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Next cohort Sept 15

Data Analytics

Become a working data analyst in 16 weeks.

Three modules — Microsoft T-SQL, Python with Pandas, and Power BI data modeling and dashboards. Built to take career-changers from spreadsheets to job-ready data analyst.

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Next cohort starts

September 15, 2026

Save your spot for the next cohort.

Duration

16 weeks

Courses

3

Class schedule

Twice weekly · evenings MST

Curriculum

3 courses across the 16-week program.

Each course runs as a self-contained unit with live instruction, weekly assignments, and a hands-on lab. Click any course to see the full lesson list.

DA101

Querying with SQL

9 lessons

Microsoft T-SQL from beginner through advanced — querying, joins, set theory, subqueries, table expressions, and window functions. Interpret a business question and write the SQL that answers it.

  1. LESSON 01

    Querying Data

    • Selecting Data
    • Sorting Data
    • Data Types and Nullability
    • Limiting Results
  2. LESSON 02

    Filtering Data & Preparing Outputs

    • The Where Clause
    • Operators
    • Working with Null
  3. LESSON 03

    Aggregation

    • Group By
    • SQL Aggregate Functions
    • Filtering with Aggregates
  4. LESSON 04

    Expressions & Functions

    • Conditional Logic
    • Handling unknowns
    • String Functions
    • Date Functions
    • Other Functions
  5. LESSON 05

    Joins

    • Keys discussion
    • Cross joins
    • Filtering with Inner Join
    • Outer joins — mismatches and unknowns
  6. LESSON 06

    Set Theory

    • Union, Intersect, Except
    • Precedence
  7. LESSON 07

    Subqueries

    • Correlated vs Non-Correlated
    • Using IN
    • EXISTS Operator
  8. LESSON 08

    Table Expressions

    • Common Table Expressions
    • Derived Tables
    • Views
  9. LESSON 09

    Window Functions

    • Numbering, Counting, and Ranking
    • Frames and Panes
DA102

Python for Data Analysis

5 lessons

Python from foundations through end-to-end data analysis — Jupyter notebooks, Pandas, data cleaning, regular expressions, and connecting to multiple sources. Capstone: an end-to-end pipeline that connects, joins, transforms, and saves data.

  1. LESSON 01

    Introduction to Python and Jupyter Notebooks

    • Why Python and where it fits
    • Overview of Jupyter Notebooks
    • Basic math operations
    • Data types and variables
    • Functions
  2. LESSON 02

    Data Structures & Control Flow

    • Data structures
    • Control flow
    • Comprehensions
    • Functions and keywords
    • Local and global scopes
    • Exception handling
  3. LESSON 03

    Reading and Writing Data

    • Intro to Pandas
    • Files on the local computer
    • Text files (.csv, .xlsx, .txt)
    • SQL Server connections
    • Spreadsheets
  4. LESSON 04

    Exploring & Transforming Data

    • Data exploration with Pandas
    • Transformation
    • Handling missing data and outliers
    • Manipulating strings
    • Using regular expressions
    • Working with numeric data and dates
  5. LESSON 05

    End-to-End Data Analytics Pipeline

    • Connecting to data
    • Joining multiple tables (SQL Server)
    • Joining multiple sources (database, files)
    • Transforming data
    • Saving data
DA103

Power BI

8 lessons

Tell stories with data — connect to multiple sources, model relationships, write DAX, and ship interactive reports through the Power BI Service. By the end you'll deploy a multi-page report with row-level security.

  1. LESSON 01

    Business Requirement Document

    • Understanding business scope
    • Requirement analysis
    • Documenting business questions
  2. LESSON 02

    Design

    • Dissecting business questions
    • Metrics, slicers, and KPIs
    • Identifying data sources
    • Data profiling
  3. LESSON 03

    Development

    • Import data
    • Live connection
    • Direct Query
    • Dual / mixed / composite (DQ + Import)
  4. LESSON 04

    Relationships

    • Single and bi-directional
    • Many-to-many
    • Active / passive
  5. LESSON 05

    Enhancing Your Data Model

    • Date table
    • Metric table
    • Calculated tables
    • Hiding and renaming tables and columns
    • Creating hierarchies
    • Modeling best practices
  6. LESSON 06

    Intro to DAX

    • DAX overview and functions
    • Aggregate functions (SUM, COUNT, DISTINCTCOUNT, COUNTROWS, MIN, MAX, AVERAGE)
    • Logical functions (IF, ISBLANK, AND, OR, COALESCE, SWITCH)
    • Date and string functions
    • Filter functions (FILTER, IN, ALL)
    • Time intelligence (Same Period Last Year, Parallel Period, Total YTD, Dates YTD)
    • USERELATIONSHIP, RELATED
  7. LESSON 07

    Visualization

    • Building reports
    • Wireframes and mock-ups
    • Design patterns — choosing the right visual
    • Backgrounds, custom themes, and custom visuals
    • Filters, slicers, and interactions
    • Syncing slicers
    • Row-Level Security
  8. LESSON 08

    Deployment

    • Power BI Service overview
    • Workspaces vs. apps
    • OneLake Data Hub
    • Metrics and Monitoring Hub
    • Scheduled refresh and subscriptions
    • Deployment pipelines
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