Python has become one of any data scientist's favorite tools for doing Predictive Analytics. In this hands-on course, you will learn how to build predictive models with Python. During the course, we will talk about the most important theoretical concepts that are essential when building predictive models for real-world problems. The main tool used in this course is scikit -learn, which is recognized as a great tool: it has a great variety of models, many useful routines, and a consistent interface that makes it easy to use. All the topics are taught using practical examples and throughout the course, we build many models using real-world datasets.
Topic identification is a top-of-the-list need for organizations working with large volumes of online, social, and enterprise text. Along with entity resolution, relation extraction, summarization, and sentiment analysis, topic modeling is a key natural language processing (NLP) function. Premise number 2: Applied NLP -- text analytics -- remains as much art as science, requiring a combination of domain and technical expertise. How better to explore topic modeling and NLP advances than via an interview with a leading practitioner? This article features an interview with Lev Konstantinovskiy, a data scientist who is community manager for gensim, which offers open-source topic modeling for Python programmers.
I've discussed parts of what I'm going to mention here in other articles, but now I want to give a few directions on what's not data science and how not to learn it. So let's start with the basics. Data science not just knowing some programming languages, math, statistics and have "domain knowledge". We've created a new field, or something like that. There's a lot of things to say and study in this field.
Here is a link to the podcast. Vicki: Thank you so much for having me. Hugo: It's an absolute pleasure to have you on the show. I'm really excited to talk about your work in Python education, full stack data science, end-to-end data science, what these things actually mean, and your work in consulting. Before we get into all of that, I'd love to know a bit about you. I'm wondering what you're known for in the data community. Vicki: Probably first and foremost, terrible puns and memes about all sorts of data and programming related things. My strategy is a little bit like BuzzFeed, right? Hit them with the memes and then sneak in serious content in between. Vicki: I've written a lot of blog posts about how to do specific things in Python, how to do specific things in data, and then just talking about like where we are in the data community in general, so very high level articles, and talking about things that break down complicated concepts into easy to understand analogies. I love that secondary is the content and that primary are terrible puns and memes. I don't mean to put you on the spot, but what's one of the worst puns you've said or come up with or heard? I have this series of puns where it's basically me pretending to talk to a TV producer to pitch them on possible shows or movies, and so that series is s pretty terrible series of tweets. I thought I would just mention that you're also, in terms of content, in the process of creating a DataCamp course.
The Python programming language has become a major player in the world of Data Science and Analytics. This course introduces Python's most important tools and libraries for doing Data Science; they are known in the community as "Python's Data Science Stack". This is a practical course where the viewer will learn through real-world examples how to use the most popular tools for doing Data Science and Analytics with Python. Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years of experience in analytical roles. He worked in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data.