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Machine Learning A-Z™: Hands-On Python & R In Data Science Part 1

Hi Data Friends, A few people have been raving about the “Machine Learning A-Z™: Hands-On Python & R In Data Science” available on Udemy: I thought I’d check it out over the weekend, but it is a massive course. I mean we are talking 41 hours of video, so at the moment I am only 51% through it. I’ll continue to review if I have some time next weekend. Here’s what I have covered so far: Part 1 Data Preprocessing Handling missing data Categorical data Splitting into train and test datasets Feature scaling Part 2 Regression Simple linear regression Multiple linear regression Polynomial regression Support Vector Regression Decision Tree Regression Random For

Why Git?

Every data scientist should learn and use Git. Git can save your team time and pain. Preserve your sanity by learning Git. Links to resource

Managing Data Science Teams - The Top Down Approach

The Problem So, we have a serious problem in Data Science at the moment. We have been doing Data Science for a few years now, anecdotally we know many AI projects fail, companies are struggling to retain data science managers and data scientists and the project sponsors of Data Science initiatives are starting to ask questions. In our excitement about Data Science and AI it seems we put together teams of Data Scientists, flicked them some data and said “Go for it guys!” without the right structures, framework and strategy in place for them to succeed. What we need is a bit of a reset and a process for executing data science projects with the right people in place to execute these projects. F

How do I become a Data Scientist and how do I excel in Data Science?

This is by far the most common I am asked, but also the trickiest question to answer. The answer is difficult, depends on the individual and depends on your passion, skills and interests. My book “The Data Scientist’s Journey: The Guide for Aspiring Data Scientists” goes into much more detail, but here is a summary if you didn’t feel like reading the 220+ pages in the book. Step 1: The Motivation for Data Science The first question you have to ask is: “Why do I even want to become a Data Scientist?” This is important to establish your motivation, work out your passion and what you are likely to excel at. You’ll be working a long time, so you might as well enjoy what you do. This motivation w

Effective Learning in Three Steps

Why learn? The most important skill for the age we live in is learning. If you are able to learn quickly you will not really be worried about the robots taking our jobs, or the industry you are in dying. You will be able to transform yourself, you will be adaptable. Like your ancestors before you, you will be able to survive the Ice Age, or as in our case the equivalent is to learn that damn python module - actually, it doesn’t really compare does it? Sadly, very few people make learning a part of their lives. As Data Scientists we have to learn all the time, the field is vast and moving rapidly. This is a good thing, but keeping up can be a challenge. Learning, and in particular efficient l