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📊 Data & Machine LearningBeginner LevelEvidence-Based Skill Profile

Hands-on Mastery in Data Analysis

Master data collection, cleaning, SQL, Python analysis, statistical inference, BI dashboards, and AI-assisted automation to drive business decisions with your dedicated AI Agent coach.

44 Modules • 12 Production Projects • Dedicated AI Coach
44 Modules
AI FearFilter Faculty

What You Will Learn

Frame clear analytical questions from vague business goals and identify required data sources.
Ingest and clean multi-source datasets, resolving missing values, schema inconsistencies, and outliers.
Write advanced relational SQL queries utilizing Common Table Expressions, joins, and window functions.
Perform exploratory data analysis and hypothesis testing in Python using NumPy, Pandas, and SciPy.
Design interactive BI dashboards and executive visualizations that highlight actionable business metrics.
Automate analytical workflows, ETL pipelines, and executive reporting using modern AI coding agents.
Architect and deploy a complete production-style end-to-end data analysis platform and executive deck.

Curriculum & Weekly Roadmap

44 Structured Modules

Module 1: Introduction to Data Analytics

  • Core Principles & The Analytics Value Chain
  • Descriptive vs Diagnostic vs Predictive Analytics
  • The End-to-End Data Analysis Lifecycle

Module 2: What Does a Data Analyst Do?

  • Roles in the Modern Data Team
  • A Day in the Life of a Data Analyst
  • Key Tools & Cross-Functional Collaboration

Module 3: Data Analyst Thinking

  • Formulating Testable Business Hypotheses
  • The 5 Whys and Root-Cause Analysis
  • Translating Ambiguous Questions into Data Queries

Module 4: Types of Data

  • Structured vs Unstructured Data Formats
  • Quantitative: Continuous vs Discrete Variables
  • Qualitative: Nominal, Ordinal & Categorical Encodings

Module 5: Data Sources and Data Collection

  • Primary vs Secondary Data Sources
  • Database Replication & Data Warehouses
  • APIs, Webhooks & Real-time Event Tracking

Module 6: Structured vs Unstructured Data

  • Relational Tables & Schema Normalization
  • Semi-Structured Formats: JSON & XML
  • Unstructured Logs & Text Analytics Preparation

Module 7: Spreadsheets for Data Analysis

  • Spreadsheet Architecture & Grid Mechanics
  • Relative vs Absolute Cell Referencing ($A$1 vs A1)
  • Logical Formulas: IF, AND, OR & Nested Logic

Module 8: Excel Fundamentals for Data Analysts

  • Lookup Functions: XLOOKUP vs VLOOKUP & INDEX/MATCH
  • Summarizing with Pivot Tables & Calculated Fields
  • Multi-Condition Aggregations: SUMIFS & COUNTIFS

Module 9: Data Cleaning Fundamentals

  • The 6 Dimensions of Data Quality
  • Identifying Common Data Smells & Anomalies
  • Establishing a Systematic Data Cleansing Workflow

Module 10: Handling Missing Data

  • Mechanisms of Missingness: MCAR, MAR & MNAR
  • Imputation Strategies: Central Tendencies & Domain Rules
  • Dropping vs Imputing: Evaluating Bias & Information Loss

Module 11: Handling Duplicates and Errors

  • Exact vs Fuzzy Duplicate Identification
  • Detecting Outliers: Z-Score & Interquartile Range (IQR)
  • Standardizing Text Entries with Regex & String Trimming

Module 12: Data Transformation

  • Type Casting & Parsing Temporal Datetime Objects
  • Pivoting, Melting & Reshaping Wide vs Long Data
  • Categorical Encoding & Value Discretization (Binning)

Module 13: Data Validation

  • Schema Validation & Data Type Contracts
  • Range & Boundary Value Assertions
  • Referential Integrity & Cross-Table Reconciliations

Module 14: SQL Fundamentals

  • Relational Database Management Systems (RDBMS)
  • Table Schemas, Data Types & Primary Keys
  • SQL Execution Order vs Syntax Order

Module 15: SQL SELECT and Filtering

  • The SELECT Statement & Aliasing Columns
  • Filtering Rows with WHERE & Comparison Operators
  • Pattern Matching with LIKE & Handling NULL Logic (IS NULL)

Module 16: Sorting, Grouping and Aggregation

  • Sorting Results with ORDER BY & Multi-Column Sorts
  • Aggregating Metrics: COUNT, SUM, AVG, MIN, MAX
  • Grouped Aggregations with GROUP BY & Filtering with HAVING

Module 17: SQL Joins

  • Relational Joins: Understanding Cartesian Products
  • INNER JOIN vs LEFT/RIGHT OUTER JOINS
  • FULL OUTER JOINS, Self-Joins & Join Optimization

Module 18: Subqueries and CTEs

  • Scalar & Table Subqueries in WHERE and FROM
  • Common Table Expressions (WITH Clause) for Modularity
  • Recursive CTEs & Hierarchical Query Patterns

Module 19: Window Functions

  • Window Function Basics: OVER() and PARTITION BY
  • Ranking Functions: ROW_NUMBER, RANK, DENSE_RANK
  • Positional Functions: LEAD, LAG & Running Totals

Module 20: SQL for Real-World Analysis

  • Customer Cohort Retention & Churn Analysis
  • Calculating Customer Lifetime Value (LTV) in SQL
  • Query Optimization: Indexes, SARGability & EXPLAIN

Module 21: Python for Data Analysis

  • Python Environment & Jupyter Interactive Notebooks
  • Data Structures: Lists, Dictionaries & Tuples
  • Writing Reusable Functions & Modular Code

Module 22: NumPy Fundamentals

  • The NumPy Ndarray & Memory-Efficient Contiguity
  • Vectorized Array Math vs Python Iterative Loops
  • Array Slicing, Boolean Masking & Universal Functions

Module 23: Pandas Fundamentals

  • Pandas Data Structures: Series vs DataFrame
  • Ingesting Data from CSV, Excel, Parquet & SQL Databases
  • Initial Dataframe Inspection: info(), describe(), shape

Module 24: DataFrames and Data Manipulation

  • Selecting & Filtering Rows/Columns (loc vs iloc)
  • Adding, Modifying & Vectorizing New Columns
  • Merging, Concatenating & Reshaping DataFrames

Module 25: Exploratory Data Analysis

  • The Exploratory Data Analysis (EDA) Blueprint
  • Univariate Distribution Profiling & Histogram Analysis
  • Bivariate Relationships: Scatter Plots & Cross-Tabs

Module 26: Statistical Thinking for Data Analysts

  • Sample vs Population: Deductive Statistical Reasoning
  • The Central Limit Theorem & Standard Error
  • Cognitive Biases: Survivorship, Selection & Confirmation Bias

Module 27: Descriptive Statistics

  • Measures of Central Tendency: Mean, Median, Mode
  • Measures of Dispersion: Variance & Standard Deviation
  • The 5-Number Summary & Boxplot Interpretations

Module 28: Correlation and Relationships

  • Quantifying Associations: Pearson vs Spearman Correlation
  • Visualizing Correlation with Heatmaps & Scatter Matrices
  • Confounding Variables & The Fallacy of Causation

Module 29: Data Visualization Fundamentals

  • Visual Perception & Gestalt Principles for Designers
  • Choosing Accessible Color Palettes & Contrast Ratios
  • Minimizing Chartjunk & Maximizing Data-Ink Ratio

Module 30: Matplotlib and Visualization with Python

  • Matplotlib Object-Oriented Hierarchy: Figure vs Axes
  • Customizing Bar Charts, Line Plots & Histograms
  • Enhancing Visuals with Seaborn Themes & Categorical Plots

Module 31: Charts and Choosing the Right Visualization

  • Comparison Charts: Bar, Column & Bullet Graphs
  • Composition Charts: Stacked Bars vs TreeMaps
  • Trend & Distribution: Line, Area, Box & Violin Charts

Module 32: Dashboard Fundamentals

  • Dashboard Purpose: Strategic, Analytical vs Operational
  • Visual Information Hierarchy & The F-Pattern Layout
  • Designing Interactive Filtering & Drill-Downs

Module 33: Business Metrics and KPIs

  • Financial & Revenue Metrics: ARR, MRR & Gross Margins
  • Growth & Acquisition: CAC, LTV & Payback Periods
  • Engagement & Retention: Churn, MAU/DAU & NPS

Module 34: Finding Insights from Data

  • Extracting High-Value Insights from Flat Metrics
  • Anomaly Detection: Spotting Sudden Spikes and Dips
  • Segment Divergence: Finding Top & Bottom Performers

Module 35: Storytelling with Data

  • The Narrative Arc: Setup, Conflict & Data Resolution
  • Contextualizing Numbers with Benchmarks & Targets
  • Crafting High-Impact Headlines vs Descriptive Titles

Module 36: Presenting Data-Driven Insights

  • Slide Deck Architecture: The 10-Slide Executive Deck
  • Presenting to C-Suite Stakeholders & Business Leaders
  • Anticipating Skepticism & Defending Analysis Rigor

Module 37: AI for Data Analysts

  • Generative AI Architecture for Data Professionals
  • Prompt Engineering: Few-Shot Prompts for Analytical Queries
  • Guardrails: Preventing Hallucinated Data & Verification

Module 38: Using AI for SQL and Python

  • Using AI to Generate Complex SQL Queries with CTEs
  • Accelerating Python & Pandas Scripting with AI Assistants
  • Automated Code Refactoring & Query Performance Tuning

Module 39: AI-Assisted Data Cleaning and Analysis

  • AI-Powered Anomaly & Dirty Data Detection
  • Synthesizing Regex for Complex String Extractions
  • Automating EDA Profiling Reports with AI Prompts

Module 40: AI-Assisted Insight Generation

  • Synthesizing Executive Summaries from Query Outputs
  • Formulating Business Action Items via AI Brainstorming
  • Generating Automated Weekly Analytical Digest Briefs

Module 41: Data Analysis Automation

  • Scheduling Automated Scripts with Cron & Task Schedulers
  • Auto-Generating Formatted PDF & HTML Data Reports
  • Configuring Automated Email & Slack Anomaly Alerts

Module 42: Data Analyst Projects

  • Scoping High-Impact Real-World Data Case Studies
  • Acquiring & Cleaning Real-World Open Datasets
  • Creating Documented, Reproducible Analysis Repositories

Module 43: Building a Job-Ready Data Analyst Portfolio

  • Structuring a Standout Data Analyst GitHub Portfolio
  • Crafting Executive Summary Readmes & Live Demos
  • Nailing the Technical SQL/Python Take-Home Assessment

Module 44: Final Production-Style Data Analysis Project

  • Capstone Part 1: Business Problem & Data Ingestion
  • Capstone Part 2: SQL/Python Analysis & KPI Dashboard
  • Capstone Part 3: Executive Presentation & Strategy Memo

Who This Course Is For

Aspiring Data Analysts, Business Intelligence Developers, Analytics Engineers, Software Developers, and Students wanting to master practical data collection, cleaning, SQL, Python, statistics, BI dashboards, and AI automation.

Key Skills Developed:

Data Foundations, Literacy & Analytical Problem SolvingExcel, Pivot Tables, Advanced Formulas & Data PreparationRelational SQL, Joins, Aggregations, Window Functions & CTEsPython Data Stack: NumPy Arrays & Pandas Tabular ManipulationMulti-Source Ingestion: CSV, JSON, Parquet, REST APIs & WarehousesData Cleaning, Missing Value Imputation & Outlier DetectionExploratory Data Analysis (EDA) & Statistical InferenceHypothesis Testing, Correlation & A/B ExperimentationData Visualization Principles, Matplotlib, Seaborn & PlotlyBusiness Intelligence, Executive Dashboards (Power BI & Tableau)Core Business Metrics: CAC, LTV, Retention, Churn & RFM CohortsPredictive Modeling, Time Series Forecasting & Trend DecompositionPrompt Engineering & Autonomous AI Agents for Data AnalysisAutomated Data Cleaning, Schema Profiling & Pipeline ETLExecutive Data Storytelling, Slide Decks & Stakeholder PresentationsData Ethics, Privacy Governance, GDPR & Production Capstone Platform

Course Faculty & Development

AI FearFilter Faculty

Data Analytics & AI Engineering Team

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