Data Private

Data Analysis Fundamentals (DATA101)

2 days
Data

Learn a repeatable data analysis process for framing business questions, assessing and cleaning data, exploring patterns, and reporting defensible findings.

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Course Overview

Build a repeatable, methodical approach to turning business questions into defensible answers. This introductory course follows the data analysis lifecycle, including framing stakeholder questions, selecting appropriate tools, collecting and assessing data, addressing quality issues, and conducting exploratory analysis with descriptive and introductory inferential statistics. Participants also examine bias, ethics, and responsible reporting before applying the process in an end-to-end mini-analysis.

Course Benefits

  • Match organizational questions to descriptive, diagnostic, predictive, or prescriptive analytics.
  • Frame stakeholder needs as focused, answerable analytical questions.
  • Compare primary and secondary data sources and assess their reliability, sampling methods, and potential bias.
  • Select analytical tools based on project needs, strengths, and limitations.
  • Explore and profile datasets to understand their structure, distributions, outliers, relationships, and analytical potential.
  • Identify and address missing values, duplicates, inconsistent structures, and other common data quality issues.
  • Produce and interpret graphical and numerical summaries of a dataset's shape, center, and spread.
  • Apply introductory inferential methods to identify and evaluate patterns in data.
  • Use ethical principles to guide data collection, analysis, privacy, and reporting.
  • Apply the complete data analysis lifecycle in a focused mini-analysis.

Delivery Methods

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

Course Outline

  1. Day 1: Framing the Problem and Collecting Data
    1. Understanding the Data Project: defining the business need, audience, expected output, and scope; turning vague concerns into answerable questions
    2. The Four Pillars of Analytics: descriptive, diagnostic, predictive, and prescriptive analytics; aligning analytical goals with organizational needs; matching question types to the appropriate pillar
    3. Data Sources and Collection Methods: observational studies and experiments; primary and secondary sources; sampling; surveys, interviews, and observations; collection bias
    4. Choosing the Right Analytical Tool: Excel, SQL, and Python; tool strengths and limitations; matching tools to project requirements
  2. Day 2: Data Quality, Exploratory Analysis, and Introductory Inference
    1. Data Discovery and Profiling: data wrangling; examining distributions, outliers, and relationships; assessing fitness for analytical use
    2. Cleaning and Structuring Data: handling missing values; removing duplicates; normalization and standardization; transforming and validating data
    3. Exploratory Analysis, Descriptive Statistics, and Introductory Inference: creating graphical and numerical summaries of shape, center, and spread; identifying initial trends and patterns
    4. Bias, Ethics, and Responsible Reporting: data assumptions and human bias; privacy considerations; evaluating data for bias; ethical frameworks
    5. Capstone—End-to-End Mini-Analysis: applying the full lifecycle of framing a question, selecting a source, profiling and cleaning data, exploring results, and reporting findings

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:

Participants should have basic computer literacy and basic familiarity with Microsoft Excel.

Experience in the following would be useful for this Data class:

No prior data analytics or programming experience is needed.

Prerequisite Courses

Courses that can help you meet these prerequisites:

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