Trending February 2024 # Don’t Miss Out On These 24 Amazing Python Libraries For Data Science # Suggested March 2024 # Top 2 Popular

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Overview

Check out our pick of the top 24 Python libraries for data science

We’ve divided these libraries into various data science functions, such as data collection, data cleaning, data exploration, modeling, among others

Any Python libraries you feel we should include? Let us know!

Introduction

I’m a massive fan of the Python language. It was the first programming language I learned for data science and it has been a constant companion ever since. Three things stand out about Python for me:

Its ease and flexibility

Industry-wide acceptance: It is far and away the most popular language for data science in the industry

The sheer number of Python libraries for data science

In fact, there are so many Python libraries out there that it can become overwhelming to keep abreast of what’s out there. That’s why I decided to take away that pain and compile this list of 24 awesome Python libraries covering the end-to-end data science lifecycle.

That’s right – I’ve categorized these libraries by their respective roles in data science. So I’ve mentioned libraries for data cleaning, data manipulation, visualization, building models and even model deployment (among others). This is quite a comprehensive list to get you started on your data science journey using Python.

Python libraries for different data science tasks:

Python Libraries for Data Collection

Beautiful Soup

Scrapy

Selenium

Python Libraries for Data Cleaning and Manipulation

Pandas

PyOD

NumPy

Spacy

Python Libraries for Data Visualization

Matplotlib

Seaborn

Bokeh

Python Libraries for Modeling

Scikit-learn

TensorFlow

PyTorch

Python Libraries for Model Interpretability

Lime

H2O

Python Libraries for Audio Processing

Librosa

Madmom

pyAudioAnalysis

Python Libraries for Image Processing

OpenCV-Python

Scikit-image

Pillow

Python Libraries for Database

Psycopg

SQLAlchemy

Python Libraries for Deployment

Flask

Python Libraries for Data Collection

Have you ever faced a situation where you just didn’t have enough data for a problem you wanted to solve? It’s an eternal issue in data science. That’s why learning how to extract and collect data is a very crucial skill for a data scientist. It opens up avenues that were not previously possible.

So here are three useful Python libraries for extracting and collection data.

One of the best ways of collecting data is by scraping websites (ethically and legally of course!). Doing it manually takes way too much manual effort and time. Beautiful Soup is your savior here.

Beautiful Soup is an HTML and XML parser which creates parse trees for parsed pages which is used to extract data from webpages.  This process of extracting data from web pages is called web scraping.

Use the following code to install BeautifulSoup:

pip install beautifulsoup4

Here’s a simple code to implement Beautiful Soup for extracting all the anchor tags from HTML:



I recommend going through the below article to learn how to use Beautiful Soup in Python:

Scrapy is another super useful Python library for web scraping. It is an open source and collaborative framework for extracting the data you require from websites. It is fast and simple to use.

Here’s the code to install Scrapy:

pip install scrapy

It is a framework for large scale web scraping. It gives you all the tools you need to efficiently extract data from websites, process them as you want, and store them in your preferred structure and format.

Here’s a simple code to implement Scrapy:

View the code on Gist.

Here’s the perfect tutorial to learn Scrapy and implement it in Python:

Selenium is a popular tool for automating browsers. It’s primarily used for testing in the industry but is also very handy for web scraping. Selenium is actually becoming quite popular in the IT field so I’m sure a lot of you would have at least heard about it.

We can easily program a Python script to automate a web browser using Selenium. It gives us the freedom we need to efficiently extract the data and store it in our preferred format for future use.

I wrote an article recently about scraping YouTube video data using Python and Selenium:

Python Libraries for Data Cleaning and Manipulation

Alright – so you’ve collected your data and are ready to dive in. Now it’s time to clean any messy data we might be faced with and learn how to manipulate it so our data is ready for modeling.

Here are four Python libraries that will help you do just that. Remember, we’ll be dealing with both structured (numerical) as well as text data (unstructured) in the real-world – and this list of libraries covers it all.

When it comes to data manipulation and analysis, nothing beats Pandas. It is the most popular Python library, period. Pandas is written in the Python language especially for manipulation and analysis tasks.

The name is derived from the term “panel data”, an econometrics term for datasets that include observations over multiple time periods for the same individuals. – Wikipedia

Pandas come pre-installed with Python or Anaconda but here’s the code in case required:

pip install pandas

Pandas provides features like:

Dataset joining and merging

Data Structure column deletion and insertion

Data filtration

Reshaping datasets

DataFrame objects to manipulate data, and much more!

Here is an article and an awesome cheatsheet to get your Pandas skills right up to scratch:

Struggling with detecting outliers? You’re not alone. It’s a common problem among aspiring (and even established) data scientists. How do you define outliers in the first place?

Don’t worry, the PyOD library is here to your rescue.

PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects. Outlier detection is basically identifying rare items or observations which are different significantly from the majority of data.

You can download pyOD by using the below code:

pip install pyod

How does PyOD work and how can you implement it on your own? Well, the below guide will answer all your PyOD questions:

NumPy, like Pandas, is an incredibly popular Python library. NumPy brings in functions to support large multi-dimensional arrays and matrices. It also brings in high-level mathematical functions to work with these arrays and matrices.

NumPy is an open-source library and has multiple contributors. It comes pre-installed with Anaconda and Python but here’s the code to install it in case you need it at some point:

$ pip install numpy

Below are some of the basic functions you can perform using NumPy:

Array creation

View the code on Gist.

output - [1 2 3] [0 1 2 3 4 5 6 7 8 9] Basic operations

View the code on Gist.

output - [1. 1.33333333 1.66666667 4. ] [ 1 4 9 36]

And a whole lot more!

We’ve discussed how to clean and manipulate numerical data so far. But what if you’re working on text data? The libraries we’ve seen so far might not cut it.

Step up, spaCy. It is a super useful and flexible Natural Language Processing (NLP) library and framework to clean text documents for model creation. SpaCy is fast as compared to other libraries which are used for similar tasks.

To install Spacy in Linux:

pip install -U spacy python -m spacy download en

To install it on other operating systems, go through this link.

Of course we have you covered for learning spaCy:

Python Libraries for Data Visualization

So what’s next? My favorite aspect of the entire data science pipeline – data visualization! This is where our hypotheses are checked, hidden insights are unearthed and patterns are found.

Here are three awesome Python libraries for data visualization.

Matplotlib is the most popular data visualization library in Python. It allows us to generate and build plots of all kinds. This is my go-to library for exploring data visually along with Seaborn (more of that later).

You can install matplotlib through the following code:

$ pip install matplotlib

Below are a few examples of different kind of plots we can build using matplotlib:

Histogram 3D Graph

Since we’ve covered Pandas, NumPy and now matplotlib, check out the below tutorial meshing all these three Python libraries:

Seaborn is another plotting library based on matplotlib. It is a python library that provides high level interface for drawing attractive graphs. What matplotlib can do, Seaborn just does it in a more visually appealing manner.

Some of the features of Seaborn are:

A dataset-oriented API for examining

relationships

between

multiple variables

Convenient views onto the overall

structure

of complex datasets

Tools for choosing

color palettes

that reveal patterns in your data

You can install Seaborn using just one line of code:

pip install seaborn

Let’s go through some cool graphs to see what seaborn can do:

View the code on Gist.

Here’s another example:

View the code on Gist.

Got to love Seaborn!

Bokeh is an interactive visualization library that targets modern web browsers for presentation. It provides elegant construction of versatile graphics for a large number of datasets.

Bokeh can be used to create interactive plots, dashboards and data applications. You’ll be pretty familiar with the installation process by now:

pip install bokeh

Feel free to go through the following article to learn more about Bokeh and see it in action:

Python Libraries for Modeling

And we’ve arrived at the most anticipated section of this article – building models! That’s the reason most of us got into data science in the first place, isn’t it?

Let’s explore model building through these three Python libraries.

Like Pandas for data manipulation and matplotlib for visualization, scikit-learn is the Python leader for building models. There is just nothing else that compares to it.

In fact, scikit-learn is built on NumPy, SciPy and matplotlib. It is open source and accessible to everyone and reusable in various contexts.

Here’s how you can install it:

pip install scikit-learn

Scikit-learn supports different operations that are performed in machine learning like classification, regression, clustering, model selection, etc. You name it – and scikit-learn has a module for that.

I’d also recommend going through the following link to learn more about scikit-learn:

Developed by Google, TensorFlow is a popular deep learning library that helps you build and train different models. It is an open source end-to-end platform. TensorFlow provides easy model building, robust machine learning production, and powerful experimentation tools and libraries.

An entire ecosystem to help you solve challenging, real-world problems with Machine Learning – Google

TensorFlow provides multiple levels of abstraction for you to choose from according to your need. It is used for building and training models by using the high-level Keras API, which makes getting started with TensorFlow and machine learning easy.

Go through this link to see the installation processes. And get started with TensorFlow using these articles:

What is PyTorch? Well, it’s a Python-based scientific computing package that can be used as:

A replacement for NumPy to use the power of GPUs

A deep learning research platform that provides maximum flexibility and speed

Go here to check out the installation process for different operating systems.

PyTorch offers the below features:

Hybrid Front-End

Tools and libraries: An active community of researchers and developers have built a rich ecosystem of tools and libraries for extending PyTorch and supporting development in areas from computer vision to reinforcement learning

Cloud support: PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling through prebuilt images, large scale training on GPUs, ability to run models in a production scale environment, and more

Here are two incredibly detailed and simple-to-understand articles on PyTorch:

Python Libraries for Data Interpretability

Do you truly understand how your model is working? Can you explain why your model came up with the results that it did? These are questions every data scientist should be able to answer. Building a black box model is of no use in the industry.

So, I’ve mentioned two Python libraries that will help you interpret your model’s performance.

LIME is an algorithm (and library) that can explain the predictions of any classifier or regressor. How does LIME do this? By approximating it locally with an interpretable model. Inspired from the paper “Why Should I Trust You?”: Explaining the Predictions of Any Classifier”, this model interpreter can be used to generate explanations of any classification algorithm.

Installing LIME is this easy:

pip install lime

This article will help build an intuition behind LIME and model interpretability in general:

I’m sure a lot of you will have heard of chúng tôi They are market leaders in automated machine learning. But did you know they also have a model interpretability library in Python?

H2O’s driverless AI offers simple data visualization techniques for representing high-degree feature interactions and nonlinear model behavior. It provides Machine Learning Interpretability (MLI) through visualizations that clarify modeling results and the effect of features in a model.

Go through the following link to read more about H2O’s Driverless AI perform MLI.

Python Libraries for Audio Processing

Audio processing or audio analysis refers to the extraction of information and meaning from audio signals for analysis or classification or any other task. It’s becoming a popular function in deep learning so keep an ear out for that.

LibROSA is a Python library for music and audio analysis. It provides the building blocks necessary to create music information retrieval systems.

Here’s an in-depth article on audio processing and how it works:

The name might sound funny, but Madmom is a pretty nifty audio data analysis Python library. It is an audio signal processing library written in Python with a strong focus on music information retrieval (MIR) tasks.

You need the following prerequisites to install Madmom:

NumPy

SciPy

Cython

Mido

And you need the below packages to test the installation:

PyTest

PyAudio

PyFftw

The code to install Madmom:

pip install madmom

We even have an article to learn how Madmom works for music information retrieval:

pyAudioAnalysis is a Python library for audio feature extraction, classification, and segmentation. It covers a wide range of audio analysis tasks, such as:

Classify unknown sounds

Detect audio events and exclude silence periods from long recordings

Perform supervised and unsupervised segmentation

You can install it by using the following code:

pip install pyAudioAnalysis

Python Libraries for Image Processing

So make sure you’re comfortable with at least one of the below three Python libraries.

When it comes to image processing, OpenCV is the first name that comes to my mind. OpenCV-Python is the Python API for image processing, combining the best qualities of the OpenCV C++ API and the Python language.

It is mainly designed to solve computer vision problems.

OpenCV-Python makes use of NumPy, which we’ve seen above. All the OpenCV array structures are converted to and from NumPy arrays. This also makes it easier to integrate with other libraries that use NumPy such as SciPy and Matplotlib.

Install OpenCV-Python in your system:

pip3 install opencv-python

Here are two popular tutorials on how to use OpenCV in Python:

Another python dependency for image processing is Scikit-image. It is a collection of algorithms for performing multiple and diverse image processing tasks.

You can use it to perform image segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and much more.

We need to have the below packages before installing scikit-image:

And this is how you can install scikit-image on your machine:

pip

install

-

U

scikit

-

learn

Pillow is the newer version of PIL (Python Imaging Library). It is forked from PIL and has been adopted as a replacement for the original PIL in some Linux distributions like Ubuntu.

Pillow offers several standard procedures for performing image manipulation:

Per-pixel manipulations

Masking and transparency handling

Image filtering, such as blurring, contouring, smoothing, or edge finding

Image enhancing, such as sharpening, adjusting brightness, contrast or color

Adding text to images, and much more!

How to install Pillow? It’s this simple:

pip install Pillow

Check out the following AI comic illustrating the use of Pillow in computer vision:

Python Libraries for Database

Learning how to store, access and retrieve data from a database is a must-have skill for any data scientist. You simply cannot escape from this aspect of the role. Building models is great but how would you do that without first retrieving the data?

I’ve picked out two Python libraries related to SQL that you might find useful.

The current psycopg2 implementation supports:

Python version 2.7

Python 3 versions from 3.4 to 3.7

PostgreSQL server versions from 7.4 to 11

PostgreSQL client library version from 9.1

Here’s how you can install psycopg2:

pip install psycopg2

Ah, SQL. The most popular database language. SQLAlchemy, a Python SQL toolkit and Object Relational Mapper, gives application developers the full power and flexibility of SQL.

It is designed for efficient and high-performing database access. SQLAlchemy considers the database to be a relational algebra engine, not just a collection of tables.

To install SQLAlchemy, you can use the following line of code:

pip install SQLAlchemy

Python Libraries for Deployment

Do you know what model deployment is? If not, you should learn this ASAP. Deploying a model means putting your final model into the final application (or the production environment as it’s technically called).

Flask is a web framework written in Python that is popularly used for deploying data science models. Flask has two components:

Werkzeug: It is a utility library for the Python programming language

Jinja: It is a template engine for Python

Check out the example below to print “Hello world”:

View the code on Gist.

The below article is a good starting point to learn Flask:

End Notes

In this article, we saw a huge bundle of python libraries that are commonly used while doing a data science project. There are a LOT more libraries that are out there but these are the core ones every data scientist should know.

Related

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Don’t Miss These Tips To Use Chatgpt For Brainstorming!

The best use of this AI tool is brainstorming, which helps in almost every field around the world. People from all walks of life can use AI tools to solve their problems or use ideas as a guide to make perfect long-term decisions. All of these tools can gather information from thousands of sources that were previously difficult to access.

However, it is not always necessary for the tool to provide the correct answers, so you need to be extra vigilant when searching for answers. You should double-check sources before finalizing an answer. OpenAI’s ChatGPT still needs a lot of improvement. With that said, we are here to discuss AI tools’ positive aspects.

6 Best Tips To Use ChatGPT For Brainstorming Amazing Ideas…

ChatGPT is not trained on updated data, so it is best for users to stick to general questions. For example, you can ask about films to watch, restaurant opening times, game results, and much more. At $20 per month for its services, ChatGPT with GPT-4 is not a bad deal. The following 6 tips can help you use ChatGPT for brainstorming:

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Start Clear And Clever:

You should not rely entirely on ChatGPT just because a strong algorithm powers it. If you want to have a good brainstorming session, start with your raw idea. Find a core question and draw some points to start your research. You can use this information to create several prompts. Let’s start with a bunch of short questions. This is because long questions are sometimes confusing, and you may not get the information you want.

Know The Limitations Of The Tool:

The best way to use ChatGPT for brainstorming is to start by learning about the chatbot. For example, learn what the chatbot is struggling with or what an effective approach is to get good prompts. This is a good approach as it will help you improve your efforts. Moreover, measure the response time of the chatbot, i.e., how much time it takes to generate answers to long and short questions.

Try To Get Persistent Prompts:

Get Prompts With Longer Lists:

The longer the lists, the better the answers. For example, if you ask for ways to write creative content, try searching for 20 ways to write creative content. Or, 90 ways to impress a girlfriend – this is a bonus tip for you. With such a variety of answers, you can find the best. If you don’t get the answer you’re looking for in one go, try repeating and rephrasing your questions. For example, 70 ways to write engaging content or something similar.

Touch Different Aspects Of Potential Applications:

Mock Up A Few Examples:

The most interesting thing about humans is that they can learn from examples. It is a better idea to ask ChatGPT for examples or case studies. For example, if you want to become a Prompt Engineer, start by asking how to become a Prompt Engineer. What skills do I need to become a Prompt Engineer? You can ask a variety of questions related to this area.

A/B Testing For Data Science Using Python – A Must

Overview

A/B testing is a popular way to test your products and is gaining steam in the data science field

Here, we’ll understand what A/B testing is and how you can leverage A/B testing in data science using Python

Introduction

Statistical analysis is our best tool for predicting outcomes we don’t know, using the information we know.

Picture this scenario – You have made certain changes to your website recently. Unfortunately, you have no way of knowing with full accuracy how the next 100,000 people who visit your website will behave. That is the information we cannot know today, and if we were to wait until those 100,000 people visited our site, it would be too late to optimize their experience.

This seems to be a classic Catch-22 situation!

This is where a data scientist can take control. A data scientist collects and studies the data available to help optimize the website for a better consumer experience. And for this, it is imperative to know how to use various statistical tools, especially the concept of A/B Testing.

A/B Testing is a widely used concept in most industries nowadays, and data scientists are at the forefront of implementing it. In this article, I will explain A/B testing in-depth and how a data scientist can leverage it to suggest changes in a product.

Table of contents:

What is A/B testing?

How does A/B testing work?

Statistical significance of the Test

Mistakes we must avoid while conducting the A/B test

When to use A/B test

What is A/B testing?

A/B testing is a basic randomized control experiment. It is a way to compare the two versions of a variable to find out which performs better in a controlled environment.

For instance, let’s say you own a company and want to increase the sales of your product. Here, either you can use random experiments, or you can apply scientific and statistical methods. A/B testing is one of the most prominent and widely used statistical tools.

In the above scenario, you may divide the products into two parts – A and B. Here A will remain unchanged while you make significant changes in B’s packaging. Now, on the basis of the response from customer groups who used A and B respectively, you try to decide which is performing better.

Source

It is a hypothetical testing methodology for making decisions that estimate population parameters based on sample statistics. The population refers to all the customers buying your product, while the sample refers to the number of customers that participated in the test.

How does A/B Testing Work?

The big question!

In this section, let’s understand through an example the logic and methodology behind the concept of A/B testing.

Let’s say there is an e-commerce company XYZ. It wants to make some changes in its newsletter format to increase the traffic on its website. It takes the original newsletter and marks it A and makes some changes in the language of A and calls it B. Both newsletters are otherwise the same in color, headlines, and format.

Objective

Our objective here is to check which newsletter brings higher traffic on the website i.e the conversion rate. We will use A/B testing and collect data to analyze which newsletter performs better.

1.  Make a Hypothesis

Before making a hypothesis, let’s first understand what is a hypothesis.

A hypothesis is a tentative insight into the natural world; a concept that is not yet verified but if true would explain certain facts or phenomena.

It is an educated guess about something in the world around you. It should be testable, either by experiment or observation. In our example, the hypothesis can be “By making changes in the language of the newsletter, we can get more traffic on the website”.

In hypothesis testing, we have to make two hypotheses i.e Null hypothesis and the alternative hypothesis. Let’s have a look at both.

Null hypothesis or H0:

The null hypothesis is the one that states that sample observations result purely from chance. From an A/B test perspective, the null hypothesis states that there is no difference between the control and variant groups. It states the default position to be tested or the situation as it is now, i.e. the status quo. Here our H0 is ” there is no difference in the conversion rate in customers receiving newsletter A and B”.

Alternative Hypothesis or H0:

The alternative hypothesis challenges the null hypothesis and is basically a hypothesis that the researcher believes to be true. The alternative hypothesis is what you might hope that your A/B test will prove to be true.

In our example, the Ha is- “the conversion rate of newsletter B is higher than those who receive newsletter A“.

Now, we have to collect enough evidence through our tests to reject the null hypothesis.

2. Create Control Group and Test Group

Once we are ready with our null and alternative hypothesis, the next step is to decide the group of customers that will participate in the test. Here we have two groups – The Control group, and the Test (variant) group.

The Control Group is the one that will receive newsletter A and the Test Group is the one that will receive newsletter B.

For this experiment, we randomly select 1000 customers – 500 each for our Control group and Test group.

Randomly selecting the sample from the population is called random sampling. It is a technique where each sample in a population has an equal chance of being chosen. Random sampling is important in hypothesis testing because it eliminates sampling bias, and it’s important to eliminate bias because you want the results of your A/B test to be representative of the entire population rather than the sample itself.

Another important aspect we must take care of is the Sample size. It is required that we determine the minimum sample size for our A/B test before conducting it so that we can eliminate under coverage bias. It is the bias from sampling too few observations.

3. Conduct the A/B Test and Collect the Data

One way to perform the test is to calculate daily conversion rates for both the treatment and the control groups. Since the conversion rate in a group on a certain day represents a single data point, the sample size is actually the number of days. Thus, we will be testing the difference between the mean of daily conversion rates in each group across the testing period.

When we run our experiment for one month, we noticed that the mean conversion rate for the Control group is 16% whereas that for the test Group is 19%.

Statistical significance of the Test

Now, the main question is – Can we conclude from here that the Test group is working better than the control group?

The answer to this is a simple No! For rejecting our null hypothesis we have to prove the Statistical significance of our test.

There are two types of errors that may occur in our hypothesis testing:

Type I error: We reject the null hypothesis when it is true. That is we accept the variant B when it is not performing better than A

Type II error: We failed to reject the null hypothesis when it is false. It means we conclude variant B is not good when it performs better than A

To avoid these errors we must calculate the statistical significance of our test.

An experiment is considered to be statistically significant when we have enough evidence to prove that the result we see in the sample also exists in the population.

That means the difference between your control version and the test version is not due to some error or random chance. To prove the statistical significance of our experiment we can use a two-sample T-test.

Source

To understand this, we must be familiar with a few terms:

Significance level (alpha):

The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. Generally, we use the significance value of 0.05

P-Value: It is the probability that the difference between the two values is just because of random chance. P-value is evidence against the null hypothesis. The smaller the p-value stronger the chances to reject the H0. For the significance level of 0.05, if the p-value is lesser than it hence we can reject the null hypothesis

Confidence interval: The confidence interval is an observed range in which a given percentage of test outcomes fall. We manually select our desired confidence level at the beginning of our test. Generally, we take a 95% confidence interval

Next, we can calculate our t statistics using the below formula:

Let’s Implement the Significance Test in Python

Let’s see a python implementation of the significance test. Here, we have a dummy data having an experiment result of an A/B testing for 30 days. Now we will run a two-sample t-test on the data using Python to ensure the statistical significance of chúng tôi can download the sample data here.

Python Code:



sns.distplot(data.Conversion_B)

At last, we will perform the t-test:

t_stat, p_val= ss.ttest_ind(data.Conversion_B,data.Conversion_A) t_stat , p_val (3.78736793091929, 0.000363796012828762)

For our example, the observed value i.e the mean of the test group is 0.19. The hypothesized value (Mean of the control group) is 0.16. On the calculation of the t-score, we get the t-score as .3787. and the p-value is 0.00036.

SO what does all this mean for our A/B Testing?

Here, our p-value is less than the significance level i.e 0.05. Hence, we can reject the null hypothesis. This means that in our A/B testing, newsletter B is performing better than newsletter A. So our recommendation would be to replace our current newsletter with B to bring more traffic on our website.

What Mistakes Should we Avoid While Conducting A/B Testing?

There are a few key mistakes I’ve seen data science professionals making. Let me clarify them for you here:

Invalid hypothesis: The whole experiment depends on one thing i.e the hypothesis. What should be changed? Why should it be changed, what the expected outcome is, and so on? If you start with the wrong hypothesis, the probability of the test succeeding, decreases

Testing too Many Elements Together: Industry experts caution against running too many tests at the same time. Testing too many elements together makes it difficult to pinpoint which element influenced the success or failure. Thus, prioritization of tests is indispensable for successful A/B testing

Ignoring Statistical Significance: It doesn’t matter what you feel about the test. Irrespective of everything, whether the test succeeds or fails, allow it to run through its entire course so that it reaches its statistical significance

Not considering the external factor: Tests should be run in comparable periods to produce meaningful results. For example, it is unfair to compare website traffic on the days when it gets the highest traffic to the days when it witnesses the lowest traffic because of external factors such as sale or holidays

When Should We Use A/B Testing?

A/B testing works best when testing incremental changes, such as UX changes, new features, ranking, and page load times. Here you may compare pre and post-modification results to decide whether the changes are working as desired or not.

A/B testing doesn’t work well when testing major changes, like new products, new branding, or completely new user experiences. In these cases, there may be effects that drive higher than normal engagement or emotional responses that may cause users to behave in a different manner.

End Notes

To summarize, A/B testing is at least a 100-year-old statistical methodology but in its current form, it comes in the 1990s. Now it has become more eminent with the online environment and availability for big data. It is easier for companies to conduct the test and utilize the results for better user experience and performance.

There are many tools available for conducting A/B testing but being a data scientist you must understand the factors working behind it. Also, you must be aware of the statistics in order to validate the test and prove it’s statistical significance.

To know more about hypothesis testing, I will suggest you read the following article:

Related

Data Science: The 10 Commandments For Performing A Data Science Project

Machine learning has the ultimate goal of creating a model that is generalizable. It is important to select the most accurate model by comparing and choosing it correctly. You will need a different holdout than the one you used to train your hyperparameters. You will also need to use statistical tests that are appropriate to evaluate the results.

It is crucial to understand the goals of the users or participants in a data science project. However, this does not guarantee success. Data science teams must adhere to best practices when executing a project in order to deliver on a clearly defined brief. These ten points can be used to help you understand what it means.

1. Understanding the Problem

Knowing the problem you are trying to solve is the most important part of solving it. You must understand the problem you are trying to predict, all constraints, and the end goal of this project.

Also read: iPhone 14 Pro Max Is Apple’s New iPhone To Be Launched In September (Know The Release Date, Specification, Rumour & More)

2. Know Your Data

Knowing what your data means will help you understand which models are most effective and which features to use. The data problem will determine which model is most successful. Also, the computational time will impact the project’s cost.

You can improve or mimic human decision-making by using and creating meaningful features. It is crucial to understand the meaning of each field, especially when it comes to regulated industries where data may be anonymized and not clear. If you’re unsure what something means, consult a domain expert.

3. Split your data

What will your model do with unseen data? If your model can’t adapt to new data, it doesn’t matter how good it does with the data it is given.

You can validate its performance on unknown data by not letting the model see any of it while training. This is essential in order to choose the right model architecture and tuning parameters for the best performance.

Splitting your data into multiple parts is necessary for supervised learning. The training data is the data the model uses to learn. It typically consists of 75-80% of the original data.

This data was chosen randomly. The remaining data is called the testing data. This data is used to evaluate your model. You may need another set of data, called the validation set.

This is used to compare different supervised learning models that were tuned using the test data, depending on what type of model you are creating.

You will need to separate the non-training data into the validation and testing data sets. It is possible to compare different iterations of the same model with the test data, and the final versions using the validation data.

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4. Don’t Leak Test Data

It is important to not feed any test data into your model. This could be as simple as training on the entire data set, or as subtle as performing transformations (such as scaling) before splitting.

If you normalize your data before splitting, the model will gain information about the test set, since the global minimum and maximum might be in the held-out data.

5. Use the Right Evaluation Metrics

Every problem is unique so the evaluation method must be based on that context. Accuracy is the most dangerous and naive classification method. Take the example of cancer detection.

We should always say “not cancer” if we want to build a reliable model. This will ensure that we are correct 99 percent of the time.

Also read: Top 10 Business Intelligence Tools of 2023

6. Keep it simple

It is important to select the best solution for your problem and not the most complex. Management, customers, and even you might want to use the “latest-and-greatest.” You need to use the simplest model that meets your needs, a principle called Occam’s Razor.

This will not only make it easier to see and reduce training time but can also improve performance. You shouldn’t try to kill Godzilla or shoot a fly with your bazooka.

7. Do not overfit or underfit your model

Overfitting, also called variance, can lead to poor performance when the model doesn’t see certain data. The model simply remembers the training data.

Bias, also known as underfitting, is when the model has too few details to be able to accurately represent the problem. These two are often referred to as “bias-variance trading-off”, and each problem requires a different balance.

Let’s use a simple image classification tool as an example. It is responsible for identifying whether a dog is present in an image.

8. Try Different Model Architectures

It is often beneficial to look at different models for a particular problem. One model architecture may not work well for another.

You can mix simple and complex algorithms. If you are creating a classification model, for example, try as simple as random forests and as complex as neural networks.

Interestingly, extreme gradient boosting is often superior to a neural network classifier. Simple problems are often easier to solve with simple models.

9. Tune Your Hyperparameters

These are the values that are used in the model’s calculation. One example of a hyperparameter in a decision tree would be depth.

This is how many questions the tree will ask before it decides on an answer. The default parameters for a model’s hyperparameters are those that give the highest performance on average.

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10. Comparing Models Correctly

Machine learning has the ultimate goal of creating a model that is generalizable. It is important to select the most accurate model by comparing and choosing it correctly.

You will need a different holdout than the one you used to train your hyperparameters. You will also need to use statistical tests that are appropriate to evaluate the results.

Why Python Alone Will Make You Fail In Data Science Job?

Python though is a higher-level language with data centric libraries and easy to read syntax, it cannot perform the tasks efficiently at all the stages

A data science job requires programming knowledge. Data science mostly uses Python programming language. These are some ideas data science job seekers usually come across. Most of the opinions on the internet revolve around these ideas, which are only partial truths. Search for ‘most desirable data science skills’ only to find Python as one of the top skills required for data science. Indeed, Python, as a programming language has ruled the data science world ever since it was developed. This doesn’t mean learning Python alone would be sufficient to land a data science job. The reason might be on the part of the project’s requirement with respect to Python’s features or the aspirant’s programming capability – depending on python would be like putting all the eggs in one basket. Python, the popular language which is presumed to be indispensable for a data scientist is losing its ground to other programming languages. A data science project goes through different stages from data extraction to data modelling to model deployment. Python though is a higher-level language with data-centric libraries and easy-to-read syntax, it cannot perform the tasks efficiently at all the stages. The newcomers include SQL, R, Scala, Julia, etc with benefits like better Cloud Native performances and the ability to run on modern hardware, etc.

Python Vs Others – a comparison:

SQL comes into the picture when we look at how much and where the companies store data. For a successful database analysis, the data should be retrieved simultaneously from servers, which Python lags way behind when compared to the query language, SQL. No wonder SQL though holds equal importance appears trailing Python in the list of required skills. SQL is used for data retrieval which is an essential step for even getting started with the project. Employers look for people who are multitaskers within the data science domain adept at basic skills because most part of data science project involves gathering and cleaning data. Perhaps this is the reason why SQL has ranked higher than Python in the Stack overflow survey. SQL syntax comes in different formats which companies use according to the demands of the project. MySQL, and SQL Server, are a few of them, you need to give a try.

R was the most popular language for data science application in 2024-16 overtaken by Python in the last 2 to 3 years. R is more for seasoned pros for it is coded heavy and has a steep learning curve. Given the emerging trends which suggest machine learning moving away from data, there is very much chance that R might become the must-learn language for beginners. Whether to use R or Python shouldn’t be a question because the purpose or the data analysis goal differs. R is optimized for deep statistical analysis which data researchers employ for deep analytics and data visualisation features while Python is more suitable for data wrangling. When Burtch Works did a comprehensive survey of data scientists and analytics professionals, R was found more popular with experienced pros and Python with beginners.

Julia, an emerging language is still considered an add-on. It shares many features with Python, R, and other programming languages like Go and Ruby, it’s worth learning right in the beginning because it has the potential to replace Python for its superior performance. With Julia, it is possible to achieve C-like performance, and hand-crafted profiling techniques without optimization, which in Python’s case, is impossible. Why employ Python in the first place if Julia can make the job better?  Besides, Julia is good at working with external libraries, and memory management by default, and otherwise.

Looking beyond the Python paradigm

As said in the beginning, programming knowledge is not the be-all and end-all solution to securing a data science job. It is pretty much an obscure fact that employers look for problem solvers rather than number crunchers. Learning coding without paying attention to why you are doing it will take you nowhere. Learning data structures will not teach how to apply them to a given database for a particular problem. Well, there are many contenders like Scala and Swift which are fast making their way into the list of viable if not popular programming languages. To survive as a data scientist, better to let Python let be a necessity rather than a sufficient requirement.

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Mastering Exploratory Data Analysis(Eda) For Data Science Enthusiasts

This article was published as a part of the Data Science Blogathon

Overview

Step by Step approach to Perform EDA

Resources Like Blogs, MOOCS for getting familiar with EDA

Getting familiar with various Data Visualization techniques, charts, plots

Demonstration of some steps with Python Code Snippet

What is that one thing that differentiates one data science professional, from the other?

Not Machine Learning, Not Deep Learning, Not SQL, It’s Exploratory Data Analysis (EDA). How good one is with the identification of hidden patterns/trends of the data and how valuable the extracted insights are, is what differentiates Data Professionals.

1. What Is Exploratory Data Analysis

EDA assists Data science professionals in various ways:-

3 Getting a better understanding of the problem statement

[ Note: the dataset in this blog is being opted as iris dataset]

2. Checking Introductory Details About Data

The first and foremost step of any data analysis, after loading the data file, should be about checking few introductory details like, no. Of columns, no. of rows, types of features( categorical or Numerical), data types of column entries.

Python Code Snippet

Python Code:



data.head()              For displaying first five rows

data.tail()   For Displaying last Five Rows

3. Statistical Insight

This step should be performed for getting details about various statistical data like Mean, Standard Deviation, Median, Max Value, Min Value

Python Code Snippet

data.describe() 

  4. Data cleaning

This is the most important step in EDA involving removing duplicate rows/columns, filling the void entries with values like mean/median of the data, dropping various values, removing null entries

Checking Null entries

data.IsNull().sum   gives the number of missing values for each variable

Removing Null Entries

data.dropna(axis=0,inplace=True)     If null entries are there

Filling values in place of Null Entries(If Numerical feature)

Values can either be mean, median or any integer

Python Code Snippet

Checking Duplicates

data.duplicated().sum()  returning total number of duplicates entries

Removing Duplicates

data.drop_duplicates(inplace=True)

5. Data Visualization

Data visualization is the method of converting raw data into a visual form, such as a map or graph, to make data easier for us to understand and extract useful insights.

The main goal of data visualization is to put large datasets into a visual representation. It is one of the important steps and simple steps when it comes to data science

You Can refer to the blog below for getting more details about Data Visualization

Various Types of Visualization analysis is: a. Uni Variate analysis:

This shows every observation/distribution in data on a single data variable. It can be shown with the help of various plots like Scatter Plot, Line plot,  Histogram(summary)plot, box plots, violin plot, etc.

b. Bi-Variate analysis: c. Multi-Variate analysis:

Scatterplots, Histograms, box plots, violin plots can be used for Multivariate Analysis

Various Plots

Below are some of the plots that can be deployed for Univariate, Bivariate, Multivariate analysis

a. Scatter Plot Python Code Snippet

sns.scatterplot(data[‘sepal_length’],data[‘sepal_width’],hue =data[‘species’],s=50)

For multivariate analysis Python Code Snippet

sns.pairplot(data,hue=”species”,height=4)

b. Box Plot Python Code Snippet

plt.show()

c. Violin Plot

More informative, than box plot, and shows full distribution of data

Python Code Snippet

plt.show()

d. Histograms

It can be used for visualizing the Probability density function(PDF)

Python Code Snippet

.add_legend();

Email: [email protected]

You can refer to the blog being, mentioned below for getting familiar with Exploratory Data Analysis

Exploratory Data Analysis: Iris Dataset

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

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