Sertan Arkan

Researcher at Uppsala vitro, in vivo, clinical science, imaging, bioinformatics, data analysis - visualization. R and Python.

[Per Uhlén @peruhlen]

Discover our new TRISCO method, which enables 3D RNA analysis at cellular resolution in intact brains. Explore a 3D rendering that reveals the deep brain expression pattern of the Parvalbumin gene, implicated in schizophrenia.
@ScienceMagazine.
https://t.co/tqysZ36zRz

[Sertan Arkan @atlantis08_07]

@gamzeli_Gamze06 @onedio Sever misin ?

[Brian Northan @truenorth_ia]

A little bit into the python API weeds but here is a 2 part video on how to deal with a tough multiscale segmentation problem with Cellpose: part 1 https://t.co/3HeQijfxqX and part 2 https://t.co/GCSBAPmTl2

[Sertan Arkan @atlantis08_07]

Annotate Images Like a Pro: Python Image Annotation Tool Walkthrough https://t.co/EcxTuNcW4c via @YouTube

[Sertan Arkan @atlantis08_07]

341 - Sholl Analysis using python coding https://t.co/JYvulyYgMc via @YouTube

[Sertan Arkan @atlantis08_07]

https://t.co/e8jo6BSEFw

[Mindful Maven @mindfulmaven_]

7. Becoming an Entrepreneur

Topics covered:

• Identifying lucrative business opportunities
• Crafting and refining your product
• Debunking common entrepreneurial myths
• Setting clear, actionable startup goals

https://t.co/UDjtO1c8xJ

[Mindful Maven @mindfulmaven_]

4. The Science of Uncertainty and Data

Topics:
• Probabilistic Calculations
• Basics of Probabilistic Models
• Introduction to Inference Method
• Random Variables and Their Distributions

https://t.co/5hyIjrK2nH

[Mindful Maven @mindfulmaven_]

5. Startup Success: How to Launch a Technology Company in 6 Steps

Topics covered:
• Assess product ideas
• Understand the start-up team
• Prepare and evaluate strategies and more

https://t.co/06XoEWyp8n

[Mindful Maven @mindfulmaven_]

6. Data Analysis: Statistical Modeling

A hands-on introduction to the interplay between statistics and computation for real data analysis.

Learn models, form hypotheses, and perform statistical analysis on real data.

https://t.co/U6tdZy2Oli

[Mindful Maven @mindfulmaven_]

3. Machine Learning with Python

Topics:
• Model Selection
• ML Project Execution
• Model Implementation & Analysis
• Training, Validation, Tuning, Features

https://t.co/V5Ri8HWjKe

[Mindful Maven @mindfulmaven_]

2. Introduction to Computational Thinking and Data Science.

Topics:
• Monte Carlo simulations
• Plotting with pylab
• Stochastic programming
• Statistical thinking

https://t.co/cxgRvngf33

[Mindful Maven @mindfulmaven_]

1. Introduction to Computer Science and Programming Using Python.

Topics:
• Simple algorithms
• Understanding computation
• Data structures and beyond
• Python programming essentials
• Intro to algorithmic complexity

https://t.co/KoguVk7ykt

[Mindful Maven @mindfulmaven_]

MIT University just released free online courses.

No payment is required.

Here are 7 courses you don't want to miss in 2024:

[David Rowland @DavidCRowland]

Really proud to publish this fascinating paper on receptors responsible for sexual touch. It's easy to get prudish and Victorian on this sort of work, but sexual and reproductive health is extremely important
https://t.co/Dmfma4Aj70

[Gina Acosta @ginacostag_]

1. Introduction to Computer Science and Programming Using Python

What you'll learn:

- A Notion of computation
- The Python programming language
- Testing and debugging
- An informal introduction to algorithmic complexity
- Data structures

https://t.co/x1B9cm0jVJ

[Gina Acosta @ginacostag_]

2. Introduction to Computational Thinking and Data Science

What you'll learn:

- Plotting with the pylab package
- Stochastic programming and statistical thinking
- Monte Carlo simulations

https://t.co/RW20pQjH6f

[Gina Acosta @ginacostag_]

3. Machine Learning with Python

What you'll learn:

- Machine learning problem principles
- Implement and analyze models
- Choosing suitable models for different applications
- Implement and organize ML projects

https://t.co/JXLPegsrSu

[Sertan Arkan @atlantis08_07]

https://t.co/GSBvLtYVBQ

[Matt Dancho (Business Science) @mdancho84]

It took me 10 years to master all 24 of these statistics concepts. In the next 24 days, I'll teach them to you one by one (with examples of how I've used them in business cases). Here's what's coming:

1. Probability Distribution
2. Regression
3. Hypothesis Testing
4. Central Tendency
5. Bayesian Statistics
6. Descriptive Statistics
7. Distribution
8. Sampling
9. Confidence Interval
10. Correlation
11. Covariance
12. Dimensionality Reduction
13. Central Limit Theorem
14. Normal Distribution
15. Population and Sample
16. Skewness
17. Variance
18. Data Types
19. Probability
20. Conditional Probability
21. P Values and Level of Significance
22. Variability
23. Over and Under-Sampling
24. Estimation

There you have it-- My top 24 stats concepts every data scientist should know!

But the next problem you’ll face is how to apply data science to business.

I'd like to help.

I’ve spent 100 hours consolidating my learnings into a free 5-day course, How to Solve Business Problems with Data Science. It comes with:

300+ lines of R and Python code
5 bonus trainings
2 systematic frameworks
1 complete roadmap to avoid mistakes and start solving business problems with data science, TODAY.

👉 Here it is for free: https://t.co/YXG4pL97ZN