Learn Data Science 2020 – Best Data Science courses & Best Data Science tutorials & Best Data Science books

Best Data Science Courses 2020


Best Data Science tutorials 2020

The Data Science Course 2020: Complete Data Science Bootcamp

The data scientist is one of the professions best suited to prosper this century. It is digital, programming and analytical oriented. Therefore, it is not surprising that the demand for data scientists has increased in the job market. Data science is a multidisciplinary field. It covers a wide range of topics:

Understanding of the field of data science and the type of analysis performed
Application of advanced statistical techniques in Python
Data visualization
Machine learning
Deep learning

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t learn these skills in the right order. For example, one would struggle in applying machine learning techniques before understanding the underlying mathematics. Or, it can be difficult to study regression analysis in Python before you know what a regression is.

You will learn:

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
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 2020.

Data Science A-Z™: Real-Life Data Science Exercises Included

Extremely practical … incredibly practical … incredibly real!

It’s not one of those mellow classes where everything goes as it should and your workout goes smoothly. This course takes you to the depths.

In this course, you will experience all the PAIN that a Data Scientist goes through on a daily basis. Corrupted data, anomalies, irregularities – you name it!

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 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:
Linear regression
K Nearest neighbors
K means grouping
Decision trees
Random forests
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
K-Means clustering
Logistic regression
Linear regression
Random forest and decision trees
Natural language processing and spam filters
Neural networks
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 live 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 2020




SaleBestseller No. 1
Python Data Science Handbook: Essential Tools for Working with Data
  • O\'Reilly Media
  • VanderPlas, Jake (Author)
  • English (Publication Language)
  • 548 Pages - 12/13/2016 (Publication Date) - O'Reilly Media (Publisher)
Bestseller No. 2
Data Science for Beginners: 4 Books in 1: Python Programming, Data Analysis, Machine Learning. A...
  • Amazon Kindle Edition
  • Park, Andrew (Author)
  • English (Publication Language)
  • 518 Pages - 01/20/2020 (Publication Date)
SaleBestseller No. 3
Data Science from Scratch: First Principles with Python
  • Grus, Joel (Author)
  • English (Publication Language)
  • 406 Pages - 05/16/2019 (Publication Date) - O'Reilly Media (Publisher)
SaleBestseller No. 4
Storytelling with Data: A Data Visualization Guide for Business Professionals
  • Nussbaumer Knaflic, Cole (Author)
  • English (Publication Language)
  • 288 Pages - 11/02/2015 (Publication Date) - Wiley (Publisher)
SaleBestseller No. 5
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
  • O'Reilly Media
  • Provost, Foster (Author)
  • English (Publication Language)
  • 414 Pages - 08/27/2013 (Publication Date) - O'Reilly Media (Publisher)
SaleBestseller No. 6
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
  • O Reilly Media
  • Wickham, Hadley (Author)
  • English (Publication Language)
  • 520 Pages - 01/10/2017 (Publication Date) - O'Reilly Media (Publisher)
Bestseller No. 7
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
  • Amazon Kindle Edition
  • Brunton, Steven L. (Author)
  • English (Publication Language)
  • 489 Pages - 02/28/2019 (Publication Date) - Cambridge University Press (Publisher)
Bestseller No. 8
Data Science: A Comprehensive Beginner’s Guide to Learn the Realms of Data Science
  • Damien, Liam (Author)
  • English (Publication Language)
  • 154 Pages - 12/02/2019 (Publication Date) - Independently published (Publisher)
SaleBestseller No. 9
Naked Statistics: Stripping the Dread from the Data
  • W W Norton Company
  • Wheelan, Charles (Author)
  • English (Publication Language)
  • 304 Pages - 01/13/2014 (Publication Date) - W. W. Norton & Company (Publisher)
Bestseller No. 10
PYTHON FOR DATA SCIENCE: The Ultimate Beginners’ Guide to Learning Python Data Science Step by...
  • Williams, Mr Ethan (Author)
  • English (Publication Language)
  • 199 Pages - 08/18/2019 (Publication Date) - Independently published (Publisher)


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