Trending December 2023 # How To Deal With Your Crush’S Death: 10 Steps (With Pictures) # Suggested January 2024 # Top 17 Popular

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Accept the fact that your crush is gone. This may be the most difficult part, especially if you had deep feelings for that person, and you never had an opportunity to share those feelings.

Go ahead and grieve, shed your tears, and let the pain wash over and through you. This is difficult, but it is an unavoidable part of the process. The depth of the hurt reflects the depth of your humanity, and your love for the one who has died.

Put your thoughts down in a journal or diary. List each thought you have about the person, and after you have written it down, think about it, even immerse yourself into it. Until you do this, you will not be able to get past it toward acceptance and peace.

Find out if there is an online page dedicated to the person who has died. Often there will be memorial pages, with blogs or links so you can write your feelings for the world to share anonymously. If there is not, you can begin one.

Write a letter to the person, tell them everything you ever felt about them, and how it feels to lose them. Seal it in a plain envelope with no name or address, and put it in a safe place. This will verbalize your feelings, and make them a permanent part of your own history and memories.

Talk to your friends about what you feel. If your feelings are too personal or you think it would be embarrassing, you can talk in general terms about it, but you need to share what you are feeling, and receive support from people who care about you.

Go and pay your last respects, either at the funeral, or if you cannot deal with that level of emotion, to a place you associate with the person. Drop some flowers there, or something you believe they would like, sit and let another flood of tears flow over you if you need to, then walk away with the knowledge that you are beyond the place where you can give them any more.

Tell your parents, a very close friend, or a religious leader (if you’re religious) about your hurt. Do not let depression become a prison for you. It is normal to feel depressed for a time, and the feelings of grief and regret will continue to come around for a long time, even the rest of your life, but again, that is just a reminder of your own humanity, and your care for another person.

Get back into life. Return to school, and other activities you are expected to be involved in. It may seem hard to do at first, but being engaged in something challenging, productive, and familiar will allow you to focus on things at hand, and not your regrets.

Tell your parents if you feel at the least like you just can’t deal with this on your own. There are counselors and other professionals who can offer help in healing from your loss if it is too much for you.

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How To Deal With Missing Data Using Python

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

Overview of Missing Data

Real-world data is messy and usually holds a lot of missing values. Missing data can skew anything for data scientists and, A data scientist doesn’t want to design biased estimates that point to invalid results. Behind, any analysis is only as great as the data. Missing data appear when no value is available in one or more variables of an individual. Due to Missing data, the statistical power of the analysis can reduce, which can impact the validity of the results.

This article will help you to a guild the following topics.

The reason behind missing data?

What are the types of missing data?

Missing Completely at Random (MCAR)

Missing at Random (MAR)

Missing Not at Random (MNAR)

Detecting Missing values

Detecting missing values numerically

Detecting missing data visually using Missingno library

Finding relationship among missing data

Using matrix plot

Using a Heatmap

Treating Missing values

Deletions

Pairwise Deletion

Listwise Deletion/ Dropping rows

Dropping complete columns

Basic Imputation Techniques

Imputation with a constant value

Imputation using the statistics (mean, median, mode)

K-Nearest Neighbor Imputation

let’s start…..

What are the reasons behind missing data?

Missing data can occur due to many reasons. The data is collected from various sources and, while mining the data, there is a chance to lose the data. However, most of the time cause for missing data is item nonresponse, which means people are not willing(Due to a lack of knowledge about the question ) to answer the questions in a survey, and some people unwillingness to react to sensitive questions like age, salary, gender.

Types of Missing data

Before dealing with the missing values, it is necessary to understand the category of missing values. There are 3 major categories of missing values.

Missing Completely at Random(MCAR):

A variable is missing completely at random (MCAR)if the missing values on a given variable (Y) don’t have a relationship with other variables in a given data set or with the variable (Y) itself. In other words, When data is MCAR, there is no relationship between the data missing and any values, and there is no particular reason for the missing values.

Missing at Random(MAR):

Let’s understands the following examples:

Women are less likely to talk about age and weight than men.

Men are less likely to talk about salary and emotions than women.

familiar right?… This sort of missing content indicates missing at random.

MAR occurs when the missingness is not random, but there is a systematic relationship between missing values and other observed data but not the missing data.

Let me explain to you: you are working on a dataset of ABC survey. You will find out that many emotion observations are null. You decide to dig deeper and found most of the emotion observations are null that belongs to men’s observation.

Missing Not at Random(MNAR):

The final and most difficult situation of missingness. MNAR occurs when the missingness is not random, and there is a systematic relationship between missing value, observed value, and missing itself. To make sure, If the missingness is in 2 or more variables holding the same pattern, you can sort the data with one variable and visualize it.

Source: Medium

‘Housing’ and ‘Loan’ variables referred to the same missingness pattern.

Detecting missing data

Detecting missing values numerically:

First, detect the percentage of missing values in every column of the dataset will give an idea about the distribution of missing values.

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings # Ignores any warning warnings.filterwarnings("ignore") train = pd.read_csv("Train.csv") mis_val =train.isna().sum() mis_val_per = train.isna().sum()/len(train)*100 mis_val_table = pd.concat([mis_val, mis_val_per], axis=1) mis_val_table_ren_columns = mis_val_table.rename( columns = {0 : 'Missing Values', 1 : '% of Total Values'}) mis_val_table_ren_columns = mis_val_table_ren_columns[ mis_val_table_ren_columns.iloc[:,:] != 0].sort_values( '% of Total Values', ascending=False).round(1) mis_val_table_ren_columns

Detecting missing values visually using Missingno library :

Missingno is a simple Python library that presents a series of visualizations to recognize the behavior and distribution of missing data inside a pandas data frame. It can be in the form of a barplot, matrix plot, heatmap, or a dendrogram.

To use this library, we require to install and import it

pip install missingno import missingno as msno msno.bar(train)

The above bar chart gives a quick graphical summary of the completeness of the dataset. We can observe that Item_Weight, Outlet_Size columns have missing values. But it makes sense if it could find out the location of the missing data.

The msno.matrix() is a nullity matrix that will help to visualize the location of the null observations.

The plot appears white wherever there are missing values.

Once you get the location of the missing data, you can easily find out the type of missing data.

Let’s check out the kind of missing data……

Both the Item_Weight and the Outlet_Size columns have a lot of missing values. The missingno package additionally lets us sort the chart by a selective column. Let’s sort the value by Item_Weight column to detect if there is a pattern in the missing values.

sorted = train.sort_values('Item_Weight') msno.matrix(sorted)

The above chart shows the relationship between Item_Weight and Outlet_Size.

Let’s examine is any relationship with observed data.

data = train.loc[(train["Outlet_Establishment_Year"] == 1985)]

data

The above chart shows that all the Item_Weight are null that belongs to the 1985 establishment year.

The Item_Weight is null that belongs to Tier3 and Tier1, which have outlet_size medium, low, and contain low and regular fat. This missingness is a kind of Missing at Random case(MAR) as all the missing Item_Weight relates to one specific year.

msno. heatmap() helps to visualize the correlation between missing features.

msno.heatmap(train)

Item_Weight has a negative(-0.3) correlation with Outlet_Size.

After classified the patterns in missing values, it needs to treat them.

Deletion:

The Deletion technique deletes the missing values from a dataset. followings are the types of missing data.

Listwise deletion:

Listwise deletion is preferred when there is a Missing Completely at Random case. In Listwise deletion entire rows(which hold the missing values) are deleted. It is also known as complete-case analysis as it removes all data that have one or more missing values.

In python we use dropna() function for Listwise deletion.

train_1 = train.copy() train_1.dropna()

Listwise deletion is not preferred if the size of the dataset is small as it removes entire rows if we eliminate rows with missing data then the dataset becomes very short and the machine learning model will not give good outcomes on a small dataset.

Pairwise Deletion:

Pairwise Deletion is used if missingness is missing completely at random i.e MCAR.

Pairwise deletion is preferred to reduce the loss that happens in Listwise deletion. It is also called an available-case analysis as it removes only null observation, not the entire row.

All methods in pandas like mean, sum, etc. intrinsically skip missing values.

train_2 = train.copy() train_2['Item_Weight'].mean() #pandas skips the missing values and calculates mean of the remaining values.

Dropping complete columns

If a column holds a lot of missing values, say more than 80%, and the feature is not meaningful, that time we can drop the entire column.

Imputation techniques:

The imputation technique replaces missing values with substituted values. The missing values can be imputed in many ways depending upon the nature of the data and its problem. Imputation techniques can be broadly they can be classified as follows:

Imputation with constant value:

As the title hints — it replaces the missing values with either zero or any constant value.

We will use the SimpleImputer class from sklearn.

from sklearn.impute import SimpleImputer train_constant = train.copy() #setting strategy to 'constant' mean_imputer = SimpleImputer(strategy='constant') # imputing using constant value train_constant.iloc[:,:] = mean_imputer.fit_transform(train_constant) train_constant.isnull().sum()

Imputation using Statistics:

The syntax is the same as imputation with constant only the SimpleImputer strategy will change. It can be “Mean” or “Median” or “Most_Frequent”.

“Mean” will replace missing values using the mean in each column. It is preferred if data is numeric and not skewed.

“Median” will replace missing values using the median in each column. It is preferred if data is numeric and skewed.

“Most_frequent” will replace missing values using the most_frequent in each column. It is preferred if data is a string(object) or numeric.

Before using any strategy, the foremost step is to check the type of data and distribution of features(if numeric).

train['Item_Weight'].dtype sns.distplot(train['Item_Weight'])

Item_Weight column satisfying both conditions numeric type and doesn’t have skewed(follow Gaussian distribution). here, we can use any strategy.

from sklearn.impute import SimpleImputer train_most_frequent = train.copy() #setting strategy to 'mean' to impute by the mean mean_imputer = SimpleImputer(strategy='most_frequent')# strategy can also be mean or median train_most_frequent.iloc[:,:] = mean_imputer.fit_transform(train_most_frequent) train_most_frequent.isnull().sum()

Advanced Imputation Technique:

Unlike the previous techniques, Advanced imputation techniques adopt machine learning algorithms to impute the missing values in a dataset. Followings are the machine learning algorithms that help to impute missing values.

K_Nearest Neighbor Imputation:

The KNN algorithm helps to impute missing data by finding the closest neighbors using the Euclidean distance metric to the observation with missing data and imputing them based on the non-missing values in the neighbors.

train_knn = train.copy(deep=True) from sklearn.impute import KNNImputer knn_imputer = KNNImputer(n_neighbors=2, weights="uniform") train_knn['Item_Weight'] = knn_imputer.fit_transform(train_knn[['Item_Weight']]) train_knn['Item_Weight'].isnull().sum()

The fundamental weakness of KNN doesn’t work on categorical features. We need to convert them into numeric using any encoding method. It requires normalizing data as KNN Imputer is a distance-based imputation method and different scales of data generate biased replacements for the missing values.

Conclusion

There is no single method to handle missing values. Before applying any methods, it is necessary to understand the type of missing values, then check the datatype and skewness of the missing column, and then decide which method is best for a particular problem.

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

Simple Methods To Deal With Categorical Variables In Predictive Modeling

Introduction

Categorical variables are known to hide and mask lots of interesting information in a data set. It’s crucial to learn the methods of dealing with such variables. If you won’t, many a times, you’d miss out on finding the most important variables in a model. It has happened with me. Initially, I used to focus more on numerical variables. Hence, never actually got an accurate model. But, later I discovered my flaws and learnt the art of dealing with such variables.

If you are a smart data scientist, you’d hunt down the categorical variables in the data set, and dig out as much information as you can. Right? But if you are a beginner, you might not know the smart ways to tackle such situations. Don’t worry. I am here to help you out.

After receiving a lot of requests on this topic, I decided to write down a clear approach to help you improve your models using categorical variables.

What are the key challenges with categorical variable?

I’ve had nasty experience dealing with categorical variables. I remember working on a data set, where it took me more than 2 days just to understand the science of categorical variables. I’ve faced many such instances where error messages didn’t let me move forward. Even, my proven methods didn’t improve the situation.

But during this process, I learnt how to solve these challenges. I’d like to share all the challenges I faced while dealing with categorical variables. You’d find:

A categorical variable has too many levels. This pulls down performance level of the model. For example, a cat. variable “zip code” would have numerous levels.

A categorical variable has levels which rarely occur. Many of these levels have minimal chance of making a real impact on model fit. For example, a variable ‘disease’ might have some levels which would rarely occur.

There is one level which always occurs i.e. for most of the observations in data set there is only one level. Variables with such levels fail to make a positive impact on model performance due to very low variation.

If the categorical variable is masked, it becomes a laborious task to decipher its meaning. Such situations are commonly found in data science competitions.

You can’t fit categorical variables into a regression equation in their raw form. They must be treated.

Most of the algorithms (or ML libraries) produce better result with numerical variable. In python, library “sklearn” requires features in numerical arrays. Look at the below snapshot. I have applied random forest using sklearn library on titanic data set (only two features sex and pclass are taken as independent variables). It has returned an error because feature “sex” is categorical and has not been converted to numerical form.

Python Code:

Proven methods to deal with Categorical Variables

Here are some methods I used to deal with categorical variable(s). A trick to get good result from these methods is ‘Iterations’. You must know that all these methods may not improve results in all scenarios, but we should iterate our modeling process with different techniques. Later, evaluate the model performance. Below are the methods:

Convert to Number

Convert to number: As discussed above, some ML libraries do not take categorical variables as input. Thus, we convert them into numerical variables. Below are the methods to convert a categorical (string) input to numerical nature:

Label Encoder: It is used to transform non-numerical labels to numerical labels (or nominal categorical variables). Numerical labels are always between 0 and n_classes-1. A common challenge with nominal categorical variable is that, it may decrease performance of a model. For example: We have two features “age” (range: 0-80) and “city” (81 different levels). Now, when we’ll apply label encoder to ‘city’ variable, it will represent ‘city’ with numeric values range from 0 to 80. The ‘city’ variable is now similar to ‘age’ variable since both will have similar data points, which is certainly not a right approach.

Above, you can see that variable “Age” has bins (0-17, 17-25, 26-35 …). We can convert these bins into definite numbers using the following methods:

Using label encoder for conversion. But, these numerical bins will be treated same as multiple levels of non-numeric feature. Hence, wouldn’t provide any additional information

Combine Levels

Combine levels: To avoid redundant levels in a categorical variable and to deal with rare levels, we can simply combine the different levels. There are various methods of combining levels. Here are commonly used ones:

Using frequency or response rate: Combining levels based on business logic is effective but we may always not have the domain knowledge. Imagine, you are given a data set from Aerospace Department, US Govt. How would you apply business logic here? In such cases, we combine levels by considering the frequency distribution or response rate.

To combine levels using their frequency, we first look at the frequency distribution of of each level and combine levels having frequency less than 5% of total observation (5% is standard but you can change it based on distribution). This is an effective method to deal with rare levels.

We can also combine levels by considering the response rate of each level. We can simply combine levels having similar response rate into same group.

Finally, you can also look at both frequency and response rate to combine levels. You first combine levels based on response rate then combine rare levels to relevant group.

Dummy Coding

Note: Assume, we have 500 levels in categorical variables. Then, should we create 500 dummy variables? If you can automate it, very well. Or else, I’d suggest you to first, reduce the levels by using combining methods and then use dummy coding. This would save your chúng tôi method is also known as “One Hot Encoding“.

End Notes

In this article, we discussed the challenges you might face while dealing with categorical variable in modelling. We also discussed various methods to overcome those challenge and improve model performance. I’ve used Python for demonstration purpose and kept the focus of article for beginners.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

How To Manage Your Passwords With Bitwarden

A password manager is a savior in this unsecure world where a different complicated password is required for each and every account that requires login. However, with so many password managers out there, it can be difficult to identify the good ones from the rest. Luckily, we found Bitwarden, which is a comprehensive tool to manage passwords.

Bitwarden is an open-source password manager that supports almost all computing platforms, comes with plenty of features and is free. It is available for Windows, Linux, Mac, Android, and iOS and supports almost all browsers.

Note: in this review we will focus on the Linux version.

Install Bitwarden

Alternatively, you can download the AppImage from Bitwarden’s Download page. Grant it an executable permission and will work in most Linux distributions.

Note: Bitwarden is also available in the Snap store. It can be installed with the command:

sudo

snap

install

bitwarden Getting Started with Bitwarden

On the first run, Bitwarden will prompt you to create a new account. The next time you use it, you just have to log in using your email address and the password.

Add Browser Extension

Using Bitwarden on its own would be too clunky. You will have to go back and forth between the application and your browser to endlessly copy and paste passwords.

To sidestep this problem, you can use an extension for your browser. This extension will act as a connector to Bitwarden, allowing it to grab your password and autofill it in the browser. It can also prompt you to save the password when setting up a new account.

After you add the extension to your browser, it will show a panel where you will have to enter your login details to gain access to your secure vault.

Save Passwords as You Go

From this point on, whenever Bitwarden detects that you entered a password for a site that isn’t stored yet in its vault, it will offer to remember it. After that, whenever you revisit the same website, Bitwarden will autocomplete your password for you.

Upgrade Your Security

Since Bitwarden can remember complicated passwords for you, your next step is to replace all your existing passwords with ones that are more complex. Bitwarden can help you in that, thanks to its included Password Generator, that you will find in its “View” menu, or by pressing the Ctrl + G combination on your keyboard.

The Password Generator window offers options for you to customize the complexity of the password. You can set the length of password, and whether it should contain uppercase, lowercase, digits, symbols, etc.

It also allows you to generate passphrases instead of passwords. Those consist of words that, theoretically, should be easier to remember than a random string of characters.

Saving Your Password Vault

By default, your password vault is saved in Bitwarden’s server. (That’s why you need a login account.) The good thing about this is that you can access your password vault wherever you are. However, if you are concerned about your data security, you can self-host it on your own server. This requires technical skill and (plenty of) time, so it is definitely not for everyone.

Going Premium

While there are plenty of password managers out there, Bitwarden is one of the very few that provides nearly all features for free. You should give it a try if you are not using any system to manage your passwords yet.

Odysseas Kourafalos

OK’s real life started at around 10, when he got his first computer – a Commodore 128. Since then, he’s been melting keycaps by typing 24/7, trying to spread The Word Of Tech to anyone interested enough to listen. Or, rather, read.

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5 Ways Companies Deal With The Data Science Talent Shortage

Specialized fields like data science have been hit especially hard with recruitment and retention challenges amid the shortage of talent in the tech industry.

Tech leaders say companies need to reconsider how they source and retain data science talent.

Read on to learn how different companies are combating the data science talent shortage through improved hiring practices, increased retention focus, and a heavier emphasis on efficient tools and teams:

Also read: Today’s Data Science Job Market

When a company is struggling to find new talent for their data science teams, it’s often worth the time and resources to look internally first.

Current employees are likely to already have some of the skill sets that the company needs, and they already know how the business works. Many companies are upskilling these employees who want to learn and find a new role within the company or expand their data science responsibilities.

Waleed Kadous, head of engineering at Anyscale, an artificial intelligence (AI) application scaling and development company, believes that employees with the right baseline skills can be trained as data scientists, particularly for more straightforward data science tasks.

“It depends on the complexity of the tasks being undertaken, but in some cases, internal training of candidates who have a CS or statistics background is working well,” Kadous said. “This doesn’t work well for highly complex data science problems, but we are still at a stage of having low-hanging fruit in many areas.

“This often works well with the central bureau model of data science teams, where data scientists embed within a team to complete a project and then move on. … The central bureau incubates pockets of data science talent through the company.”

Continue your data science education: 10 Top Data Science Certifications

In many cases, data science teams already have all of the staffing they need, but inefficient processes and support hold them back from meaningful projects and progress.

Marshall Choy, SVP of product at SambaNova Systems, an AI innovation and dataflow-as-a-service company, believes many tasks that are handled by internal data scientists can be better administered by third-party strategic vendors and their specialized platforms.

“Some companies are taking a very different approach to the talent shortage issue,” Choy said. “These organizations are not acquiring more talent and instead are making strategic investments into technology adoption to achieve their goals.

“By shifting from a DIY approach with AI adoption to working with strategic vendors that provide higher-level solutions, these companies are both reducing cost and augmenting their data science talent.

“As an example, SambaNova Systems’ dataflow-as-a-service eliminates the need for large data science teams, as the solution is delivered to companies as a subscription service that includes the expertise required to deploy and maintain it.”

Dan DeMers, CEO and co-founder of Cinchy, a dataware company, also believes that third-party solutions can solve data science team pain points and reduce the need for additional staff. Great tools also have the potential to draw in talent who want access to these types of resources.

“Data is seen as inextricably intertwined with the applications used to generate, collate, and analyze it, and along the way, some of those functions have become commoditized. That’s partly why data science has gone from being the discipline du jour to a routine task.

Kon Leong, CEO at ZL Technologies, an enterprise unstructured data management platform, thinks that one of the biggest inefficiencies on data science teams today is asking specialized data scientists to focus on menial tasks like data cleaning.

“In many ways, the data cleanup and management challenge has eclipsed the analysis portion. This creates a mismatch where many professionals end up using their skills on tedious work that they’re overqualified for, even while there is still a shortage of top talent for the most difficult and pressing business problems.

“Some companies have conceived creative ways to tackle data cleanup, such as through cutting-edge data management and analytics technologies that enable non-technical business stakeholders to leverage insights. This frees up a company’s data scientists to focus on the toughest challenges, which only they are trained to do. The result is a better use of existing resources.”

Improve data quality with the right tools: Best Data Quality Tools & Software

These newer data professionals are hungry to showcase their learned skills, but they also want opportunities to keep learning, try hands-on tasks, and build their network for professional growth.

Sean O’Brien, senior VP of education at SAS, a top analytics and data management company, thinks it’s important for retention for companies to offer curated networking opportunities, where new data scientists can build their network and peer community within an organization.

“Without as much face time, new and early career employees have lost many of the networking and relationship-building opportunities that previously created awareness of hidden talent,” O’Brien said.

“Long-serving team members already have established relationships and knowledge of the work processes. New employees lack this accumulated workplace social capital and report high dissatisfaction with remote work.

“Companies can set themselves apart by creating opportunities for new employees to generate connections, such as meetings with key executives, leading small projects, and peer-to-peer communities.”

O’Brien also emphasized the importance of having a strong university recruiting and education strategy, so companies can engage data science talent as early as possible.

“Creating an attractive workplace for analytics talent isn’t enough, however,” O’Brien said. “Companies need to go to the source for talent by working directly with local universities.

“Many SAS customers partner with local college analytics and data science programs to provide data, guest speakers, and other resources, and establish internship and mentor programs that lead directly to employment.

“By providing real-world data for capstone and other student projects, graduates emerge with experience and familiarity with a company’s data and business challenges. SAS has partnerships with more than 400 universities to help connect our customers with new talent.”

The importance of data to your business: Data-Driven Decision Making: Top 9 Best Practices

Data science professionals at all levels want transparency, not only on salary and work expectations but also on what career growth and paths forward could look like for them.

Jessica Reeves, SVP of operations at Anaconda, an open-source data science platform, explained the importance of being transparent with job candidates and current employees across salary, communication, and career growth opportunities.

“Transparency is a critical characteristic that allows Anaconda to attract and retain the best talent,” Reeves said.

“This is seen through salary transparency for each employee with benchmarks in the industry for your title, where you live, and how your salary stacks comparative to other jobs with the same title. We also encourage transparency by having an open-door policy, senior leadership office hours, and anonymous monthly Ask Me Anything sessions with senior leadership.

“Prioritizing career growth also helps attract top talent. Now more than ever, employees want a position where they can have opportunities to get to the next level and know what that path is. Being a company that makes its potential trajectory clear from the start allows us to draw in the best data practitioners worldwide.

“To showcase their growth potential at Anaconda, we have clear career mapping tracks for individual contributors and managers, allowing each person to see the steps necessary to reach their goal.”

Read next: Data Analytics Industry Review

Developing and projecting a recognizable brand voice is one of the most effective indirect recruiting tactics in data science.

If a job seeker has heard good things about your company or considers you a top expert in data science, they are more likely to find and apply for your open positions.

“One thing that is becoming increasingly important is supporting data scientists in sharing their work through blog posts and conferences,” Kadous said. “Uber’s blog is a great example of that.

“It’s a bit tricky because sometimes data science is the secret sauce, but it’s also important as a recruiting tool: It demonstrates the cool work being done in a particular place.

Reeves at Anaconda also encourages her teams to find different forums and mediums to give their brand more visibility.

“Our Anaconda engineering team is very active in community forums and events,” Reeves said. “We strive to ingrain ourselves into the extensive data and engineering community by engaging on Twitter, having guest appearances on webinars and podcasts, or authoring blog posts on data science and open-source topics.”

Read next: Top 50 Companies Hiring for Data Science Roles

How To Root Your Android Phone With Supersu

Android devices have historically been easy to root. With rooting, users get root access to the device’s file system. This allows users to have customization capabilities outside of what is usually possible with an unmodified version of Android. Here, we show you how to root your Android phone with the SuperSU tool.

Prerequisites

Before we get started, there are a few things you will need to have in place:

You will need to unlock your boot loader.

A custom recovery like TWRP must be installed on your device (we have a TWRP setup guide for you here).

You will need to download the latest SuperSU file. Be sure to download the flashable ZIP file.

Root Your Android Phone

To get started, you need to place the SuperSU file that you downloaded in the root directory of your phone’s storage. You can do this by either downloading the file directly from your browser or by connecting your Android phone to a computer and transfering it over.

Next, boot your phone into recovery mode using the key combination to do so with your phone. Now, from the installation menu of your custom recovery, you will need to select the SuperSU file from earlier.

After selecting the SuperSU flashable ZIP file, you need to confirm that you want to flash this file to your device.

After some time, you will be notified that the the file was successfully flashed to your device. You can then reboot your device by going to the main menu of the TWRP recovery and selecting Reboot.

Your device should now be rooted. Hopefully everything went smoothly. To ensure that this is the case, you’ll need to do a quick check to see if your device has indeed been rooted. You should now be able to see the SuperSU app in your phone’s menu.

To verify that the rooting process was successful, download the Root Checker app.

When you run the app, you should see a SuperSU prompt asking you if you wish to grant the app Superuser permissions.

Grant the app Superuser permissions. You should see that your device is rooted on the main screen of the app.

If none of these things happen, and there is no indication that your device has been granted Superuser permissions, then you may have to go back and retry the rooting process.

If it does indicate that you have root access, then you’re golden! You can now download root apps to your heart’s desire. When running a root app for the first time, you’ll usually be greeted with a prompt asking if you want to grant the app in question Superuser access.

Simply grant the app Superuser access, and you will be able to make use of all of its features.

Some Notes About the SuperSU App

If you don’t respond to the prompt in time, then you won’t be able to make full use of the app’s features. If this was unintended, simply go to the SuperSU app, select the app want to grant superSU access to, and under the “Access” section of the resulting pop-up, select Grant.

You have the option of doing this with any root app that you have run on your device. The options for access are Prompt, Grant, and Deny.

Frequently Asked Questions Does rooting wipe my phone?

No, rooting shouldn’t wipe your phone, though there is always a risk that if you root your phone wrong you may brick your phone! While rooting doesn’t wipe your phone, installing a custom OS may wipe your phone. So bear that in mind, given that a lot of people root specifically to install custom OS like LineageOS.

Is SuperSU still in development?

The latest version of SuperSU is 2.82, which was released back in 2023. So it’s been a while since we’ve had a new version of the app. From that, it doesn’t look like the app is in development any more.

Is SuperSU Pro worth it?

SuperSU Pro is a license key that unlocks some additional features for SuperSU, mainly in the way of security, with some extra anti-malware features and others. Given that it’s all free these days and can be downloaded from the SuperSU site, there’s no harm in downloading SuperSU Pro after SuperSU then using the APK to unlock the extra features on SuperSU.

Who owns SuperSU?

Since 2023, SuperSU was actively developed by Chainfire, something of a legend on the Android modding scene. In 2023, Chainfire transferred ownership to a company called CCMT, then later in 2023 Chainfire announced the end of his work developing SuperSU, as it’s now being developed by another team. In truth however, they don’t seem to be doing much the the app. Very little is known about CCMT, leading many users to jump ship and use Magisk instead.

Rooting is useful, since it gives you full access to your file system, thus allowing you to customize your device and install apps in a way that isn’t possible with an unrooted device. If you just want to uninstall bloatware from your phone, you can do it without having to root your phone. Also, here’s how to take a scrolling screenshot on Android.

Robert Zak

Content Manager at Make Tech Easier. Enjoys Android, Windows, and tinkering with retro console emulation to breaking point.

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