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data science books

Ayesha Saleem - Digital content creator - Author
Ayesha Saleem
| September 9

In this blog, we will introduce you to the highly rated data science statistics books on Amazon. As you read the blog, you will find 5 books for beginners and 5 books for advanced-level experts. We will discuss what’s covered in each book and how it helps you to scale up your data science career. 

Statistics books

Advanced statistics books for data science 

1. Naked Statistics: Stripping the Dread from the Data – By Charles Wheelan 

Naked statistics by Charles Wheelan

The book unfolds the underlying impact of statistics on our everyday life. It walks the readers through the power of data behind the news. 

Mr. Wheelan begins the book with the classic Monty Hall problem. It is a famous, seemingly paradoxical problem using Bayes’ theorem in conditional probability. Moving on, the book separates the important ideas from the arcane technical details that can get in the way. The second part of the book interprets the role of descriptive statistics in crafting a meaningful summary of the underlying phenomenon of data. 

Wheelan highlights the Gini Index to show how it represents the income distribution of the nation’s residents and is mostly used to measure inequality. The later part of the book clarifies key concepts such as correlation, inference, and regression analysis explaining how data is being manipulated in order to tackle thorny questions. Wheelan’s concluding chapter is all about the amazing contribution that statistics will continue to make to solving the world’s most pressing problems, rather than a more reflective assessment of its strengths and weaknesses.  

2. Bayesian Methods For Hackers – Probabilistic Programming and Bayesian Inference, By Cameron Davidson-Pilon 

Bayesian methods for hackers

We mostly learn Bayesian inference through intensely complex mathematical analyses that are also supported by artificial examples. This book comprehends Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. 

Davidson-Pilon focused on improving learners’ understanding of the motivations, applications, and challenges in Bayesian statistics and probabilistic programming. Moreover, this book brings a much-needed introduction to Bayesian methods targeted at practitioners. Therefore, you can reap the most benefit from this book if you have a prior sound understanding of statistics. Knowing about prior and posterior probabilities will give an added advantage to the reader in building and training the first Bayesian model.    

Read this blog if you want to learn in detail about statistical distributions

The second part of the book introduces the probabilistic programming library for Python through a series of detailed examples and intuitive explanations, with recent core developments and the popularity of the scientific stack in Python, PyMC is likely to become a core component soon enough. PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy, SciPy, and Matplotlib. This book is filled with examples, figures, and Python code that make it easy to get started solving actual problems.  

3. Practical Statistics for Data Scientists – By Peter Bruce and Andrew Bruce  

Practical statistics for data scientists

This book is most beneficial for readers that have some basic understanding of R programming language and statistics.  

The authors penned the important concepts to teach practical statistics in data science and covered data structures, datasets, random sampling, regression, descriptive statistics, probability, statistical experiments, and machine learning. The code is available in both Python and R. If an example code is offered with this book, you may use it in your programs and documentation.  

The book defines the first step in any data science project that is exploring the data or data exploration. Exploratory data analysis is a comparatively new area of statistics. Classical statistics focused almost exclusively on inference, a sometimes-complex set of procedures for drawing conclusions about large populations based on small samples.  

To apply the statistical concepts covered in this book, unstructured raw data must be processed and manipulated into a structured form—as it might emerge from a relational database—or be collected for a study.  

4. Advanced Engineering Mathematics by Erwin Kreyszig 

Advanced engineering mathematics

Advanced Engineering Mathematics is a textbook for advanced engineering and applied mathematics students. The book deals with calculus of vector, tensor and differential equations, partial differential equations, linear elasticity, nonlinear dynamics, chaos theory and applications in engineering. 

Advanced Engineering Mathematics is a textbook that focuses on the practical aspects of mathematics. It is an excellent book for those who are interested in learning about engineering and its role in society. The book is divided into five sections: Differential Equations, Integral Equations, Differential Mathematics, Calculus and Probability Theory. It also provides a basic introduction to linear algebra and matrix theory. This book can be used by students who want to study at the graduate level or for those who want to become engineers or scientists. 

The text provides a self-contained introduction to advanced mathematical concepts and methods in applied mathematics. It covers topics such as integral calculus, partial differentiation, vector calculus and its applications to physics, Hamiltonian systems and their stability analysis, functional analysis, classical mechanics and its applications to engineering problems. 

The book includes a large number of problems at the end of each chapter that helps students develop their understanding of the material covered in the chapter. 

5. Computer Age Statistical Inference by Bradley Efron and Trevor Hastie 

Computer age statistical inference

Computer Age Statistical Inference is a book aimed at data scientists who are looking to learn about the theory behind machine learning and statistical inference. The authors have taken a unique approach in this book, as they have not only introduced many different topics, but they have also included a few examples of how these ideas can be applied in practice.

The book starts off with an introduction to statistical inference and then progresses through chapters on linear regression models, logistic regression models, statistical model selection, and variable selection. There are several appendices that provide additional information on topics such as confidence intervals and variable importance. This book is great for anyone looking for an introduction to machine learning or statistics. 

Computer Age Statistical Inference is a book that introduces students to the field of statistical inference in a modern computational setting. It covers topics such as Bayesian inference and nonparametric methods, which are essential for data science. In particular, this book focuses on Bayesian classification methods and their application to real world problems. It discusses how to develop models for continuous and discrete data, how to evaluate model performance, how to choose between parametric and nonparametric methods, how to incorporate prior distributions into your model, and much more. 

5 Beginner level statistics books for data science 

6. How to Lie with Statistics by Darrell Huff 

How to lie with statistics

How to Lie with Statistics is one of the most influential books about statistical inference. It was first published in 1954 and has been translated into many languages. The book describes how to use statistics to make your most important decisions, like whether to buy a house, how much money to give to charity, and what kind of mortgage you should take out. The book is intended for laymen, as it includes illustrations and some mathematical formulas. It’s full of interesting insights into how people can manipulate data to support their own agendas. 

The book is still relevant today because it describes how people use statistics in their daily lives. It gives an understanding of the types of questions that are asked and how they are answered by statistical methods. The book also explains why some results seem more reliable than others. 

The first half of the book discusses methods of making statistical claims (including how to make improper ones) and illustrates these using examples from real life. The second half provides a detailed explanation of the mathematics behind probability theory and statistics. 

A common criticism of the book is that it focuses too much on what statisticians do rather than why they do it. This is true — but that’s part of its appeal! 

 7. Head-first Statistics: A Brain-Friendly Guide Book by Dawn Griffiths  

Head first statistics

If you are looking for a book that will help you understand the basics of statistics, then this is the perfect book for you. In this book, you will learn how to use data and make informed decisions based on your findings. You will also learn how to analyze data and draw conclusions from it. 

This book is ideal for those who have already completed a course in statistics or have studied it in college. Griffiths has given an overview of the different types of statistical tests used in everyday life and provides examples of how to use them effectively. 

The book starts off with an explanation of statistics, which includes topics such as sampling, probability, population and sample size, normal distribution and variation, confidence intervals, tests of hypotheses and correlation.  

After this section, the book goes into more advanced topics such as regression analysis, hypothesis testing etc. There are also some chapters on data mining techniques like clustering and classification etc. 

The author has explained each topic in detail for the readers who have little knowledge about statistics so they can follow along easily. The language used throughout this book is very clear and simple which makes it easy to understand even for beginners. 

8. Think Stats By Allen B. Downey 

Think stats book

Think Stats is a great book for students who want to learn more about statistics. The author, Allen Downey, uses simple examples and diagrams to explain the concepts behind each topic. This book is especially helpful for those who are new to mathematics or statistics because it is written in an easy-to-understand manner that even those with a high school degree can understand. 

The book begins with an introduction to basic counting, addition, subtraction, multiplication and division. It then moves on to finding averages and making predictions about what will happen if one number changes. It also covers topics like randomness, sampling techniques, sampling distributions and probability theory. 

The author uses real-world examples throughout the book so that readers can see how these concepts apply in their own lives. He also includes exercises at the end of each chapter so that readers can practice what they’ve learned before moving on to the next section of the book. This makes Think Stats an excellent resource for anyone looking for tips on improving their math skills or just wanting to brush up on some statistical basics! 

9. An Introduction To Statistical Learning With Applications In R By Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani 

An introduction to statistical learning

Statistical learning with applications in R is a guide to advanced statistical learning. It introduces modern machine learning techniques and their applications, including sequential decision-making, Gaussian mixture models, boosting, and genetic programming. The book covers methods for supervised and unsupervised learning, as well as neural networks. The book also includes chapters on Bayesian statistics and deep learning. 

It begins with a discussion of correlation and regression analysis, followed by Bayesian inference using Markov chain Monte Carlo methods. The authors then discuss regularization techniques for regression models and introduce boosting algorithms. This section concludes with an overview of neural networks and convolutional neural networks (CNNs). The remainder of the book deals with topics such as kernel methods, support vector machines (SVMs), regression trees (RTs), naive Bayes classifiers, Gaussian processes (GP), gradient ascent methods, and more. 

This statistics book is recommended to researchers willing to learn about statistical machine learning but do not have the necessary expertise in mathematics or programming languages 

10. Statistics in Plain English By Timothy C. Urdan 

Statistics in plain English

Statistics in Plain English is a writing guide for students of statistics. Timothy in his book covered basic concepts with examples and guidance for using statistical techniques in the real world. The book includes a glossary of terms, exercises (with solutions), and web resources. 

The book begins by explaining the difference between descriptive statistics and inferential statistics, which are used to draw conclusions about data. It then covers basic vocabulary such as mean, median, mode, standard deviation, and range. 

In Chapter 2, the author explains how to calculate sample sizes that are large enough to make accurate estimates. In Chapters 3–5 he gives examples of how to use various kinds of data: census data on population density; survey data on attitudes toward various products; weather reports on temperature fluctuations; and sports scores from games played by teams over time periods ranging from minutes to seasons. He also shows how to use these data to estimate the parameters for models that explain behavior in these situations. 

The last 3 chapters define the use of frequency distributions to answer questions about probability distributions such as whether there’s a significant difference between two possible outcomes or whether there’s a trend in a set of numbers over time or space 

Which data science statistics books are you planning to get? 

Build upon your statistical concepts and successfully step into the world of data science. Analyze your knowledge and choose the most suitable book for your career to enhance your data science skills. If you have any more suggestions for statistics books for data science, please share them with us in the comments below.  

Data Science Dojo
Phuc Duong
| April 21

O’Reilly has been a staple in data science learning. That doesn’t mean we can’t have a little fun. Here are some spoofs of my favorite data science books.

Data science books

You may have come to this post actually looking for books to study data science. If that’s you, take a look at the O’reilly website. They have compiled “Free Data Ebooks” from O’Reilly editors, authors, and Strata speakers. Happy reading!

O’Reilly spoofs

Explaining the unorthodox approach to optimization
Distribute learning
To learn distributed machine learning in excel
A definitive guide to intro to arbitrary P-value thresholds

Not getting the O’Reilly Books jokes? Attend our Boot camp to figure out why they are funny.

The pocket guide on effective Hadoop on small datasets
Deep learning
Book about essential deep learning with JavaScript


Data Science Dojo
Nathan Piccini
| October 28

Learning different concepts in data science can often be daunting. Here are 6 books to help lift the burden.

List of books to help you learn data science

List of books:

Check out the below list of 6 data science books that help you kick off your learning journey.

1. Machine Learning: A probabilistic approach

This is an almost *exhaustive* book on machine learning topics ranging from the very basics of probability to mixture models, variational inference, and deep learning. Even though I first encountered this book as a companion textbook for a university course, I think calling this a textbook is doing it a disservice. It is an encyclopedia and can serve as a detailed reference for any data scientist or machine learning engineer.

The book doesn’t shy away from proper mathematical notation, which might be jarring for some, which is why the first couple of chapters about the basics are so important to get your feet wet. There are diagrams exploring the characteristics of models, pseudocode, fully worked examples, and even exercises at the end of chapters.

There are a bunch of fantastic online learning resources for stats, ML, and data science topics but most of them shy away from the maths and theoretical aspects which is where this book shines.

  • Author: Kevin Murphy
  • Education Level: Beginner – Advanced

2. Fundamentals of deep learning (O’Reilly)

Deep Learning is only getting more and more popular each year and with that, the wealth of online tutorials and courses about each topic keeps increasing. My main issue with most of these is that they are either too focused on the implementation (feeling more like a tutorial for Keras than deep learning as a field) or they skip out on key theoretical concepts.

Deep Learning by Ian Goodfellow, while being a very detailed exploration of the field and its roots, is (in my opinion) not the best jumping-off point for beginners or even many people who understand the basics.

This book, The Fundamentals of Deep Learning (O’Reilly), doesn’t have this problem. It uses easy-to-understand notation and minimal derivation while still covering the breadth of the field’s most common concepts (this is less ‘complete’ than the Goodfellow text).

The major advantage this book has as an introductory text is the inclusion of companion code samples in Tensorflow (the most popular DL framework) which makes the jump from reading and learning a topic in the book to implementing and experimenting seamless.

  • Author: Nikhil Buduma
  • Education Level: Beginner

3. An introduction to statistical learning (with Application in R)

An Introduction to Statistical Learning (popularly known as ‘ISLR’) is easy, one of the most popular textbooks available on machine learning. The text builds your machine learning concepts step-by-step. Also, despite consciously restricting the discussions a little short of details on ‘mathematical derivations’ and ‘statistical jargon; the text gives a complete treatment to respective topics.

  • Author: Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
  • Education Level: Beginner

4. The elements of statistical learning: Data mining, inferencing, and prediction

The Elements of Statistical Learning (popularly known as ‘ESL’) is often recommended as the next step in learning for machine learning (ISRL being the first step). In my opinion, the ESL text demands an advanced level facility with Algebra, Calculus, and Statistics. Like ISLR, ESL does find mentioned as either an assigned or a recommended textbook in leading master’s programs in Data Science, Statistics, and Business Analytics.

  • Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman
  • Education Level: Advanced

5. R for everyone

The solution to the often-thought problem that R requires too much knowledge for non-statisticians, R for Everyone draws on making learning easy and intuitive. This book starts with the basics, walking you through downloading and installing R, but takes you through more advanced problems so you’ll be able to “tackle statistical problems you care about the most”.

You can expect to build both linear and non-linear models, use data mining techniques, and use LaTeX, RMarkdown, and Shiny to make your code reproducible.

“This guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks.

  • Author: Jared P. Lander
  • Education Level: Beginner

6. The cartoon guide to statistics

Used as a textbook in Data Science Dojo’s data science Bootcamp, The Cartoon Guide to Statistics covers everything needed for a basic understanding of statistics. The authors use cartoons and humor to explain the concepts many find hard to learn. This book is great if you’re just starting to learn statistics and data science, or if you want a good laugh while you refresh your memory.

The last page reads: “Well, that’s it! By now, you should be able to do anything with statistics, except lie, cheat, steal, and gamble. We left those subjects to the bibliography.”

  • Author: Larry Gonick and Woollcott Smith
  • Education Level: Beginner
Data Science Dojo
Ali Haider Shalwani
| August 5

The best data science toolkit to help you succeed. Find leading blogs, podcasts, YouTube channels, project ideas, and numerous other data science resources in one place.

100s of 1000s of people are everyday planning to get started with their data science journey while most of them are actively & continuously looking out for data science resources & sites, to begin with. With tons of resources & sites available, one might wonder where to get the most useful & up-to-date data science material

And, if you are one of those, then you have landed on the correct page because I have curated a list of resources that have helped me a lot to learn data science & should probably help you out too.

Data science blogs

List Data Science Blogs
List of Data Science Blogs

Being a data scientist, you would want to stay updated with the recent happenings in data science, machine learning, and artificial intelligence. There are some quality blogs producing engaging and interesting content day in day out. These blogs can also be regarded as excellent data science resources.

1. KDnuggets

It has always been on the top of my list; they provide new blogs on data science, machine learning, artificial intelligence, and analytics on a routine basis. So, if you need a new data science blog frequently or on daily basis then KDnuggets is your option to go with.

Link to site: https://www.kdnuggets.com/

2. Data Science Central

From data science cheat sheets to PDFs to infographics, you can find all the useful material here with just one search.

Link to site: https://www.datasciencecentral.com/

3. Towards Data Science

With towards data science, you can clarify your data science, ML, and AI basics & fundamentals. Additionally, they provide a wide range of blogs on statistics & mathematics that can further aid your learning journey. Link to site: https://towardsdatascience.com/

4. Data Science Dojo

From big data to data analytics to statistics, Data Science Dojo is providing some useful blogs on several different areas of data science. Though, the blogs are limited in number but are highly recommended for all those who are just getting started with data science. Link to site: https://datasciencedojo.com/blog/

5. R Bloggers

Undoubtedly, you can find some most amazing blogs on R, Python, regression, and statistics here. Hence, if you are looking for clarity in any of the aforementioned areas, then R bloggers is your way to go. Link to site: https://www.r-bloggers.com/

PRO TIP: Join our data science bootcamp program today to enhance your data analysis skillset!

Data science communities

data Science Communities
List of Data Science Communities

Online forums and communities can be a really interactive way of learning data science. With a number of enthusiasts all over the world, these online spaces can serve as a resource for staying on track and updated, being a valuable contribution to your data science toolkit.

1. Kaggle

One of the most useful online communities for data scientists & practitioners; with Kaggle you can learn some of the most essential Python, machine learning, and data science concepts.

Link to site: https://www.kaggle.com/

2. Reddit

This online community can help you find some helpful data science articles on a routine basis, which can enhance your knowledge & understanding.

Link to site: https://www.reddit.com/r/datascience/ 

3. Stack Overflow

If you are currently in the learning phase, then it is one of the most useful online community for you to find the answers to your questions.

Link to site: https://stackoverflow.com/

4. Mathematics Stack Exchange

Need help with mathematics? Then this mathematics forum can help you with it at any level. You can easily find answers to your math-related queries here.

Data science YouTube channels

Data Science YouTube Channels
List of Data Science YouTube Channels

There are a number of YouTube channels sharing the concepts of data science. You, obviously, will not be able to go through all the videos. Here are some of the best data science resources when it comes to YouTube.

1. Ken Jee

Following his channel can make it a lot easier for you to break into the field of data science. Ken Jee shares his own learning experience & makes some useful career-related suggestions.

Link to channel: https://www.youtube.com/results?search_query=ken+jee 

2. Data professor

Learn about data science, machine learning, bioinformatics, research, and teaching with these amazing videos & content produced by data professor. 

Link to channel: https://www.youtube.com/results?search_query=data+professor

3. Data Science Dojo

Are you new to the field of data science? Then Data Science Dojo can help you with learning some of the most significant concepts of machine learning, artificial intelligence, Python programming, and R programming.

Link to channel: https://www.youtube.com/results?search_query=data+science+dojo

Check out our Data Science Bootcamp now, to begin with, your career. 

4. Krish Naik

To get the know-how of how data science, machine learning, deep learning, and artificial intelligence work in a real-life scenario, follow his amazing tutorials & content.

Link to channel: https://www.youtube.com/results?search_query=krish+naik

5. Code Basics

Start learning programming in the easiest & untaught manner. So, if you are looking for a veracious channel to learn to program, then Code Basics is your channel to trust on.

Link to channel: https://www.youtube.com/results?search_query=code+basic

For more Data Science related information, check out our other blog posts.

Data science podcasts

Data Science Podcasts
List of Data Science Podcasts

Podcasts are an excellent way of staying updated in the world of data science. I have listed a few useful data science podcasts on Sound-Cloud, Apple Podcast, and Spotify to help you learn and make the most out of your time.

1. Spotify

a. Data Skeptic

These podcasts from data skeptic will bring amazing tutorials on statistics, machine learning, big data, and data science. Start learning now with them.

Link to Podcast: https://open.spotify.com/show/1BZN7H3ikovSejhwQTzNm4

b. SuperDataScience:

With SuperDataScience you can boom your analytics career. It includes podcasts on statistics, R, Python, SQL programming, tableau, machine learning, Hadoop, databases, and other analytical tools.

Link to Podcast: https://open.spotify.com/show/1n8P7ZSgfVLVJ3GegxPat1

2. Apple Podcast

a. Women in Data Science

Hear from women leaders across the data science profession, their advice on data science & lessons that can help you build your career.

Link to Podcast: https://podcasts.apple.com/us/podcast/women-in-data-science/id1440076586

b. Data Science in Production

This podcast primarily focuses on the tools & techniques that can help you put your models into production faster.

Link to Podcast: https://podcasts.apple.com/us/podcast/data-science-in-production/id1455613667

3. Sound Cloud

a. Data Hack Radio

This series of Podcast features Kunal Jain from Analytics Vidhya along with top data science leaders & practitioners.

Link to Podcast: https://soundcloud.com/datahack-radio

b. O’Reilly Data Show Podcast

With these podcasts, you can explore the opportunities that are driving the field of data science & big data.

Link to Podcast: https://soundcloud.com/oreilly-radar/sets/the-oreilly-data-show-podcast

Data science movies to watch

Data Science Movies
List of Data Science Movies to Watch

The world of data science is not just about writing code and building models. Data science has a lot of influence, both directly and indirectly, on the entertainment industry. In fact, movies and tv shows can be used as one of the data science resources by aspiring scientists and engineers. When you need to freshen up or take a break from tough problems, these movies can be of great help.

1. Minority Report (2002)

An action-thriller directed by Steven Spielberg, starring Tom Cruise. We have generally seen data being used to infer new information, but here the data is being used to predict crime predisposition. 

2. Interstellar (2014)

Christopher Nolan’s cinematic success won an Oscar for best visual effects and grossed over $677 million worldwide. The movie includes quadrilateral robots like TARS & CASE that are true examples of the that we have made within the AI domain. 

3. The Imitation Game (2014)

The move is based on the real-life story of Alan Turing & also describes the process of creating the first-ever machine within the field of cybersecurity & cryptography. 

4. The Queen’s Gambit (2020)

One of the most popular Netflix series, with over 62 million viewers, tells the story of Beth Harmon; a made-up chess star who beats all odds in life from being orphaned as a child to battling drug addiction & chess competitions. Though the series is not really related to data science, but how Beth mentally plays the game by visualizing the chessboard on the ceiling is much like how an AI system works. For the past few years, AI researchers are trying to build a computer-generated bot version of Beth.

Top data science books

Data Science Books
List of Top Data Science Books

Books are one of the best additions to any individual’s data science toolkit. There is an immense amount of literature out there, helping aspiring data scientists clarify some concepts and acquire valuable information.

1. An Introduction to Statistical Learning- With Applications in R

This book provides an overview of the field of statistical learning, which covers essential tools that can help in handling vast data sets varying from biology to marketing to finance. 

2. The Hundred-Page Machine Learning Book

This book covers a wide range of topics in just 100 pages. Some of machine learning’s core concepts are explained here in just a few words. 

3. The Cartoon Guide to Statistics

By using cartoons & humor, the author explains some of the essential statistical concepts that one might find difficult to comprehend. This book is highly recommended if you are just getting started with data science & statistics. 

4. Forecasting- Principles & Practice

Making decisions based on the future forecasts is required at several instances, for example, whether to build up a new power plant in the next five years or not? Such decisions can only be based on forecasts. This book can assist you with understanding the basics & principles of forecasting.

Data science newsletters

Data Science Newsletters
List of Data Science Newsletters

Similar to blogs and podcasts, Newsletters can be a valuable addition to your data science toolkit. You’ll get curated articles at regular intervals to stay on top of things.

1. Mode Analytics

This collaborative platform combines SQL, Python, and R together in one place. You can subscribe to amazing data science-related newsletters with mode.

Link to site: https://mode.com/ 

2. Data Science Weekly

You can find curated articles here for data science news, jobs, and blogs for free. So, if you are looking for routine data science stuff, then data science weekly is your way to go with. 

3. Data Science Dojo

They provide a weekly newsletter on a wide array of topics including data, programming, AI, infrastructure, Ops, data science, and ML. With them, you are subscribing to blogs & articles that are relevant to you & your learning. 

Link to site: https://datasciencedojo.com/newsletter/

4. Data Elixir

A weekly dose for you all the top data science picks, covering machine learning, data visualization, analytics, and strategy. Stay up to date in data science with them. 

Link to site: https://dataelixir.com/

Data science datasets

Data Science Datasets
List of Data Science Datasets

If you want to test and polish your newfound skills, the following valuable datasets can serve as one of the best data science resources.

1. Kaggle Datasets

These datasets can help you with exploring, sharing, and analyzing quality data. 

Find the datasets here: https://www.kaggle.com/datasets 

2. Data Market

You can find 100-million-time series from UN, World Bank, Eurostat, and other important data providers, which can ultimately help you with visualizing world economies & societies. 

3. Datacatalogs.org

It includes a comprehensive list of data portals from around the world i.e. Canada, United States, EU, and more. 

Find the dataset here: http://datacatalogs.org/ 

4. NASDAQ Data Store

With NASDAQ, you can access all the market & stock data. 

5. Data Science Dojo

You can find a wide range of datasets here, including, consensus income, Dow Jones Index, car evaluation, real estate evaluation, and more. 

Top Data Science LinkedIn pages to follow

Data Science LinkedIn Pages
List of Data Science LinkedIn Pages

LinkedIn can serve as another top data science resource, particularly if you’re looking to read short, engaging articles and get inspired by the stories of individuals. The pages listed below are worth following.

1. Machine Learning Mastery

You can find some useful machine learning articles & resources here that can help you to get started with applied ML. So, if you are into ML then Machine Learning Mastery is your place, to begin with.

2. Towards AI

With having 1800+ contributing writers from university professors to industry experts, they have a wide range of articles on tech, science, mathematics, engineering, and the future. If you are looking for some high-quality articles, then start scrolling through them. 

3. Machine Learning India

Looking for useful infographics & PDFs? Then start following Machine Learning India because they have a ton of useful infographics, data science PDFs, and cheat sheets. 

4. Data Science Dojo

Are you new to data science? Do you need daily content? Then I highly recommend you to start following Data Science Dojo. They share useful data science resources; be it an infographic, a cheat sheet, a blog, or a joke for humor. It doesn’t really matter if you are a beginner or an expert in the field, they have the right mix of content for everyone. Adding on, their weekly polls can help you test your data science skills, while their frequently held online webinars can help you with enhancing your knowledge. 

5. Data Science Central

Similar to their blog, they have amazing data science articles on their LinkedIn profile as well. If you are a LinkedIn Freak, then you should start following their page now.

Data science free tools

Free Data Science Tools
List of Free Tools for Data Science

A data science toolkit devoid of tools and software is not really a toolkit, to be fair. There are some quality tools out there, including open-source software, that a data scientist can benefit from. Here are the best data science resources in the realm of software applications.

1. TensorFlow

It is a free & open-source software library for machine learning. TensorFlow is commonly used for neural networks, though, it can be used for a wide range of tasks. 

Link to site: https://www.tensorflow.org/ 

2. Anaconda

It is used for the scientific computing of Python & R programming languages, which helps in package management & deployment. The distribution includes data science packages for Windows, Linux, and macOS. 

Link to site: https://www.anaconda.com/ 

3. GitHub

One of the largest & most developed platforms in the world, where millions of companies & developers build & maintain their software on. 

Link to site: https://github.com/ 

4. Good Colab

This amazing product of google allows anyone to write & execute random Python code through the browser. Generally, it is a good fit for machine learning, data analysis, and education. Additionally, colab is a hosted Jupyter notebook that requires no setup & provides free access to computing resources. 

Data science projects

Data Science Projects
List of Data Science Projects

The world of data science is nothing without practical experience and real-world projects. In your data science toolkit, therefore, you should have some quality projects. This will not only help you gain valuable experience but also strengthen your portfolio.

 1. Beginner Level 

a. Fake News Detection

If you are new to data science then this project can assist you to level up your data science career. Using Python, you can detect false & misleading news across social media & online channels. 

b. Forest Fire Prediction

Using K-means clustering one can identify the hotspots of forest fires & severity, which can help in lessening & controlling the ecosystem damage. 

c. Twitter Sentiment Analysis

One of the widely used text mining techniques, this project includes sentiment analysis of the text (tweets) in form of positive, negative, and neutral.

2. Intermediate Level 

a. Recognition of Speech Emotion

Willing to learn on the usage of different libraries? Then you must go with this project idea. With different editor tools, you can tell how the speech emotion is appearing. This program model can be built as a data science project. 

b. Gender & Age Detection with Data Science

This type of real-time project can help you grab the recruiter’s attention during an interview. Additionally, with this project, you can also learn convolutional neural networks. 

c. Chatbots

One of the highly demanded & crucial elements for all businesses these days. Thereby, working on this data science project can help you uplift your career.

3. Advance-level 

a. Credit Card Fraud Detection

Once you are through practicing the beginner & intermediate level of projects, you can move to this level. With the Credit Card Fraud Detection project, you can learn about how to use R with different algorithms like decision trees & logistic regression.

b. Traffic Sign Recognition

The purpose of this project is to achieve a higher level of accuracy in self-driving car technologies using CNN techniques, which can help in identifying different types of traffic signals by the input of an image. 

c. Customer Segmentations

One of the most popular & important data science projects that can help marketers to reach the targeted & relevant group of people via marketing activities. Methods of clustering can play a vital role here that can assist in dividing the audience within age brackets, income, gender, and interest.

Summing up the data science toolkit

Whether you are a beginner or an expert in the field of data science, this comprehensive data science toolkit can be your ultimate support at all career levels. Bookmark this post for future assistance & use.

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