Learn SQL Basics for Data Science

I'm thinking about working on my SQL with the Modern Big Data Analysis with SQL Specialization course.

Most courses that teach SQL focus on traditional relational databases, but today, more and more of the data that’s being generated is too big to be stored there, and it’s growing too quickly to be efficiently stored in commercial data warehouses. Instead, it’s increasingly stored in distributed clusters and cloud storage. These data stores are cost-efficient and infinitely scalable.

To query these huge datasets in clusters and cloud storage, you need a newer breed of SQL engine: distributed query engines, like Hive, Impala, Presto, and Drill. These are open source SQL engines capable of querying enormous datasets. This Specialization focuses on Hive and Impala, the most widely deployed of these query engines.

But there's also a SQL Spark course.

On top of that I want to do Udacity's Data Engineering nanodegree program free for a month.

I'm lost.

Codeacademy - https://www.codecademy.com/catalog/language/sql

Coursera - https://www.coursera.org/specializations/learn-sql-basics-data-science

SQL Cookbook by O'Reilly publications - https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjI57S-k7PsAhVv_XMBHW9JBUAQFjAAegQIBBAC&url=http%3A%2F%2F188.166.46.4%2Fget%2FPDF%2FAnthony%2520Molinaro-SQL%2520Cookbook_177.pdf&usg=AOvVaw3AFjA9Kvafl5HfdiS32wxt

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Hope this helps!

Hi!

I took Python for Everybody and it was a very good introduction. There's a Python for Data Science (also from U Mich) that apparently is pretty good too: https://www.coursera.org/specializations/data-science-python

Another good class I took prior to the OMSA was Mathematics for Machine Learning (a three-course specialization): https://www.coursera.org/specializations/mathematics-machine-learning It was very helpful to have a general idea of what Machine Learning is and a good refresher of Linear Algebra and Calculus.

I have heard pretty good comments from this class: https://www.edx.org/course/linear-algebra-foundations-to-frontiers They use Matlab (which is one of the two programming languages you can use in the ML class, so it would be nice to have that skill).

Apparently the hardest compulsory class of the program is Data and Visual Analytics which has 4 50-60-hour projects your are meant to finish in 3 weeks each. So you will need to quickly understand tools such as SQL (https://www.coursera.org/specializations/learn-sql-basics-data-science), Linux Command Line (https://www.coursera.org/courses?query=linux%20commands), D3.js (https://www.coursera.org/specializations/information-visualization), among others in the cloud.

In general I agree that most of what is taught during the master can be learned on each class as their are self-contained, but being prepared in advance can help you have a better experience and learn more in-depth content.

All the best!

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Esse é o case.

Eu estou estudando no momento Python por essas fontes aqui (essa, essa e essa).

Eu também estou realizando esse curso. E quando terminar esses pretendo começar esses aqui (1, 2, 3 e 4) eu digo que pretendo, pq já fiz o pedido de auxilio financeiro do courseira só estou esperando aprovação.

Fora isso eu tenho alguns livros aqui comigo de algebra linear, matematica discreta e estatistica que foram bastante recomendados e eu baix.. aluguei na loucadora do Paulo Coelho, e eu estou acompanhando um curso de estatistica da USP.

São todos cursos gratuitos, mas estou curtindo bastante, especialmente esses do Coursera que parace ter um projeto aplicado para portfólio.

Mas digo que estou estudando isso a 1 mês mais ou menos, mas as coisas ainda não clicaram comigo ainda não.

No worries, I'm quite bored in my last year anyways.

Our generation is blessed with online resources. Indeed I learnt quite some skills beyond formal education. Most of the SWE skills have to be self-taught (perhaps you could take mods under CS, but I'm unfamiliar with those). The math and stats requirement should be very familiar to you.

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Here's a list of courses/specialisation I've taken and found pretty useful:

[Ranked by recommended order of learning]

Data Viz

1. Storytelling with Data (the book clearly explains what is a good viz, very practical book and strongly recommended)
2. Tableau courses (GUI based data viz software, commonly used in many companies)

Others

1. Learn SQL Basics for Data Science Specialization
2. Google Cloud Platform, focus on basics such as how to use GCP and BigQuery, Google Data Studio
3. Git and GitHub (I mostly read documentation for Git, the commonly used commands are quite few)

Data Science

1. Applied Data Science with Python Specialization by University of Michigan (Python)
2. HarvardX Data Science Professional Certification (R)
3. Kaggle online learning (Short courses for beginner)

Deep Learning (less used by companies outside of tech, recommend to learn after mastering the traditional statistical learning models)

1. TensorFlow Developer Professional Certificate (together with Google TensorFlow certification exam)
2. And other specialised courses offer by deeplearning.ai