This is a book containing 12 comprehensive case studies focused primarily on data manipulation, programming and computional aspects of statistical topics in authentic research applications. The aim is to provide students, researchers and faculty with exposure to the entire thought process of approaching the computations of a complete data analysis project. This differs from teaching a programming language. Instead, it illustrates how to think about programming with very concrete and complete examples. We also emphasize testing and validating computations, when and how to make them faster, and give the reader insight into how high-level programming evolves.
Each chapter works through all of the computations and programming to acquire, transform and explore the data or create the simulations. We discuss different aspects of the analysis and show results. However, readers have the opportunity to take the analyses much further, building on the core computational work described in the chapters.
The data scientist's guide for writing papers
Scientific papers are written by students, graduate students, and established scientists. The writing of such papers is an obligatory part of work on a dissertation.
The purpose of this article is to provide a clear guide on how to write a science research paper. For this guide, we used the advice from the best essay writing service.
How to write a data science paper
First of all, let's define what a scientific paper is. A scientific piece examines one or several interrelated problems of a particular topic. We can say that a scientific paper is a full-fledged mini research on a certain narrow topic.
Second, a scientific paper should bring the change to the current state of a certain problem. It should not restate already known things; instead, it has to provide new solutions to the issues that are still not settled.
Third, a scientific article should be unique and free from plagiarism. Maybe you have a question: "Who can write my essay?". You cannot mention the ideas of other scientists, researchers, or journalists without mentioning them in your paper or article. Your citations must be formatted accordingly to the citation style required by your professor or scientific advisor. Otherwise, your content will be considered plagiarized; it can even end up in all your scientific accomplishments achieved earlier.
- The following types of scientific papers are distinguished:
- Scientific-theoretical - describing the results of research carried out based on a theoretical search and explanation of phenomena and their patterns.
- Scientific and practical (empirical) - built on the basis of experiments and real experience.
- Survey - devoted to the analysis of scientific achievements in a particular area over the past few years.
The scientific paper involves presenting conclusions and intermediate or final results of scientific research, as well as experimental or analytical activities. Such an article should contain the author's developments, conclusions, and recommendations.
This means that, first of all, the scientific paper should have the effect of novelty: the results presented in it should not have been published earlier. By publishing a scientific article, the author secures a priority in the chosen field of research.
How to publish a scientific paper
If you are preparing an article for a particular publication in a magazine or scientific journal, you first of all study the requirements for the articles accepted in it: volume, design, range of topics.
Then, you can reflect on the topic of the article. First, review the material you already have and think about how you can use it to write data science paper. The more narrowly and specialized the topic of the article is presented, the better. Don't try to embrace the immensity. The topic should be relevant to science and interesting to you.
Having defined the topic, sketch out a rough outline of the article, think about how and in what sequence to present the material. Now you need to decide what materials you lack for full and well-reasoned conclusions.
Go to the laboratory, archives, library to collect the missing information, to conduct additional experiments. Turn to a professional essay writer for additional help. Be sure to pay attention to new publications on your topic that have appeared over the past year or two. Flip through scientific journals, conference proceedings, magazines, newspapers. The content of the paper should be relevant and based on the latest developments of other researchers.
Having collected the necessary material, group it, analyze, and summarize. For a better perception of the amount of work done and the results of your activity, present the material in a visual form: draw up diagrams, graphs, tables. This will help you to organize not only the information received, but also make your readers understand you better and use your material in their activities.
Can't find inspiration to write such a voluminous work? Contact real experts in this field for data science paper help.
Not sure where to start writing the text? Start in the middle and just write down everything that comes into your head.
- Here are the most useful science writing tips:
- Do not try to find the right words and the right phrases immediately. The main thing is to form the skeleton of a future article.
- Set aside the written text for a few days. All the while, your brain will continue to work, and when you reopen the file with your notes, work will go much faster.
- Write the paper's main body first, then the conclusions and introduction, and then move on to the title, annotation, and keywords.
- When you start writing a scientific article, imagine who you are writing it for. Provide comments that are difficult and obscure for your audience, but here it is important to strike a balance and not explain elementary and well-known truths.
- Do not use unreasonable borrowings.
- Don't forget to divide your text into paragraphs. If the article is long, use subheadings. Such paper is easier to read.
Using KML and Google Earth to visualize data
Occurrences of technical words in Kaggle Job Posts
The case studies form 3 basic groups (with overlap in most chapters)
- data analysis and statistical methods
- data technologies
- exploratory data analysis (EDA),
- naïve Bayes,
- k-nearest neighbors,
- classification and regression trees,
- repeated measurements and time series,
- non-linear least squares and optimization,
- cross validation,
- connections and text processing,
- regular expressions,
- UNIX shell tools,
- relational databases and SQL,
- scraping Web pages and HTML,
- XML, KML.
The chapters also provide rich examples of some more advanced aspects of R, including
- object-oriented progamming with both S3 and reference classes,
- dealing with large data with, e.g., the bigmemory package
- profiling R code
- making code faster
- interfacing to C code