Bitfount is a federated analytics and machine learning platform that makes extracting value from sensitive data easy, fast, private, and secure. For data custodians and data scientists or researchers partnering to achieve better insights from data, Bitfount combines the best of data collaboration design, with advanced privacy-preserving capabilities, while playing nicely with all of your existing tools and crucially not requiring the transfer of any raw data. Data collaboration today is a painful, messy business. As anyone who has attempted to set up a collaboration around sensitive data will know, the current process is generally very painful and slow. Valuable datasets languish in silos as a result of regulatory or commercial sensitivity concerns, incompatible data management solutions, lengthy contractual processes, or just plain lack of understanding of what data is available for which purposes within an organisation.
For decades, managing data essentially meant collecting, storing, and occasionally accessing it. That has all changed in recent years, as businesses look for the critical information they can pull from the massive amounts of data generated, accessed, and stored in myriad locations, from corporate data centers to the cloud and the edge. Given that, data analytics – helped by such modern technologies as artificial intelligence (AI) and machine learning – has become a must-have capability and in 2022, the importance will be amplified. Enterprises need to rapidly parse through data – much of it unstructured – to find the information that will drive business decisions. They also need to create a modern data environment in which to make that happen.
This article was published as a part of the Data Science Blogathon. Jupyter Notebook is a web-based interactive computing platform that many data scientists use for data wrangling, data visualization, and prototyping of their Machine Learning models. It is easy to use the platform, and we can do programming in many languages like Python, Julia, R, etc. By default, it comes with Ipython kernels, and if necessary, we can install other language kernels. We'll need more tools to see how our prototype model works in a production environment and how visualizations look in a dashboard because they can only be used to prototype models and do things like Data wrangling and Data Visualization.
Would you be amazed if food delivery apps like Zomato or Ubereats suggest what you want to eat on a specific day, keep a check on your food cravings and cheat-day plans? Well, it's a matter of a couple of years and this would be a real phenomenon. With customer expectations touching the sky to increased competition, businesses seek an edge in bringing products and services to stand apart in the market and deliver incredible customer experiences. Where customers expect businesses to read their minds and surprise them with something new, businesses are always one step ahead to do so. With the emergence of companies such as Netflix and Spotify that deliver personalized insights daily, customers expect to have recommendations catered to their needs. Customers expect other companies also to meet the expected standards and bring something unique to their table, how can businesses jump to this level of proactivity? How can you anticipate needs, trends, and behaviors?
This stock forecast is designed for investors and analysts who need predictions of the best stocks for the whole Pharmaceutical sector (see Pharma Stocks Package). Package Name: Pharma Stocks Forecast Recommended Positions: Long Forecast Length: 7 Days (3/16/22 – 3/23/22) I Know First Average: 25.8% The algorithm correctly predicted 10 out of 10 the suggested trades in the Pharma Stocks Forecast Package for this 7 Days forecast. The prediction with the highest return was SPPI, at 85.74%. YMAB and BBIO also performed well for this time horizon with returns of 40.69% and 26.44%, respectively.
Ishaan and Elizabeth, both graduate students in business, are attending a marketing strategy lecture at a business school in the Northeast. While learning about the principles of market segmentation, Ishaan texts "outdated" followed by three thinking--face emojis to Elizabeth. He wonders how demographic-, geographic-, or psychographic-based segmentation--the topic of the lecture--can help his family's franchise restaurant deal with the hundreds of sometimes-not-so-positive online reviews and social media posts. Meanwhile, Elizabeth hopes that the fast-food restaurant where she ordered her lunch understands that she now belongs to the segment of'extremely displeased' customers. Earlier, she used the restaurant's new app to order a burrito without cheese and sour cream, only to discover that the meal included both offending ingredients. Her lunch went straight into the trash can and she angrily tweeted her disappointment to the restaurant. This simple vignette illustrates an important point. Organizations of every size are challenged with capitalizing on enormous amounts of unstructured organizational data--for instance, from social media posts--particularly for applications such as market segmentation. The purpose of this article is to give the reader an idea of the challenges and opportunities faced by businesses using market segmentation, including the impacts of big data. Our research will demonstrate what market segmentation might look like in the near future, as we also offer a promising approach to implementing market segmentation using unstructured data.
This forecast is part of the Dividends Package, as one of I Know First's quantitative investment solutions. We determine the best stocks carrying a dividend by screening our database daily using our advanced algorithm. Package Name: Dividend Stocks Forecast Recommended Positions: Long Forecast Length: 1 Year (3/3/21 – 3/3/22) I Know First Average: 30.38% Several predictions in this 1 Year forecast saw significant returns. The algorithm had correctly predicted 10 out of 10 stock movements.
This stock market forecast is part of the World Indices Package, as one of I Know First's quantitative investment solutions. We determine our world indices forecast by screening our database daily using our advanced algorithm. Package Name: Indices Forecast Recommended Positions: Long Forecast Length: 3 Days (3/3/22 – 3/6/22) I Know First Average: 11.45% For this 3 Days forecast the algorithm had successfully predicted the changes in 9 out of 10 indices. The prediction with the highest return was VXEFA, at 31.19%.
The Energy Stocks Package is based on the I Know First algorithm and is designed for investors and analysts who need recommendations for the best performing stocks for the whole Energy Industry. Package Name: Energy Stocks Forecast Recommended Positions: Long Forecast Length: 7 Days (2/27/22 – 3/6/22) I Know First Average: 9.25% Several predictions in this 7 Days forecast saw significant returns. The algorithm had correctly predicted 10 out of 10 stock movements. MTDR was the top performing prediction with a return of 17.27%. LPI and AR saw outstanding returns of 12.3% and 12.23%.
This forecast is part of the Dividends Package, as one of I Know First's quantitative investment solutions. We determine the best stocks carrying a dividend by screening our database daily using our advanced algorithm. Package Name: Dividend Stocks Forecast Recommended Positions: Long Forecast Length: 1 Year (2/7/21 – 2/7/22) I Know First Average: 41.1% For this 1 Year forecast the algorithm had successfully predicted 10 out of 10 movements. The highest trade return came from BHLB, at 61.06%. The suggested trades for CMA and STLD also had notable 1 Year yields of 55.88% and 55.01%, respectively.