MachineHack, in association with TheMathCompany, is launching a fortnight-long hiring hackathon for data scientists and machine learning practitioners from July 02 to July 19, 2021. The winners will get the exclusive opportunity to build a rewarding analytics career at TheMathCompany.
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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.
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With a surge in AI and ML applications to improve business process efficiencies, there has been an increase in data science hiring across companies. It is one of the fastest-growing areas and often requires a robust hiring process to get the best candidates in the team. We got in touch with Sreekanth Menon, VP, Data Science at Genpact to understand their data science hiring process. Sreekanth shares that not only their hiring mechanism looks for the best available candidate but also retains them by providing opportunities to improve their skills over time.
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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
Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.
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.
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.
Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.
**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.
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MachineHack, in association with Analytics India Magazine, has come up with yet another hackathon for the machine learning community — the Workation Price Prediction Challenge.
In the light of the new normal, different websites have started providing packages to work from different locations. The concept of workation — a portmanteau of work and vacation– is gaining currency. However, it is challenging to find a good place with all the amenities, including high-speed internet and a comfortable stay within the budget.
Thus, to solve the real-world problem of finding the best deals for workations, MachineHack is challenging the machine learning community to build a model for predicting the price per person for workation trips.
To facilitate this, MachineHack has collected workation packages in and around India — starting from Kashmir to Kanyakumari and from Gujarat to Assam. The data has more than 18000+ rows of different packages with details like start location, hotel type, cost per person, destination, itinerary, and many more. Using this dataset, along with the knowledge of machine learning, deep learning, and model building, the participants need to create a model that can efficiently and accurately predict a workation trip’s expense.
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