Efficient Python Tricks and Tools for
Data Scientists - By Khuyen Tra

GithubRepo : https://github.com/khuyentran1401/Efficient_Python_tricks_and_tools_for_data_scientists
Stars ⭐️: 675
Forked By: 202

Hello Dear😊!!!
Have you heard of The Python For Machine Learning International Bootcamp coming up on the 12th of September?
Link: Click Me
If you haven't, Global AI Hub is organizing a FREE ONE-MONTH INTENSIVE boot camp on python for machine learning.
This is a chance to improve yourselves in subjects such as Python😍, #machinelearning😍, #datascience😍, and #deeplearning😍!!!
In addition, you will be able to develop your portfolios ☺️ with the project work😃 that you will do from scratch under the guidance of mentors!!!😁
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Link: Click Me
DEADLINE😱😱 : 7th September 2022

Implementing DBSCAN in Python

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based unsupervised learning algorithm. It computes nearest neighbor graphs to find arbitrary-shaped clusters and outliers. Whereas the K-means clustering generates spherical-shaped clusters.

Learn more about working with it in this article
Link

The Applied Data Science Lab is open for applications!

This program is oragnised by World Quant University.
The Applied Data Science Lab is a credentialed offering where students tackle real-world meaningful, and complex problems.
By completing a series of end-to-end data science projects, they build the wrangling, analysis, model-building, and communication skills to prepare them for success in data-centric careers in both the private and public sectors.

What you will cover:
⭐️Leverage Real-World Data
⭐️Access All the Tools you Need
⭐️Guides by Your Side
⭐️Develop The Skills to Build a Professional Portfolio

Link: Register for Free

DPhi Python Basics for Data Science Bootcamp

At the end of this Bootcamp you will know the following things:

➡️ Installing Anaconda and introduction to Jupyter Notebook

➡️ Getting familiar with Python syntaxes and writing your first Python program

➡️ Variables, Data Types, and Operators in Python

➡️ Data Structures and Data Types in Python

➡️ Python Functions and Packages/
Register Here

Data Analyst Boot camp 2022: Get Ready to Be a Data Analyst.

Rating ⭐️: 3.8 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨‍🏫: 24,500
Created by: TemoTech Learning Academy

🔗 Course link

SQL Boot Camp 2022: Complete SQL Course

Rating ⭐️: 4.1 out of 5
Duration ⏰: 4 hours on-demand video
Students 👨‍🏫: 10,304
Created by: Temo Tech Academy

🔗 Course link

SQL Course 2022: SQL for Data Analysis and Data Science.

Rating ⭐️: 3.4 out of 5
Duration ⏰: 5.5hours on-demand video
Students 👨‍🏫: 110,841
Created by: TemoTech Learning Academy

🔗 Course link

#sql #datascience #datanalysis

FREE UDEMY COURSES

Complete Linear Regression Analysis in Python

Rating ⭐️: 4.5 out of 5
Duration ⏰: 7.5 hours on-demand video
Students 👨‍🏫: 150,747
Created by: Star Tech Academy

🔗 Course link

Data Analytics A-Z with Python

Rating ⭐️: 4.1 out of 5
Duration ⏰: 4 hours on-demand video
Students 👨‍🏫: 56,145
Created by: Yaswanth Sai Palaghat

🔗 Course link

Object Detection Web App with TensorFlow, OpenCV and Flask

Rating ⭐️: 4.6 out of 5
Duration ⏰: 1 hour on-demand video
Students 👨‍🏫: 32,020
Created by: Yaswanth Sai Palaghat

🔗 Course link

Logistic Regression in R Studio

Rating ⭐️: 4.6 out of 5
Duration ⏰: 6 hours on-demand video
Students 👨‍🏫: 82,771
Created by: Star Tech Academy

🔗 Course link

Logistic Regression in Python

Rating ⭐️: 4.3 out of 5
Duration ⏰: 7.5hours on-demand video
Students 👨‍🏫: 95,949
Created by: Star Tech Academy

🔗 Course link

#python #datanalysis #data_science #deep_learning #machinelearning
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group

Multilingual NLI Dataset

Moritz Laurer, a PhD researcher working with NLP at Vrije Univeristy of Amsterdam, announced on his Twitter account that new multilingual dataset is ready!

New dataset contains 2 730 000 NLI text pairs in 26 languages. It was created from previous English dataset using the latest open-source machine translation model.

The dataset can be loaded here.

Free Machine Learning Course

Learn ML engineering in 4 months in a free online course by Al_Grigor from DataTalksClub

What you will learn:
- Linear and logistic regression
- Tree-based models
- Neural networks
- Deployment with AWS, Serverless, Kubernetes

Register here: Link

Understanding The Structure of Your Data

1) Univariate Visualization: This visualization is used to gain a summary statistics of each feature in your dataset. The goal of univariate visualization is to have a solid understanding of the data in order to start querying and visualizing our data in various ways. It uses tools such as barplots and histograms to reveal the structure of the data.

2) Bivariate Visualization: This visualization is used when you need to find relationships between two variables in your dataset where one of the variable could be the target variable. It uses correlations, scatter plots and line plots to reveal structure of the data.

3) Multivariate Visualization: This is employed to understand interactions between different fields in the dataset. It uses line plots, scatter plots, and matrices with multiple colors to understand the relationship between various features of a dataset.

Machine Learning YouTube Courses

This repo contains some of the best and most recent machine learning courses available on YouTube.

Creator: dair-ai
Stars⭐️: 8.1k
Fork: 977
Repo Link: Click Me

FREE UDEMY COURSES

Artificial Neural Networks (ANN) with Keras in Python and R

Rating ⭐️: 4.3 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨‍🏫: 153,510
Created by: Star Tech Academy

🔗 Course link

Marketing Analytics: Forecasting Models with Excel

Rating ⭐️: 4.5 out of 5
Duration ⏰: 7 hours on-demand video
Students 👨‍🏫: 134, 539
Created by: Star Tech Academy

🔗 Course link

Decision Trees, Random Forests, Bagging & XGBoost: R Studio

Rating ⭐️: 4.6 out of 5
Duration ⏰: 6 hours on-demand video
Students 👨‍🏫: 60,768
Created by: Star Tech Academy

🔗 Course link

#machine_learning l #datascience #datanalysis #neural_networks #deep_learning #ai #python

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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group

GOOGLE CLOUD FREE MACHINE LEARNING AND AI COURSE

Learn how to implement the latest machine learning and artificial intelligence technology by exploring training on BigQuery, TensorFlow, Cloud Vision, Natural Language API, and more

what you will learn:
👌Big Data & Machine Learning Fundamentals
👌Perform Foundational Data, ML, and AI Tasks in Google Cloud
👌Machine Learning on Google Cloud
👌Advanced Machine Learning with TensorFlow on Google Cloud Platform
👌MLOps (Machine Learning Operations) Fundamentals
👌ML Pipelines on Google Cloud
👌Build and Deploy Machine Learning Solutions on Vertex AI
👌Create Conversational AI Agents with Dialogflow CX

https://cloud.google.com/training/machinelearning-ai

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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group

Data Cleaning Checklist

Data cleaning takes up 80% of the data science workflow. Use this checklist to identify and resolve any quality issues with your data.

Link

I understand data science is not all about programming but, as far as I know, Python comes into play to some extent on this matter. How much should I know about programming to do data science?

This question was asked earlier today by one our community member in our main channel @bigdataspecialist.
I decided to share my answer here since it might be interesting to some of you and I am pretty sure great majority of you haven't even noticed his question/my answer.

TL:DR
Programming is important, but you don't have to be an expert. You just need some basic to intermediate skills to prepare your data (which you are going to use in data science tasks), possibly make some data visualizations to gain insights and at the end to create your machine learning models. These basic skills could probably be gained in a month, especially if you are not complete newbie who has never heard of programming 😅

You can get mentioned skills from this course: https://www.coursera.org/learn/python-data-analysis
Teacher is Christopher Brooks and course is created by University of Michigan.
Note: I know it says its paid one, buy you can apply for financial aid and get course for free. That's how I got this course when I just started learning data science.



Keep in mind that Python is not only programming language which comes to mind when you think about doing data science.
For example, for almost all data science and machine learning tasks, I use Java. It's very specific and usually data scientists don't do that, but platform developed by my company is receiving 4k requests per second, so we need something blazing fast, and Python is pretty slow. That's why we use Java.
But if I am going to test something locally, or I need some easy data preparation or data visualizations, I use Python. Creating charts to gain some insights would be real nightmare with Java. But for you as a beginner Python is probably best choice

Long story short:
If you want it fast and easy, python is way to go
IF you want it very fast (but probably pretty hard to make it work) - Java.
If you want to perform advanced calculations and visualizations - R
If you want to show your visualizations dynamically on some web page, then certain JavaScript libraries like D3.js or chart.js.

Hope this helps.

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Join @datascience_bds for more cool data science materials.
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Huge collection of Data Science materials
By Harvard

You will find lecture notes/Notebooks for every data science/machine learning topic you heard about 🤯

https://harvard-iacs.github.io/2019-CS109A/pages/materials.html

Understanding and Handling Overfitting

When a machine learning method fits the Training Data really well but makes poor predictions, we say that it is Overfit to the Training Data.

Let's try to undertstand how this occurs and how to handle it,

1) Overfitting in Machine Learning
Link

2) Overfitting and Underfitting With Machine Learning Algorithms
Link

3) Overfitting by IBM
Link

4) The Complete Guide on Overfitting and Underfitting in Machine Learning
Link