Common statistics program packages differ considerably in terms of their strengths, weaknesses, and handling. The decision as to which system is the best fit should be made with care. Changing to a new system can result in high costs for things like new licenses and re-training. This article introduces and contrasts the market leaders - R, Python, SAS, SPSS, and STATA - to help to illustrate their relative pros and cons, and help make the decision a bit easier.
R Programming a popular, open-source statistics environment that can be extended by packages almost at will. R is commonly used with RStudio, a comfortable development environment that can be used locally or in a client-server installation via a web browser. R applications can also be used directly and interactively on the web via Shiny.
A data scientist should know how to effectively use statistics to gain insights from data. Here are five useful and practical statistical concepts that every data scientist must know.
Data Science can be practically defined as the process by which we get extra information from data. When doing Data Science, what we’re really trying to do is explain what all of the data actually meansin the real-world, beyond the numbers.
To extract the information embedded in complex datasets, Data Scientists employ a number of tools and techniques including data exploration, visualisation, and modelling. One very important class of mathematical technique often used in data exploration is statistics.
A dramatic upswing of data science jobs facilitating the rise of data science professionals to encounter the supply-demand gap.
By 2024, a shortage of 250,000 data scientists is predicted in the United States alone. Data scientists have emerged as one of the hottest careers in the data world today. With digitization on the rise, IoT and cognitive technologies have generated a large number of data sets, thus, making it difficult for an organization to unlock the value of these data.
With the constant rise in data science, those fail to upgrade their skill set may be putting themselves at a competitive disadvantage. No doubt data science is still deemed as one of the best job titles today, but the battles for expert professionals in this field is fierce.
The Era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multi-cloud support, machine learning, and real-time analytics.
The Era of Big Data passed away on June 5, 2019, with the announcement of Tom Reilly' s upcoming resignation from Cloudera and subsequent market capitalization drop. Coupled with MapR's recent announcement intending to shut down in late June, which will be dependent on whether MapR can find a buyer to continue operations, June of 2019 accentuated that the initial Era of Hadoop-driven Big Data has come to an end. Big Data will be remembered for its role in enabling the beginning of social media dominance, its role in fundamentally changing the mindset of enterprises in working with multiple orders of magnitude increases in data volume, and in clarifying the value of analytic data, data quality, and data governance for the ongoing valuation of data as an enterprise asset.
As I give a eulogy of sorts to the Era of Big Data, I do want to emphasize that Big Data technologies are not actually 'dead,' but that the initial generation of Hadoop-based Big Data has reached a point of maturity where its role in enterprise data is established. Big Data is no longer part of the breathless hype cycle of infinite growth but is now an established technology.
Explore the future of Machine Learning and ML algorithms.Introduction to Machine Learning
Machine Learning is an application of Artificial Intelligence. It allows software applications to become accurate in predicting outcomes. Machine Learning focuses on the development of computer programs, and the primary aim is to allow computers to learn automatically without human intervention.
Google says "Machine Learning is the future," and the future of Machine Learning is going to be very bright. As humans become more addicted to machines, we’re witnesses to a new revolution that’s taking over the world, and that is going to be the future of Machine Learning.