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

This course is designed for participants new to data analysis who need a repeatable, methodical approach for turning a business question into a defensible answer. It covers the full data analysis lifecycle, from framing stakeholder questions and selecting tools through collecting, profiling, and cleaning data, and on to exploratory analysis that identifies initial trends and patterns using descriptive and introductory inferential statistics.

Course Benefits

  • Frame a vague business problem as an answerable analytical question and align it to the appropriate analytics pillar: descriptive, diagnostic, predictive, or prescriptive.
  • Evaluate primary and secondary data sources and their collection methods for reliability, credibility, and bias.
  • Assess which tools are suitable for an analytics project, given the project's requirements, including dataset size and complexity, the business goals, and the project budget.
  • Prepare a raw dataset for analysis and interpret it using descriptive and introductory inferential statistics.
  • Perform a data quality assessment of an analysis for bias, accuracy, and ethical risk, and report findings with an honest statement of uncertainty.

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 and expected output; setting scope; turning vague concerns into answerable questions.
    2. The Four Pillars of Analytics: Descriptive, diagnostic, predictive, and prescriptive; aligning analysis goals with organizational needs; matching question type to pillar.
    3. Data Sources and Collection Methods: Observational studies vs. 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; profiling a dataset for distribution, outliers, and relationships; assessing data fitness for analytical use.
    2. Cleaning and Structuring Data: Handling missing values; removing duplicates; normalization and standardization; data transformation; validation techniques.
    3. Exploratory Analysis, Descriptive Statistics, and Introductory Inference: Producing graphical and numerical summaries of a dataset's shape, center, and spread; identifying initial trends and patterns.
    4. Bias, Ethics, and Responsible Reporting: Data assumptions and human biases; privacy considerations; evaluating data quality for bias; ethical frameworks.
    5. Capstone: End-to-End Mini-Analysis: Applying the complete lifecycle: question, source, profile, clean, explore, report.

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:

Basic proficiency 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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