A Crash Course in Applied Linear Algebra. Many people think of Linear Algebra as intimidating, difficult, and great for ending conversations at parties. The truth is that Linear Algebra is extraordinarily useful, often unreasonably so. By studying one equation, y = Ax, you will add an arsenal of tools and intuition to your skillset that can be applied in any technical situation (even nonlinear ones).

**Description**

Many people think of Linear Algebra as intimidating, difficult, and great for ending conversations at parties. The truth is that Linear Algebra is extraordinarily useful, often unreasonably so. By studying one equation, y = Ax, you will add an arsenal of tools and intuition to your skillset that can be applied in any technical situation (even nonlinear ones).

**Abstract**

Chances are good that during your education you were required to take a Mathematics course on Linear Algebra, during which you probably covered topics including null spaces, reduced row echelon forms, independence, and a host of similarly abstract concepts. How much of that material do you remember, much less use on a regular basis? Are your eyes glazing over already?

It may amaze you to discover the number of things in your life, from your movie recommendations, to your GPS, to your 401k portfolio, that depend on concepts from Linear Algebra. Linear Algebra provides the theory for many core techniques in Data Science and Statistics, notably linear regression and PCA. You can't even talk about a normal distribution in more than one dimension without introducing matrices!

This talk will cover the highlights of Applied Linear Algebra. We'll discuss the impacts of familiar topics like eigenvalues and rank and introduce some likely unfamiliar topics such as low-rank approximations, quadratic forms, and definiteness. Throughout the talk, I'll bring in geometric interpretations of the math to help create a visual sense for what is happening, as well as application examples from different science and engineering disciplines. Each concept will be demonstrated using Python and Numpy, often in shockingly few lines of code.

My goal is to leave you with an intuition for matrices and linear systems that will unlock your ability to dive into deeper subjects as you continue in your own growth and exploration.

Prerequisites: a basic knowledge of vectors, matrices, and matrix multiplication

This tutorial on Python vs R vs SAS will help you understand the difference between Python, R and SAS so you know should learn that in 2020. First, you will learn the history of Python, R, and SAS. You will look at how they differ in terms of cost, speed, and ease of learning. You will get an idea about the different data handling and data visualization packages available in Python, R, and SAS. You will understand their usage in the industries, customer, and community support. Finally, you will see the job trends, their popularity, and their preference in the industry. Now, let's get started with learning Python vs R vs SAS.

This tutorial on Python vs R vs SAS will help you understand the fundamental difference between the three most popularly used programming languages in the field of analytics. First, you will learn the history of Python, R, and SAS and see how it has evolved over the years. You will look at how they differ in terms of cost, speed, and ease of learning. You will get an idea about the different data handling and data visualization packages available in Python, R, and SAS. You will understand their usage in the industries, customer, and community support. Finally, you will see the job trends, their popularity, and their preference in the industry. Now, let's get started with learning Python vs R vs SAS.

If you are thinking to learn a new programming language then also Python is a good choice, particularly if you are looking to move towards a lucrative career path of Data Science and Machine learning which has lots of opportunities. In this article, I am going to share some of the best online courses to learn Python in 2020...

Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options.

Python is relatively simple, so it's easy to learn since it requires a unique syntax that focuses on readability. Developers can read and translate Python code much easier than other languages. In turn, this reduces the cost of program maintenance and development because it allows teams to work collaboratively without significant language and experience barriers.

Additionally, Python supports the use of modules and packages, which means that programs can be designed in a modular style and code can be reused across a variety of projects. Once you've developed a module or package you need, it can be scaled for use in other projects, and it's easy to import or export these modules.

In recent years, Python has also become a default language for Data Science and Machine learning Projects and that's another reason why many experienced programmers are learning Python .

If you are thinking to learn a new programming language then also Python is a good choice, particularly if you are looking to move towards a lucrative career path of Data Science and Machine learning which has lots of opportunities. In this article, you will find free online courses in python programming, but not only will you find one, but you will also find 5 more courses on Python! I am going to share some of the best online courses to learn Python in 2020

They are high quality courses with more than 4 star rating (from 0 to 5 stars), that means if you are starting your career with the python programming language, these are the best courses that will take you step-by-step , to start and learn from scratch the fundamentals about this language that so professional and useful has been in recent years.

This is one of the most popular course to learn Python on Udemy and more than 250,000 students have enrolled in it. That speaks volumes for the quality of the course.

This is a comprehensive but straight-forward course to learn the Python programming language on Udemy! and useful for all levels of programmers.

In this course, you will learn Python 3 in a practical manner. You will start by downloading and setting up Python on your machine and then slowly move on to different topics.

It's also a practical course where an instructor will show you live coding and explain what he does.

The course also comes with quizzes, notes and homework assignments as well as 3 major projects to create a Python project portfolio! which complements your learning.

In early 2016, Python passed Java as the #1 beginners language in the world. Why? It's because it's simple enough for beginners yet advanced enough for the pros.

You can not only write simple scripts to automate stuff but also create a complex program to handle trades. You can even use Python for it for IOT, Web Development, Big Data, Data Science, Machine learning and more.

This is a very practical course and useful not just for beginners but also for programmers who know other programming languages e.g. Java, C++ and want to learn Python.

In 30 days this course will teach you to write complex Python applications to scrape Data from nearly any website and Build your own Python applications for all types of automation. It's perfect for busy developers who learn by doing serious stuff.

This online Python course is taught by Ardit Sulce ,This Python course has everything you need to know to start coding in Python and not even that, by the end of the course you will know how to build complete programs and also build graphical user interfaces for your programs so you can impress your employer or your friends. This course will guide you step by step starting from the basics and always assuming you don't have previous programming experience or a computer science degree. In fact, most people who learn Python come from a vast variety of careers.

This course has all you need to get you started. After you take it you will be ready to go to the next level of specializing in any of the Python paths such as data science or web development. Python is one of the most needed skills nowadays. Sign up today!

This is another fantastic course to learn Python on Udemy. This course is taught by Tim Buchalka,I am a big fan of Tim Buchalka and have attended a couple of his courses.

This course is aimed at complete beginners who have never programmed before, as well as existing programmers who want to increase their career options by learning Python.

The fact is, Python is one of the most popular programming languages in the world – Huge companies like Google use it in mission critical applications like Google Search.

And Python is the number one language choice for machine learning, data science and artificial intelligence. To get those high paying jobs you need an expert knowledge of Python, and that’s what you will get from this course.

By the end of the course you’ll be able to apply in confidence for Python programming jobs. And yes, this applies even if you have never programmed before. With the right skills which you will learn in this course, you can become employable and valuable in the eyes of future employers.

This course was developed by Ziyad Yehia , a renowned instructor on Udemy. Currently, This course has nearly 78,000 students and excellent star ratings.

This is a project-based course and you will build 11 Projects int this Python Course.

If you enjoy hands-on learning while working on the project rather than learning individual concept then this course is for you.

This is a comprehensive, in-depth and meticulously prepared course and teaches you everything you need to know to program in Python. It delivers what is promised in the title, A-Z, it's all here!

That's all about the best courses to learn Python in depth. you can begin with these courses, don't need to buy all of them, just choose the one where you can connect with instructor.

These courses will give you a solid foundation and confidence to use Python in your project.

==========================================================

Thanks for reading

Data science is linked to numerous other modern buzzwords such as big data and machine learning, but data science itself is built from numerous domains, where you can get your expertise. These domains include the following: * Statistics *...

Data science is linked to numerous other modern buzzwords such as big data and machine learning, but data science itself is built from numerous domains, where you can get your expertise. These domains include the following:

- Statistics
- Visualization
- Data mining
- Machine learning
- Pattern recognition
- Data platform operations
- Artificial intelligence
- Programming

Math and statistics

Statistics and other math skills are essential in several phases of the data science project. Even in the beginning of data exploration, you'll be dividing the features of your data observations into categories: - Categorical
- Numeric:
- Discrete
- Continuous

Continuous values have an infinite number of possible values and use real numbers for the representation. In a nutshell, discrete variables are like points plotted on a chart, and a continuous variable can be plotted as a line.

Another classification of the data is the measurement-level point of view. We can split data into two primary categories:

Qualitative: - Nominal
- Ordinal
- Quantitative:
- Interval
- Ratio

Nominal variables can't be ordered and only describe an attribute. An example would be the color of a product; this describes how the product looks, but you can't put any ordering scheme on the color saying that red is bigger than green, and so on. Ordinal variables describe the feature with a categorical value and provide an ordering system; for example Education—elementary, high school, university degree, and so on.

**Visualizing the types of data**

Visualizing and communicating data is incredibly important, especially with young companies that are making data-driven decisions for the first time, or companies where data scientists are viewed as people who help others make data-driven decisions. When it comes to communicating, this means describing your findings, or the way techniques work to audiences, both technical and non-technical. Different types of data have different ways of representation. When we talk about the categorical values, the ideal representation visuals would be these:

- Bar charts
- Pie charts
- Pareto diagrams

Frequency distribution tables

A bar chart would visually represent the values stored in the frequency distribution tables. Each bar would represent one categorical value. A bar chart is also a baseline for a Pareto diagram, which includes the relative and cumulative frequency for the categorical values:

Bar chart representing the relative and cumulative frequency for the categorical values

If we'll add the cumulative frequency to the bar chart, we will have a Pareto diagram of the same data:

Pareto diagram representing the relative and cumulative frequency for the categorical values

Another very useful type of visualization for categorical data is the pie chart. Pie charts display the percentage of the total for each categorical value. In statistics, this is called the relative frequency. The relative frequency is the percentage of the total frequency of each category. This type of visual is commonly used for market-share

*Statistics *

A good understanding of statistics is vital for a data scientist. You should be familiar with statistical tests, distributions, maximum likelihood estimators, and so on. This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren't) a valid approach. Statistics is important for all types of companies, especially data-driven companies where stakeholders depend on your help to make decisions and design and evaluate experiments.

**Machine learning**

A very important part of data science is machine learning. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.

**

**Choosing the right algorithm****

When choosing the algorithm for machine learning, you have to consider numerous factors to properly choose the right algorithm for the task. It should not only be based on the predicted output: category, value, cluster, and so on, but also on numerous other factors, such as these:

- Training time
- Size of data and number of features you're processing
- Accuracy
- Linearity
- Number of possible parameters

Training time can range from minutes to hours, depending not only on the algorithm but also on the number of features entering the model and the total amount of data that is being processed. However, a proper choice of algorithm can make the training time much shorter compared to the other. In general, regression models will reach the fastest training times, whereas neural network models will be on the other side of the training time length spectrum. Remember that developing a machine-learning model is iterative work. You will usually try several models and compare possible metrics. Based on the metrics captured, you'll fine-tune the models and run comparisons again on selected candidates and choose one model for operations. Even with more experience, you might not choose the right algorithm for your model at first, and you might be surprised that other algorithms can outperform the first chosen candidate, as shown:

**Big data**

Big data is another modern buzzword that you can find around the data management and analytics platforms. The big does not have to mean that the data volume is extremely large, although it usually is. learn more Data science online course

**SQL Server and big data**

Let's face reality. SQL Server is not a big-data system. However, there's a feature on the SQL Server that allows us to interact with other big-data systems, which are deployed in the enterprise. This is huge!

This allows us to use the traditional relational data on the SQL Server and combine it with the results from the big-data systems directly or even run the queries towards the big-data systems from the SQL Server. The answer to this problem is a technology called PolyBase: