Myah  Conn

Myah Conn


Bored Games - Musings of a locked-down Data Scientist

Let’s face it — after getting the scrabble board out for the 10th time this month, the novelty of more time at home is starting to wear off.
Now while I don’t want to promote spurious correlations, I find the trends in people searching for “Bored” and “Board games” during the UK lockdown period rather telling. Spurred on by this, and a free couple of hours (roughly the time it takes me to pick my next word in Scrabble), I wondered what more I could find out about the UK’s new favourite past-time.

Kaggle Datasets to the rescue!

A brief search led me to a wonderful dataset taken from BoardGameArena¹, which provides a whole host of information for over 100k games! After seeing what data was available in the dataset, the two areas I found myself wanting to dive into were the age-ratings (“Suitable for ages 12+” etc.)and the wealth of star-ratings that have been collected for many of the games. The following questions sprang to mind:

  1. Are games created for younger players designed to be played more quickly?
  2. Are there certain publishers of games that are lagging behind others in creating high-quality, and well-liked, games?
  3. Given this varied dataset, am I able to predict the rating a certain game will receive, based on its characteristics?

The first two questions can be answered with a quick _exploratory analysis — _that is, visually inspecting certain slices of the data on their own — so let’s get cracking.

Are games created for younger players designed to be played more quickly?

Below is a boxplot² showing how the recommended-play-time varies with the age-range of the game. I also show how many games are rated for each age with the grey bars.

Notice how the median play-time (the orange line in each box) increases as the minimum age increases from zero, however — above 12 years, the suggested play-time appears to decrease.

It appears as though the suggested play-time increases as the recommended age increases from zero, but then hits a turning point over the age of 12, after which it decreases again.

One possible reason for this could be that age-restricted games, which here I’m considering as 15+, are designed with evening/after-dinner parties in mind, perhaps necessitating a quicker game.

Another possible cause could be the reduced sample size of these age-ranges, though with a little over 1,000 games in each range, I am comfortable putting this to one side for now.

Are there certain publishers of games that are lagging behind others in creating high-quality, and well-liked, games?

#linear-regression #data-analysis #python #data-science #board-games

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Bored Games - Musings of a locked-down Data Scientist
Siphiwe  Nair

Siphiwe Nair


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Java Questions

Java Questions


50 Data Science Jobs That Opened Just Last Week

Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

(The jobs are sorted according to the years of experience r

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**Location: **Bibinagar, Telangana

Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.

#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Ian  Robinson

Ian Robinson


Data Science: Advice for Aspiring Data Scientists | Experfy Insights

Around once a month, I get emailed by a student of some type asking how to get into Data Science, I’ve answered it enough that I decided to write it out here so I can link people to it. So if you’re one of those students, welcome!

I’ll segment this into basic advice, which can be found quite easily if you just google ‘how to get into data science’ and advice that is less common, but advice that I’ve found very useful over the years. I’ll start with the latter, and move on to basic advice. Obviously take this with a grain of salt as all advice comes with a bit of survivorship bias.

Less Basic Advice:

1. Find a solid community

2. Apply Data Science to Things you Enjoy

3. Minimize the ‘Clicks to Proof of Competence’

4. Learn Through Research or Entry Level Jobs

#big data & cloud #data science #data scientist #statistics #aspiring data scientist #advice for aspiring data scientists

5 Indian Companies Recruiting Data Scientists In Large Numbers

According to a recent study on analytics and data science jobs, the number of vacancies for data science-related jobs in India has increased by 53 per cent, since India eased the lockdown restrictions. Moreover, India’s share of open data science jobs in the world has seen a steep rise from 7.2 per cent in January to 9.8 per cent in August.

Here is a list of 5 such companies, in no particular order, in India that are currently recruiting Data Scientists in bulk.

#careers #data science #data science career #data science jobs #data science recruitment #data scientist #data scientist jobs