Through this blog, I will list out the major reasons why you should learn Python and the 8 major data-driven reasons for learning it.
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5 Things You Dont Know About PyCaret
In comparison with the other open source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with a few words only.
Originally from KDnuggets https://ift.tt/3210uh9
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Top 5 Jupyter Widgets to boost your productivity!
A guide to install, configure and use the most useful jupyter widgets.
We can’t thank Project Jupyter enough for bringing in those versatile and interactive IPython notebooks, FOR FREE!. Hands down, jupyter has changed the way experimentation is done using conventional scripts and has become the De facto of modern-day python programming. Why is it so well known? Why is everyone using it? because:
- Each cell can be modified at any point of time even when the program is running.
- Documentation using jupyter notebooks is a cup of tea, export your notebook as a PDF, make it into a python script or export it as an HTML file.
- Complete output is visible for every cell.
- Step-by-Step implementation of complex algorithms makes it easy to progress with the project.
- Jupyter maintains state of notebook therefore code can be undone and deleted cells can be restored.
Now that we know why jupyter is a must, let’s take a look at how we can improve it further to make the experience even more streamlined.
Introducing Notebook Widgets!
Jupyter widgets enhance the jupyter experience by introducing liveliness to your notebooks and are useful for tasks such as easy navigation, smooth presentations, template generation, code formatting and hassle less visualisations. Let’s straightaway dive into it.
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Prerequisite installations:
Using condo package manager:
conda install -c conda-forge jupyter_contrib_nbextensions
conda install -c conda-forge jupyter_nbextensions_configurator
Using pip package manager:
pip install jupyter_nbextensions_configurator jupyter_contrib_nbextensions
jupyter contrib nbextension install --user
jupyter nbextensions_configurator enable --user
Now the type following command in terminal and launch jupyter notebook
jupyter notebook
You must see the following addition in the menu
Uncheck the following button to start using widgets!
Now that you have done that we are ready to go!
For activating a widget search for the name in the search bar as indicated above and check the button.
- Collapsible headings
One of my absolute favourites is collapsible headings widget which allows you to enclose a snippet of code within a heading until next heading of the same size is found. Sometimes the programs get too long and reach to a point where navigation through the code is a tedious task. Use this widget to hide long scripts of code and make your notebook more presentable and easy to understand.
2. Autopep8
“You primarily write your code to communicate with other coders, and, to a lesser extent, to impose your will on the computer.”
— Guido van Rossum, Python founder
Another widget to make your code more presentable is Autopep8. PEP stands for Python Enhancement Proposal, it is a style for writing python code to make it more readable and understandable. It defines a few formatting conventions such as:
- While writing a function use a lowercase word or words. Separate words by underscores to improve readability — my_func(), prime()
- While declaring a variable use a lowercase single letter, word, or words. Separate words with underscores to improve readability — count, df, first_list
- While declaring a constant use an uppercase single letter, word, or words. Separate words with underscores to improve readability — BASE_PATH,DIMS,ITERATIONS
That’s exactly what Autopep8 does for you automatically! Have a look at the before and after:
All you have to do is just click on the cell you want to correct the formatting of and hit the button below which should be on your jupyter toolbar:
3. Notify
If the task at hand is time consuming then having a widget which can notify user on completing cell execution can be quite handy. One such extension is notify, it basically sends a browser notification every time a cell completes its execution. Say today you have to work on retrieving data from remote databases or perform complex operations on a large dataframe and it is taking so long that you almost forget about its execution, later you check on after an hour and find out it had completed execution half an hour earlier, to avoid the frustration that will follow you can simply use this widget 🙂
Do not forget to allow notifications access to jupyter.
4. Code folding
Like other IDEs such as PyCharm or Eclipse, code folding can also be used in jupyter using the Code folding widget. Here’s an example:
It’s a pretty simple tool and is just plug-and-play, doesn’t require any setup.
5. Hinterland
Hinterland is an auto-complete extension for jupyter that provides suggestions as you type and it works quite well. If you are among those who tend to write repetitive code for any reason, hinterland is the thing for you!. Look at this demo:
Conclusion:
These were some of the most used jupyter widgets to help programmers better manage the code they write, explore more and use those suites you. Do let me know if you find any interesting widgets! Till then, see you next time.
Previous blogs:
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Top 5 Jupyter Widgets to boost your productivity! was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
What are the Best Practices for Software Testing?
Introduction
Software testing is a significant kind of tradition software expansion. It is the procedure that gauges the competency of a system or program to decide if it meets the prerequisite outcomes. Recognizing mistakes or errors is the essential part and job of software testing; this procedure is even significant for many other several reasons. Software testing is made use to assure that any given program or method encounters design necessities. They are extremely made in use to measure if any program and product functions within a suitable period. With the help of software testing services, anybody can check if a given software can be used in the proper surroundings and if it meets shareholder potentials.
Are you aware of the several software testing methods accomplished by testers in the entire globe? If not, let’s check below in the article best practices techniques of testing and the types of testing.
Best Practices for Software Testing
software testing is a test that numerous product expansion teams go through. There are methods and performances. Below are a few of the best practices where the software engineers make use of at the time of testing procedure:
- Give primary response: Test engineers must be comprised initially in the expansion procedure to give feedback on an extensive diversity of aspects involving possibility, planning and maintenance as it relies on the software development.
2. Allow the greater team to shift quicker: Test engineers must be accountable for the automation of the product test outline by retorting to the requirements of the expansion team.
3. Stimulate originality: the test engineers add the test procedure by mixing fresh ideas for test examples. When new ideas are added to cases, software engineers can tackle difficulties in an original manner.
4. The flexibility of Test: when the expansion team requires fast feedback, test engineers concentrates mainly on investigative serve and testing quickly. This is obliging on lesser common examples, which is difficult to test automatically.
5. Involve with the outdoor: As and when problems increase in customer support area test engineers is able to examine perseverance through the customer and arrange the harshness and danger. This would permit software developers to concentrate on fitting imperfections.
6. The mixture of testing and development: teams can increase as well as maintain talent with the help of sharing duties all over the team. Revolving the testing tasks with expansion parts offers members a fresh viewpoint. Similarly, inspiring software developers to participate in the testing lets project members know about testing thoroughly.
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What are the stages of Software testing?
• Requirements analysis
• Planning phase
• Analysis phase
• Test case development
• Environment setup
• Test case execution
• Test cycle closure
Software testing types
Software application and product advancement include the procedures of code investigation, code configuration, code improvement and the most basic procedure programming testing. This procedure of QA testing includes testing at the product level as well as module level and testing all in all unit. Many companies use software testing and it is an important part. Some companies make use of manual testing while many other companies have taken over automation kind of Test.
Though there are diverse kinds of software testing in practice; nevertheless, the two significant types are Functional and Non-functional types of testing; however, the two main kind of software testing are:
1. Manual Testing: This is a test procedure that is accompanied physically without making use of automated or software scripting to recognize flaws. A test plan text is organized that acts as a controller to the testing procedure to have a complete reporting of the Test.
2. Automation Testing: Automation testing is a Software testing method to test and associate the real consequence with the predictable result. This could be attained by writing test writings or using an automated testing tool. Test automation is used to mechanize monotonous errands and additional testing errands which are problematic to achieve physically.
Summary
In summary, the significance of software testing can be accredited to significant places that is less preservation, enlarged user assurance, and matching to necessities. To avoid delayed testing impacts, enterprises must know the different kinds of software testing techniques and should take the full benefit of testing by leveraging a testing services provider.
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What are the Best Practices for Software Testing? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
Understanding Time Series with R
Analyzing time series is such a useful resource for essentially any business, data scientists entering the field should bring with them a solid foundation in the technique. Here, we decompose the logical components of a time series using R to better understand how each plays a role in this type of analysis.
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source https://365datascience.weebly.com/the-best-data-science-blog-2020/understanding-time-series-with-r
New Course! Data Visualization with Python, R, Tableau, and Excel
Hey, my name is Elitsa – a Computational Biologist turned data science professional and a course creator at 365 Data Science.
And I’m happy to announce the brand-new addition to our Program: The Data Visualization Course with Python, R, Tableau, and Excel!
In this post, I’ll take you through all the features of the course, its structure, and the in-demand skills it will help you develop. Finally, I’ll tell you a bit more about myself and the projects I’ve worked on.
The 365 Data Science Data Visualization Course
In my career, I’ve worked with multiple datasets on various problems. But what they all had in common was the need to visualize the data to gain some insight, or to present what I’ve discovered in front of an audience.
That is why I decided to create Data Visualization with Python, R, Tableau, and Excel – to help people who work with data to visualize and interpret their findings accurately. This high-powered, practical course will teach you how to create a rich variety of graphs and charts and develop superior data interpretation skills to secure a career in data science or business intelligence. And I hope that once you complete it, creating and understanding data visualizations will come as intuitively to you as it does for me.
Who is this course for?
This course is a perfect match for beginners. But it is also highly beneficial for anyone who wants to advance their career by adding value to their workplace with data visualization proficiency.
What is the structure of the course?
The Data Visualization course is based in 4 different technologies: Excel, Tableau, Python, and R.
And in each section, we’ll explore a specific chart and learn how to create it in all these environments.
It doesn’t matter what your preferred software is. You will be able to master the art of beautiful data visualizations in no time! In addition, you have immediate access to ready-to-use templates for all charts studied in the course. All you have to do is download the course files, replace the dataset, and start creating!
Now, the course follows a simple structure that is suitable for everyone’s data visualization journey, even if you are just getting started.
In the first section, you’ll get familiar with the highest level in data visualization theory – how to select the most appropriate chart, chart color, and so on.
In the second section, you’ll explore in detail how to install the different software to make sure you are all set to learn.
The subsequent sections are organized in a very consistent way.
- Each section revolves around a given chart type;
- The first lecture of each section introduces the type of visualization and the dataset we’ll be working with;
- The following 4 lectures show the practical implementation in Excel, Tableau, Python, and R. You are free to learn all 4 software, or simply stick to your preferred one;
- Finally, we conclude with 1 or more lectures on chart usage and interpretation.
What will you learn?
You’ll learn how to create stunning visualizations with:
- Bar charts
- Pie charts
- Stacked area charts
- Line charts
- Histograms
- Scatter plot and a Scatter plot with a trendline (regression plot)
- Combo charts, race bar charts, and correlograms
Not only that – you will grasp how to label and style data visualizations to achieve a ready-for-presentation graph; interpret different types of charts; and choose the right chart to provide the most meaningful visualization of the data you are working with.
About the author
As I mentioned earlier, I am a Computational Biologist. I have deep expertise in the fields of algorithms and data structures, phylogenetics, as well as population genetics. My academic background is in Bioinformatics with publications on constructing Phylogenetic Networks and Trees. I am also one of the authors of the course Customer Analytics in Python in the 365 Data Science Program. If you’re curious to learn about my experience and projects, you can find more details in this interview.
The Data Visualization course is part of the 365 Data Science Program, so current subscribers can access the courses at no extra cost.
To learn more about the 365 Data Science Program curriculum or enroll in the 365 Data Science Program, please visit our Courses page.
Want to explore the curriculum or sign up 15 hours of beginner to advanced video content for free? Click on the button below.
The post New Course! Data Visualization with Python, R, Tableau, and Excel appeared first on 365 Data Science.
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What Is a SARIMAX Model?
What Is a SARIMAX model?
Although we have dedicated a series of blog posts to time series models, we are yet to discuss one very important topic – seasonality.
Each of the models we examined so far – be it AR, MA, ARMA, ARIMA or ARIMAX has a seasonal equivalent.
As you can probably guess, the names for these counterparts will be SARMA, SARIMA, and SARIMAX respectively, with the “S” representing the seasonal aspect.
Therefore, the full name of the model would be Seasonal Autoregressive Integrated Moving Average Exogenous model.
We can all agree that it’s a mouthful, so we’ll stick with the abbreviation.
Additionally, the SARMA and SARIMA can be considered simpler cases of the SARIMAX, where we don’t use integration or exogenous variables, so we’ll mainly focus our attention to the SARIMAX in this tutorial.
What Is Seasonality?
In case you need a hint, seasonality occurs when certain patterns aren’t consistent, but appear periodically. For instance, check out the weekly YouTube searches for Christmas songs like “Jingle Bells”.
These occur much more frequently over the festive period in December every year. However, the number of times these songs are played is usually a lot lower in June or July.
Therefore, a simple autoregressive component won’t describe the data well.
To elaborate, a simple AR component would severely understate the number of times Christmas songs are played in December, based on the stats from November (1 lag ago). At the same time, it would also greatly overstate the number in January, basing them off of the values recorded in December, since this genre usually experiences a dip after Christmas.
How Do We Handle Seasonality?
To account for such a pattern, we need to include the values recorded during the previous festive period into the model. In this specific example, that would mean relying on the number of times the songs were played last December. Of course, we CAN also include the data from two Decembers back, or even more.
It’s a bit like having another series which is further spread out in time than our original one. Going back to the musical example, the original time series contains values a month apart, while the seasonal one would hold values 12 months apart.
The SARIMAX Model Definition
Now that we’re familiar with the general idea of seasonal models, let’s look at the notation we use and what each value means. Compared to the ARIMAX, the SARIMAX requires 4 additional orders.
This might sound like a lot, but there’s no need to worry!
The first 3 of these 4 orders are just seasonal versions of the ARIMA orders.
In other words, we have a seasonal autoregressive order denoted by upper-case P, an order of seasonal integration denoted by upper-case D, and a seasonal moving average order signified by upper-case Q. To make differentiation easier, econometricians have agreed to use lower-case letters for their non-seasonal equivalents.
The 4th, and last, order is the length of the cycle. For instance, if we have hourly data, and the cycle length is 24, then the seasonal pattern appears once every 24 hours.
What Is the Length of the Cycle in Seasonal Models?
Another way to think about it is “The number of periods necessary to pass before the tendency reappears”. If we want to inspect a seasonal trend, we need to make sure to set the appropriate cycle length. We represent the last order with a lower-case “s” because it sets the length of each season.
How Do We Interpret Seasonal Orders?
Let’s quickly explain how the 4 new orders work in unison.
Essentially, the length – “s”, – expresses how far away the seasonal components will be from the current period. So, if we have a model with seasonal orders of (2,0,1 and 5), then we’re including the lagged values from 5, and 10 periods ago, as well as the error term from 5 periods ago. Each cycle is “5” periods long and we’re taking 2 lagged seasonal values. So, we’re simply including the values from 5 and 10 periods ago. Similarly, we add the error term from 5 periods ago.
To generalize, we’re interested in every “s”-th value. We start from the “s”-th and go all the way up to “s, times p”. The equivalent is true for seasonal integrated values and seasonal errors as well.
What Is the Equation of a SARIMAX Model?
Let’s see what the equation of a SARIMAX model of order (1,0,1) and a seasonal order (2,0,1,5) looks like.
The interesting part here is that every seasonal component also comprises additional lagged values. If you want to learn why that is so, you can find a detailed explanation of the math behind the SARIMAX model here.
So, what can we see from the equation? The total number of coefficients we are estimating equals the sum of seasonal and non-seasonal AR and MA orders. In other words, we’re looking at a total of “P plus Q, plus, p plus q” – many coefficients.
The non-seasonal ones are expressed with lower-case ϕ and θ; while their seasonal counterparts are expressed with upper-case Φ and Θ respectively. Just like with the orders, the capital letters denote the seasonal components and the lower-case ones – the non-seasonal.
So, this is the basic knowledge of seasonal models you need. However, if you want to learn more about time series and time-series data, make sure to check out our article on the topic.
If you’re new to Python, and you’re enthusiastic to learn more, this comprehensive article on learning Python programming will guide you all the way from the installation, through Python IDEs, Libraries, and frameworks, to the best Python career paths and job outlook.
Try Introduction to Python course for free!
The post What Is a SARIMAX Model? appeared first on 365 Data Science.
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Learn Python ML Deep Learning Data Visualization and more in Italy with BIG DIVE
Do you want to learn or upgrade your data data proficiency and push your career forward? This year, under the umbrella of BIG DIVE, TOP-IX presents four full-time 1-week courses from beginner to advanced levels. Read more and register now.
Originally from KDnuggets https://ift.tt/2ZaRq7u
Pull and Analyze Financial Data Using a Simple Python Package
We demonstrate a simple Python script/package to help you pull financial data (all the important metrics and ratios that you can think of) and plot them.
Originally from KDnuggets https://ift.tt/3gHHCrA
365 Data Science 