Head First SQL
Here's a brain friendly guide to learning SQL for beginners

Author:Lynn Beighley
Pages: 586
Link: Click Me!

Statistics Guide for Data Science
Learning Statistics for Data Science can be quite overwhelming for beginners without a Statistics background. One can get confused on which topics to learn or books to read up to equip their knowledge

You don't have to learn it all. Here are essential topics you can learn

1) Know what a p value is and its limitations
2) Linear Regression and its Assumptions
3) Different Statistical Distributions and when to use them
4) Mean, Variance for Normal, Poisson, and Uniform Distribution
5) Sampling Techniques and Common Designs(eg: A/B)
6) Bayes Theorems and it's application
7) Measurements and Interpretation of Confidence Intervals
8) Logistics Regressions and ROC curves
9) Resampling(Cross Validation and Bootstrapping)
10) Tree Based Models

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

Where to find Data for Machine Learning

High quality data is key for building useful machine learning models. Models learn their behaviour from data. So, finding the right data is a big part of the work to build machine learning into your products.

This article gives a concise explanation on finding the right data for your models.

https://towardsdatascience.com/where-to-find-data-for-machine-learning-e375e2a515c8

SQL Free Resources
Looking to learn SQL for free? Here is a curated list of websites you can use to upgeade your SQL skill level or practice writing queries. Remember SQL is a necessary skill to have in your toolkit as a data professional.

1. W3 Schools

https://w3schools.com/sql

2. SQL Zoo

http://sqlzoo.net

3. SQLBolt

http://sqlbolt.com

4. Khan Academy

https://khanacademy.org/computing/computer-programming/sql

5. FreeCode Camp

https://youtu.be/HXV3zeQKqGY

To Practice what you have learned and build your skill at hte same time , you can use these:

6. Hacker Rank

https://hackerrank.com/domains/sql

7. SQL Murder Mystery Game

https://mystery.knightlab.com

#datascience #SQL

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

Machine Learning with Python: Zero to GBMs

This is a practical and beginner-friendly introduction to supervised machine learning, decision trees, and gradient boosting using Python. This is a self-paced course where you can:

👌Watch hands-on coding-focused video tutorials
👌Practice coding with cloud Jupyter notebooks
👌Build an end-to-end real-world course project
👌Earn a verified certificate of accomplishment
👌Interact with a global community of learners
👌You will solve 2 coding assignments & build a course project where you'll train ML models using a large real-world datasets

Link: https://jovian.ai/learn/machine-learning-with-python-zero-to-gbms

Text Classification with TensorFlow

This is an intermediate-level Python course taught by MIT grad student Kylie Ying. You can code along at home in your browser.

You'll use TensorFlow to train Neural Networks, visualize a diabetes dataset, and perform Text Classification on wine reviews. (2 hour YouTube course)

Link: https://www.freecodecamp.org/news/text-classification-tensorflow/

Introduction to Machine Learning, IIT Kharagpur

🆓 Free Online Course
💻 44 Lecture Videos
🏃‍♂️ Self paced
Teacher 👨‍🏫 : Prof. S. Sarkar

🔗 https://nptel.ac.in/courses/106105152

The Scikit-Learn Guide

Looking to improve your knowledge on machine Learning ALgorithms, there's no better place to start from than to check the sklearn documentation

There is alot of interesting information you can gain there

https://scikit-learn.org/stable/

Want to make sure your Spark applications reach the best performance?

We invite you to our Dynamic Talks #90 | Spark performance mastery!
⏰ Date and time: July 20, 6:30 pm (CET)

The speaker is Iñigo San Aniceto Orbegozo, Staff Big Data Engineer at Grid Dynamics.

💻 Participation is free but registration is required: https://forms.gle/UVvfWG5LeZAXTuNQ6

More about event: https://fb.me/e/1U9Vq4epw

Just wanted to share this 👆 here as well in case somebody is interested.

**A List Of Free Data Science Tutorials**

🔘Python for Data Science - Great Learning
Rating ⭐️: 4.2 out of 5
Duration ⏰: 1 hour 55 mins on-demand video
Students 👨‍🏫: 25,605
Created by: Bharani Akella
🔗 Course link

🔘A - Z™ Python crash course for Data Science 2021
Rating ⭐️: 4.4 out of 5
Duration ⏰: 2 hours on-demand video
Students 👨‍🏫: 7,012
Created by: Abb Selec
🔗 Course link

🔘An Athlete’s Guide To Data Science
Rating ⭐️: 3.0 out of 5
Duration ⏰: I hour 1 min on-demand video
Students 👨‍🏫: 1,975
Created by: Jon pierre Jones
🔗 Course link

🔘NumPy for Data Science Beginners: 2021
Rating ⭐️: 4.0 out of 5
Duration ⏰: I hour 51 mins on-demand video
Students 👨‍🏫: 11,535
Created by: Abb Selec
🔗 Course link

🔘Learn Data Science With R Part 1 of 10
Rating ⭐️: 4.1 out of 5
Duration ⏰: 8 hours 42 mins on-demand video
Students 👨‍🏫: 32,824
Created by: Ram Reddy
🔗 Course link

🔘Data Science with Analogies, Algorithms and Solved Problems
Rating ⭐️: 4.1 out of 5
Duration ⏰: 1 hour 19 mins on-demand video
Students 👨‍🏫: 15,706
Created by: Ajay Dhruv, Neha Mayekar, Shreya Pattewar, Shubham Patil
🔗 Course link

🔘Data Science, Machine Learning, Data Analysis, Python & R
Rating ⭐️: 3.8 out of 5
Duration ⏰: 8 hours 7 mins on-demand video
Students 👨‍🏫: 89,564
Created by: DATAhill Solutions Srinivas Reddy
🔗 Course link

🔘Intro to Data for Data Science
Rating ⭐️: 4.6 out of 5
Duration ⏰: 1 hour 1 min on-demand video
Students 👨‍🏫: 9,727
Created by: Matthew Renze
🔗 Course link

🔘Learn NumPy Fundamentals (Python Library for Data Science)
Rating ⭐️: 4.3 out of 5
Duration ⏰: 1 hour 49 mins on-demand video
Students 👨‍🏫: 27,038
Created by: Derrick Sherrill
🔗 Course link

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

Fundamentals of Data Visualization

A primer on making informative and compelling figures
Author: Claus . O . Wike
Book Link; Read Me!

A Guide to Understanding Different Types of Data

Hey There😃!!
Do you know the different formats your data can be in and how to identify them?😌
Here's a guide that can help you😉

Structured Data : It is in a standardized format, has a well-defined structure, complies to a data model, follows a persistent order, and is easily accessed by humans and programs. This data type is generally stored in a database. Normally in a table or number of tables.
Examples: Data from surveys, different sensors, point-of-sale details, and financial information

Unstructured Data: It does not conform to any other model and has no easily identifiable structure. There is no organization to it and it cannot be stored in any logical way. Unstructured data does not fit into any database structure, has no rules or format, and it cannot be easily used by programs.
Examples: raw videos from surveillance cameras, reports, file shared with corporate documents, images, and memos.

Semi Structured Data: It is not in a relational database, does not conform to a data model, but has some elements of structure. It cannot be stored in rows and columns or databases. This data contains metadata and tags which helps it to be grouped appropriately and describes the way it is stored. Semi-structured data is organized hierarchically, although the entities within that group may not have the same properties or attributes. It is difficult to automate and manage and is hard for programs to access.
Examples: wikipedia pages with links, collection of scientific papers in JSON format with authors, emails, zipped files, web files, and binary executables.

Different Data Sources and How They Are Collected

1) Company Data Sources:
Web Events, Survey Data,
Customer Data,
Logistics Data and Financial Transactions.

2) Open Data Sources:
Public Data APIs,
Public Records
APIs request data over the internet. Interesting API's include:
Twitter, Wikipedia, Yahoo Finance, Google Maps etc
Public records data can be collected by international organisations like World Bank, UN, WTO

3) National Statistical Offices:
Censuses
Surveys

4) Government Agencies:
Weather Data
Environment Data
Population Data