Data Analytics Course | DevelopIntelligence

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.

Purpose

Learn how to locate, manipulate, and analyse data with Python, no matter the size of the data set.

Audience

Developers with some experience in Python interested in learning more about Python, Data Science, or Data Visualizations.

Role

Business Analyst - Data Engineer - Data Scientist - Software Developer - Web Developer

Skill Level

Introduction

Style

Workshops

Duration

3 Days

Related Technologies

Python

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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.

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about our training

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

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
- COALESCING
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