Data Science Tips for Beginners!

Data Science is all about applying the right kind of knowledge. Following are some of the resources which would help you get into data science quickly Data Mining And Analysis: Fundamental Concepts and Algorithms. 👉   link 👈 Fundamental Numerical Methods and Data Analysis 👉   link 👈 Modelling With Data 👉   link 👈 An Introduction to Statistical Learning 👉   link 👈 Bayesian Statistics Made Simple  👉 link 👈 A Course in Machine Learning  👉 link 👈 The First Encounter with Machine Learning  👉 link 👈 Machine Learning Notebooks  👉 link 👈 I hope. its helpful

ML systems are increasingly used in day to day applications. Data scientists often spend a lot of time in designing the model but little on the model post-deployment. In contrast, the software development life cycle emphasizes on testing and production systems post-deployment. ML systems will flourish and can make more impact if they mimic the best practices of software engineering. Machine learning systems directly or indirectly can influence the customers that they are intended to serve. A mis-classification could result in a catastrophe for some random customer. Therefore, it is prudent for data scientists to meticulously emphasize on the testing and production. This article is an extension of the tweet that I posted. I would like to thank Practical AI podcast members and Tania Allard for sharing useful tips. Tips on testing and production — ask these

Hello , In this post, I am going to share a list of 40 great data science and machine learning resources. Authoring Books with R Markdown Feature Selection: The 10-dimensional burrito From scatter plot to slope chart Using Big Data for Machine Learning Analytics in Manufacturing A Complete Tutorial on Linear Regression with R Statistical Computing with Stata Build an AI Writer - Machine Learning for

'Data Science' is a very attractive career path today. A huge lot of individuals start walking on this path and majority of them fall in the trap of these luring mistakes. I also belong to this majority. That is why I am writing this post so that you learn from my mistakes as it is rightly said “Intelligent is the one who learns from others’ mistakes.” Learning theory first and implementing later : Like many others we start our journey by taking a course and learn ‘Data Science’. Mine was no different. Majority of these courses will give you lots and lots of theory. Many courses have quizzes and assignments, but still the practical aspects lack in them because you don’t practically implement things along with the course. Theories are definitely important but they are of no use if you don’t implement and understand its actual use. Relying just on the

Commonly used  jupyter Notebook Shortcuts. Tab code completion or indent Shift-Tab tooltip Ctrl-] indent Ctrl-[ dedent Ctrl-A select all Ctrl-Z undo Ctrl-S­hift-Z redo Ctrl-Y redo Ctrl-Home go to cell start Ctrl-Up go to cell start Ctrl-End go to cell end Ctrl-Down go to cell end Ctrl-Left go one word left Ctrl-Right

Great introduction to data science for total beginners "I wanna be a data scientist, but… how?". Where to start, and what to do step-by-step. Plus, the author gives a few recommendations for what classes to take and lists what skills are required to become a data scientist. Great visualizations too! I wish I had it a year ago. Instead, I had to go through hundreds of job postings to see what skills are required by the job market, and doing many-hours research on upvoted online courses. I found Kaggle and GitHub through my LinkedIn connections and learned them by myself (by the way, there are courses for everything). Good luck in your

I guess, we all make mistakes and even scientists do. Here is an article highlighting  3 most common mistakes for Data Scientists . As for me, I do tend to jump between libraries when I accidentally find something interesting. And I want to cover all at once. What about you? Where are yours?

This could be the best explanation for how XGBoost works for Regression and Classification. Definitely worth investing learning about it on the links below. Click Here ->  XGBoost for Regression Click Here ->  XGBoost for Classification Click Here ->  Mathematical Details Do share more resources in comments if you have. Nevertheless, nothing beats original paper.

Statistics free book - know your theory first. It's ideal for any data scientist: engineers, BI analysts, statisticians, operations research, AI and machine learning professionals, economists, data scientists, biologists, and quants, ranging from beginners to executives. It includes: Machine Learning Fundamentals and NLP Applied Probability and Statistical Science New Foundations of Statistical Science Case Studies, Business Applications Additional Topics Click Here to Download The Book!

Hello to everyone, We all know that the internet offers us very useful information. Sometimes we want to improve ourselves so much that the information is not enough for us. Therefore, I am writing links to help you reach free courses available on different platforms. I wish everyone the best in the way of being a data scientist. Congnitive Class Python for Data Science :  https://cognitiveclass.ai/courses/python-for-data-science/ Machine Learning with Python :   https://cognitiveclass.ai/courses/machine-learning-with-python/ Data Visualization with Python :   https://cognitiveclass.ai/courses/data-visualization-with-python/ Introduction to Data Science :   https://cognitiveclass.ai/courses/data-science-101/ Deep Learning Fundementals :   https://cognitiveclass.ai/courses/introduction-deep-learning/ Data Analysis with Python :   https://cognitiveclass.ai/courses/data-analysis-python/ Deep Learning with TensorFlow : ...

Data Science is a competitive field, and people are quickly building more and more skills and experience. This has given rise to the booming job description of Machine Learning Engineer, and therefore, my advice for 2020 is that all Data Scientists need to be developers as well. To stay competitive, make sure to prepare yourself for new ways of working that come with new tools. 1. Agile Agile is a method of organizing work that is already much used by dev teams. Data Science roles are filled more and more by people who’s original skillset is pure software development, and this gives rise to the role of Machine Learning Engineer. More and more, Data Scientists/Machine Learning Engineers are managed as developers: continuously making improvements to Machine Learning elements in an existing code base. For this type of role, Data Scientists have to know the Agile way of working based on the Scrum method. It defines several roles for different people, and this role defi...