Data Private

Analytics Boot Camp (DATA200)

5 days
DataMicrosoft ExcelMicrosoft Power BI

Run a complete analytics engagement, from framing the question and preparing data through statistics, Power BI dashboards, and a stakeholder briefing.

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  • Private class for your team
  • Live expert instructor
  • Online or on‑location
  • Customizable agenda
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Course Overview

This course is designed for participants who need to run a complete analytics engagement, from a vague business concern through a decision-ready recommendation. Through intense, hands-on work with a running case study, participants learn to frame questions and gather requirements, convert and combine raw data into analysis-ready form, apply statistics to interpret the results, and present their findings as charts, dashboards, and a stakeholder briefing.

Course Benefits

  • Apply a structured framework to turn a broad business concern into a focused analytics question with defined requirements, scope, and expected output.
  • Gather, vet, and combine data from multiple sources, judging each for relevance, completeness, and credibility.
  • Convert raw data into usable form using functions, text tools, and transformations, then clean, join, and reshape it for analysis.
  • Apply descriptive statistics, probability distributions, hypothesis testing, confidence intervals, and regression to interpret results and quantify confidence in findings.
  • Build charts, dashboards, and presentation-ready visuals in Excel, Power BI, and PowerPoint that reveal trends and support a message.
  • Carry a case end-to-end and communicate the findings through data storytelling in a presentation that moves stakeholders toward a decision.

Delivery Methods

Private Class
Delivered for your team at your site or online.

Microsoft Certified Partner

Webucator is a Microsoft Certified Partner. We design and deliver training that aligns directly with Microsoft's official technologies, standards, and learning objectives.

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Course Outline

  1. Day 1: Framing and Getting the Data
    1. Starting the Analytics Engagement: Defining the business need; identifying the audience and expected output; setting scope; turning a vague concern into an answerable question.
    2. Building the Question Structure: Separating symptoms from causes; matching the question to the type of analysis needed; deciding what "done" looks like.
    3. Collecting and Reviewing Data: Identifying candidate data sources; weighing internal vs. external data; assessing completeness, credibility, and source of truth.
    4. Getting Data into Excel: Importing files; understanding workbook and table structure; setting up a clean working dataset.
  2. Day 2: Preparing Data for Analysis
    1. Inspecting the Raw Dataset: Reviewing file structure; spotting blanks, inconsistent entries, and unexpected values; prioritizing cleanup.
    2. Converting Data into Usable Form: Transforming raw fields with functions and text tools; standardizing formats; removing duplicates.
    3. Combining and Reorganizing Data: Joining related tables; filtering to relevant records; aggregating and reshaping for downstream use.
    4. Power Query and Reporting Prep: Using Power Query to clean and shape inputs; structuring fields for analysis views; validating the output.
  3. Day 3: Statistics for Interpreting Results
    1. Summarizing the Data: Measures of center, spread, and distribution; describing what the data actually shows.
    2. Probability and Sample Thinking: Likelihood and uncertainty; how samples support conclusions about a whole.
    3. Distribution Patterns: Recognizing common distribution shapes; understanding clustering and outliers; anticipating expected results.
    4. Testing Results: Hypotheses, test logic, and p-values; distinguishing statistical results from practical meaning.
  4. Day 4: Estimation, Modeling, and Machine Learning Orientation
    1. Estimation with Confidence Intervals: Using sample results to bound likely ranges; expressing uncertainty responsibly.
    2. Regression-Based Predictive Analysis: Statistical modeling; studying relationships between variables; reading direction and strength; supporting prediction-oriented planning.
    3. Orientation to Machine Learning Concepts: What machine learning adds beyond regression; when learned models fit a business problem; risks of automated prediction.
    4. Selecting the Right Visual: Chart selection; visualization best practices; matching the visual to the message and audience.
  5. Day 5: Telling the Story and Closing the Case
    1. Building Dashboards: Assembling visuals into dashboards in Power BI; balancing interactivity and clarity; designing for the stakeholder's question.
    2. Reporting the Story: Structuring findings as a narrative; separating evidence from message; preparing the stakeholder briefing.
    3. Capstone Presentation: Completing the full workflow from question to recommendation; presenting to a business audience.

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following is required for this Data class:

Data Analysis Fundamentals or equivalent experience (proficiency with spreadsheet navigation, basic data manipulation, and descriptive statistics).

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