CrestML

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How to plan a successful Data Science Project

Planning is an important aspect of all projects to ensure that the strategic objectives of the project are accomplished. It is critical to breakdown a project into manageable phases or tasks to monitor progress and ensure timely completion. As a data scientist, it is important to have a working and well-thought plan of the end-to-end execution of a project to encourage yourself and have tangible outputs to show to stakeholders. Tools like Jira and Microsoft DevOps can be used to break down a data science project into different tasks for your team and it also gives team members visibility and progress updates. It makes collaboration much easier and code sharing to eliminate people solving the same problem. In one of my previous roles, Jira made collaboration easier because everyone with access to the project could raise issues/user stories and explain what and how they want the issue to be addressed. Also, it is good practice to write acceptance criteria in user stories in order ...

A broad overview of Machine Learning Algorithms and its classifications

In its simplest form, Machine Learning (ML) could be described as when a machine learns something. Another general definition of ML is “the art and science of programming computers so they can learn from data”. My favourite definition is the one below because it tells you instantly what a Machine Learning program does. According to Tom Mitchell (1997) “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. We use Machine Learning every day in most of the apps that we use, from recommendation systems to our email. A general example of a Machine Learning program that we are all familiar with is our email spam filter. The email examples that the spam filter system uses to learn are the training set, and each of the email used are known as a training instance. The task here is to check new emails if they are spam or not, and the experience E is the tr...

AI and automation are changing the Construction Industry

We’ve all heard about the fourth industrial revolution (industry 4.0) and the technological disruption it will bring to the world of work. To show its popularity, a 7-Day search on Google Trends reveals that the query ‘industrial revolution’ had peak popularity of 100 on September 27 2018, at 3:00 AM in the UK. 100 is the highest rating for search queries popularity on Google Search. The 'popularity' shows two things; there is an increased awareness about the topic and businesses are evaluating its impact on their operations and strategies. So, what does the fourth industrial revolution represent? The simplest definition of Industry 4.0 is, it is a move towards digitalisation. In other words, it is a period when machines will self-optimise and self-configure to produce quality goods and services using artificial intelligence (AI). Development in big data analytics will enable industries to be more agile through real-time data processing that generates insights faster for...

Increasing your ROI: How recruitment technology and analytics can drive performance and growth

Majority of business leaders agree that embracing digital transformation and automation is key to both short-term and long-term growth. In fact, most businesses now have a plan in place for their digital journey with some of them gaining substantial media coverage. Having a plan is a step in the right direction, but it is also important to know what will be critical to realise the maximum return on investment and gain some competitive advantage. This blog will be sharing some research-proven points that should inform every digital transformation plan. Digital recruitment technologies include websites, CRM and job boards. The four recommendations are from the study conducted by Parry and Tyson (2008). -Drive traffic to your corporate website : Recruiters need to take steps to drive users to their site if they do not have a prominent brand, as job seekers rarely look at an unknown site. Job boards can be used to drive traffic to a company’s corporate website through the use of a web...

Essential characteristics of Good Jobs

With record employment level in most G7 countries and the increase in economic growth, little attention is given to analysing the quality of work created. Creating more jobs is paramount to make a nation prosperous and for the redistribution of wealth. Some of us were taught the quantitative measure of labour demand and supply in economics. The chart shows increasing economic growth and employment rate in the UK. UK GDP and Employment Rate, 2010-2018 Qualitative measures regarding the quality of work is also important for a happier workforce and society. Work should be evaluated both quantitatively and qualitatively for measurable improvements and a balanced society. Personally, I do know that if I am happy at work, I feel and perform better both at work and home. Same goes for most of my family and friends. Providing good work for all citizens should be a national strategic objective in every modern society. There is a consensus among existing studies that with the right leg...

How to create End-to-End Machine Learning models pipeline in Microsoft Azure

As a data scientist, your work life will focus on value from data. Working on data and deriving valuable information from this asset will always be your top priority. You only do not want to explore the data, but you also want the uncovered hidden patterns and trends in the data to be used for decision making. With the new problem being more data and less information, it is critical to have a strategy in place regarding how to make the information extracted from data available to end users. That is, you want the information derived from the data to be actioned and used by relevant stakeholders. Otherwise, the effort used in exploring the data, building models and making predictions may not realise the intended business benefits. This sounds counter-intuitive but it is the reality of many data scientists with just PowerPoint presentations the only thing from their work or a report of their findings. You can change this with more accessible and easy to deploy technolog...