Updated May 22, 2023
Best Data Science Books 2023
Best Data Science Courses 2023
Best Data Science tutorials 2023
The Data Science Course 2023: Complete Data Science Bootcamp
This data science course covers:
The course provides the whole toolkit you need to become a data scientist
Fill your CV with sought after data science skills: statistical analysis, Python programming with NumPy, pandas, matplotlib and Seaborn, advanced statistical analysis, Tableau, machine learning with stat models and scikit-learn, deep learning with TensorFlow
Impress interviewers by showing an understanding of the field of data science during your data science interview
Learn how to preprocess data
Understanding the math behind machine learning (an absolute must that other courses don’t teach!)
Start coding in Python and learn how to use it for statistical analysis
Perform linear and logistic regressions in Python
Perform cluster and factor analysis
Be able to create machine learning algorithms in Python, using NumPy, statsmodels and scikit-learn
Apply your skills to real business cases
Use cutting-edge deep learning frameworks like Google’s TensorFlow Develop business intuition while coding and solving tasks with big data
Unleash the power of deep neural networks
Improve machine learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross-validation, testing, and how hyperparameters could improve performance
Warm your fingers as you will be eager to apply everything you have learned here to more and more real life situations
This is the best Data Science course in 2023.
Data Science A-Z™: Real-Life Data Science Exercises Included
This course will give you a comprehensive overview of the data science journey. By the end of this course, you will know:
How to clean and prepare your data for analysis
How to perform a basic visualization of your data
How to model your data
How to adjust the curve of your data
And finally, how to present your results and wow the audience
This course will give you so many hands-on exercises that the real world will look like a piece of cake when you graduate. This class offers homework exercises that are so stimulating and stimulating that you will want to cry … But you will not give up! You are going to crush it. In this course, you will develop a good understanding of the following tools:
You will learn:
Complete all stages of a complex data science project
Create basic Tableau visualizations
Explore data in Tableau
Understand how to apply the statistical chi-square test
Apply the ordinary least squares method to create linear regressions
Evaluate R-Squared for all types of models
Evaluate the adjusted R-squared for all types of models
Create a simple linear regression (SLR)
Create multiple linear regression (MLR)
Create dummy variables
Interpreting the coefficients of an MLR
Read the output of the statistical software for the models created
Use backward elimination, forward selection, and two-way elimination methods to build statistical models
Create a logistic regression
Intuitively understand a logistic regression
Use false positives and false negatives and know the difference
Reading a confusion matrix
Create a robust geodemographic segmentation model
Transform independent variables for modeling purposes
Derive new independent variables for modeling purposes
Check multicollinearity using VIF and the correlation matrix
Understanding the intuition of multicollinearity
Apply the cumulative precision profile (CAP) to evaluate models
Building the CAP curve in Excel
Use training and testing data to build robust models
Get information from the CAP curve
Understanding the odds ratio
Derive business information from the coefficients of a logistic regression
Understand what model deterioration actually looks like
Apply three levels of model maintenance to prevent model deterioration
Install and Navigate in SQL Server
Install and browse Microsoft Visual Studio Shell
Clean data and find anomalies
Use SQL Server Integration Services (SSIS) to upload data to a database
Create conditional splits in SSIS
Deal with text qualifier errors in RAW data
Create scripts in SQL
Apply SQL to Data Science Projects
Create stored procedures in SQL
Present Data Science projects to stakeholders
Python for Data Science and Machine Learning Bootcamp
Are you ready to start your journey to becoming a Data Scientist!
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!
Data Scientist has been ranked number one on Glassdoor and the average salary for a data scientist exceeds $ 120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!
This course is designed for beginners with some programming experience or for seasoned developers looking to take the leap into data science!
This comprehensive course is comparable to other Data Science bootcamps which typically cost thousands of dollars, but now you can learn all of this information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for each lecture, this is one of the most comprehensive data science and machine learning courses on Udemy!
We’ll teach you how to program with Python, create amazing data visualizations, and use machine learning with Python! Here are some of the wide variety of topics we’ll learn:
Programming with Python
NumPy with Python
Using Pandas Data Frameworks to Solve Complex Tasks
Use pandas to manage Excel files
Web scraping with python
Connect Python to SQL
Use matplotlib and seaborn for data visualization
Use plot for interactive visualizations
Machine learning with SciKit Learn, including:
K Nearest neighbors
K means grouping
Natural language processing
Neural networks and deep learning
Support vector machines
You will learn
Use Python for data science and machine learning
Use Spark for Big Data Analysis
Implement machine learning algorithms
Learn how to use NumPy for numeric data
Learn how to use Pandas for data analysis
Learn how to use Matplotlib for Python plotting
Learn how to use Seaborn for statistical graphs
Use Plotly for interactive dynamic visualizations
Using SciKit-Learn for Machine Learning Tasks
Random forest and decision trees
Natural language processing and spam filters
Support vector machines
Data Science: Deep Learning in Python
In this course, you will build SEVERAL hands-on systems using natural language processing, or NLP – the branch of machine learning and data science that deals with text and speech. This course is not in my deep learning series, so it doesn’t contain difficult math – just coding in Python. All material for this course is FREE.
After a brief discussion of what NLP is and what it can do, we’ll start creating some really useful stuff. The first thing we’re going to create is an encrypted decryption algorithm. These have applications in warfare and espionage. We will learn how to build and apply several useful NLP tools in this section, namely character level language models (using Markov’s principle) and genetic algorithms.
The second project, in which we start to use more traditional “machine learning”, is to build a spam detector. You probably receive very little spam these days, compared to the early 2000s, because of systems like these.
Next, we’ll create a sentiment analysis model in Python. It’s something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market.
We’ll go over some handy tools and techniques like the NLTK (natural language toolkit) library and Latent Semantic Analysis or LSA.
Finally, we end the course by building an article spinner. It is a very difficult problem and even the most popular products these days do not do it correctly. These lectures are designed to get you started and to give you ideas on how you could improve them yourself. Once mastered, you can use it as an SEO or search engine optimization tool. Internet marketers around the world will love you if you can do it for them!
This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It is not a question of “remembering the facts”, but of “seeing for yourself” through experimentation. It will teach you how to visualize what is going on in the model internally. If you want more than just a cursory glimpse into machine learning models, this course is for you.
You will learn
Write your own decryption algorithm using genetic algorithms and language modeling with Markov models
Write your own spam detection code in Python
Write your own sentiment analysis code in Python
Perform latent semantic analysis or latent semantic indexing in Python
Get an idea of how to write your own article spinner in Python
R Programming A-Z™: R For Data Science With Real Exercises!
Learn R programming by doing! There are a lot of R courses and conferences out there. However, R has a very steep learning curve and students are often overwhelmed. This course is different! This course is really step by step. In each new tutorial, we build on what has already been learned and take one more step forward. After each video, you learn a valuable new concept that you can apply immediately. And the best part is you learn through practical examples.
This training is full of real-life analytical challenges that you will learn to solve. We will solve some of these problems together, others will make them homework exercises.
In summary, this course has been designed for all skill levels and even if you have no programming or statistics experience, you will be successful in this course!
You will learn
Learn to program in R at a good level
Learn how to use R Studio
Learn the basics of programming
Learn how to create vectors in R
Learn how to create variables
Learn about integer, doubles, logical, character, and more types in R
Learn how to create a while () loop and a for () loop in R
Learn how to create and use matrices in R
Learn the matrix () function, learn rbind () and cbind ()
Learn how to install packages in R
Learn how to customize R Studio to suit your preferences
Understand the law of large numbers
Understanding the normal distribution
Practice working with statistical data in R
Practice working with financial data in R
Practice working with sports data in R
Best Data Science books 2023
Data Science from Scratch: First Principles with Python 2nd Edition
Data Science from Scratch First Principles with Python by Joel Grus shows you how these tools and algorithms work by implementing them from scratch. This perfect book will help you get started with the math and statistics at the heart of data science, and the hacking skills he needs to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book provides you how to find the gems in today data science. This is the Best Data Science book for beginners. You will:
Take a crash course in Python
Learn the basics of linear algebra, statistics, and probability, and how and when they are used in data science.
Collect, explore, clean, equip and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k nearest neighbors, naive bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund introduces you to R, RStudio, and tidyverse, a collection of R packages designed to work together to make data science fast, fluid, and fun. Suitable for readers with no prior programming experience, R for Data Science is designed so that you can do data science as quickly as possible.
Authors Hadley Wickham and Garrett Grolemund walk you through the steps of importing, discussing, exploring, and modeling your data and reporting the results. You’ll gain a comprehensive and holistic understanding of the data science cycle, along with the basic tools you need to handle the details. Each section of the book takes associated exercises to help you put into practice what you have learned along the way. This is one of the Best Data Science books for R. You will learn to:
Wrangle: turn your data sets into a convenient form for analysis
Program: Discover powerful R tools to solve data problems more clearly and easily
Explore – examine your data, generate hypotheses and test them quickly
Model – Provide a reduced-size summary that captures the actual ‘signals’ in your dataset
Communicate – Learn R Markdown to integrate prose, code, and results.
Python Data Science Handbook: Essential Tools for Working with Data
Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas. For many researchers, Python programming language is a first-class tool primarily due to its libraries for storing, manipulating, and previewing data. There are several resources for individual pieces of this data science stack, but only the Python Data Science Handbook allows you to get them all: IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. This is the Best Data Science book for Python.
Practicing scientists and data users familiar with reading and writing Python code will find this comprehensive desktop reference ideal for solving everyday problems: manipulating, transforming, and cleaning data; visualize different types of data; and use the data to create statistical or machine learning models. It is simply the essential reference for scientific computing in Python.
With this manual you will learn how to use:
IPython and Jupyter – Provide computing environments for data scientists using Python
NumPy – Includes ndarray for efficient storage and manipulation of dense data arrays in Python
Pandas – Includes DataFrame for efficient storage and manipulation of labeled / columnar data in Python
Matplotlib – Includes capabilities for a flexible range of data visualizations in Python
Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
An Introduction to Statistical Learning: with Applications in R
An Introduction to Statistical Learning: with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani provides accessible information in the field of statistical learning, a set of essential tools for making sense of the large and complex data sets that have emerged in fields ranging from biology and finance to marketing and research, astrophysics during the last twenty years. This book covers some of the most important modeling and prediction techniques, as well as relevant applications. Topics include linear regression, classification, resampling methods, reduction approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real life examples are used to illustrate the methods presented. Since the purpose of this manual is to facilitate the use of these statistical learning techniques by professionals in science, industry, and other fields, each chapter contains a tutorial on implementing the analysis and methods presented in R, a software platform. extremely popular open source statistician.
Two of the authors were co-authors of The Elements of Statistical Learning (Hastie, Tibshirani, and Friedman, 2nd ed. 2009), a popular reference work for researchers in statistics and machine learning. An introduction to statistical learning covers many of the same topics, but at a level accessible to a much wider audience. This book is intended for both statisticians and non-statisticians who want to use state-of-the-art statistical learning techniques to analyze their data. The text assumes only a preliminary course in linear regression and no knowledge of matrix algebra.
Deep Learning (Adaptive Computation and Machine Learning series)
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville provides a mathematical and conceptual foundation, covering relevant fundamental concepts in linear algebra, probability and information theory, numerical calculus, and machine learning. Describes deep learning techniques used by industry professionals, including deep feedback networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and studies applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, the book offers research perspectives, covering theoretical topics such as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python by Peter Bruce, Andrew Bruce and Peter Gedeck adds comprehensive Python examples, gives practical advice on applying statistical methods to data science, shows you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you are familiar with the R or Python programming languages and have some knowledge of statistics, this quick reference bridges the gap in an accessible and readable format.
With this book you will learn:
Why Exploratory Data Analysis is a Key Early Stage in Data Science
How Random Sampling Can Reduce Bias and Produce a Better Data Set, Even with Big Data
How the principles of experimental design provide definitive answers to questions
How to use regression to estimate results and detect anomalies
Key classification techniques for predicting which categories a record belongs to
Statistical Machine Learning Methods That “Learn” From Data
Unsupervised learning methods to extract meaning from unlabeled data