With the rapid pace at which technology is driving innovation in machine learning and artificial intelligence, it has become immensely important to keep pace with the ongoing trends in data science. However, it can become challenging to read everything that’s out there.
Hence, I have started a fortnightly series 'Top 10 Data Science and Blockchain articles". It has been possible thanks to the support of the previous post. But however, this week I'll not restrict myself to articles. I will include some podcasts / ebooks as well.
So, let's begin!
- Analytics in a Big Data World: The Essential Guide to Data Science and its Applications Why I recommend this?: Simplicity. Professor Bart has done an excellent job of putting introduction to some of the most complex topics in the Analytics industry in a much simpler way.
- Dataskpetic Podcasts: With episodes ranging from anywhere between 15 minutes to an hour, the Data Skeptic is a great way to introduce yourself to the world of Data Science podcasts. The topics include interviews with data science practitioners to talk about real world data science challenges, simple academic concepts like feature selection, NLP, decision trees, among many others.
- The importance of Soft Skills in Data Science: Although I recommend this podcast as a whole, this particular episode of this podcast really hit the bullseye. Soft skills are often under-rated in this space, and yet are essential especially in the leadership of data science teams.
- Become a data scientist: This is by far my most favorite article in the domain of 'Getting started with Data Science'.
- Cryptocurrency Data Wrangling : Same blog as #4. Data Wrangling is the most tedious and time consuming part of a data analyst pipeline. This shows you how to wrangle dataset which will help you in momentum trading.
- Kullback Leibler Divergence
Kullback Leibler divergence, or KL divergence, is a measure of information loss when you try to approximate one distribution with another distribution. Understanding this can help you see the structure of many machine learning loss functions. - Monitor Machine Learning Algorithms: “Learning the learner: Using machine learning to track performance of machine learning algorithms”. One of the best podcasts I have ever heard.
- Data Exploration using Elastic Search and Kibana: Exploratory Data Analysis (EDA) helps us to uncover the underlying structure of data and its dynamics through which we can maximize the insights.
- Complete Study of Air pollution: A newcomer in this field can learn how to prepare the reports a data scientist makes after his analysis through this post.
- What no one tells you about Real Time machine learning: A very interesting read and I recommend you to read too!
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