Data Analysis tip #1

Here are the five main steps in the data analysis lifecycle that you must be comfortable with:

Define the Problem: This initial stage involves understanding the business question or problem you’re trying to solve with data analysis.

Data Collection and Preparation: Once you know the problem you’re tackling, it’s time to gather the relevant data. This may involve collecting data from internal databases, external sources, or a combination of both. Data preparation involves cleaning, organizing, and formatting the data to ensure its accuracy and usability for analysis.

Data Exploration and Analysis: Getting to know your data through summary statistics and visualization to uncover patterns, trends, and relationships within the data.

Model Building and Evaluation: This stage is optional. Some data analysis projects will not involve model building. This stage is often used in data analysis projects, particularly those involving predictive modeling. Here, you would develop a model based on the data and then evaluate its effectiveness in achieving your desired outcome. This may involve techniques like machine learning or regression analysis.

Data Presentation: The final step involves sharing your findings and putting them into action. This may involve creating reports, visualizations, or presentations to communicate insights to stakeholders.

You want to get comfortable with these steps? Start your 50-day journey to learn data analysis with Python if you have not started yet. Link below:

50 Days of Data Analysis with Python: The Ultimate Challenge Book for Beginners

0 comments

Write a comment

Shut Up and Do the Work

50 Days of Python A Challenge a Day: Three Years Later

Master Python Classes: Object-Oriented Programming Crash Course

[TRY THIS CHALLENGE] On Conditional Statements

Working With Databases - Master Python Fundamentals

See all posts from Benjamin Bennett Alexander