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Page is translated, but not showing in local language (translation)
Done please push through ja language translation as submitted starting around 04:57, 27 December 2021 (UTC). I would like to refer this page to the Wikimedia jargon glossary we are compiling on jawp. Appreciate your kind arrangement, --Omotecho (talk) 07:48, 8 January 2022 (UTC)
- If you link to Home/ja, it should work. --Ameisenigel (talk) 16:12, 22 February 2022 (UTC)
- @Ameisenigel, hi, that s a great top to learn, thank you for telling me. It is showing and I am smiling. Cheers, arigatō, -- Omotecho (talk) 20:19, 22 February 2022 (UTC)
East Turkestan
Historical state in Central Asia, on the territory of present-day Xinjiang, at the moment a province of ChinaHistorical state in Central Asia, on the territory of present-day Xinjiang, at the moment a province of China Afon99 (talk) 11:09, 19 July 2022 (UTC)
Punctuation errors on the "Financial Reports" page
This page uses en dashes to identify financial years, when the dash should be restricted to ranges of years. That is according to the WP-en MOS and to general punctuation guidelines used by the publishing industry. I can't edit the page myself to fix.
It's arguable that the "2021-2022" financial year, with a hyphen, should be "2021/2022". But it's clearly wrong to typeset "2018–2019" with an en dash. It's not a period of two years, 2018 through 2019, but a single year that spans part of 2018 and part of 2019. At the very least it should be changed to a simple hyphen, but slashes are the norm for financial and school years. With a slash, unlike a hyphen, it's clear that the punctuation isn't a mistake for an en dash. Kwamikagami (talk) 21:38, 31 August 2022 (UTC)
Top Data Science Myths That Must Be Debunked
Top Data Science Myths That Must Be Debunked
Although there are many employment openings in the data science profession, many people are still unsure of what data scientists perform. The numerous misconceptions regarding the function of a data scientist are partly to blame for this confusion. We will debunk the top ten data science fallacies in this article. You will know more about the job of a data scientist and what it takes to be one at the end of this essay.
Due to data science's growing popularity, many misconceptions about it are spread. It's critical to be aware of and dispel these fallacies if you're thinking about a career in data science. Every data function is identical Because their occupations, tasks, and duties are all extremely distinct from one another, data analysts, data engineers, and data scientists are all performing the same action, which is entirely incorrect. We recognise the confusion this causes because all of these folks fall under the big data category. Let's look at what data engineers do first. They are in charge of working on the fundamentals of engineering and creating the scalable data pipelines necessary for the extraction of raw data from various sources, transformation, and injection into downstream systems. Data scientists and data analysts rely on this procedure because it allows them to turn data into information, which is what matters.
To work as a data scientist, you must have a PhD.
This is also entirely incorrect, but it also greatly relies on the kind of job role you want. For instance, a master's or doctoral degree is required if you want to work in research. However, if you want to solve complicated data riddles and work with deep learning or machine learning, you will be the one to work on data science projects employing libraries and data analysis techniques. Therefore, a master's degree is not required. Nowadays, everything revolves around skills; therefore, if you possess the necessary skill set for a data scientist, you may certainly enter the field.
Data Scientists must be proficient coders Additionally, this is completely incorrect because a data scientist's job requires them to work with large amounts of data, and when we talk about pro coding, we mean working excessively on the competitive programming side or having a very in-depth understanding of common data structures and algorithms. A data scientist must undoubtedly be skilled at handling complicated problems. In the realm of data science, we have languages like Python and R that offer crucial support through a variety of libraries that can be utilized to tackle complex data challenges. To build the best data models and machine learning models, you should strive to grasp how to use these libraries and their modules as a data scientist. Take up the training in Data Science Course with placement in Hyderabad today to gain a competitive edge.
Even Programming professionals should study data science One of the most crucial myths to dispel is this one. Although a growing number of young people choose to major in science, primarily drawn by the increasing employment available in the technological sector, most people do not already haical backgrounds for the data science profile because they excel at problem-solving and comprehending commercial use cases. In order to succeed in data science interviews, it's crucial to comprehend these concepts. Companies don't focus on a programmeve a technical background. Employers frequently choose candidates with non-technr's traditional technical skills; instead, they want to know if an applicant is strong in the aptitude test.
Predictive modeling is all there is to data science.
Not everyone knows that data scientists spend 80% of their time cleaning and transforming data and 20% of their time on data modeling. As a result, a data scientist who wants to produce extremely accurate data and a machine learning model must clean and transform data. We know that working on a specific Big Data solution involves a number of processes, the first of which is crucially important: data transformation. In the modern era, data comes to us from many different sources, and the raw data can occasionally contain invalid records. We won't be able to get actionable transformation data and won't be able to do anything if we can't clean our data.
Data Science Needs a Solid Mathematical Foundation
This is also untrue to the fullest extent possible because one of the crucial aspects of working as a data scientist on a daily basis is having strong math skills. These mathematical ideas, such as statistics and probability, would be helpful while analyzing the data, but they are not necessary for a data scientist to possess to be successful in their field. Unless you need to innovate or develop a new algorithm, you don't need to be a mathematics expert to execute the standard mathematical calculations and computations because we have excellent programming languages like Python and R that also provide great support for amazing libraries.
Conclusion Do not be deterred by these fallacies if data science is something you are interested in. People of different backgrounds are welcome to work in data science. Anyone can become a data scientist with the trending skills and knowledge. Learn more about the tools with data science course in Hyderabad , and become an IBM-certified data scientist. Sidimeenu (talk) 08:59, 26 December 2022 (UTC)