## 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

Mathematics

Statistics

Python

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:

SQL

SSIS

Board

Gretl

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

### Bestsellers

- O\'Reilly Media
- VanderPlas, Jake (Author)
- English (Publication Language)
- 548 Pages - 12/13/2016 (Publication Date) - O'Reilly Media (Publisher)

- Amazon Kindle Edition
- Park, Andrew (Author)
- English (Publication Language)
- 518 Pages - 01/20/2020 (Publication Date)

- Grus, Joel (Author)
- English (Publication Language)
- 406 Pages - 05/16/2019 (Publication Date) - O'Reilly Media (Publisher)

- Nussbaumer Knaflic, Cole (Author)
- English (Publication Language)
- 288 Pages - 11/02/2015 (Publication Date) - Wiley (Publisher)

- O'Reilly Media
- Provost, Foster (Author)
- English (Publication Language)
- 414 Pages - 08/27/2013 (Publication Date) - O'Reilly Media (Publisher)

- O Reilly Media
- Wickham, Hadley (Author)
- English (Publication Language)
- 520 Pages - 01/10/2017 (Publication Date) - O'Reilly Media (Publisher)

- Amazon Kindle Edition
- Brunton, Steven L. (Author)
- English (Publication Language)
- 489 Pages - 02/28/2019 (Publication Date) - Cambridge University Press (Publisher)

- Damien, Liam (Author)
- English (Publication Language)
- 154 Pages - 12/02/2019 (Publication Date) - Independently published (Publisher)

- W W Norton Company
- Wheelan, Charles (Author)
- English (Publication Language)
- 304 Pages - 01/13/2014 (Publication Date) - W. W. Norton & Company (Publisher)

- Williams, Mr Ethan (Author)
- English (Publication Language)
- 199 Pages - 08/18/2019 (Publication Date) - Independently published (Publisher)