I recently had the pleasure of giving a talk on Geospatial Analytics at the Munich Business School (MBS). My talk “Geospatial Intelligence” covered a brief introduction into Geospatial data, what it looks like, its challenges, and how it can be applied towards business strategies. Some students had a few interesting questions which I thought were interesting to elaborate further on in more detail here.
Julia is a fairly modern scientific programming language that is free, high-level, fast, and bundles a bunch of awesome features that makes Julia working with data great again. Amazon Athena is an interactive query service which allows you to easily analyze your data collecting dust in Amazon S3 storage using your good old friend SQL. Julia is great for working with data, Athena is great for querying data, how can we use both together? Rather than manually export CSV files and use CSV.jl to load CSV files in Julia, I’ll be showing you how to query the data using Athena directly from Julia, loading the resulting set of data into a DataFrame using DataFrames.jl for you to work with.
The “science” in Data Science is supposed to refer to the fact that doing Data Science involves conducting experiments with data. In any other field failed experiments are accepted, you wouldn’t want a drug company to push out a new drug which failed internal tests, so why is it frowned upon in Data Science?
There is a common misconception that being a Data Scientist means that you do not need to care about writing good clean code, because you’re not a Software Developer. If you’re a firm believer of this, please take some time to hear me out. I hope to have at least gotten you to reconsider by the end of this post.
Data Science competitions have become extremely popular in the past 5 years. They are not only popular on sites such as Kaggle, but also when interviewing with companies for a role on the job market in the form of a candidate test. These tests have quickly turned into a way to judge the performance of a participant/candidate’s submission and determine whether they have succeeded in the challenge or not.
The General Data Protection Regulation (GDPR) will come into effect on the 25th of May, 2018; along with it a number of penalties for companies who fail to abide by these rules. These penalties can be very harsh, up to €20M or 4% of your company’s annual worldwide revenue. The European Commission (EC) has stated that no exceptions will be made after the 25th of May, and failure to comply will result in hefty fines. Despite these harsh penalties, not all companies are taking this new regulation seriously who will no doubt find themselves in a world of panic just before GDPR hits. Many more fail to understand the point of this new regulation and what it swears to protect. In light of this, I thought I’d give a brief overview of this new regulation and what it entails.
Are you tired of your employees accessing social networks at work? Maybe you feel that your employees are spending more time checking their Facbook feed rather than crunching the numbers. Whether it’s Facebook, Twitter, or YouTube, how dare they do something else other than what you’re paying them for? In fact, why don’t you just strip out all the applications from their computers, lock them down, which let’s face it they probably already are, and leave Excel installed and call it ExcelOS, patent pending.
Have you ever looked at a job ad, skimmed through it, and decided not to apply because you thought that you could not even come close to matching the requirements the ad was asking for? Maybe you were job hunting and felt like you were being flooded with job ads having impossible requirements making you shake your head in disbelief.
I am a data scientist who is extremely passionate about being able to derive insight from data. I currently work for a leading car rental company in the automotive industry based in Munich, but curious to all fields where data has a huge impact. I have learnt a lot on my journey thus far, topics which include machine learning, deep learning, recommender systems, parallel computing, and high performance computing. I am constantly setting myself out to learn new things, be it from online articles and discussions, to books and podcasts. Many people do not understand why I do so, especially at late hours of the day, but it is very simple to me as learning new things is something that I find great joy in doing.
I recently had the pleasure of giving a talk on Geospatial Analytics at the Munich Business School (MBS). My talk “Geospatial Intelligence” covered a brief introduction into Geospatial data, what it looks like, its challenges, and how it can be applied towards business strategies. Some students had a few interesting questions which I thought were interesting to elaborate further on in more detail here.
Julia is a fairly modern scientific programming language that is free, high-level, fast, and bundles a bunch of awesome features that makes Julia working with data great again. Amazon Athena is an interactive query service which allows you to easily analyze your data collecting dust in Amazon S3 storage using your good old friend SQL. Julia is great for working with data, Athena is great for querying data, how can we use both together? Rather than manually export CSV files and use 
The “science” in Data Science is supposed to refer to the fact that doing Data Science involves conducting experiments with data. In any other field failed experiments are accepted, you wouldn’t want a drug company to push out a new drug which failed internal tests, so why is it frowned upon in Data Science?
There is a common misconception that being a Data Scientist means that you do not need to care about writing good clean code, because you’re not a Software Developer. If you’re a firm believer of this, please take some time to hear me out. I hope to have at least gotten you to reconsider by the end of this post.
Data Science competitions have become extremely popular in the past 5 years. They are not only popular on sites such as Kaggle, but also when interviewing with companies for a role on the job market in the form of a candidate test. These tests have quickly turned into a way to judge the performance of a participant/candidate’s submission and determine whether they have succeeded in the challenge or not.
Even though it has 1-based indexing.
The General Data Protection Regulation (GDPR) will come into effect on the 25th of May, 2018; along with it a number of penalties for companies who fail to abide by these rules. These penalties can be very harsh, up to €20M or 4% of your company’s annual worldwide revenue. The European Commission (EC) has stated that no exceptions will be made after the 25th of May, and failure to comply will result in hefty fines. Despite these harsh penalties, not all companies are taking this new regulation seriously who will no doubt find themselves in a world of panic just before GDPR hits. Many more fail to understand the point of this new regulation and what it swears to protect. In light of this, I thought I’d give a brief overview of this new regulation and what it entails.
Are you tired of your employees accessing social networks at work? Maybe you feel that your employees are spending more time checking their Facbook feed rather than crunching the numbers. Whether it’s Facebook, Twitter, or YouTube, how dare they do something else other than what you’re paying them for? In fact, why don’t you just strip out all the applications from their computers, lock them down, which let’s face it they probably already are, and leave Excel installed and call it ExcelOS, patent pending.
Have you ever looked at a job ad, skimmed through it, and decided not to apply because you thought that you could not even come close to matching the requirements the ad was asking for? Maybe you were job hunting and felt like you were being flooded with job ads having impossible requirements making you shake your head in disbelief.