Uzay

I am creating content about Data Science to keep you and myself learning actively.

[Uzay @NVirtualis]

Hi everyone!

Have you ever wondered about:
Why is data cleaning crucial?🤔
What does dirty data mean?🤔
And how can we clean our data to be tailored for our goals? 🎯

Let's get into this data-cleansing thread🧹💡 #DataCleaning #DataScience

[Uzay @NVirtualis]

@levikul09 Great explanation Levi! Thanks a lot for your labour and time

[Levi @levikul09]

How to describe distribution?

Skewness 🧵

[Uzay @NVirtualis]

🌟 Navigating Missing Data Mechanisms: A Gym Survey Depiction 💪🌐

Let's delve into the types of missing values in this thread 🧵#DataScience

[Uzay @NVirtualis]

Understanding these missing value mechanisms is vital for an accurate approach to handling missing data in our data sets so that we can decide whether to apply data imputation or deletion. 🌐📊#DataScience

[Uzay @NVirtualis]

2️⃣ MAR (Missing At Random):

Scenario: Participants skip or elderly ones forget weight based on self-reported height and age (X)
Explanation: Missing weights depend on observed variable (X), like height, age. Varying heights may lead to more skips. 📏🤔 #DataScience

[Uzay @NVirtualis]

3️⃣ MNAR (Missing Not At Random):

Scenario: Participants hide weight, linked to their weight itself (Y).
Explanation: Missing weights are tied to the variable being measured. Those with a higher body weight might be more likely to skip. 💡#DataScience

[Uzay @NVirtualis]

1️⃣ MCAR (Missing Completely At Random):

Scenario: Gym scale malfunctions during survey.
Explanation: Participants face random weight value gaps, unrelated to any characteristics (X or Y). A glitch, not tied to survey factors. 🏋️‍♂️#DataScience

[Uzay @NVirtualis]

🌟 Navigating Missing Data Mechanisms: A Gym Survey Depiction 💪🌐

Let's delve into the types of missing values in this thread 🧵#DataScience

[Uzay @NVirtualis]

Stay tuned for upcoming content where we dive deeper into Data Science. 🚀🔍 #DataCleansing #DataScience

[Uzay @NVirtualis]

In other words, clean data equals clean decisions. Having clean data will ultimately boost overall productivity and provide the highest quality information for your decision-making.

[Uzay @NVirtualis]

As for dealing with typos in 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗰𝗮𝗹 𝗰𝗼𝗹𝘂𝗺𝗻𝘀, "if it is a lot and varying", the Levenshtein distance metric could be a good option for fixing misspellings.

[Uzay @NVirtualis]

𝗪𝗵𝘆 𝗶𝘀 𝗱𝗮𝘁𝗮 𝗰𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗰𝗿𝘂𝗰𝗶𝗮𝗹?

Data cleaning is highly important because it ensures the accuracy, reliability, consistency, compatibility, and completeness of our data resulting in meaningful insights and sound decisions to achieve our goals.

[Uzay @NVirtualis]

Hi everyone!

Have you ever wondered about:
Why is data cleaning crucial?🤔
What does dirty data mean?🤔
And how can we clean our data to be tailored for our goals? 🎯

Let's get into this data-cleansing thread🧹💡 #DataCleaning #DataScience