Trending December 2023 # 15 Must Read Books For Entrepreneurs In Data Science # Suggested January 2024 # Top 13 Popular

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The roots of entrepreneurship are old. But, the fruits were never so lucrative as they have been recently. Until 2010, not many of us had heard of the term ‘start-up’. And now, not a day goes by when business newspapers don’t quote them. There is sudden gush in the level of courage which people possess.

Today, I see 1 out of 5 person talking about a new business idea. Some of them even succeed too in establishing their dream company. But, only the determined ones sustain. In data science, the story is bit different.

The success in data science is mainly driven by knowledge of the subject. Entrepreneurs are not required to work at ground level, but must have sound knowledge of how it is being done. What algorithms, tools, techniques are being used to create products & services.

In order to gain this knowledge, you have two ways:

You work for 5-6 years in data science, get to know things around and then start your business.

You start reading books along the way and become confident to start in first few years.

I would opt for second option.

Why read books ?

Think of our brain as a library. And, it’s a HUGE library.

How would an empty library look like? If I close my eyes and imagine, I see dust, spider webs, brownian movement of dust particles and darkness. If this imagination horrifies you, then start reading books.

The books listed below gives immense knowledge and motivation in technology arena. Reading these books will give you the chance to live many different entrepreneurial lives. Take them one by one. Don’t get overwhelmed. I’ve displayed a mix of technical and motivational books for entrepreneurs in data science. Happy Reading!

Note: I do not intend to promote any book. Neither I have any affiliation with amazon. However, for your perusal, I have shared amazon links in case you are interested to buy any.

List of Books Data Science For Business

This book is written by Foster Provost & Tom Fawcett. It gives a great head start to anyone, who is serious about doing business with big data analytics. It makes you believe, data is now business. No business in the world, can now sustain without leveraging the power of data. This books introduces you to real side of data analysis principles and algorithms without technical stuff. It gives you enough intuition and confidence to lead a team of data scientists and recommend what’s required. More importantly, it teaches you the winning approach to become a master at business problem solving.

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Big Data at Work

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Lean Analytics

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This book is written by Michael Lewis. It’s a brilliant tale which sprinkles some serious inspiration. A guy named billy bean does what most of the world failed to imagine, just by using data and statistics. He paved the path to victory when situations weren’t favorable. Running a business needs continuous motivation. This can be a good place to start with. However, this book involves technical aspects of baseball. Hence, if you don’t know baseball, chances are you might struggle in initial chapters. A movie also has been made on this book. Do watch it!

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Elon Musk

This book is written by Ashlee Vance. I’m sure none of us are fortunate to live the life of Elon Musk, but this book let’s us dive in his life and experience rise of fantastic future. Elon is the face behind Paypal, Tesla and SpaceX. He has dreamed of making space travel easy and cheap. Recently, he was applauded by Barack Obama for the successful landing of his spaceship in an ocean. People admire him. They want to know his secrets and this is where you can look for. As on entrepreneur, you will learn about must have ingredients which you need to a become successful in technology space.

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Keeping up with the Quants

This book is written by Thomas H Davenport and Jinho Kim. As we all know, data science is driven by numbers & maths (quants). Inspired from moneyball, this book teaches you the methods of using quantitative analysis for decision making. An entrepreneur is a terminal of decision making. One must learn to make decisions using numbers & analysis, rather than intuition. The language of this book is easy to understand and suited for non-maths background people too. Also, this book will make you comfortable with basics statistics and quantitative calculations in the world of business.

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The Signal and the Noise

The author of this book is Nate Silver, the famous statistician who correctly predicted US Presidential elections in 2012. This books shows the real art and science of making predictions from data. This art involves developing the ability to filter out noise and make correct predictions. It includes interesting examples which conveys the ultimate reason behind success and failure of predictions. With more and more data, predictions have become prone to noise errors. Hence, it is increasingly important to understand the science behind making predictions using big data science. The chapters of this book are interesting and intuitive.

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When Genius Failed

This book is written by Roger Lowenstein. It is an epic story of rise and failure of a hedge fund. For an entrepreneur, this book has ample lessons on investing, market conditions and capital management. It’s a story of a small bank, which used quantitative techniques for bond pricing throughout the world and ensured every invested made gives a profitable results. However, they didn’t sustain for long. Their quick rise was succeeded by failure. And, the impact of their failure was so devastating that US Federal bank stepped in to rescue the bank, because the fund’a bankruptcy would have large negative influence on world’s economy.

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Lean Startup

This book is written by Eric Ries. In one line, it teaches how to not to fail at the start of your business. It reveals proven strategies which are followed by startups around the world. It has abundance of stories to make you walk on the right path. An entrepreneur should read it when he/she feel like draining out of motivation. It teaches to you to learn quickly, implement new methods and act quickly if something doesn’t work out. This book applies to all industries and is not specific to data science.

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Web Analytics 2.0

This book is written by Avinash Kaushik. It is one of the best book to learn about web analytics. Internet is the fastest mode of collecting data. And, every entrepreneur must learn the art of internet accounting. Most of the businesses today face the challenge of weak presence on social media and internet platforms. Using various proven strategies and actionable insights, this book helps you to solve various challenges which could hamper your way. It also provides a winning template which can be applied in most of the situations. It focuses on choosing the right metric and ways to keep them in control.

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Predictive Analytics

This book is written by Eric Seigel. It is a good follow up book after web analytics 2.0. So, once you’ve understood the underlying concept of internet data, metrics and key strategies. This book teaches you the methods of using that knowledge to make predictions. It’s simple to understand and covers many interesting case studies displaying how companies predict our behavior and sell us products. It doesn’t cover technical aspects, but explains the general working on predictive analytics and its applications. You can also check out this funny rap video by Dr. Eric Seigel:

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This book is written by Steven D Levitt and Stephen J Dubner. It shows the importance of numbers, data, quantitative analysis using various interesting stories. It says, there is a logic is everything which happens around us. Reading this book will make you aware of the unexplored depth at which data affects our real lives. It draws interesting analogy between school teachers and sumo wrestlers. Also, the bizarre stories featuring cases of criminal acts, real-estate, drug dealers will certainly add up to your exciting moments.

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Founders at Work

This book is written by Jessica Livingston. Again, this isn’t data science specific but a source of motivation to get you moving forward. It’s a collection of interviews with the founders of various startups across the world. The focus has been kept on early days i.e. how did they act when they started. This book will give you enough proven ideas, strategies and lessons to anticipate and avoid pitfalls in your initial days of business. It consist of stories by Steve Wozniak (Apple), Max Levchin (Paypal), Caterina Fake (Flikr) and many more. In total, there are 32 interviews listed which means you have the chance to learn from 32 mentors in one single book. Must read for entrepreneurs.

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Bootstrapping a Business

This book is written by Greg Gianforte and Marcus Gibson. It teaches about the things to do when you are running short of money and still don’t want to stop. This is a must read book for every entrepreneur. Considering the amount of investment required in data science startups, this book should have a special space in an entrepreneur’s heart. It reveals various eye opening truths and strategies which can help you build a great company. Greg and Marcus proves that money is not always the reason for startup failure, it’s all about founder’s perspective. This book has stories of success and failures, again a great chance for you to live many lives by reading this book.

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Analytics at Work

This book is written by Thomas H Davenport, Jeanne G Harris and Robert Morrison. This books reveals the increased use of analytical tools & concepts by managers to make informed business decisions. The decision making process has accelerated. For a greater impact, it also consists of examples from popular companies like chúng tôi best buy and many more. It talks about recruiting, coordination with people and the use of data and analytics at an enterprise level. Many of us are aware of data and analytics. But, only a few know how to use them together. This quick book has it all !

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Disclosure: The amazon links in this article are affiliate links. If you buy a book through this link, we would get paid through Amazon. This is one of the ways for us to cover our costs while we continue to create these awesome articles. Further, the list reflects our recommendation based on content of book and is no way influenced by the commission.

End Notes

This marks the end of this list. While compiling this list, I realized most of these books are about sharing experience and learning from the mistake of others. Also, it is immensely important to posses quantitative ability to become good in data science. I would suggest you to make a reading list and stick to it throughout the year. You can take up any book to start. I’d suggest to start with a motivational book.

Check out Live Competitions and compete with best Data Scientists from all over the world.


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The 7 Best Sites To Read Comic Books For Free

Whether you’re a seasoned comic book enthusiast or a total newcomer to the world of comics, you’ll find that it can be rather expensive to buy all the comic books you want to read. Or, it may be hard to find specific issues (especially if they’re old or rare). 

If you want to read some comics right now, and for free, the best thing you can do is head online to the numerous websites that provide comic books in a digital form. Although these won’t help with comic book collecting, they are the best places to start a new series or catch up on one. 

Table of Contents

Below you’ll find seven of the best sites to read comic books for free so you can get started right away!

Here, you’ll find a whole ton of titles from the Golden era of comics, all from the public domain and thus easily accessible on this site. You can search through several comic book publishers to find what you’re looking for or discover new titles. 

You can download any comic here and read them at your leisure, or you can preview to read them online. The site is clean and easy to navigate, so you can find exactly what you want without hassle. 

If you’re looking for more popular comic series, especially from Marvel or DC, Comic Extra is an excellent option for finding these for free. There are tons of comic book scans on this site, both new and old, so you’re likely to find whatever you’re searching for here.

The scans on this site are good quality, and it’s easy to navigate through each comic, making reading them as fun as it should be. 

Comixology is one of the best sites for free comic books, giving you access to tons of different types, including graphic novels, indie comics, and more. The site’s purpose is to sell comic books, but you can find hundreds of them to read online if you go to the Free Comics category. 

All you have to do is add the free comics you want to read to your cart, and you’ll receive a digital copy upon checkout. There’s no need to pay for anything. If you want to access even more comics on the site, you can also sign up for Comixology Unlimited, a service where you pay monthly to read even more comics on the site. Alternatively, use the Comixology app. 

After Marvel and DC, Dark Horse is one of the bigger names in the comic book world. They have licenses for many different franchises, including Stranger Things, World of Warcraft, Overwatch, and more. Their website has plenty of comics you can buy, but a large section is also dedicated to free comics. 

All you need to do is sign up for a free account. Then, you can read Dark Horse comics digitally. You can also download comics to a mobile device to read them offline if you wish. 

This site has a vast selection of comic book issues, no matter what you are looking for. It’s no-frills, allowing you to enjoy just reading your favorite comics without any distractions. 

The scans also tend to be very high-quality and are easy to read in your browser without downloading a thing. All the pages for each comic are on one long page if you dislike reloading the website for each new page. 

Not only can you read popular titles on this site, but you can also find a variety of lesser-known or indie comics. Use this site to discover a new series to get into, or when you can’t seem to find a particular series on other free comic sites. 

Another great site with a seemingly endless library of comics to read is Read Comics Online. You can browse comics by the publisher, alphabetically, by the latest release date, or get a random comic to read if you want something new. 

Comic book scans are uploaded weekly, and you can create an account on the site to get access to everything it has to offer. It’s easy to read each comic, as each scan is high quality with reader-friendly formatting. 

Enjoy Comic Books Online

With several choices for free reading available online, you’re not going to run out of comics to read soon. So, no matter whether you want to read comics by the biggest publishers, old-school golden age comics, or indie comics, you should be able to find it for free online on your favorite digital device for reading. 

15 Best Linux Books (2023 Update)

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Linux is an operating system based on UNIX and was first introduced by Linus Torvalds. It is based on the Linux Kernel and can run on different hardware platforms manufactured by Intel, MIPS, HP, IBM, SPARC, and Motorola.

Best Linux Books

Book Title Author Name Latest Edition Publisher Ratings Link

Daniel J. Barrett 3rd edition O’Reilly Media

William Shotts 2nd edition No Starch Press

Jason Cannon 1st edition Independently Published

Richard Blum 3rd edition Wiley

Jason Cannon 1st edition Independently Published

Linux Pocket Guide is a book written by Daniel J. Barrett. It provides an organized learning path. It also helps you to gain mastery of the most useful and important commands. This is an ideal reference book for both novice or who wants to get up to speed on Linux or experienced users.

This book features new commands for processing image files and audio files, reading and modifying the system clipboard, and manipulating PDF files.

The Linux Command Line is a book written by William Shotts. The author takes you from your very first terminal keystrokes to writing full programs using a Linux shell or command line.

In this book, you will also learn file navigation, environment configuration, pattern matching with regular expressions, etc. Apart from practical knowledge, the book also reveals the basic concept of every topic.

Linux for Beginners is a book written by Jason Cannon. The best part of this book is that you don’t need any prior knowledge of Linux OS. You will be guided using step by step logical and systematic approach.

This learning material also covers new concepts or jargon are encountered. The best thing about this tutorial book is that every detail are covered in this book in an easy to understand language and its basic concepts.

Linux Command Line and Shell Scripting Bible is a reference book written by Richard Blum. The book includes detailed instructions and abundant examples.

To use this book, you will learn how to bypass the graphical interface and communicate with your computer. This Linux book thirty pages of new functional examples that are fully updated to align with the latest Linux features.

It starts with command line fundamentals. The book gives information about shell scripting and shows you the practical application of commands for automatic, frequently performed functions.

Command Line Kung Fu is a book written by Jason Cannon. The book also includes packed with dozens of tips and over 100 practical, real-world examples. The examples given in this book help you to solve actual problems and accomplish worthwhile goals.

The book has a comprehensive index is included. So even if you want to find every example where a given command is used -even if it is not the main subject.

Linux Administration is a book written by Jason Cannon. This Linux learning material includes topics like Ubuntu Linux, Debian, Linux Mint, RedHat Linux, Fedora, SUSE Linux, Kali Linux, and more.

By the end of this Linux book, you will fully understand the most important and fundamental concepts of Linux server administration. Moreover, you will be able to put those concepts to use in various real-world situations.

The Complete Reference is a book written by Richard Petersen. The book includes various Linux features, tools, and utilities from this thoroughly updated and comprehensive resource.

This Linux book also covers use the desktops and shells, manage applications, deploy servers, and handle system and network admin tasks.

The book includes various details on the very different and popular Ubuntu and Red Hat/Fedora software installation. The book also teaches you tools used by different distributions.

How Linux Works, is a book written by Brian Ward. The book teaches you the concepts behind Linux internals. It is ideal reference material for anyone curious to know about the operating system’s inner workings.

You will also learn how development tools work and how to write effective shell scripts. In this book, you will also explore the kernel and examine key system tasks like system calls, input and output, and file systems.

This book covers more than seventy new interfaces, including POSIX asynchronous I/O, spin locks, barriers, and POSIX semaphores, etc. The book offers several chapter-length case studies, each reflecting contemporary environments.

Linux Kernel Development is a book written by Robert Love. The book gives details about the design and implementation of the Linux kernel. The writer is presenting the content in a manner that is beneficial to those writing and developing kernel code.

It is also an ideal book for programmers seeking to understand the Linux OS better. The book offers features of the Linux kernel, which includes its design, implementation, and interfaces.

This Linux book reveals the software design secrets of the original Unix designers. It also shows how they produce software that is fast, portable, reusable, modular, and long-lived.

The book covers topics like Basic of Unix Philosophy, Unix history, OS comparisons, Best practices, Finding notation that sings, etc. The book also includes 12 case studies to know the use of Linux in real-life applications.

Linux, in a Nutshell, is a book written by Stephen Figgins. The book includes programming tools, system and network administration tools, the shell, editors, etc.

This book focuses on Linux system essentials, as well as more coverage of new capabilities such as virtualization, revision control with git. It also includes an option for using the vast number of Linux commands.

The Linux Programming Interface is a book written by Michael Kerrisk. In this book, the author provides detailed descriptions of the system calls and library functions that you need to learn Linux programming, etc.

This book covers the wealth of Linux-specific features, including epoll, notify, and the /proc file system. The book emphasis on UNIX standards (POSIX.1-2001/SUSv3 and POSIX.1-2008/SUSv4). At the same time, this Linux book is also equally valuable to programmers working on other UNIX platforms.

Linux System programming is a book written by Robert Love. The book provides learning material on Linux system programming, a reference manual on Linux system calls. The book is an ideal guide to learn about writing smarter, faster code.

The book helps you to distinguish between POSIX standard functions and special services offered only by Linux. The book also includes a chapter on multithreading. This best linux books for beginners provides an in-depth look at Linux from both a theoretical and applied perspective.

Linux Administration is a book written by Wale Soyinka. The book teaches you how you can effectively set up and manage any version of Linux on individual servers or using this practical resource.

It is one of the best Linux books that offers clear explanations, step-by-step instructions, and real-world examples.

You will also learn how to configure hardware and software, work from the GUI or command line, maintain Internet and network services. This book included Software management and backup solutions.

FAQ: 🏅 What is Linux?

LINUX is an operating system or a kernel distributed under an open-source license. Its functionality list is quite like UNIX. The kernel is a program at the heart of the Linux operating system that takes care of fundamental stuff, like letting hardware communicate with software.

📚 Which are the Best Linux Books?

Following are some of the Best Linux Books for Beginners & Advanced Programmers:

🚀 What are the basic requirements to learn Linux?

Nothing! This book list is designed for beginners to expert-level linux professionals.

Flask Python Tutorial For Data Science Professionals

As a Data Science Enthusiast, Machine Learning Engineer, or data science practitioner, it’s not just about creating a machine learning model for a specific problem. Presenting your solution to the audience or clients is equally important, as your goal is to impact society. Deploying your solution to the cloud requires the assistance of a web framework, and Flask Python is one such micro web framework that simplifies the process.

This article will guide you through creating a Flask Python web application for Machine Learning and Data Science. We will delve into the following table of contents, providing detailed explanations and practical implementations.

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

What is Web-Framework, and Micro Web-Framework?

A web application framework is a package of libraries and modules that simplifies the development process by handling protocol details and application maintenance. Python Django is an example of a traditional web framework, an enterprise framework.

On the other hand, a micro-framework offers developers more flexibility and freedom. Unlike traditional frameworks, micro-frameworks do not require extensive setup and are commonly used for small web application development. This approach saves time and reduces maintenance costs.

What is Flask Python?

Flask, a web framework written in Python, enables developers to quickly and rapidly develop web applications, configuring the backend and front end seamlessly. It grants developers complete control over data access and is built on Werkzeug’s WSGI toolkit and Jinja templating engine. Flask offers several key features, including:

Simplified REST API development: Flask streamlines the creation of REST APIs by providing convenient libraries, tools, and modules for handling user requests, routing, sessions, form validation, and more.

Versatility for various projects: Flask utilizes various applications, such as blog websites, commercial websites, and other web-based projects.

Minimal boilerplate code: Flask eliminates the need for excessive boilerplate code, allowing developers to focus on the core functionality of their applications.

Lightweight and essential components: Flask is a micro-framework that offers only essential components, allowing developers to implement additional functionalities through separate modules or extensions.

Extensibility through Flask extensions: Flask boasts a vast ecosystem of extensions that can be seamlessly integrated to enhance its functionalities, providing developers with flexibility and the ability to expand its capabilities.

Why Use Flask? Structure of Flask Web Application

To ensure the smooth execution of your Flask application, it is crucial to maintain the files according to Jinja templating guidelines. Follow these steps to organize your working directory for your Flask project:

Create a folder for your Flask project: In your working directory, set up a dedicated folder to hold all the files related to your Flask project.

Maintain the following folder structure:

Create a folder named “templates”: This folder will store your HTML files. Place all the HTML templates used in your Flask application inside this folder.

Create a folder named “static”: This folder will contain CSS, JavaScript, and any additional images you utilize in your application.

Note: The pickle file in the image may not be present in your current setup. It will generate when you implement the Flask application. For now, focus on creating the three components mentioned above: the templates folder, the static folder, and the main Flask file.

Following this standard structure, you can easily organize and maintain your Flask application, ensuring smooth execution and better collaboration with other developers.

Checkout: Develop and Deploy Image Classifier using Flask: Part 1 & Part 2

Key Aspects of Flask: WSGI and Jinja2

Everywhere it is said and written that Flask is WSGI compliant or Flask uses Jinja templating. But what is the actual meaning of these terms and what significance does this play in the flask development lifecycle? Let’s explore one by one each of two terminology.

What is Web Server Gateway Interface(WSGI)?

WSGI is a standard that describes the specifications concerning the communication between a client application and a web server. The benefit of using WSGI is that it helps in the scalability of applications with an increase in traffic, maintains efficiency in terms of speed, and maintains the flexibility of components.

What is Jinja2?

Template means frontend application designed using HTML, CSS, and whose content is displayed to a user in an interactive way. Flask helps to render the web page for the server with some specified custom input. In simple words, Flask helps connect your backend workflow with the frontend part and act as client-side scripting means It helps you to access the data that the user provides on frontend designed application and process the inputs by passing values to backend application and again rendering the output to HTML content is the task of Jinja templating.

Jinja2 has vast functionalities like a template inheritance which means when you create multiple templates (pages) for the application then some code or design is the same on each page so you do not need to write it again. It can be inherited from another template.

Setting Up Flask Environment

Now you have a good understanding of the theory of flask. let us enhance our understanding by making our hands dirty while trying something to implement using a flask.

It is good to create a new virtual environment if you start working on any new project. In your python working directory from anaconda prompt or command prompt create a new environment using the below code.

Install Flask

Now the first thing is to install a flask. use the pip command to install Flask.

pip install Flask Test Flask Installation

Write the below code in created python app file and run it from the command line in a working directory using the below code.

from flask import Flask app = Flask(__name__) @app.route('/') def hello_world(): return 'Hello World’ if __name__ == '__main__':

There are parameters that can be defined in the app run function. the run function basically runs the application on a local development server., port, debug, options)

host – It defines that on what hostname to listen to. we are running at localhost(default is

Port – on which port to call the application. The default port is 5000.

options – The options are forwarded to the werkzeug server.

Important Flask Python Terminologies Routes

The route is a decorator in a python flask. It basically tells the application which function to be run or on which URL the user should be rendered. in a route function, the escape sequence describes the URL. The function after defining the route is created and you can pass parameters as a normal python function.

Flask support dynamic routing as well. you can modify the URL or while rendering you can put various conditions with custom data to send.

HTTP Methods

HTTP methods are the core communication block between various parties on the worldwide web. It helps to get, send, cached data from different websites or files. let us explore the different HTTP methods that Flask support and which method is used for what purpose.

1. GET

It is the most basic form of sending data to websites by concatenating the content to URL. The GET method is most commonly used to fetch the data from files or load a new HTML page. It can be used where you send the non-confidential data which if disclosed is not an issue.


POST method is the most used method after the GET request. It is used to send the data to a server using encryption. The data is not appended to URL, it is sent and displayed in a body of HTML using jinja python templating. The POST method is mostly used when we are working with forms to send receive user data and after processing sending output back to display in HTML body.

POST method is the most-trusted method and is used to send confidential data like login credentials.


The head method is similar to the Get method but it can be cached on the system. The passed data is unencrypted and it must have a response. Suppose if some URL requests for a large file download, now by using HEAD method URL can request to get file size.

4. PUT

The PUT method is similar to the POST method. The only difference lies in when we call POST request multiple times then that many time request is made, and in PUT method it opposes the multiple requests and replaces the new request with an old response.


delete is a simple HTTP method that is used to delete some particular resource to a server.

Implement your First ML Web App Using Flask Python

Now we have a practical and basic understanding of the flask framework works. Now you must be wondering how can deploy our machine learning model using flask so that the Public can use it and provide new data. So, it is a simple task where the inputs(best features you have chosen) your machine learning model requires is taken from a user in form of HTML form or flask form, and using the flask GET method you access data at the backend. After providing the user data to the model and model gives you an output. Using POST request you render the output to an HTML page and give it to a user. The process is simple and works really very fast. let us implement this on a dataset.

Problem Statement

We are using a Healthcare expense dataset from Kaggle. You can find details and the dataset here. The data basically aims to predict the individual healthcare expenses given age, family details, BMI, gender. The particular dataset is chosen because it contains different input variables so you will learn how to access different inputs from the front end using a flask. Our main aim here is not for implementing any generalized machine learning model. our main aim is to understand the development of flask applications for any machine learning model.

Prepare Machine Learning Model

Before implementing the Flask application it is important to have a machine learning model in any form like pickle, h5, etc. so lets us load the data, preprocess the data, and train a linear regression algorithm on it. After modeling, we will save the model using the pickle module. The code snippet of complete model preparation is given below.

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.linear_model import LinearRegression import pickle data = pd.read_csv("/kaggle/input/insurance/insurance.csv") le = LabelEncoder()['sex']) data['Sex'] = le.transform(data['sex'])['smoker']) data['Smoker'] = le.transform(data['smoker'])['region']) data['Region'] = le.transform(data['region']) #independent and dependent columns x = data[["age", "bmi", "children", "Sex", "Smoker", "Region"]] y = data['charges'] #split in train and test x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0) #model training linreg = LinearRegression(), y_train) #model testing predictions = linreg.predict(x_test) linreg.score(x_test,y_test) #save the model file = open("expense_model.pkl", 'wb') pickle.dump(linreg, file)

Also Read: How to Deploy Machine Learning Models using Flask (with Code!)

Creating a Flask Application

Now it’s time to get ready and build a flask application. we will move in a step-by-step procedure by designing an HTML page, server-side flask application.

Step 1: Create a HTML Page

In templates, folders create an HTML file with any name. we have kept it as index.html. In this, we have to design a form that will visible to a user through which the user will provide us a data. I have created a simple web interface to observe everything clearly. you can give design it more and can also add a CSS file from a static folder. The code snippet is below and its explanation is given below code.

{{ prediction_text }}

Healthcare Expense Predictor







{{ prediction_text }}

In HTML form we have used a dynamic URL building of the Jinja template. It means that when a form is submitted then where should a user be redirected. in our form whenever the to submit button will be triggered then it will call to prediction URL and our flask application will capable to access the data.

Dynamic URL Building 

It is a method used to dynamically build URLs at run-time. url_for method is used to achieve this. the first parameter is accepted as a folder name or function name and the second is the filename. If you use it in flask then it is used to redirect to some URL after success or failure of some event where URL changes as per input. In simple words means the URL keeps changing based on inputs. Consider the below script as a demo in a flask of how to build a dynamic URL.

@app.route('/user/') def hello_user(name): if name =='admin': return redirect(url_for('admin')) else: return redirect(url_for('guest', guest = name))

In the above code, it accepts the name from the frontend through some form, and on hitting a post request hello user function accepts the name and check that it is admin then redirect to the admin panel else on the guest panel. The same dynamic URL building you can use if you are working on some big project or you want to redirect the user to different HTML pages based on his inputs.

Control Statements in Flask jinja Template

After creating the HTML form, we use the H2 tag with jinja syntax to display our output. In Flask, when you need to display data from the server on the front end, you enclose the variable in double curly braces within the desired tag. You can also use loops and conditions for printing data. Loops and control statements are written in single curly braces followed by a modulo sign. Below is a sample snippet demonstrating how to display an array of numbers and determine if they are even or odd. This code is for reference purposes only and must not be added to your actual code files. It helps you understand how to use control statements in jinja.

{% for i in arr%} {% if i%2 == 0%} <h4> {{i}} </h4> <p>Even</p> {% else %} <h4> {{i}} </h4> <p>Odd</p> {% endif %} {% endfor %}

Step 2: Create a Flask Python Application

Now let us edit our python app file. here we will write a complete logic of how our web app routing will happen, and what action to take each time. The complete explanation of each term is described below the code.

from flask import Flask, render_template, request import pickle app = Flask(__name__) model = pickle.load(open('expense_model.pkl','rb')) #read mode @app.route("/") def home(): return render_template('index.html') @app.route("/predict", methods=['GET','POST']) def predict(): if request.method == 'POST': #access the data from form ## Age age = int(request.form["age"]) bmi = int(request.form["bmi"]) children = int(request.form["children"]) Sex = int(request.form["Sex"]) Smoker = int(request.form["Smoker"]) Region = int(request.form["Region"]) #get prediction input_cols = [[age, bmi, children, Sex, Smoker, Region]] prediction = model.predict(input_cols) output = round(prediction[0], 2) return render_template("index.html", prediction_text='Your predicted annual Healthcare Expense is $ {}'.format(output)) if __name__ == "__main__": Stage 1

First, we have to import the Flask class and define our app. After that, we have loaded the model that we have saved in our working directory. The first routing is at our home page which is given by a single escape sign. It means that if the user heat at the “/” URL then redirects to our HTML page which is our home application.

Stage 2

After this, the main predict function is there. It means if the user makes a POST request means to hit a submit button then load the site to “/predict”, and the flask will access the data inserted in HTML form. when we have the data we pass it to our loaded model in 2-dimension to get the desired output. As we get an output to redirect the user to the same page with prediction and using jinja templating we have printed output on the HTML page.

Final Stage

The inputs from the flask form are accessed using the name attribute that we have provided to each label while creating an HTML page. the request make a request to that input label and match it with the name and collect the value selected or entered by the user. so when you create an HTML form for a machine learning model do provide each input with an appropriate name so you can access the value easily. And the input variable is categorical then using the value attribute we have encoded it to numeric. so when a request fetches the data it gets the value as specified but when it brings the data, it is always in string format so we typecast it to an integer.

Step 3: Run the Flask application locally

That sits, and by following these simple two to three easy steps you can deploy your machine learning model and make it available for the public to use. Now let’s check this from our command prompt whether everything is running fine or not. open your command prompt and go into the working directory and just run the app file.

On running the app file you will get a localhost URL, copy the URL and open it in the browser and you will see your web app in your browser.

Now Provide some random data in a form and check whether you are getting predictions or not.

Also Read: Which is Better for Machine Learning: Flask vs Django

Ready to Use Flask Python?

Hence, we have successfully made our first flask application, and we are getting an output. Now you can use any cloud platform to deploy your application and make it available for the audience to use.

Frequently Asked Questions

Q1. What is Flask Python used for?

A. Flask Python is a web framework used for building web applications in Python. It provides tools and libraries for handling web development tasks efficiently.

Q2. Is Flask Python or Django?

A. Flask and Django are both Python web frameworks but differ in their design philosophies and feature sets. Flask is minimalistic and flexible, while Django is a full-featured framework with batteries included.

Q3. What is Python Django vs Flask?

A. Python Django is a high-level web framework that follows the Model-View-Controller (MVC) architectural pattern. On the other hand, Flask is a microframework that follows a simpler and more lightweight approach.

Q4. Is Python Flask an API?

A. Yes, Python Flask can be used to build APIs. It provides the tools and libraries to handle HTTP requests and responses, making it suitable for building RESTful APIs or web services.

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Hypothesis Testing For Data Science And Analytics

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

Introduction to Hypothesis Testing

Every day we find ourselves testing new ideas, finding the fastest route to the office, the quickest way to finish our work, or simply finding a better way to do something we love. The critical question, then, is whether our idea is significantly better than what we tried previously.

These ideas that we come up with on such a regular basis – that’s essentially what a hypothesis is. And testing these ideas to figure out which one works and which one is best left behind, is called hypothesis testing.

The article is structured in a manner that you will get examples in each section. You’ll get to learn all about hypothesis testing, p-value, Z test, t-test and much more.

Fundamentals of Hypothesis Testing

Let’s take an example to understand the concept of Hypothesis Testing. A person is on trial for a criminal offence and the judge needs to provide a verdict on his case. Now, there are four possible combinations in such a case:

First Case: The person is innocent and the judge identifies the person as innocent

Second Case: The person is innocent and the judge identifies the person as guilty

Third Case: The person is guilty and the judge identifies the person as innocent

Fourth Case: The person is guilty and the judge identifies the person as guilty

As you can clearly see, there can be two types of error in the judgment – Type 1 error, when the verdict is against the person while he was innocent and Type 2 error, when the verdict is in favour of the Person while he was guilty.

The basic concepts of Hypothesis Testing are actually quite analogous to this situation.

Steps to Perform for Hypothesis Testing

There are four steps to performing Hypothesis Testing:

Set the Hypothesis

Compute the test statistics

Make a decision

1. Set up Hypothesis (NULL and Alternate): Let us take the courtroom discussion further. The defendant is assumed to be innocent (i.e. innocent until proven guilty) and the burden is on a prosecutor to conduct a trial to show evidence that the defendant is not innocent. This is the Null Hypothesis.

Keep in mind that, the only reason we are testing the null hypothesis is that we think it is wrong. We state what we think is wrong about the null hypothesis in an Alternative Hypothesis.

In the courtroom example, the alternate hypothesis can be – the defendant is not guilty. The symbol for the alternative hypothesis is ‘H1’.

2. Set the level of Significance – To set the criteria for a decision, we state the level of significance for a test. It could 5%, 1% or 0.5%. Based on the level of significance, we make a decision to accept the Null or Alternate hypothesis.

Don’t worry if you didn’t understand this concept, we will be discussing it in the next section.

3. Computing Test Statistic – Test statistic helps to determine the likelihood. A higher probability has a higher likelihood and enough evidence to accept the Null hypothesis.

We’ll be looking into this step in later lessons.

4. Make a decision based on p-value – But What does this p-value indicate?

We can understand this p-value as the measurement of the Defense Attorney’s argument. If the p-value is less than ⍺ , we reject the Null Hypothesis or if the p-value is greater than ⍺, we fail to reject the Null Hypothesis.

Critical Value (p-value)

We will understand the logic of Hypothesis Testing with the graphical representation for Normal Distribution.

Typically, we set the Significance level at 10%, 5%, or 1%. If our test score lies in the Acceptance Zone we fail to reject the Null Hypothesis. If our test score lies in the critical zone, we reject the Null Hypothesis and accept the Alternate Hypothesis.

Critical Value is the cut off value between Acceptance Zone and Rejection Zone. We compare our test score to the critical value and if the test score is greater than the critical value, that means our test score lies in the Rejection Zone and we reject the Null Hypothesis. On the opposite side, if the test score is less than the Critical Value, that means the test score lies in the Acceptance Zone and we fail to reject the null Hypothesis.

But why do we need a p-value when we can reject/accept hypotheses based on test scores and critical values?

p-value has the benefit that we only need one value to make a decision about the hypothesis. We don’t need to compute two different values like critical values and test scores. Another benefit of using a p-value is that we can test at any desired level of significance by comparing this directly with the significance level.

This way we don’t need to compute test scores and critical values for each significance level. We can get the p-value and directly compare it with the significance level.

Directional Hypothesis

Great, You made it here! Hypothesis Testing is further divided into two parts –

Direction Hypothesis

Non-Direction Hypothesis

In the Directional Hypothesis, the null hypothesis is rejected if the test score is too large (for right-tailed and too small for left tailed). Thus, the rejection region for such a test consists of one part, which is right from the centre.

Non-Directional Hypothesis

In a Non-Directional Hypothesis test, the Null Hypothesis is rejected if the test score is either too small or too large. Thus, the rejection region for such a test consists of two parts: one on the left and one on the right.

What is Z test?

z tests are a statistical way of testing a hypothesis when either:

We know the population variance, or

We do not know the population variance but our sample size is large n ≥ 30

If we have a sample size of less than 30 and do not know the population variance, then we must use a t-test.

One-Sample Z test

We perform the One-Sample Z test when we want to compare a sample mean with the population mean.


Let’s say we need to determine if girls on average score higher than 600 in the exam. We have the information that the standard deviation for girls’ scores is 100. So, we collect the data of 20 girls by using random samples and record their marks. Finally, we also set our ⍺ value (significance level) to be 0.05.

In this example:

The mean Score for Girls is 641

The size of the sample is 20

The population mean is 600

The standard Deviation for the Population is 100

Since the P-value is less than 0.05, we can reject the null hypothesis and conclude based on our result that Girls on average scored higher than 600.

Two- Sample Z Test

We perform a Two-Sample Z test when we want to compare the mean of two samples.


Here, let’s say we want to know if Girls on average score 10 marks more than the boys. We have the information that the standard deviation for girls’ Scores is 100 and for boys’ scores is 90. Then we collect the data of 20 girls and 20 boys by using random samples and record their marks. Finally, we also set our ⍺ value (significance level) to be 0.05.

In this example:

The mean Score for Girls (Sample Mean) is 641

The mean Score for Boys (Sample Mean) is 613.3

The standard Deviation for the Population of Girls is 100

The standard deviation for the Population of Boys is 90

The Sample Size is 20 for both Girls and Boys

The difference between the Mean Population is 10

Thus, we can conclude based on the P-value that we fail to reject the Null Hypothesis. We don’t have enough evidence to conclude that girls on an average score of 10 marks more than the boys. Pretty simple, right?

What is a T-Test?

In simple words, t-tests are a statistical way of testing a hypothesis when:

We do not know the population variance

Our sample size is small, n < 30

One-Sample T-Test

We perform a One-Sample t-test when we want to compare a sample mean with the population mean. The difference from the Z Test is that we do not have the information on Population Variance here. We use the sample standard deviation instead of the population standard deviation in this case.


Let’s say we want to determine if on average girls score more than 600 in the exam. We do not have the information related to variance (or standard deviation) for girls’ scores. To a perform t-test, we randomly collect the data of 10 girls with their marks and choose our ⍺ value (significance level) to be 0.05 for Hypothesis Testing.

In this example:

The mean Score for Girls is 606.8

The size of the sample is 10

The population mean is 600

The standard deviation for the sample is 13.14

Our P-value is greater than 0.05 thus we fail to reject the null hypothesis and don’t have enough evidence to support the hypothesis that on average, girls score more than 600 in the exam.

Two-Sample T-Test

We perform a Two-Sample t-test when we want to compare the mean of two samples.


Here, let’s say we want to determine if on average, boys score 15 marks more than girls in the exam. We do not have the information related to variance (or standard deviation) for girls’ scores or boys’ scores. To perform a t-test. we randomly collect the data of 10 girls and boys with their marks. We choose our ⍺ value (significance level) to be 0.05 as the criteria for Hypothesis Testing.

In this example:

The mean Score for Boys is 630.1

The mean Score for Girls is 606.8

Difference between Population Mean 15

The standard Deviation for Boys’ scores is 13.42

The standard Deviation for Girls’ scores is 13.14

Thus, P-value is less than 0.05 so we can reject the null hypothesis and conclude that on average boys score 15 marks more than girls in the exam.

Deciding between Z Test and T-Test

So when we should perform the Z test and when we should perform the t-Test? It’s a key question we need to answer if we want to master statistics.

If the sample size is large enough, then the Z test and t-Test will conclude with the same results. For a large sample size Sample Variance will be a better estimate of Population variance so even if population variance is unknown, we can use the Z test using sample variance.

Similarly, for a  Large Sample, we have a high degree of freedom. And since t-distribution approaches the normal distribution, the difference between the z score and t score is negligible.


In this article, we learn about a few important techniques to solve the real problem such as:-

what is hypothesis testing?

steps to perform for hypothesis testing


directional hypothesis

Non- directional hypothesis

what is Z-test?

One-sample Z-test with example

Two-sample Z-test with example

what is a t-test?

One-sample t-test with example

Two-sample t-test with example

If you want to read my previous blogs, you can read Previous Data Science Blog posts from here.

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A Master’s In Data Science And A Six

A Master’s in Data Science and a six-figure salary are mutually exclusive in the present generation

While a bachelor’s degree in math, statistics, computer science, or a similar field of study is normally required for data scientists, some companies prefer applicants with a master’s in data science. These employers will pay more for this qualification in addition to preferring it. That is how a Master’s in Data Science and a Six- figure Salary go hand in hand.

Graduates of data science master’s programs can earn six- figure salary, beginning with base salaries, and incentives are not included in this figure. A large part of many of the best master’s programs in data science emphasizes experiential learning. In many decision-making processes, students have practical experience transforming complex data into visual representations. Students gain collaboration skills through working on practical data science projects with academic and commercial partners.

Most data science master’s programs have only been around for five to seven years, making them a relatively recent phenomenon. Before the availability of complete data science programs, many data scientists sought regular master’s degrees in statistics or computer science. After graduation, you may have a variety of possibilities if you’re considering how to find a career in data science and what a master’s in data science pay might look like. There are many more high-paying jobs, but these are just a few to think about:

Data Scientist – Many people join the profession as generalists in data science, typically at the IC-II level. Building models, preprocessing data, and validating data are the responsibilities of data scientists.

Data Architect – The creation of data infrastructure is occasionally delegated to data architects. Typically, architects and data engineers work closely together.

Machine Learning Engineer – Machine learning, deep learning, and AI specializations are available in several master’s degrees in data science. Graduates with these degrees are more prepared for specialized ML positions like engineers, scientists, and AI specialists.

Data Science Manager – A master’s degree is sometimes a requirement for the position, even though master’s programs aren’t explicitly created to prepare you for management. You might graduate and be hired as a manager if you have prior managerial experience. Some of the highest salaries in the industry are paid to data science managers. 

Data Scientists and Mathematical Science Occupations: $100,000 or more (IN USA) – Data scientists and those in related fields make an average salary of $103,930 per year, according to a BLS report from May 2023. You can also have the opportunity to make extra money depending on the sector.

Market Research Analysts: $66,000 (IN USA).

Market research might be a difficult but rewarding place to start if you’re considering how to break into the data science field. According to BLS data from 2023, market research analysts made a median yearly compensation of $65,810.

Computer and Information Research Scientists: $127,000 (IN USA).

The median master’s in data science income for computer and information research scientists with a degree in computer science or a related field was $126,830 per year in 2023, according to the BLS.

A master’s degree in data science will help you progress your career and possibly earn higher pay while making you a competitive applicant for these roles. The completion of a master’s program in data science demonstrates to potential employers not only your commitment to your studies but also your expertise in the industry.

You can access and analyze information in ways that others can’t by obtaining essential skills in statistics, analytical techniques, programming, and business through a master’s in data science. When figuring out how to get a job in data science, these abilities might make you stand out from other jobs.


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