AI News, Data Science Workflow: Overview and Challenges

Data Science Workflow: Overview and Challenges

Millions of professionals in fields ranging from science, engineering, business, finance, public policy, and journalism, as well as numerous students and computer hobbyists, all perform this sort of programming on a daily basis.

There are four main phases, shown in the dotted-line boxes: preparation of the data, alternating between running the analysis and reflection to interpret the outputs, and finally dissemination of results in the form of written reports and/or executable code.

However, anecdotes and empirical studies indicate that a significant amount of data analysis is still done on desktop machines with data sets that fit on modern hard drives (i.e., less than a terabyte).

Reformat and clean data: Raw data is probably not in a convenient format for a programmer to run a particular analysis, often due to the simple reason that it was formatted by somebody else without that programmer's analysis in mind.

Many of the scientists I interviewed for my dissertation workcomplained that these tasks are the most tedious and time-consuming parts of their workflow, since they are unavoidable chores that yield no new insights.However, the chore of data reformatting and cleaning can lend insights into what assumptions are safe to make about the data, what idiosyncrasies exist in the collection process, and what models and analyses are appropriate to apply.

For example, Christian Bird, an empirical software engineering researcher that I interviewed at Microsoft Research, obtains raw data from a variety of .csv and XML files, queries to software version control systems and bug databases, and features parsed from an email corpus.

In closing, the following excerpt from the introduction of the book Python Scripting for Computational Science summarizes the extent of data preparation chores: Scientific Computing Is More Than Number Crunching: Many computational scientists work with their own numerical software development and realize that much of the work is not only writing computationally intensive number-crunching loops.

Very often programming is about shuffling data in and out of different tools, converting one data format to another, extracting numerical data from a text, and administering numerical experiments involving a large number of data files and directories.

The figure below shows that in the analysis phase, the programmer engages in a repeatediteration cycleof editing scripts, executing to produce output files, inspecting the output files to gain insights and discover mistakes, debugging, and re-editing.

Lastly, data scientists do not write code in a vacuum: As they iterate on their scripts, they often consult resources such as documentation websites, API usage examples, sample code snippets from online forums, PDF documents of related research papers, and relevant code obtained from colleagues.

student might meet with her research advisor every week to show the latest graphs generated by her analysis scripts.The inputs to meetings include printouts of data visualizations and status reports, which form the basis for discussion.

Upon inspecting the charts and tables that my analyses generated each day, he often asked me to adjust my scripts or to fork my analyses to explore multiple alternative hypotheses (e.g., "Please explore the effects of employee location on bug fix rates by re-running your analysis separately for each country.").

Make comparisons and explore alternatives: The reflection activities that tie most closely with the analysis phase are making comparisons between output variants and then exploring alternatives by adjusting script code and/or execution parameters.

The figure below shows an example set of graphs from social network analysis research, where four variants of a model algorithm are tested on four different input data sets:

The final phase of data science is disseminating results, most commonly in the form of written reports such as internal memos, slideshow presentations, business/policy white papers, or academic research publications.

For example, computer graphics and user interface researchers currently submit a video screencast demo of their prototype systems along with each paper submission, but it would be ideal if paper reviewers could actually execute their software to get a "feel"

Before colleagues can execute one's code (even on the same operating system), they must first obtain, install, and configure compatible versions of the appropriate software and their myriad of dependent libraries, which is often a frustrating and error-prone process.

Similarly, it is even difficult to reproduce the results of one's own experiments a few months or years in the future, since one's own operating system and software inevitably get upgraded in some incompatible manner such that the original code no longer runs.

For instance, academic researchers need to be able to reproduce their own results in the future after submitting a paper for review, since reviewers inevitably suggest revisions that require experiments to be re-run.

As an extreme example, my former officemate Cristian Cadar used to archive his experiments by removing the hard drive from his computer after submitting an important paper to ensure that he can re-insert the hard drive months later and reproduce his original results.

is part of this model, and there are other aspects of this model that clearly relate to evaluation, I find that defining meaningful metrics, applying them, verifying that they are being applied correctly, and then using those to assess effectiveness, are all important challenging aspects to many data science efforts.

these reservations, I do find this to be a clear and insightful presentation of many of the key concepts in data science, and I am particularly pleased that it is appearing outside the paywall that restricts access to so many of the other worthwhile insights shared through ACM.

is part of this model, and there are other aspects of this model that clearly relate to evaluation, I find that defining meaningful metrics, applying them, verifying that they are being applied correctly, and then using those to assess effectiveness, are all important challenging aspects to many data science efforts.

Despite these reservations, I do find this to be a clear and insightful presentation of many of the key concepts in data science, and I am particularly pleased that it is appearing outside the paywall that restricts access to so many of the other worthwhile insights shared through ACM.

Data Science Workflow: Overview and Challenges

Millions of professionals in fields ranging from science, engineering, business, finance, public policy, and journalism, as well as numerous students and computer hobbyists, all perform this sort of programming on a daily basis.

There are four main phases, shown in the dotted-line boxes: preparation of the data, alternating between running the analysis and reflection to interpret the outputs, and finally dissemination of results in the form of written reports and/or executable code.

However, anecdotes and empirical studies indicate that a significant amount of data analysis is still done on desktop machines with data sets that fit on modern hard drives (i.e., less than a terabyte).

Reformat and clean data: Raw data is probably not in a convenient format for a programmer to run a particular analysis, often due to the simple reason that it was formatted by somebody else without that programmer's analysis in mind.

Many of the scientists I interviewed for my dissertation workcomplained that these tasks are the most tedious and time-consuming parts of their workflow, since they are unavoidable chores that yield no new insights.However, the chore of data reformatting and cleaning can lend insights into what assumptions are safe to make about the data, what idiosyncrasies exist in the collection process, and what models and analyses are appropriate to apply.

For example, Christian Bird, an empirical software engineering researcher that I interviewed at Microsoft Research, obtains raw data from a variety of .csv and XML files, queries to software version control systems and bug databases, and features parsed from an email corpus.

In closing, the following excerpt from the introduction of the book Python Scripting for Computational Science summarizes the extent of data preparation chores: Scientific Computing Is More Than Number Crunching: Many computational scientists work with their own numerical software development and realize that much of the work is not only writing computationally intensive number-crunching loops.

Very often programming is about shuffling data in and out of different tools, converting one data format to another, extracting numerical data from a text, and administering numerical experiments involving a large number of data files and directories.

The figure below shows that in the analysis phase, the programmer engages in a repeatediteration cycleof editing scripts, executing to produce output files, inspecting the output files to gain insights and discover mistakes, debugging, and re-editing.

Lastly, data scientists do not write code in a vacuum: As they iterate on their scripts, they often consult resources such as documentation websites, API usage examples, sample code snippets from online forums, PDF documents of related research papers, and relevant code obtained from colleagues.

student might meet with her research advisor every week to show the latest graphs generated by her analysis scripts.The inputs to meetings include printouts of data visualizations and status reports, which form the basis for discussion.

Upon inspecting the charts and tables that my analyses generated each day, he often asked me to adjust my scripts or to fork my analyses to explore multiple alternative hypotheses (e.g., "Please explore the effects of employee location on bug fix rates by re-running your analysis separately for each country.").

Make comparisons and explore alternatives: The reflection activities that tie most closely with the analysis phase are making comparisons between output variants and then exploring alternatives by adjusting script code and/or execution parameters.

The figure below shows an example set of graphs from social network analysis research, where four variants of a model algorithm are tested on four different input data sets:

The final phase of data science is disseminating results, most commonly in the form of written reports such as internal memos, slideshow presentations, business/policy white papers, or academic research publications.

For example, computer graphics and user interface researchers currently submit a video screencast demo of their prototype systems along with each paper submission, but it would be ideal if paper reviewers could actually execute their software to get a "feel"

Before colleagues can execute one's code (even on the same operating system), they must first obtain, install, and configure compatible versions of the appropriate software and their myriad of dependent libraries, which is often a frustrating and error-prone process.

Similarly, it is even difficult to reproduce the results of one's own experiments a few months or years in the future, since one's own operating system and software inevitably get upgraded in some incompatible manner such that the original code no longer runs.

For instance, academic researchers need to be able to reproduce their own results in the future after submitting a paper for review, since reviewers inevitably suggest revisions that require experiments to be re-run.

As an extreme example, my former officemate Cristian Cadar used to archive his experiments by removing the hard drive from his computer after submitting an important paper to ensure that he can re-insert the hard drive months later and reproduce his original results.

is part of this model, and there are other aspects of this model that clearly relate to evaluation, I find that defining meaningful metrics, applying them, verifying that they are being applied correctly, and then using those to assess effectiveness, are all important challenging aspects to many data science efforts.

these reservations, I do find this to be a clear and insightful presentation of many of the key concepts in data science, and I am particularly pleased that it is appearing outside the paywall that restricts access to so many of the other worthwhile insights shared through ACM.

is part of this model, and there are other aspects of this model that clearly relate to evaluation, I find that defining meaningful metrics, applying them, verifying that they are being applied correctly, and then using those to assess effectiveness, are all important challenging aspects to many data science efforts.

Despite these reservations, I do find this to be a clear and insightful presentation of many of the key concepts in data science, and I am particularly pleased that it is appearing outside the paywall that restricts access to so many of the other worthwhile insights shared through ACM.

Ultimate guide to deal with Text Data (using Python) – for Data Scientists Engineers

One of the biggest breakthroughs required for achieving any level of artificial intelligence is to have machines which can process text data.

From social media analytics to risk management and cybercrime protection, dealing with text data has never been more important.

In this article we will discuss different feature extraction methods, starting with some basic techniques which will lead into advanced Natural Language Processing techniques.

We will also learn about pre-processing of the text data in order to extract better features from clean data.

Before starting, let’s quickly read the training file from the dataset in order to perform different tasks on it.

Note that here we are only working with textual data, but we can also use the below methods when numerical features are also present along with the text.

The basic intuition behind this is that generally, the negative sentiments contain a lesser amount of words than the positive ones.

Here, we simply take the sum of the length of all the words and divide it by the total length of the tweet: Generally, while solving an NLP problem, the first thing we do is to remove the stopwords.

Before diving into text and feature extraction, our first step should be cleaning the data in order to obtain better features.

The next step is to remove punctuation, as it doesn’t add any extra information while treating text data.

As we discussed earlier, stop words (or commonly occurring words) should be removed from the text data.

We can also remove commonly occurring words from our text data First, let’s check the 10 most frequently occurring words in our text data then take call to remove or retain.

Similarly, just as we removed the most common words, this time let’s remove rarely occurring words from the text.

Because they’re so rare, the association between them and other words is dominated by noise. You can replace rare words with a more general form and then this will have higher counts All these pre-processing steps are essential and help us in reducing our vocabulary clutter so that the features produced in the end are more effective.

In that regard, spelling correction is a useful pre-processing step because this also will help us in reducing multiple copies of words.

is used as ‘ur’. We should treat this before the spelling correction step, otherwise these words might be transformed into any other word like the one shown below:

Unigrams do not usually contain as much information as compared to bigrams and trigrams. The basic principle behind n-grams is that they capture the language structure, like what letter or word is likely to follow the given one.

Therefore, we can generalize term frequency as: TF = (Number of times term T appears in the particular row) / (number of terms in that row) To understand more about Term Frequency, have a look at this article.

Instead, sklearn has a separate function to directly obtain it: We can also perform basic pre-processing steps like lower-casing and removal of stopwords, if we haven’t done them earlier.

Bag of Words (BoW) refers to the representation of text which describes the presence of words within the text data. The intuition behind this is that two similar text fields will contain similar kind of words, and will therefore have a similar bag of words.

For implementation, sklearn provides a separate function for it as shown below: To gain a better understanding of this, you can refer to this article.

So, before applying any ML/DL models (which can have a separate feature detecting the sentiment using the textblob library), let’s check the sentiment of the first few tweets.

Here, we only extract polarity as it indicates the sentiment as value nearer to 1 means a positive sentiment and values nearer to -1 means a negative sentiment.

We can easily obtain it’s word vector using the above model: We then take the average to represent the string ‘go away’

Data Science at the Command Line

Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line.

Jeroen expertly discusses how to bring that philosophy into your work in data science, illustrating how the command line is not only the world of file input/output, but also the world of data manipulation, exploration, and even modeling.

Cook Consultant in applied mathematics, statistics, and technical computing If you find this content useful, please consider supporting the work by either: This work is licensed under the Creative Commons Attribution-NoDerivatives 4.0 International License.

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