This is the first article in a series about Azure DevOps, which will cover cross-tenant architecture, Power BI CICD Lifecycles, unit testing within Databricks, and end-to-end MLOps Lifecycles.
Large organizations tend to have complex IT infrastructure due to many acquired subsidiaries, which makes managing a centralized hub quite complicated, and is often traded for distributed architecture. However, the recent move to adopt Advanced Analytics to deliver business value has demanded a central location for Data Processing.
This creates a problem of how to connect different resources for DevOps and Data Engineers. …
Prior to starting any Data Science task, the first choice one must make is to choose a programming language that best resembles the task. Whether it is a student working on an assignment, a professional executing a use case, an individual making a career change into the field of Data Science, or simply a data enthusiast engaged in self learning, this is an equally important question to ask. This article aims to debate the pros and cons of Python and R, and hopefully clear any doubts on choosing the most appropriate programming language for the desired task.
Python, developed during…
Exploratory Data Analysis (EDA) is used to analyze and understand data sets to unearth hidden patterns and insights. In Data Science, this is often employed as the preliminary step in any Advanced Analytics task.
There are numerous objectives of conducting an EDA:
● Maximizing the insight that can be gained from a data set.
● Uncovering the underlying structure.
● Extracting important variables.
● Detecting outliers and anomalies.
● Testing underlying assumptions.
● Developing and testing hypothesis.
● Determining the optimal factor settings.
If done properly, EDA answers following core questions:
It widely accepted that the retail industry has noticed that people’s buying patterns have changed drastically due to the adverse effects of the pandemic. Customers look towards purchasing lower priced items, fewer non-essential items and more important items in bulk, especially when there is pending news of lockdowns in cities. There are also visible shifts in purchasing trends of many items including fresh food items, items with longer shelf-lives, and self-care items. …
A mere claim that an intervention will make a difference is no longer sufficient to make business sense. A solid, mathematically driven method is needed to measure and guarantee the difference the said intervention will make. However, measuring the impact in an economically volatile time such as the new normal would be very challenging as few people outside the discipline know that the field of economics contains many powerful impact assessment tools that have been perfected for years.
What is Econometrics?
Econometrics is the branch of Economics that contains a wealth of statistical and mathematical tools used in Data Analysis…
The retail industry is constantly in search of new ways to enhance the shopping experience of customers. In this article, let us spotlight customer churn and how data can be used to mitigate it.
What is customer churn?
When a customer leaves or stops transacting with the business, the business loses the opportunity for potential sales or cross selling. When a customer leaves the business without any form of advice, the company may find it hard to respond and take corrective action. Ideally companies should be proactive and identify potential churners prior to them leaving.
Why customer churn is important?
Welcome to the finale of the four-part series featuring the Women@OCTAVE: Chamodi Adikaram, Data Scientist; Dinusha Dissanayake, Data Scientist; Lamana Mulaffer, Senior Data Scientist; Nadeesha Ekanayake, Senior Data Scientist; and Shashini Gregory, Visualisation Analyst. Today, they will reveal the trailblazing women who inspired them and take an insightful look at the future of women in Data Science. Here are their stories.
Missed part 3? Read it here!
Who is the woman Scientist that inspires you the most?
Chamodi: Helen Ling was a member of the first team of female Engineers at NASA and is known for having offered the team…
Welcome back to the four-part series featuring the Women@OCTAVE: Chamodi Adikaram, Data Scientist; Dinusha Dissanayake, Data Scientist; Lamana Mulaffer, Senior Data Scientist; Nadeesha Ekanayake, Senior Data Scientist; and Shashini Gregory, Visualisation Analyst. Today, they will share their advice to young girls enthusiastic about Data Science and Engineering and reveal some tips on how to thrive in this field! Here are their stories.
What words of encouragement do you have for a schoolgirl interested in Data?
Chamodi: First, be it Data Science or any other profession, identify your true passion and follow it without doubt. Only your skills, abilities, and commitment…
Welcome back to the four-part series featuring the Women@OCTAVE: Chamodi Adikaram, Data Scientist; Dinusha Dissanayake, Data Scientist; Lamana Mulaffer, Senior Data Scientist; Nadeesha Ekanayake, Senior Data Scientist; and Shashini Gregory, Visualisation Analyst. Today, they will share their insight into what it is like to be a part of the OCTAVE work culture, and how they balance work and fun. Here are their stories.
Missed Part 1? Read it here!
How has you experience with OCTAVE been so far?
Lamana: My colleagues have a growth mindset and are very open to collaboration and peer-learning. The work we do — a vast…
OCTAVE, the John Keells Group Centre of Excellence for Data and Advanced Analytics, is the cornerstone of the Group’s data-driven decision making.