People realize that effective uses of data can increase competitiveness, even in a challenging marketplace. Here are six industries hiring data scientists now that will likely continue doing so for the foreseeable future.
In our quest to better understand and predict business outcomes, traditional predictive modeling tends to fall flat. However, causal inference techniques along with business analytics approaches can unravel what truly changes your KPIs.
AI ops has taken Human machine collaboration to the next level where humans and machines are not just coexisting but are collaborating and working together like team members.
We might have a reasonable sense for what "noise" is as some statically random phenomena that occurs in Nature. But, how can this same characteristic be defined--and understood--within the context of making judgements, such as in human behavior, corporate decision-making, medicine, the law, and AI systems?
This blog highlights the essential features of a data integration solution that help an organization generate consistent and accurate data to keep the business running smoothly.
Django is a Python web application framework enjoying widespread adoption in the data science community. But what else can you use Django for? Read this article for 9 use cases where you can put Django to work.
So many options are now available online to learn in the field of data science. There are several factors to consider to determine if these options or a traditional degree from an academic institution is the best approach for your personal learning style and career aspirations.
After working as a Data Scientist for a year, I am here to share some things I learnt along the way that I feel are helpful and have increased my efficiency. Hopefully some of these tips can help you in your journey :)
The results of the 2021 Stack Overflow Developer Survey were recently released, which is a fascinating snapshot of today's developers and the tools they are using. Have a look at some selections from the report, particularly those which may be of interest to data professionals.
AI models are necessarily trained on historical data from the real-world--data that is generated from the daily goings on of society. If social-based biases are inherent in the training data, then will the AI predictions highlight these same biases? If so, what should we do (or not do) about making AI fair?
Everyone makes mistakes, which can be a good thing when they lead to learning and improvements over time. But, we can also try to first learn from others to expedite our personal growth. To get started, consider these lessons learned the hard way, so you don’t have to.
Once fresh out of school and ready to burst into an organization as a new hire with newly-developed skills and knowledge, many have learned that things tend to be a little different in the "real world" compared to university. A few shifts in your approach to continued learning and expanding your confidence might help you professionally reach a little further, faster.
For decades, SQL has been the foundation for how humans interact with data. Alternate approaches seem to continually attempt to replace this powerful language. However, while much progress remains in the techniques and tools for the curation and management of data, the skilled craftspeople who work with data -- through the lens of SQL -- are likely to be around for decades more.
The strategic power of AI has been established thoroughly across many industries and companies, leading to surges in model creation. Investments in the people, processes, and tools for operationalizing models, referred to as ModelOps, lag. This function of operationalizing, integrating, and deploying AI models in line with businesses value expectations is growing into a core business capability as global use of AI matures.
Artificial Intelligence and Machine Learning are the next-gen technology used in various fields. With the rise in online threats, it has become essential to include these technologies in cybersecurity. In this post, we will know what roles do AI and ML play in cybersecurity.
Data analysts are dealing with bigger datasets than ever before, making interrogation difficult. Visualized Exploratory Data Analysis, supported by advanced parallel computing, promises an answer.
Looking to up your data analytics consulting rates? Learn exactly what most freelancers are charging, and the rates you SHOULD be charging as a business intelligence and analytics consultant. This post will show you what you need to know to achieve maximum results for your data consulting career.
The Top Industries Hiring Data Scientists in 2021
Django’s 9 Most Common Applications
The Difference Between Data Scientists and ML Engineers
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