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Data Analytics

Course Summary

The Data Analytics training course covers fundamental concepts around extracting business insights from large data sets. The course will be heavily focused on extracting and aggregating data and presenting it for exterior consumption.

The course begins by teaching students how to understand the analytics process and understanding the shape of their data. Next, the course covers metrics and what they can tell you, model creation, and how to clean the data. The course concludes with error analysis and how to present your data.

Learn how to locate, manipulate, and analyse data with Python, no matter the size of the data set.
Developers with some experience in Python interested in learning more about Python, Data Science, or Data Visualizations.
Business Analyst - Data Engineer - Data Scientist - Software Developer - Web Developer
Skill Level
3 Days
Related Technologies


Productivity Objectives
  • Acquire tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
  • Describe common Python functionality and features used for data science.
  • Adopt techniques in Pandas to create, structure, manipulate, and clean data.
  • Manage various data formats within Python.
  • Visualize the data.

What You'll Learn:

In the Data Analytics training course, you'll learn:
  • Understanding the Analytics Process
    • Load, prepare, manipulate, model, and analyze
    • Collection of data
    • Transforming data
    • Cleaning of data
    • Modeling of data
  • Getting the Shape of the Data
    • Using sampling to understand the shape of the data
    • Context around data
    • Sampling
  • Understanding What Metrics Can Show and What They Cannot
    • Pattern recognition
    • Vanity Metrics vs Important metrics
    • Real-Time vs Historical data
    • Handling anomalies and outages in data
  • Model Creation
    • Measures of accuracy around models
    • Regression vs Classification
    • Predictive analytics
  • Cleaning Data
    • Handling bad values
    • NULL value removal
  • Removing Malformed/Immaterial Data
    • Data replacement
    • Dropping unnecessary noise
  • Finding Error Patterns
  • When to Remove Data
  • Signs of an Error in an Analysis
  • Presenting Data
  • Chart Types:
    • Histogram
    • Time Series
    • Line
    • Mosaic
    • Classification
  • Value of Visual Explanation
    • Setting up data updates (daily/weekly/monthly)
    • Summary
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”


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