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Typefully
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We're launching a Command Bar today with great commands and features.
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Pose a thought-provoking question.
Never run out of ideas
Get prompts and ideas whenever you write - with examples of popular tweets.
@aaditsh
I think this thread hook could be improved.
@frankdilo
On it 🔥
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Easlo
@heyeaslo
Reply with "Notion" to get early access to my new template.
Jaga
@kandros5591
Notion 🙏
DM Sent
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Auto-Split Text in Posts
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150,000+ creators and teams chose Typefully to curate their Twitter presence.
Marc Köhlbrugge@marckohlbrugge
Tweeting more with @typefully these days.
🙈 Distraction-free
✍️ Write-only Twitter
🧵 Effortless threads
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I recommend giving it a shot.
Jurre Houtkamp@jurrehoutkamp
Typefully is fantastic and way too cheap for what you get.
We’ve tried many alternatives at @framer but nothing beats it. If you’re still tweeting from Twitter you’re wasting time.
DHH@dhh
This is my new go-to writing environment for Twitter threads.
They've built something wonderfully simple and distraction free with Typefully 😍
Santiago@svpino
For 24 months, I tried almost a dozen Twitter scheduling tools.
Then I found @typefully, and I've been using it for seven months straight.
When it comes down to the experience of scheduling and long-form content writing, Typefully is in a league of its own.
Luca Rossi ꩜@lucaronin
After trying literally all the major Twitter scheduling tools, I settled with @typefully.
Killer feature to me is the native image editor — unique and super useful 🙏
Visual Theory@visualtheory_
Really impressed by the way @typefully has simplified my Twitter writing + scheduling/publishing experience.
Beautiful user experience.
0 friction.
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@aaditsh
I think this thread hook could be improved.
@frankdilo
On it 🔥
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Step by step process for Exploratory Data Analysis ↓
Before we start, let us first understand what exactly is EDA and why is it important?
Exploratory Data Analysis is an approach to analyzing and understanding data sets through visualizations and statistical techniques.
EDA is an iterative process and can be done multiple times.
📊 It is a crucial step in the data science processes as it allows analysts to:
• Gain a deeper understanding of the data they are working with
• Identify patterns and correlations
• Identify outliers that may need to be addressed before building models or making predictions.
1. Define the problem 🎲:
The first step in the process is to clearly define the problem you want to investigate. This ensures that you are gathering the necessary data to answer your question.
Gather the relevant data from various sources like databases or spreadsheets.
2. Clean and organize the data:
It is important to clean, organize the data to ensure it is ready for analysis + makes it easy to work with. This step involves -
- removing any missing/ irrelevant data
- sorting the data
- creating new columns
- merging multiple data sets, etc
3. Univariate Analysis:
This step involves analyzing variables individually to understand the distribution & identifying outliers.
Statistical measures to describe data:
- mean, median, mode, std. dev.
Viz. to understand the distribution:
- histograms, boxplots, scatterplots
4. Bivariate Analysis:
🔍 This allows you to analyze the relationship b/w pairs of variables.
- Useful for identifying patterns or correlations in the data. Visualizations such as scatter plots, line plots, and bar charts help you understand the relationship b/w variables.
5. Multivariate Analysis:
Allows you to analyze the relationship among multiple variables.
- Helps you gain a deeper understanding of the data and identify any trends or patterns.
Techniques such as factor analysis, and cluster analysis can be used.
6. Data visualization:
Once you're done with data analysis, it's time to be a data wizard and create some visuals to get some deeper insights & communicate your findings. 🧙♂️
- Use data viz libraries as Matplotlib, Seaborn, Plotly and Bokeh to create scatter plots & heat maps.
7. Draw conclusions:
At the end, you should summarize your findings, draw conclusions, and identify any next steps for further analysis or investigation.
This allows you to determine what additional analysis or research is needed to answer your initial question or problem.
That's all for this one! Thanks for reading 👋
Hope it helps.
Planning to post such threads regularly.
Give a follow if you don't want to miss out on new content :)