https://matplotlib.org/stable/contents.html
🔹 Week 8: AWS QuickSight (Dashboards)
📌 Topics
  • Connecting QuickSight to AWS RDS & S3: Configure database permissions, set up direct query mode vs. SPICE ingestion, and establish secure connections to your Brazilian E-commerce data sources
  • Building Interactive Dashboards for e-commerce analysis: Create real-time sales monitoring, regional performance comparisons, and customer behavior visualizations
  • Implementing drill-downs and filters for product categories, time periods, and geographical regions
  • Setting up automated dashboard refresh schedules and sharing permissions for stakeholders
📚 Resources
AWS QuickSight Free Tier: https://aws.amazon.com/quicksight/
Key visualizations to create:
  • Sales heat map across Brazilian states
  • Payment method distribution trends
  • Customer satisfaction metrics by seller
  • Delivery performance tracking
  • Product category performance analysis
📌 Week 9-12: Portfolio, Job Preparation & Interviews
🎯 Goal: Build a compelling data analyst portfolio and successfully land your first role.
Build Portfolio
Create 3 projects: Brazilian E-commerce customer segmentation using RFM analysis, AWS QuickSight sales dashboard, and SQL optimization case study with AWS RDS
Prepare Resume
Highlight technical skills (Python, Pandas, SQL, AWS RDS/S3/QuickSight) and quantify achievements from portfolio projects (e.g., "Improved query performance by 40%")
Practice Interviews
Master common SQL queries, explain AWS data architecture, and prepare case studies on data cleaning, RFM analysis, and visualization best practices
Apply for Jobs
Target companies using AWS stack, apply to 5 jobs weekly, focus on roles mentioning SQL, Python, and BI tools like QuickSight
🔹 Week 9-12: Resume, LinkedIn & Interviews
Build Your Professional Brand
  • Create a portfolio website highlighting your Brazilian E-commerce analysis projects
  • Optimize LinkedIn profile with AWS, Python, and SQL keywords
  • Showcase QuickSight dashboards and RFM analysis case studies
Interview Preparation
  • Practice explaining data cleaning processes with Pandas
  • Prepare examples of AWS RDS query optimization
  • Master common SQL window functions and joins
📚 Essential Resources
  • StrataScratch SQL Challenges: https://www.stratascratch.com/
  • AWS Certified Data Analytics Preparation
  • Real-world Python Data Cleaning Examples