Data Analysis / Data Science Career
Discussion
The biggest growth industry for 2019 is jobs in data so considering moving into that side.of things. My excel is decent, can do vlookups, formulas, pivot tables, index matches, etc so was thinking of trying to get a data analyst role whilst self studying SQL at home.
How tough is SQL to learn (never done programming before)??
Any one do or did data type jobs??
How tough is SQL to learn (never done programming before)??
Any one do or did data type jobs??
Definitely a growth area. SQL's probably a good thing to learn but take a look at R as well, it's a free programming language specifically for data analysis, and now has machine learning and all sorts built in.
Worth taking a look at the OR society https://www.theorsociety.com/ for an idea of what kinds of things people are looking at
Worth taking a look at the OR society https://www.theorsociety.com/ for an idea of what kinds of things people are looking at
sql isn't hard if you have a programer type of logical brain. But while it's a good solid skill with employment prospects, it's hardly cutting edge.
Python, R, anaconda etc and those type of big data analysis tools are where tomorrow's analysts will come from. I don't code but they look easier to my eyes than sql.
Python, R, anaconda etc and those type of big data analysis tools are where tomorrow's analysts will come from. I don't code but they look easier to my eyes than sql.
You can also look at up and coming products like Elastic Search ( https://www.elastic.co ) and Splunk ( Free training : https://www.splunk.com/en_us/training/free-courses... ).
Both are used heavily for data analysis and the other current trend of security.
Both are used heavily for data analysis and the other current trend of security.
Chimune said:
sql isn't hard if you have a programer type of logical brain. But while it's a good solid skill with employment prospects, it's hardly cutting edge.
Python, R, anaconda etc and those type of big data analysis tools are where tomorrow's analysts will come from. I don't code but they look easier to my eyes than sql.
Python, R, anaconda etc and those type of big data analysis tools are where tomorrow's analysts will come from. I don't code but they look easier to my eyes than sql.
hyphen said:
Chimune said:
sql isn't hard if you have a programer type of logical brain. But while it's a good solid skill with employment prospects, it's hardly cutting edge.
Python, R, anaconda etc and those type of big data analysis tools are where tomorrow's analysts will come from. I don't code but they look easier to my eyes than sql.
Python, R, anaconda etc and those type of big data analysis tools are where tomorrow's analysts will come from. I don't code but they look easier to my eyes than sql.
Mediocre Sql knowledge will be easy to pick up and is very much entry level understanding.
If you're serious about data science.. I would recommend learning software engineering basics using an online course e.g mit 6001.. Then get a book like "Hands-On Machine Learning with Scikit-Learn and TensorFlow".. And then look at doing google cloud platform courses (fundamentals and then big data / ml focused course.)
If you're serious about data science.. I would recommend learning software engineering basics using an online course e.g mit 6001.. Then get a book like "Hands-On Machine Learning with Scikit-Learn and TensorFlow".. And then look at doing google cloud platform courses (fundamentals and then big data / ml focused course.)
I've worked specifically in the data domain for the last 20 years and whilst learning to write SQL is definitely one of the easier language's to learn writing queries that run efficiently especially analytical queries is much more difficult than many people realise.
To help give you some context we have many people from analysts, developers and even people that don't really have a clue accessing one of our data warehouses. I can categorically say that no matter their back ground I still see terribly inefficient queries every day, so much so when have very stringent controls in place to kill queries that may cause issues to the overall performance of the server(s).
Learn to write efficient SQL and understanding how your data is structured is just as important as learning the syntax, thinking of data in sets is key rather than thinking about individual rows of data.
As you mentioned the areas really booming are Machine Learning, Data Science and AI. All require analysis of generally huge sets of data. In most cases SQL wont be the only language you need to know, phython and R are more common. On top of that you need to learn technologies suitable for working with large disparate structured and unstructured data sets such as Hadoop, Spark, Elasticsearch, Presto etc.
Working within cloud infrastructure is starting to make some of this easier with the likes of google cloud platform and Amazon Web Services where you can either build the technology yourself on rented resources or on their managed services but its still very complex and difficult for those without both IT operation and development experience to draw upon.
To help give you some context we have many people from analysts, developers and even people that don't really have a clue accessing one of our data warehouses. I can categorically say that no matter their back ground I still see terribly inefficient queries every day, so much so when have very stringent controls in place to kill queries that may cause issues to the overall performance of the server(s).
Learn to write efficient SQL and understanding how your data is structured is just as important as learning the syntax, thinking of data in sets is key rather than thinking about individual rows of data.
As you mentioned the areas really booming are Machine Learning, Data Science and AI. All require analysis of generally huge sets of data. In most cases SQL wont be the only language you need to know, phython and R are more common. On top of that you need to learn technologies suitable for working with large disparate structured and unstructured data sets such as Hadoop, Spark, Elasticsearch, Presto etc.
Working within cloud infrastructure is starting to make some of this easier with the likes of google cloud platform and Amazon Web Services where you can either build the technology yourself on rented resources or on their managed services but its still very complex and difficult for those without both IT operation and development experience to draw upon.
You will need a bit more than SQL 
for data science.
Being able to pull the data out is one thing, analysing the data and what its telling you is another thing.
If you want to be a data science get up to speed on probability theory, sampling etc.
I used to be traditional business intelligence / analytics developer so I had SQL, Python etc already the thing that made it easier for me to move into data science role was the fact I did a lot of probability and stats work before so I know a lot of the concepts and theory.
for data science.Being able to pull the data out is one thing, analysing the data and what its telling you is another thing.
If you want to be a data science get up to speed on probability theory, sampling etc.
I used to be traditional business intelligence / analytics developer so I had SQL, Python etc already the thing that made it easier for me to move into data science role was the fact I did a lot of probability and stats work before so I know a lot of the concepts and theory.
Edited by PostHeads123 on Friday 11th January 16:24
Hi folks.
In response to a few PMs I asked my DS lead for some recommendations for DS books and they came up with the list below
Numsense!
Machine learning for absolute beginners
Data science from scratch
The suggestion was to read the first 2 (free on Amazon Prime/Unlimited) then maybe the 3rd. In conjunction with the 3rd download Python and some of the DS Libs and have a play
Hope this helps
M
In response to a few PMs I asked my DS lead for some recommendations for DS books and they came up with the list below
Numsense!
Machine learning for absolute beginners
Data science from scratch
The suggestion was to read the first 2 (free on Amazon Prime/Unlimited) then maybe the 3rd. In conjunction with the 3rd download Python and some of the DS Libs and have a play
Hope this helps
M
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