Data Science School

Fallacies are what we call the results of faulty reasoning. Statistical fallacies, a form of   misuse of statistics , is poor statistical reasoning; you may have started off with sound data, but your use or interpretation of it, regardless of your possible purity of intent, has gone awry. Therefore, whatever decisions you base on these wrong moves will necessarily be incorrect. Source   There are infinite ways to incorrectly reason from data, some of which are much more obvious than others. Given that people have been making these mistakes for so long, many statistical fallacies have been identified and can be explained. The good thing is that once they are identified and studied, they can be avoided. Let's have a look at a few of these more common fallacies and see how we can avoid them. Out of interest, when misuse of stati...

Data Science Careers Shaping The Future 1. Data Scientist Average Salary:  $139,840 Typical Job Requirements : Data scientists analyse large amounts of complex raw and processed information to find patterns that will develop organizations and help steer vital business decisions. 2. Machine Learning Engineer Average Salary:  $114,826 Typical Job Requirements:  They create data funnels and work on software solutions. They often require strong statistics and programming skills and software engineering knowledge. 3. Machine Learning Scientist(Research Scientist or Engineer) Average Salary:  $114,121 Typical Job Requirements:  They research new data approaches and algorithms that will be applied in adaptive systems. 4. Applications Architect Average Salary:  $113,757 Typical Job Requirements:  Application architects concentrate on designing the architecture of applications and building components (e.g. user interface and infrastructure). They monitor the be...

According to recent industry trends, the data scientist job market has exploded, with data science being one of the most in-demand jobs. Many colleges are also introducing data science as a completely new programme. This shows that data analysis is already a critical role for businesses today. Data underpins every business decision. Financial organisations, in particular, are personalising customer experiences and combating fraud entirely with the help of data scientists’ important insights and assumptions. Last year, LinkedIn discovered that data analysis is one of the top three fastest-growing career categories, alongside software technology and digital content, which is unsurprising given that data science is expected to be one of the fastest-growing fields in the future. Considering this, AIM has compiled the best career advice given by industry leaders in the field.  “Aspiring data scientists have a promising future ahead. With the growing demand for data scientists and the cu...

Goto My Channel For more Info: Make your learning journey more efficient. Photo by  Kelly Sikkema  on  Unsplash My data science journey started in 2019. Those who follow me on Medium would know that I like sharing my experience of learning data science. I write about the mistakes I made, the challenges I faced, the tools I frequently use, and so on. In this article, I would like to share 3 suggestions to those who plan to become a data scientist or just started learning data science. These are based on my own experience and what I observe in the data science ecosystem. Without further ado, let’s get started. The hidden fact about data analyst and data scientist. Stay tune! and Watch till end of the video Remember to subscribe to my Channel.  #datascience #dataanalyst  #datascientist  #datascienceschool #problems https://youtu.be/2pH9q20Q_vQ 1. Be agile More and more businesses invest in data science with an aim of converting data to value. The form of this ...