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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask

18 Useful Mobile Apps for Data Scientist / Data Analysts

A
Analytics Vidhya Last Updated : 28 Jun, 2023
9 min read

Introduction

Does your passion lie in Data Science /Analytics?

Currently, data science and machine learning are changing the world. Here’s your chance to live your passion. To become better at what you do, you no longer need to stick around your laptop for long hours. Take a break and switch to faster way of learning. Switch to Mobile Apps.

Did you know you can run Python in your phone?

You heard right. Mobile apps have added immense excitement to our ways of learning. The subjects which were considered difficult to understand, are now taught using pictures and stories in your mobile / tablets. You can access them anywhere, whether you are in a bus, car, train, restaurant or anywhere else. All you need is a earphone to plug in (if necessary).

In this article, I’ve shared some useful apps (I found) which can improve your necessary data science / analytics skills. These apps can improve your listening skills, logical skills, decision making skills, mathematical skills, statistical skills and much more. They are much more powerful than one could imagine.

I’ve grouped these mobile apps in various categories. I presume, you know your weak areas, so this would help you to target those sweet spots. These are android apps available for FREE at google playstore.

Note: We do not intend to promote any mobile app using this article. These apps have been selected on the basis of their ratings and performance. 

mobile apps data science

Table of contents

Mobile Applications

Brain Training

Elevate (Downloads – 1 million, Rating – 4.5, Size – 38.80 MB)

elevate

It is a personalized brain training program designed to improve your cognitive skills. Your brain training session include 3 exercises every day. The exercises are selected on your past performance. If you are found to be weak in certain aspect, you can expect those exercises to repeat frequently.  This app has various training programs but are limited in FREE version. Still, it succeeds in providing an exhilarating experience.

Lumosity (Downloads – 10 million, Rating – 4.1, Size – 49.71 MB)

lumosity

Lumosity is a personalized brain training program and has over 40 fun games. These games are intuitive enough to challenge your core thinking. This app will help you to improve reading, writing, logical and mathematical skills. The personalized training programs are really interesting and addictive. You get 3 exercises for brain training everyday. Advanced exercises are unlocked, which can be availed with payment.

Neuro Nation (Downloads – 5million, Rating – 4.5, Size – 18.32 MB)

neuronation

This is a fitness app for your brain. This can help you with improvement in memory, concentration, logical thinking and intelligence. It comprises of various set of exercises, creatively designed more than 60 programs. It also allows you challenge your peers and measure your weekly performance. You’ll get limited programs in unpaid version. Still, they are worth trying. If you sincerely use these apps, for few minutes everyday, it can bring a huge change in your life.

Math Workout (Downloads – 5 million , Rating – 4.2, Size – 2.71 MB)

workout

Do you want to become good with numbers? This app would help you with that. You should now feel comfortable doing numerical calculations of any type. This skill helps a lot in all stages of lives. In short, you must develop your mental math skills. Train your mind such that it can do numerical computation on finger tips. This is a beginner level app. It has various interesting exercises to help you acquire mathematical intuition.

Math Tricks (Downloads – 5 million, Rating – 4.4, Size – 6.90 MB)

tricks

This app is for advanced users. I’d recommend this app for every person aspiring to become a data scientist. You need to be smart with maths. Here you’ll find useful tricks for speeding up mathematical calculations. It comprises of tricks for square number, percentage, division, addition, exponent and much more. For every trick, it has 15 levels. You can also compete with your friends in multiplayer mode. Your performance statistics are also tracked.

Programming / Data Analysis Tools

QPython (Downloads – 500k, Rating – 4.4, Size – 13.17 MB)

qpython

This app allows you to run python on your mobile. It assists your android device to run python scripts and projects. Having been highly appreciated on play store, this app works best on python 2.7. It consists of Python interpreter, console, editor, and the SL4A Library for Android. It also includes many useful python libraries. It can also execute python code & files from QR codes. Thus, you are no longer limited to run python by your machines.

Learn Python (Downloads – 10k, Rating – 4.7, Size – 4.77 MB)

learntpytho

You are no longer required to stick to your machines to learn Python. Here’s a python tutorial for your android device. This tutorial covers python basics, data types, control structures, function and modules, exceptions, working with files, functional programming, object oriented programming and much more. For an enhanced learning experience, this tutorial includes fill in blanks, true false, re-arrange and question-answers. It’s a great resources for beginners interested in python.

R Programming (Downloads – 10k, Rating – 3.8, Size – 561 KB)

programiing

Just like Python, you can learn R too on your android devices. This app introduces you to basics of R Programming. Consider it as a shorted version of swirl in R. I’d recommend this app for complete beginners. It consists of Vectors, Functions, Matrices, Factors, Data Frames, Lists and much more. After completing this tutorial, you’d become ready to undertake your first data analysis.

Excel Tutorial (Downloads – 100k, Rating – 4.2, Size – 9.05 MB)

exceltutorials

Not just R or Python, you can also learn excel on your android device. This is one of the most recommended excel tutorial on playstore. This app comprises of tutorials covering wide range of excel topics from basic to advanced levels. It cover topics such as sorting, filtering, pivot table, keyboard shortcuts, whatif analysis etc. You better start managing your time and choose to invest in these apps. Surely, you’ll experience a significant improvement in your knowledge.

Termux (Downloads – 10k, Rating – 4.8, Size – 134KB )

termux

Termux offers a powerful interface with an extensive Linux package collection. This app allows you to run text based games with frotz, projects with git and subversion, use python console, access servers, edit files with nano and vim and much more on your android devices. You can also install desired packages using an in-built apt package manager known from the Debian and Ubuntu Linux distributions.

Statistics & Mathematics

Basic Statistics (Downloads – 50k, Rating – 4.1, Size – 2.72 MB)

statistics

This app is for beginners in data science / analytics. Consider this app as your refresher on various statistical measures. It comprises of topics such as frequency distribution and graphs, data description, probability, distributions, estimations, hypothesis testing and much more. If you are preparing to take an exam, this can be an ideal tutorial for you.

Probability Distributions (Downloads – 50k, Rating – 4.5, Size – 3.21 MB)

distribu

After you have acquired basic knowledge of statistics, this app would make more sense to you. This app allows you to undertake many probabilistic functions in your android devices. It requires prior knowledge of probability distributions such as binomial. It allows you to compute probability mass function for poisson, hypergeometric and plot functions for gaussian, t test, chi-square, log-normal distributions and much more.

Statistics and Sample Size (Downloads – 10k, Rating – 4.4, Size – 3.43 MB)

sample

It is the statistical calculator for your android devices. This help can help you calculate various statistical metrics such as sample size, statistical distribution table, statistical analysis and much more. Using this app, you can save your time in doing calculations. It does in seconds. It is a handy tool for basic and well as scientific statistics. This app supports only .csv files. But, I think that should suffice with our data analysis requirements.

Khan Academy (Downloads – 100k, Rating – 4.5, Size – 21.48 MB)

khan

You can watch khan academy videos on your android devices as well. As an aspiring data scientist, you must look for linear algebra, probability and statistics course on khan academy. This should help you to get prepared for rigid mathematics calculations in machine learning algorithms. This has a redesigned interface which delivers an enhanced user experience. You can also sync your khanacademy.org progress which this app.

MOOCs

You must have well browsed their (MOOCs) website to undertake some data science / analytics course. A lot of you wouldn’t know that you can continue your learning on your android devices as well. Below are some useful apps of popular open courses:-

Udacity (Downloads – 1 million, Rating – 4.2, Size – 5.40 MB)

udacity

Udacity offers a wide range of courses. Now, you no longer need to wait for laptop accessibility. You are great if you have an android device. You can simply undertake and complete courses in your mobile as well. There is no difference in learning on their website vs app. Their app’s user interface is nice and easy to use.

Coursera (Downloads – 5 million, Rating – 4.3, Size – 16.80 MB)

coursera

Millions of students are acquiring new skills from Coursera. To improve their learning experience, coursera also has ensured their android presence. Using this app, you can get timely notifications directly in your android device. With this app, you can learn anytime, anywhere without waiting for the best time to study. You would find all sort of features which coursera has enabled on their website.

edX (Downloads – 500k, Rating – 4.2, Size – 6.07 MB)

edx

If you are edX subscriber, you would like to download this app and continue your learning experience on your android device as well. This app allows you to download course videos, receive instant notifications, stream class videos via Wifi or cellular connection and much more. Like others, edX has made learning much more accessible and enjoyable.

Udemy (Download – 5million, Rating – 4.3, Size – 27.47 MB)

udemy

With 5 million downloads, Udemy is continuously innovating users’ learning experience. With over 32000 courses and tutorials, you can learn almost anything. But, only if you are curious. People have appreciated its content and experience on play store. This app allows you to download lectures and tutorials on your android device.

Bonus

Analytics Vidhya (Download – 10k, Rating – 4.4, Size – 6.5 MB)

This is Analytics Vidhya’s own mobile application. We are including it in this list as we feel this will be super useful to any data scientist who uses it. It has all the blog posts, including AVBytes, that we publish on AV. You can also save articles to read later. It’s an ideal companion for your data science journey.

Frequently Asked Questions

Q1. What app is used for data science?

A. JupyterLab is commonly used for data science tasks. It provides an integrated development environment (IDE) with support for multiple programming languages, including Python, R, and Julia. JupyterLab offers features like notebooks, code editors, data visualization tools, and terminal access, making it a versatile and widely adopted application for data scientists to analyze, visualize, and experiment with data.

Q2. Can we do data science in mobile?

A. Yes, it is possible to perform data science tasks on mobile devices. Several mobile apps and platforms are available that allow for data analysis, visualization, and even running machine learning models. These apps provide a simplified interface and may have limitations compared to desktop environments but can still be useful for basic data exploration, on-the-go analysis, and learning purposes.

End Notes

It might difficult to download all these 18 apps in your android devices. Device memory posses untimely challenges sometime. I understand. Hence, you must consider your areas of improvement and accordingly work towards it. To reap maximum benefits from these apps, you must follow a disciplined schedule. Dedicate few minutes of daily life in these training programs to learn useful things everyday.

Did you find this article useful? Is there any other android app useful for data scientists ? Share your experience / suggestions in the comments section below.

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.

android appsbrain trainingmathematicsmobile appsprobabilitystatistics
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Responses From Readers

Clear

Dinesh
Dinesh

Awesome.apps..amazing research done..

123

sudheer.rao
sudheer.rao

Hi,..its nice to read this piece of information. It would have been great if you had linked the playstore apps . Also, Qpython for 3 is not available. Nevertheless, good article. Thanks !

123

Sarvesh
Sarvesh

Really nice list of app . I think all of apps build fundamentals of Data Scientists. Khan Academy app is not only for the Data Scientist but also all of those who need to master in basics of all fields. Thanks for such a nice list of app.

123

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