A framework for project success

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Introduction

Data Science is still a roaring field with demand continuing to outstrip supply and many business expecting to increase their IT spend drastically over the next few years.


Deep learning doesn’t have to be complex

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Introduction

When I first started learning Data Science and looking at projects, I thought you could either do a Deep Learning or regular project. This is not the case.


What I’ve learned after writing 9 articles on Medium — 10 tips for my 10th article.

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Introduction

I’ve been an avid reader on Medium for well over a year now, but I only started writing towards the end of 2020.


A data-driven approach to understanding soft skills in the UK market.

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Introduction

This article uses a web scraped dataset to analyse text and find the most common skills in data-related jobs. I’m focusing on technical/hard skills for this article and will tackle soft skills later on.

Bias

There is an inherent bias in this dataset because it is scraped. This is largely because Indeed, the platform this was scraped from, is used extensively by recruiters who tend to use more technical skills and offer larger salary ranges.


A data-driven approach to understanding technical skills in the UK market.

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Introduction

This article uses a web scraped dataset to analyse text and find the most common skills in data-related jobs. I’m focusing on technical/hard skills for this article and will tackle soft skills later on.

  • What is it?
  • What the Data Says
  • My Opinion
  • Salary
  • Learning

Basic Statistics

The dataset consists of 3,015 job titles including salaries.

Table of Contents


The guide for Non-Mathematicians

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Introduction

There are plenty of fantastic Data Science content creators on YouTube. If I’m trying to understand an algorithm, one of them typically hits the nail on the head and describes it perfectly.


One of the most widely used algorithms today is actually quite tricky to explain

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If you haven’t already, you should read my article on Decision Trees.

Understanding the Decision Tree and its flaws is paramount to understand why the Random Forest exists, and why it is powerful.

Table of Contents

Introduction

According to KDNuggets the top 3 most commonly used algorithms by Data Scientists are Linear/Logistic Regression, Decision Tree or Random Forest and Gradient Boosting models. …


The building blocks for modern, tree-based models…

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Table of Contents

Introduction

Tree-based models are some of the most widely used models today; they are very powerful, easy to implement and provide feature importances to help with interpretability. One of the most widely used tree-based models is the Random Forest, which is based on Decision Trees.


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Introduction

This article looks at a few techniques that if mastered, equips the user with the tools to deal with a wide range of data types. This article does not cover anything relation to database management such as table creation or schemas.

If these techniques are too complex, you can get up to speed in less than 10 minutes here:

Table of Contents


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Table of Contents

Introduction

If you need a more general introduction to SQL and Databases, check out the first part of this tutorial:

In this article, we will look at basic SQL syntax; selecting data from tables, filtering and working with data types. We will then look at Joins and aggregate measures.

SQL for Data Science — why?

SQL remains one of the most…

Adam Shafi

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