How to Analyze Data - A Step by Step Guide

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Data analysis is a crucial skill in today's world. Whether you're a researcher, marketer, or simply curious about a topic, understanding how to analyze data is essential. In this video, we'll take you through a step-by-step guide on how to analyze data effectively.

We'll begin by defining your hypothesis, which is the question you want to answer through your analysis. Understanding what makes a successful analysis is also crucial, as it ensures that your analysis is relevant and informative. We'll then move on to looking for trends in your data, which involves identifying patterns and relationships between variables.

Defining the key events and variables is another crucial step in data analysis, as it helps you understand the data and draw conclusions. Knowing your audience is also vital, as it helps you communicate your findings effectively.

Lastly, we'll emphasize the importance of repeating each step when necessary, as data analysis is an iterative process that often requires revisiting previous steps. By following these steps, you'll be able to analyze data effectively and draw meaningful conclusions.

So whether you're a data novice or an experienced analyst, this video will provide you with valuable insights and practical tips on how to analyze data like a pro. Don't miss out on this opportunity to enhance your analytical skills and stay ahead of the game!

#data
#dataanalysis
#businessanalysis
#insights

[drivetrainerYT]

Great video, wish clients possessed this understanding or at least eagerness to find out what is the use of collecting structured data and analysing it for business

[ronenTheBarbarian]

Great Video! I think for a follow up video, it would be interesting to take this further and apply to diverse types of analysis used today in different areas.

User analysis, product analysis, financial analysis (dealing with profit/revenue), marketing analysis (related to campaigns etc.), fraud analysis, stock market analysis, are all different 'worlds' I had to work in as a data analyst, with varying degrees of involvement, and being involved in different stages of the data analysis phase.

Example for user analysis/behaviour: - User Behaviour with a website/product:
Step 1 could be like "Why do 30% of our subscribers drop at the end of the year". The question can come from leadership, or it is something you will notice before anyone else and will have to tackle it.
Step 2 could be "Success would be Subscriber who is retained after the end of year"
Step 3: What trends should we measure for this? We should be looking at logins (active logins) of subscribers who drop, and subscribers who are retained. Or we can look at what premium features are being used by retained subscribers, and what is the engagement like for subscribers who drop? Do any of the dropped subscribers run into any technical issues? Do any of them contact customer support for any assistance with these features?
Step 4: Events needs are login event, active flag or condition (customer signed in at least once in the past 30 days), what areas of the website/product they interacted with, so actual URLs or component names
Step 5: The audience in this case product manager, senior leadership, etc.

Just trying to think of how this amazing guide can be applied to real-world scenarios out in the field. I completely agree with you that one must sometimes force clients to be a part of this, but other times, we are "stuck" on our own to come up with baselines/hypothesis with no guidance or existing KPIs in place. I think analysts would benefit greatly if they knew about all the different areas they can work in, along with the data "dialect" in each area.

Hope that made sense.

[method]

Great video. What app did you use to make your slides?

[watcherme]

nice video, can u recommend book or more details? thanks😊