Data Science Now

#DataScienceNow mid-season recap! On this episode we make a quick review of the previous sessions, covering some of the basics of Data Science, and you'll have a sneak peek of what's next. Watch the video on Youtube: https://bit.ly/DSNYoutube Subscribe to the Data Science Now newsletter here: https://bit.ly/DSNNewsletter Follow Closter on social media: - Instagram: https://instagram.com/closterteam - Twitter: https://twitter.com/ClosterTeam - Facebook: https://www.facebook.com/ClosterTeam/ - LinkedIn: https://www.linkedin.com/company/closter #DataScience #MachineLearning #FavioDataJourney #Closter #Podcast To enable screen reader support, press ⌘+Option+Z To learn about keyboard shortcuts, press ⌘slash

This session of Data Science Now is an introduction to modeling and machine learning in data science. The topics are: - What is a model? - Why do we need modeling in data science? - What is machine learning? - How to solve business problems with machine learning Watch the video on Youtube: https://bit.ly/DSNYoutube Subscribe to the Data Science Now newsletter here: https://bit.ly/DSNNewsletter Follow Closter on social media: - Instagram: https://instagram.com/closterteam - Twitter: https://twitter.com/ClosterTeam - Facebook: https://www.facebook.com/ClosterTeam/ - LinkedIn: https://www.linkedin.com/company/closter #DataScienceNow #DataScience #Closter #MachineLearning #FavioDataJourney #Podcast

On this episode of Data Science Now we'll learn how to discover valuable insights from data through Data Science. We focus on: - Historic overview of data science methodologies - Comparison, advantages, and disadvantages of data science methodologies - Getting insights from data - Introduction to modeling Watch the video on Youtube: http://bit.ly/DSNYoutube Subscribe to the Data Science Now newsletter here: https://bit.ly/DSNNewsletter Follow Closter on social media: - Instagram: https://instagram.com/closterteam - Twitter: https://twitter.com/ClosterTeam - Facebook: https://www.facebook.com/ClosterTeam/ - LinkedIn: https://www.linkedin.com/company/closter #DataScienceNow #DataScience #Closter #MachineLearning #FavioDataJourney #Podcast

On this session of Data Science Now, we talk about different techniques and trends in the exploration and preparation of data for modeling, which is crucial in every data science project. The topics are: - Data analytics - Exploratory data analysis - Data preparation and techniques - Putting it all together Watch the video on Youtube: https://bit.ly/DSNYoutubeS2E5 Subscribe to the Data Science Now newsletter here: https://bit.ly/DSNNewsletter Follow Closter on social media: - Instagram: https://instagram.com/closterteam - Twitter: https://twitter.com/ClosterTeam - Facebook: https://www.facebook.com/ClosterTeam/ - LinkedIn: https://www.linkedin.com/company/closter #DataScienceNow #DataScience #MachineLearning #FavioDataJourney #Podcast #Closter

On this episode of Data Science Now, we talk about Data Collection, how and where to collect the right datasets to solve a business problem, and great tools to make the process easier. The topics of the session are: - What is data collection? - Types of Data - Ethics of data collection - Building datasets from scratch - Using databases - Data enrichment Watch the video on Youtube: https://bit.ly/DSNYoutubeS2E4 Subscribe to the Data Science Now newsletter here: https://bit.ly/DSNNewsletter Follow Closter on social media: - Instagram: https://instagram.com/closterteam - Twitter: https://twitter.com/ClosterTeam - Facebook: https://www.facebook.com/ClosterTeam/ - LinkedIn: https://www.linkedin.com/company/closter #DataScienceNow #DataScience #DataCollection #FavioDataJourney #Podcast

On this session, we continue the conversation from the previous episode, and we discuss: - How to understand and develop KPIs - The good, the bad and the ugly of doing data science for businesses Watch the video on Youtube: https://bit.ly/DSNYoutubeS2E3 Subscribe to the Data Science Now newsletter here: https://bit.ly/DSNNewsletter Follow Closter on social media: - Instagram: https://instagram.com/closterteam - Twitter: https://twitter.com/ClosterTeam - Facebook: https://www.facebook.com/ClosterTeam/ - LinkedIn: https://www.linkedin.com/company/closter #DataScienceNow #DataScience #Closter #Business #Podcast

On the first session from the Data Science Now series about the Basics of Data Science, "Business Understanding", we cover the fundamental aspects for understanding the context of a business, we see how Data Science works in a business environment, how to get the context of a company and a project, what to ask in business meetings, how to behave in business environments, and understanding the goals of a company and its departments. Wait for part 2 on the next session. Watch the video on Youtube: https://bit.ly/DSN-YoutubeS2E2

Welcome to the second season of #DataScienceNow! This time we'll cover the basics of Data Science, from data collection, to modeling and deployment, talking about methods, infrastructure, machine learning, DataOps and much more. Watch the video on Youtube: https://bit.ly/DSNYoutubeS2E1 #DataScience #Closter #MachineLearning

On this episode, we talk about the best and most used books to study Machine Learning and AI. Watch the video on Youtube: https://youtu.be/KS6MLtS7FHg #DataScienceNow #DataScience #Closter #MachineLearning #AI #ArtificialIntelligence #Statistics #Data #Technology #Jobs #Business #Programming

Learn about the process and algorithms that Netflix and other companies use for their recommendation systems on this Episode of Data Science Now. Watch the video on Youtube: https://youtu.be/HtS9fHa_FkQ #DataScience #Closter #MachineLearning #AI #ArtificialIntelligence #Statistics #Data #Technology #Netflix

Understanding the coronavirus with Data Science. Join us on #DataScienceNow while we use the available data to analyze, plot and create simple models about the spread and control of the disease. #DataScience #Coronavirus #Closter #MachineLearning #AI #ArtificialIntelligence #Statistics #Data #Technology #COVID19

On this episode we talk about the most important programming languages for Data Science and how to learn them. #DataScienceNow #DataScience #Closter #MachineLearning #AI #ArtificialIntelligence #Statistics #Data #Technology #Jobs #Business #Programming

On this episode of Data Science Now, we talk about business understanding and its relevant function in the Data Science workflow. Watch the video on Youtube: https://youtu.be/pKlC2kOBmPc #DataScienceNow #DataScience #Closter, MachineLearning #AI #ArtificialIntelligence #Statistics #Data #Technology #Jobs #Business

The 5th episode of #DataScienceNow! Stay tuned and learn more about the crucial relationship between Math, Machine Learning and Data Science. #DataScience #Closter #MachineLearning #AI #ArtificialIntelligence #Statistics #Data #Technology #Math #Business #Jobs #LinkedInLive

On this session of Data Science Now, we talk about how to learn and use the most important Python libraries for Data Science, like TensorFlow, Pandas, Spark and much more. #DataScienceNow #DataScience #Closter #MachineLearning #AI #ArtificialIntelligence #Statistics #Data #Technology #Business #LinkedInLive #Python

How to get a job in Data Science? Find out on this episode of #DataScienceNow, we're live talking about what you need to study, how to prepare for interviews, what companies are looking for and much more. And if you're a recruiter, we'll also be discussing the right questions to ask in order to find the best data scientist. #DataScienceNow #DataScience #Podcast #Closter #LinkedInLive #AI #MachineLearning #Statistics #Data #Technology #Business #Jobs

On this episode we talk about the history of Data Science, from its beginning and advances, to the latest trends and future of the field.

In the first episode of Data Science Now, Favio Vázquez talks about Knowledge Data Science and why it could be the future of the field. We also discuss the importance of Python in the world of Data Science. Finally, we explore a Python library that can help you improve the quality and readability of your stories.