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Your Learning Roadmap

6-Month Journey

A structured path from fundamentals to advanced analytics, designed to build skills progressively with hands-on projects at every step.

Phase 1: FUNDAMENTAL​

Months 1-2 • Building Strong Foundations

Month 1: Business Statistics & Excel For Analysis (4 weeks)​

  • Week 1-2: Data Analytics foundations, business statistics, cognitive biases, Excel fundamentals
  • Week 3-4: Advanced Excel (Pivot tables, VLOOKUP, INDEX-MATCH), data visualization basics, Power Pivot, Interactive dashboard

Month 2: SQL For Data Analysis (4 weeks)​

  • Week 5-6: SQL fundamentals (SELECT, JOINs, aggregations), database concepts, CTEs
  • Week 7-8: SQL Window functions, Views, Intro to Cloud Data Warehouse (BigQuery)

Phase 2: INTERMEDIATE​

Months 3-4 • Deepening Technical Skills

Month 3: Analytical Thinking & BI Tools (Power BI) (4 weeks)​

  • Week 9-10: Build automatic dashboard with Power/Tableu, Data modeling, DAX
  • Week 11-12: Apply Analytical Thinking into Data Analysis cases

Month 4: Python Analytics Foundation (4 weeks)​

  • Week 13: Python fundamentals (data types, functions, control flow)
  • Week 14: Pandas & NumPy, data cleaning, customer churn analysis project
  • Week 15-16: Data visualization (Matplotlib/Seaborn), EDA

Phase 3: ADVANCED​

Months 5-6 • Applied Analytics & AI

Month 5: Applied Machine Learning for Data Analysts (4 weeks)​

  • Week 17-18: Basic ML algorithms for business analytics cases
  • Week 19-20: Advanced ML algorithms (classification, regression, clustering), model evaluation, customer LTV prediction

Month 6: Industry Apps & Portfolio (4 weeks)​

  • Week 21-22: Universal business metrics, domain-specific applications (Marketing/Sales/Risk Analytics), end-to-end capstone project
  • Week 23-24: Advanced AI tools, LLM integration, portfolio building, resume optimization, final capstone

Detail Syllabus

MONTH 1​

Business Statistics & Excel Mastery​

Build a solid foundation in data analytics, master Excel techniques, and understand core statistical concepts

Week 1-2: Data Analytics Foundations & Statistics​

Topics:

  • What is Data Analytics? Types: Descriptive, Diagnostic, Predictive, Prescriptive
  • Data types: Quantitative vs Qualitative, Structured vs Unstructured
  • Basic statistics: Mean, median, mode, variance, standard deviation,
  • Inferential statistics: Sampling, Hypothesis testing, Probability
  • Cognitive Biases in Data Analysis: Survivorship Bias, Confirmation Bias, Sampling Bias, Simpson's Paradox

Resources:

Week 3-4: Advanced Excel & Data Visualization Basics​

Topics:

  • Pivot tables, VLOOKUP, XLOOKUP, INDEX-MATCH
  • Conditional formatting and data validation
  • Charts and dashboards in Excel, Power Pivot, Power Query
  • Data visualization principles (choosing the right chart)

Mini Project:

📊 Sales Performance Dashboard in Excel

Resources:

MONTH 2​

SQL For Data Analysis + First AI Tools​

Master database querying with SQL, and leverage AI tools for accelerated learning

Week 5-6: SQL Fundamentals​

Topics:

  • Database concepts: Tables, rows, columns, keys
  • SELECT, WHERE, GROUP BY, ORDER BY
  • JOINs (INNER, LEFT, RIGHT, FULL)
  • Aggregations: COUNT, SUM, AVG, MAX, MIN
  • Subqueries and CTEs (Common Table Expressions)
  • Practice: SQLZoo, LeetCode SQL problems

Resources:

Week 7-8: SQL Window Functions & Intro Cloud Data Warehouse​

Topics:

  • Window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD
  • Window clauses: PARTITION BY, ORDER BY, ROWS/RANGE BETWEEN
  • Views: Creating and managing Views for reusable queries
  • Introduction to Cloud Data Warehouse: Why cloud? BigQuery vs Snowflake vs Redshift
  • Hands-on BigQuery: Querying public datasets, understanding serverless model
  • First AI experience: Using ChatGPT/Claude for SQL assistance

Mini Project:

📊 SQL Window Functions & BigQuery Analytics Challenge

Resources:

Soft Skills Development​

Essential professional skills for Month 1-2

Skills:

  • Critical thinking and problem-solving frameworks
  • Business communication basics
  • How to ask good questions
  • Understanding stakeholder needs

Resources:

MONTH 3

Analytical Thinking & BI Tools (Power BI)​

Master advanced Analytical Thinking techniques, data modeling, and create powerful interactive dashboards with Power BI

Week 9-10: Advanced Analytical Thinking & Problem Solving Strategy​

Topics:

  • Problem Framing & Business Context Mastery
  • Hypothesis-Driven Thinking (Thinking Before Data)
  • Structured Problem Decomposition (MECE & Beyond)
  • Analytical Strategy Design
  • Storytelling & Persuasive Problem Solving

Mini Project:

📊 Data Analysis Report for Business Problem

Resources:

Week 11-12: Data Analysis & Build Automatic Dashboard With BI Tools​

Topics:

  • Power BI: Data connections, Power Query, DAX basics
  • Creating interactive dashboards and reports
  • Tableau: Connecting to data, building visualizations
  • Dashboard design best practices
  • Publishing and sharing reports

Mini Project:

📊 Marketing Campaign Performance Dashboard

Resources:

MONTH 4

Python for Data Analysis​

Learn Python fundamentals, master data manipulation with Pandas, and create stunning visualizations.

Week 13: Python Fundamentals for Data Analysis​

Topics:

  • Python data types: int, float, str, bool, list, dict, tuple, set
  • Control flow: if/else, for/while loops
  • Functions: defining functions, parameters, return values, lambda functions
  • List comprehensions and dictionary comprehensions
  • Error handling: try/except basics
  • File I/O: Reading/writing CSV and JSON files
  • Installing and importing libraries (pip, import)

Mini Project:

📊 Python Data Processing Script: Read a CSV sales file, clean data, calculate summary statistics, and export results

Resources:

Week 14: Pandas & NumPy for Data Manipulation​

Topics:

  • Pandas: DataFrames, Series, indexing, filtering
  • Data cleaning: handling missing values, duplicates, outliers
  • GroupBy operations and aggregations
  • Merging and joining datasets
  • NumPy arrays and vectorization

Mini Project:

📊 Data Cleaning & EDA: Customer Churn Dataset with Pandas

Resources:

Week 15-16: Data Visualization with Python​

Topics:

  • Matplotlib and Seaborn for data visualization
  • Exploratory Data Analysis (EDA) techniques
  • Introduction to Machine Learning concepts
  • Supervised vs Unsupervised learning
  • Simple Linear Regression with scikit-learn

Mini Project:

📊 Sales Forecasting with Linear Regression

Resources:

Soft Skills Development​

Essential professional skills for Month 3-4

Skills:

  • Data storytelling and narrative building
  • Presentation skills for technical audiences
  • Translating insights into business recommendations
  • Version control with Git & GitHub (foundation for portfolio)

Git Essentials:

  • git init, clone, add, commit, push, pull
  • Branching basics: branch, checkout, merge
  • GitHub: Creating repositories, README.md, .gitignore
  • Publishing Jupyter Notebooks on GitHub

Resources:

MONTH 5

Applied Machine Learning for Data Analysts​

Apply machine learning to solve real business problems — focus on practical usage, not theory mastery

Week 17-18: Machine Learning Algorithms for Business​

Topics:

  • Classification: Logistic Regression, Decision Trees, Random Forest
  • Regression: Multiple regression, Polynomial regression
  • Clustering: K-Means, Hierarchical clustering
  • Model evaluation: Accuracy, Precision, Recall, F1-Score, ROC-AUC
  • Cross-validation and hyperparameter tuning

Mini Project:

📊 Customer Lifetime Value Prediction

Resources:

Week 19-20: AutoML & Modern AI Tools​

Topics:

  • Introduction to AutoML: H2O.ai, PyCaret, AutoGluon
  • Feature engineering automation
  • Model selection and ensemble methods
  • Deploying ML models (basics)
  • Generative AI for Analytics: Using ChatGPT/Claude for code generation, debugging, insights

Mini Project:

📊 Automated Predictive Model Pipeline with AutoML

Resources:

MONTH 6 - FINAL

Industry Applications & Portfolio Projects​

Apply your skills to real-world domains, build an impressive portfolio, and prepare for your data analytics career

Week 21-22: Domain-Specific Applications​

Essential Business Metrics (Foundation for all domains):

MetricFull NameÝ nghĩa
CACCustomer Acquisition CostChi phí để có 1 khách hàng mới
LTV / CLV(Customer) Lifetime ValueTổng giá trị 1 KH mang lại
Churn Rate—Tỷ lệ khách hàng rời bỏ
NPSNet Promoter ScoreMức độ hài lòng & giới thiệu
MRR / ARRMonthly/Annual Recurring RevenueDoanh thu định kỳ
DAU / MAUDaily/Monthly Active UsersNgười dùng hoạt động
ARPUAverage Revenue Per UserDoanh thu trung bình/user
ROIReturn on InvestmentTỷ suất lợi nhuận đầu tư

💡 Rule of thumb: LTV/CAC ≥ 3 là healthy. Nếu < 1 → đang "mua" khách hàng bằng lỗ.

Marketing Analytics:

  • Customer segmentation (RFM analysis, clustering)
  • Marketing mix modeling (MMM)
  • A/B testing and campaign optimization
  • Attribution modeling

Sales Analytics:

  • Sales forecasting (time series with Prophet, ARIMA)
  • Lead scoring with ML
  • Pipeline analysis and conversion optimization
  • Sales territory optimization

Risk Analytics:

  • Credit risk modeling
  • Fraud detection with anomaly detection
  • Churn prediction and prevention
  • Risk scoring models

Mini Project:

📊 Major Project (Choose 1): End-to-end analytics project in your chosen domain

Resources:

Week 23-24: Advanced AI & Portfolio Building​

Topics:

  • Generative AI deep dive: Prompting strategies for data analysis
  • LLM integration: Using GPT-4, Claude API for insights
  • AI-powered dashboards and reporting
  • Natural Language Processing (NLP) basics for text analytics
  • Building your data portfolio: GitHub, portfolio website
  • Creating case studies from your projects
  • Resume and LinkedIn optimization for data roles

Mini Project:

📊 Final Capstone: AI-Enhanced Analytics Solution

Resources:

Soft Skills Development​

Essential professional skills for Month 5-6

Skills:

  • Executive presentations and C-level communication
  • Stakeholder management and influencing without authority
  • Project management for analytics projects
  • Ethical considerations in data and AI
  • Networking and personal branding

Resources:

PROJECT PORTFOLIO

Hands-on project ideas across Marketing, Sales, and Risk Analytics to showcase your skills

Marketing Analytics Projects​

  • Email Campaign Performance Dashboard (Beginner) - Build an interactive dashboard analyzing email campaign metrics: open rates, click-through rates, conversions, and ROI.
    • Skills: Excel, Power BI, Visualization
  • Customer Segmentation - RFM Analysis (Intermediate) - Segment customers based on Recency, Frequency, and Monetary value using clustering algorithms for targeted marketing.
    • Skills: Python, SQL, K-Means
  • Marketing Attribution Model (Advanced) - Build a multi-touch attribution model to understand which marketing channels contribute most to conversions using ML.
    • Skills: Python, ML, AutoML

Sales Analytics Projects​

  • Sales Performance Dashboard (Beginner) - Create a comprehensive sales dashboard tracking KPIs: revenue, conversion rates, top products, and sales team performance.
    • Skills: Excel, Tableau, SQL
  • Sales Forecasting Model (Intermediate) - Build time series forecasting models to predict future sales using historical data and seasonal patterns.
    • Skills: Python, Time Series, Prophet
  • Customer Lifetime Value Prediction (Advanced) - Predict customer lifetime value using ML to identify high-value customers and optimize acquisition strategies.
    • Skills: Python, ML, Regression

Risk Analytics Projects​

  • Credit Risk Dashboard (Beginner) - Develop a dashboard to monitor credit risk metrics: default rates, credit scores distribution, and risk exposure.
    • Skills: Excel, Power BI, SQL
  • Fraud Detection System (Intermediate) - Build an anomaly detection system to identify fraudulent transactions using statistical methods and pattern recognition.
    • Skills: Python, SQL, Anomaly Detection
  • Credit Scoring Model with ML (Advanced) - Develop an ML-based credit scoring model to assess borrower creditworthiness and predict default probability.
    • Skills: Python, ML, Classification

PRACTICE DATASETS

Download curated datasets to practice and build your data analytics portfolio projects

  • Sales Analytics Dataset - Comprehensive sales transaction data including products, customers, regions, and time periods for revenue analysis and forecasting. (50K+ Records, 15 Features)
  • HR Analytics Dataset - Employee data covering demographics, performance, satisfaction, and attrition for workforce analytics and retention modeling. (15K+ Records, 20 Features)
  • CRM Analytics Dataset - Customer relationship data with interaction history, purchase patterns, and engagement metrics for churn prediction and CLV analysis. (30K+ Records, 18 Features)
  • Marketing Analytics Dataset - Multi-channel campaign data with customer journeys, touchpoints, conversions, and attribution data for ROI optimization. (40K+ Records, 22 Features)