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What's the Best Statistical Software? A Comparison of R, Python, SAS, SPSS and STATA
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
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.
Five (5) Useful Statistics, Data Scientists Need to Know
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.
Five (5) Useful Statistics, Data Scientists Need to Know
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.
The Data Scientist Job and the Future
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, loT 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.