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Avi Kumar Talaviya Profile picture
@avikumart_
Jul 26, 2022 • 12 tweets • 7 min read • Read on X
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12 data science communities you can join to enrich your data learning journey & collaborate with like-minded people📊📈

A🧵↓
List of 12 communities↓

1) #/66DaysOfData
2) Data leaders
3) Data literacy project
4) IBM Datasci community
5) Kaggle
6) Makeover Monday

↓ (Credit courtesy: @DataKwery)
7) ODSC
8) R-ladies
9) R-studio community
10) Tableau community
11) TidyTuesday community
12) WomanWhoCode

Click the link below to access all community links at once compiled by @DataKwery

🔗 datakwery.com/communities/?u…
A QUICK shoutout to @DataKwery for helping me create this thread for the community

Check out their website for 2400+ data science resources and courses to learn industry-relevant data skills (Link below)

And don't forget to sign up to stay updated!!
🔗 datakwery.com/?utm_source=av…
1) #66DaysOfData

🔗 kennethjee.com/66daysofdata

2) Data leaders

🔗 worldcongress.dataleaders.net
3) Data literacy project

🔗 forum.thedataliteracyproject.org

4) IBM Datasci community

🔗 community.ibm.com/community/user…
5) Kaggle

🔗 kaggle.com/competitions

6) Makeover Monday

🔗 makeovermonday.co.uk
7) ODSC (Open data science community)

🔗 odsc.com

8) R-ladies

🔗 rladies-community-slack.herokuapp.com
9) R-studio community

🔗 community.rstudio.com

10) Tableau community

🔗 community.tableau.com/s/
11) TidyTuesday community

🔗 tidytuesday.com

12) WomanWhoCode

🔗 womenwhocode.com
You can read the unrolled version of this thread here: typefully.com/avikumart_/MSa…
End of this thread!👍

If you've found it informative then do like, RT/QT first tweet, and comment what you think on this💬

And Don't forget to follow @datakwery and @avikumart_ for more such threads and information!😃✌

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More from @avikumart_

Avi Kumar Talaviya Profile picture
Mar 16
Data preprocessing is a crucial step in data-driven decision-making as it involves transforming raw data into a format that is suitable for analysis.

Without such methods it is impossible to transform data and derive useful insights

Learn more👇
1. Data Cleaning:

This involves handling missing or erroneous data. Techniques include imputation (replacing missing values with a sensible estimate), deletion of rows or columns with missing data, or using algorithms that can handle missing data directly.
2. Data Transformation:

This includes techniques such as normalization and standardization to rescale data to a similar scale, making it easier to compare different features.
Read 13 tweets
Avi Kumar Talaviya Profile picture
Jan 8
Time series forecasting is crucial for predicting future trends based on historical data.

It is one of the most important topics to learn in data science👇

Here are 5 methods widely used in the field: Image
1️⃣ ARIMA (AutoRegressive Integrated Moving Average):

ARIMA is a powerful and widely-used method that combines autoregression, differencing, and moving averages.

It's effective for stationary time series data, capturing trends and seasonality.
2️⃣ Exponential Smoothing (ETS):

ETS methods, including Holt-Winters, use weighted averages of past observations to forecast future values.

This approach adapts to changes in data patterns and is suitable for various time series characteristics.
Read 7 tweets
Avi Kumar Talaviya Profile picture
Dec 30, 2023
Large language models are growing rapidly as many AI startups have launched new models lately

But do you know some basic facts about LLMs?

If not then here's the thread for you👇 Image
1/ Pre-training:

Large language models are pre-trained on vast amounts of diverse and unstructured data from the internet. During this pre-training phase, the model learns to understand the complexities of language, including grammar, semantics, and context.
2/ Transfer learning:

The pre-trained models can be fine-tuned for specific tasks, allowing them to adapt to more specialized domains with less data compared to training from scratch.
Read 15 tweets
Avi Kumar Talaviya Profile picture
Dec 25, 2023
Natural language to SQL is one of the most exciting applications of large language models

Here's the step by step guide to build such application👇
1. User Input:

• Users provide natural language queries or requests as input to the application.

• Example: "Retrieve all customers who purchased in the last month." 2.
2. LLM Processing:

• Use LLM (Large Language Model) to process and understand the user's natural language input. • Extract the intent and key entities from the user query.

• Example: Identify the intent as "Retrieve" and entities like "customers" and "last month."
Read 14 tweets
Avi Kumar Talaviya Profile picture
Dec 23, 2023
CRISP-DM stands for Cross-Industry Standard Process for Data Mining.

Conceived in 1996 by leaders in the then-nascent field of data mining—DaimlerChrysler, SPSS, and NCR—it was born out of a need for a standardized data mining procedure that could grow into a business process
The Lifecycle of a Data Mining Project The framework is not a linear path but a dynamic, iterative process.

Constantly evolving business requirements and data insights mean that moving back and forth through the stages is common and necessary for success.
1. Business Problem Understanding:

Here, it's all about grasping the enterprise’s core issue. The aim is to translate business goals into a data mining problem and sculpt a preliminary plan festooned with specific tasks.
Read 10 tweets
Avi Kumar Talaviya Profile picture
Dec 7, 2023
Java is the most popular programming language among coders

here are some basics of Java to learn for every programmer👇
1/ Java environment: The Java environment is a platform that allows you to develop and run Java programs.

It consists of the Java Virtual Machine (JVM), the Java Development Kit (JDK), and the Java Runtime Environment (JRE).
2/ Java strings:

Java strings are immutable, meaning that they cannot be changed once they are created.

To create a Java string, you can use the `String` keyword.
Read 11 tweets

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