DEV Community: sinxloud

I ranked the Best TensorFlow Courses on the internet, based on your reviews

sinxloud — Wed, 03 Apr 2019 23:31:07 +0000

I know the options out there, and what skills are needed for learners.

Whether you’re an expert or a beginner, TensorFlow makes it easy develop and train ML models.

As a beginner, you may be looking for a way to get a solid understanding of TensorFlow that’s not only rigorous and practical, but also concise and fast. So without further ado, let's get started.

This piece is originally published and will be updated here >> Best TensorFlow Courses.

Also, Mathematics and Algorithms are very important for TensorFlow. So if you need a refresher or want to learn the basics, I've got you covered in this piece about the Mathematics for Machine Learning and Data Science and Algorithms for Data Science and Machine Learning.

5 Best TensorFlow Courses and Specialization with Certification

Below, I’ve curated a selection of the best TensorFlow for beginners and experts who aspire to expand their minds.

1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning

In this course, you will receive a broad introduction to TensforFlow learning for Artificial Intellegence, Machine Learning, and Deep Learning.

Is it right for you?

Upon the completion of this course, you will have a deeper understanding of how neural networks work and will equipped to build and apply scalable models to solve real-world problems with TensorFlow.

GO TO COURSE

2. TensorFlow: Getting Started

In this, course, you’ll learn use TensorFlow and create a range of machine learning and deep learning models, from simple linear regression to complex deep neural networks.

Is it right for you?

Upon the successful completion of this course, you will be equipped to take an approach to effective problem solving and interacting with the results of your work with your peers.

GO TO COURSE

3. Intro to TensorFlow

In this course, you’ll be provided with a TensorFlow model to scale out the training of that model and learn the key concepts for offering high-performance predictions using Cloud Machine Learning Engine.

Is it right for you?

Upon the completion of this course, you will be able well equipped to create machine learning models and Build Neural Network in TensorFlow.

GO TO COURSE

4. Creative Applications of Deep Learning with TensorFlow

5. AI & Deep Learning with TensorFlow

Thanks for making it to the end :-)

If you liked this article, I’ve got a few very practical reads for you. One about Best Machine Learning Courses on the Internet and one about the Best Deep Learning Courses.

I’ve also got this data science newsletter that you might be into. I send a tiny email once every fortnight (if that) with some useful and cool stuff I’ve found/made. Don’t worry, I hate spam as much as you.

Learn Machine Learning with Python and Maths: DataCamp, Stanford and Imperial College

sinxloud — Mon, 11 Mar 2019 05:24:54 +0000

Machine learning is a set of statistical techniques and algorithms designed to find and use structure and patterns in data to make interesting predictions or provide cool insights.

As a beginner, you may be looking for a way to get a solid understanding of machine learning that’s not only rigorous and practical, but also concise and fast. So without further ado, let’s start with what you need to learn.

Machine learning is all about algorithms, So if you need to learn the basics or advanced concepts, I've got you covered in this piece about Learn Algorithms for data science and machine learning.

This post was originally published here

Learn Machine Learning with Python

First off you’ll need to learn Python. Python is widely used in Data Science, IOT, Machine Learning, Web Applications or Game Development.

Unlike other Programming languages, Python’s syntax is human readable and concise. So as a beginner, this will allow you to grasp the basics quickly, with less mental strain, and you can level up to advanced Machine Learning topics faster.

There are many wonderful free online resources to get started on machine learning but if you’re having trouble getting started, I would suggest taking any Python Course and in my personal opinion Introductory course with emphasis on Data Science is much better.

These 2 courses from DataCamp are designed for beginner and intermediate learners, and they offer tremendous support to help you intellectually bootstrap your career in the field of Information Sciences.

1. Intro Python for Data Science … – Beginners

Introduction to Python for Data Science course will provide an overview of the Python ecosystem for Data Science and get you to write your first code in Python.

Most importantly you will receive an in-depth introduction to Pandas and Numpy, powerful and efficient libraries for both Machine Learning and Data Science.

Is it right for you?

DataCamp has several great introductory courses, all taught via interactive exercises. This course is suitable for any motivated individual with burning intellect and insatiable desire learn Data Science or Machine Learning.

GO TO COURSE

Once you are able to understand other people’s Python code, i strongly recommend taking any Intermediate Python course. DataCamp also offers Intermediate Python for Data Science which will introduce you to advanced topics, helpful to gain functional knowledge for Machine Learning.

2. Machine Learning with Python … – Intermediate

In this Skill track, you’ll be introduced to the world of Machine Learning and learn the fundamental concepts for Machine Learning with Python. This learning track is divided into 5 courses;

Supervised Learning with scikit-learn
Unsupervised Learning with scikit-learn
Linear Classifiers in Python
Machine Learning with the Experts: School Budgets
Deep Learning in Python

Don’t get intimidated with new concepts presented in this Machine Learning Skill track. It will be hard to finish but not impossible. Some concepts will be foreign or straight up weird, just take it again if you have to.

Is it right for you?

In this Machine Learning Skill Track, you can practice with real-life examples of Machine learning and understand the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition and artificial intelligence by using Keras, the latest version of a cutting edge library for deep learning in Python.

GO TO SKILL TRACK

If you are interested in finding about the quick Machine Learning Courses specifically, I’ve got you covered with the this article about the Best Courses in Machine Learning on the internet.

Always Stay on Top of Learning !!!

Machine learning is difficult for beginners as well as libraries for Machine Learning in python are difficult to understand.

Over the past couple month, I have been collecting latest Python Cheat Sheets for Data Science and Machine Learning from different sources.

7+ Python Cheat Sheets for Beginners and Experts
Concise Cheat Sheets of Machine Learning with Python (and Maths)

It’s possible to lose motivation while learning, so you can give any of these Python Podcasts a listen to stay current.

If you ever feel stuck, never hesitate to reach out to the fellow learners on Reddit and Stackoverflow, who are one click away to help and support you.

Also, be sure to partake in Kaggle Learning/ Challenges for Machine Learning, you will be able to stay on top of learning and grow faster.

Brush up your Math Skills !!!

Mathematics is the bedrock of any contemporary discipline of science. However, You don’t need a degree in Mathematics to succeed in Machine Learning. Yet, if you do have a math background, you’ll definitely get ahead.

You will need a structured learning approach with achievable goals to upgrade your skills required for Machine Learning. I would strongly suggest to brush up your math skills from the day one of learning Python, irrespective of your background in Mathematics.

In my personal experience, devoting at-least 30-45 minutes everyday will bear much fruit and you’ll understand and learn advanced Python topics for Maths and Statistics faster.

In coming days, I will to publish another post about the level of Mathematics you need for Machine Learning. Meanwhile, Let’s briefly dive into this last section and use the infographic below to learn about the minimum level of mathematics needed to be a Machine Learning Scientist/ Engineer.

Here are 2 best online classes from World Class faculty to help you master the vocabulary, notation, concepts, and algebra rules that all aspiring Machine Learning Scientists/ Engineers must know before moving on to a more advanced material.

1. Mathematics for Machine Learning Specialization - Highly Recommended

This Specialization is offered by Imperial College of London and is comprised of 3 courses to equip you with intuitive understanding of Mathematics and will help you apply key concepts to machine learning.

Mathematics for Machine Learning: Linear Algebra
Mathematics for Machine Learning: Multivariate Calculus
Mathematics for Machine Learning: PCA

Is it right for you?

Upon the completion of this Specialization, you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

GO TO COURSE

2. Introduction to Mathematical Thinking - High Value

This course will help you to think like a Professional mathematician to solve real problems that can arise from the everyday world, or from science, or from within mathematics itself.

This course is not about doing Mathematics but about how you can think the way mathematicians do.

Is it right for you?

You will richly benefit from this course and inculcate a powerful cognitive process for Machine Learning or Data Science.

GO TO COURSE

Thanks for Making it to the end :-)

Machine Learning is intimidating and learning without efforts is exceedingly impossible. So if you'll follow the order and keep practicing Python every day, then it will become easy after while for you to tighten your grips on it.

If you liked this article, I’ve got a practical reads for you. One about Learning Probability and Statistics for Data Science and one about Best Machine Learning Courses/ Specialization.

Cheers !!!

Ignorance of Statistics in Data Science will only Inspire dread.

sinxloud — Wed, 06 Feb 2019 14:21:34 +0000

This article was originally published here.

I want to start this article by highlighting the issues related to data science that I think as a statistician are important for its success as an emerging new field and I'm going to motivate this piece initially at least with a story about cats which comes from an article that appeared in The New York Times in August of 1989.

You might consider it to be an early example of data journalism, So without further ado let’s start with what the piece — On Landing Like a Cat: It Is a Fact, suggested.

Every year scores of cats falls from open windows in New York City, from June the 4th through November the 4th of 1984 for instance 132 such victims were admitted to the Animal Medical Center on 62nd Street in Manhattan.

Most of the cats landed on concrete most survived. It goes on to recite some statistics from the data set like 21 of 22 falling 7 or more stories actually survived, 2 of them fell together, 40% fell at night. The height of the buildings ranged from 2 to 32 stories with an average of 5.5.

And the weird thing was that there seemed to be a positive relationship between the length of the fall and the probability of survival.

May be the Cat was able to turn or relax his body or even kind of a flying squirrel thing where they're like flaps of skin underneath its arms. 😢🤣

Well, this is exceeding strange because you might ask what's going on here. Some of them surmised, maybe it's the 9lives hypothesis or maybe they interviewed some experts who'd say that the cat had some time to fall.

The giveaway is actually in the first paragraph of the article that I am highlighting out to you as it said on three two such victims were admitted to the Animal Medical Center all right dead cats are not transported to Animal Hospitals.

The whole story had no basis and the reason was it was based on this sort of found data. It's sort of a tale of caution in the interpretation of found data and the reason is because this is what statisticians might call a convenient sample.

It's expedient as the data is not collected via any sort of sampling scheme but its available. It is being used to answer questions that weren't really intended by the collectors of the data.

The main problem with this data is missingness. Missing data arises everywhere with experiments, especially with observational studies in some cases.

In this article the missingness was highly correlated with the outcome of interest which was whether or not the cat survived.

This data is what we'd call a non representative sample and by representative we mean that it's related. The data set in hand is the same as the underlying target population of interest these are statistical issues right the inference from the Cats data was unreliable because of ignorance of statistics essentially.

Learn to cultivate Statistical Thinking Skills

With the power of Python-based tools, you will rapidly get up to speed and begin thinking statistically in the Statistical Thinking Courses offered by DataCamp.

You will learn faster through DataCamp's immediate and personalized feedback on every exercise.

Thanks for making it to the end 🙂

If you liked this article, I've got a practical reads for you one about the Skills in Python for every Data Scientist and one about How to learn Data Science with Python.

Like to learn

Check sinxLoud.com where I post all about the latest and greatest AI, Machine Learning, and Data Science!

You can also read the latest and short stories from sinxLoud on our medium handle!

Deep Learning Courses from World-Class Educators

sinxloud — Fri, 11 Jan 2019 12:36:13 +0000

Deep Learning involves techniques that can’t be understood without an effective teacher.

First off, you need to have a solid understanding of advanced mathematical concepts and understanding of Algorithms for deep learning. So, if you need a refresher, I’ve got you covered in Maths for Machine Learning and Learn Algorithms for Machine Learning

This post is originally published and updated here ➡ Deep Learning Courses

Best Deep Learning from World-Class Educators [2019 Updated]

Below, I’ve curated a selection of the best available courses in Deep Learning for beginners and experts who aspire to expand their minds.

1. Deep Learning with Python - DataCamp

If you want to learn Deep Learning in Python, this course will introduce you to the fundamental concepts and terminologies used in deep learning, and understand why deep learning techniques are powerful these days.

This course will provide basics of deep learing and neural networks and also teach you a deep learning framework called Keras using which in just a few lines of code you can train very deep neural networks.

Is it right for you?

This course is suitable for beginners in Deep Learning with good knowledge of Python programming and some experience in Machine Learning.

By the end of this course you will also build a simple neural network all by yourself and generate predictions using Python’s numpy library.

GO TO COURSE

2. An Introduction to Practical Deep Learning - Intel AI Academy

An Introduction to Practical Deep Learning is taught by AI Principal Engineers at Intel.

This course is very dense and informative that aims to help learners to grasp the basics of Deep Learning.

Is it right for you?

This course is primarily aimed at learner with some background in programming and understanding of basic calculus, but are new to the field deep learning.

Go To Course

3. Deep Learning Specialization - Andrew NG

Deep Learning CourseThis is one of the best and highly recommended Deep Learning Specialization, comprised of five courses taught be the AI Pioneer - Andrew Ng, Co-Founder of Coursera, DeepLearning AI and Adjunct Professor at Stanford University.

This specialization will help to learn the foundations of Deep Learning, understand techniques to build effective neural networks, and learn how to manage successful machine learning projects.

Is it right for you?

This specialization assumes that a learner has intermediate skills in Python and basic knowledge of statistics to understand and work on case studies from healthcare, autonomous driving, music generation, sign language reading, and natural language processing.

Go To Course

4. Deep Learning with Keras - PluralSight

This course on Deep Learning with Keras is Created by Jerry Kurata, Technology Expert and best selling author of Machine Learning and Deep Learning Courses on Pluralsight and Coursera.

This course will get you up to speed with both the theory and practice of using Keras to create powerful deep neural networks.

And equip you with the various methods of using Keras for interconnecting various layers of neurons quickly and easily to form the structure of your deep neural networks.

Is it right for you?

This course is suitable for learners with a good knowledge of Python to work with Keras and will help gain the skills required to effectively create deep neural networks through the course’s combination of lecture and hands-on coding.

Go To Course

Continue reading this article "Deep Learning Courses" from the World-Class Educators.

Before You Go

You may also be interested in finding the Best TensorFlow Courses for Machine Learning and Deep Learning.

If you know of any courses that I may have missed, please let me know in the comments below!

If you found this post helpful enough, I have got two practical reads for you. One about A Beginner's Guide to Machine Learning with Python and one about the Best Courses in Machine Learning Courses on the Internet.

happy learning!!!

Guarantee Yourself a Data Science Job with this Mentor-Led BootCamp

sinxloud — Sat, 05 Jan 2019 22:35:36 +0000

The data scientist career path is on the rise and SpringBoard’s Data Science career track will help you learn all that you need to start a new career in the data science field.

If your understanding about Data Science is a big question mark, i've got a one practical read for you about How to Learn Data Science with Python (Statistics and Maths).

Data Science Career Track with Job Guarantee

Get a job, or your money back, through Springboard’s mentor-guided online bootcamp.

With a job guarantee, if you don’t land a job within six months of graduating, your tuition will be refunded.

Springboard’s Data Science Career Track graduates who accepted new jobs reported an average annual salary increase of $23,000.

And through the deferred tuition program, students can enrol for a small deposit and finish paying after starting a job.

Is it right for you?

Data Science Career Track is designed for people with some experience in statistics and programming.

This is a perfect Data Science career track for software developers, analysts, and finance professionals.

All professional and academic backgrounds are welcome, however.

Halfway through the track, you will have the option to choose a specialization that aligns with your career goals: advanced machine learning, natural language processing, or deep learning.

Throughout the program and into your job search, you’ll have support from your one-on-one mentor (a working data scientist), as well as Springboard’s career services team.

What you will learn in first?

-Overview of the Python data science stackCollect and transform data using APIs, Pandas, SQL, and NoSQL
-Use machine learning to make predictions
-Scale your analysis using Spark and large data sets
-Create real-world business impact through effective data storytelling
-Show your work to future employers through your GitHub portfolio

Go To Course

Before You Go

We hope you will learn all the important skills and also get the personalized guidance you need to land the job you want.

You may also be interested in reading 5 Must-have skills in Python for every Data Scientist or may consider starting with one the Best (and Affordable…) Data Science Courses to learn and upgrade your skills.

If you liked this article, please do share it with your friends and also sign up for our newsletter to keep up with similar content once every fortnight.

Wishing you the best with your career!

Cheers 🍻

Originally published at sinxloud.com on December 30, 2018.

5 Must-have skills in Python for every Data Scientist

sinxloud — Sun, 30 Dec 2018 16:06:46 +0000

Most Data Scientists and Machine Learning Engineers prefer using Python for Data Science and developing artificial intelligence and machine learning apps.

This post was originally published here.

Here are 5 Must-have skills in Python for every Data Scientist

If you are a data scientist or want to learn data science with Python track, here are five critical skills you need to develop as a beginner.

And to help you develop these skills, we have linked some of the best available resources to help you become a creative data practitioner.

1. Data Scraping

Gathering data from websites is one of the most logical and easily accessible sources of data.

You'll need to learn how to use Python packages like urllib2, requests, simplejson, regular expression operations, selenium and beautiful soup to make handling web requests and data formats easier.

2. SQL

You need to learn how to turn raw data into actionable insights and once you have a large amount of structured data, you will want to store and process it.

To be an effective data scientist or an engineer, you should be able to wrangle and extract data from relational databases using SQL.

3. Data Frames

SQL is important in data science and great for handling large amounts of data however it lacks Machine Learning and Data Visualization.

So you will have to go through the painful process of enabling Machine Learning services in SQL Server or use MapReduce to get data to a manageable size and then process it using Pandas.

4. Machine Learning

A lot of data science can be done with select, join, and group by (or equivalently, map and reduce) but sometimes you need to do some non-trivial machine-learning.

Before you jump into fancier algorithms, try out simpler algorithms like Naive Bayes and regularized, linear regression. In Python, these are implemented in scikit-learn.

5. Data Visualization

Data science is about communicating your findings, and data visualization is incredibly valuable part of that.

Python offers Matlab-like plotting via matplotlib, which is functional, even if it is ascetically lacking and if you are really serious about dynamic visualizations, try d3.

These skills are taught excellently in Data Scientist with Python Career Track offered by DataCamp.

DataCamp offers over 100+ courses by expert instructors on topics such as Importing Data, Data Visualization, SQL, Machine Learning, Statistical Thinking and more.

You will learn faster through DataCamp's immediate and personalized feedback on every exercise.

Before You Go

You may also be interested in reading How to Learn Data Science with Python or may want to start with one the Best (and Affordable...) Data Science Courses to learn and upgrade your skills.

If you want to learn Probability and Statistics for Data Science, i've got you covered in this article about the best online classes.

Wishing you the best with your career!

If you want to be an above average Web Security Researcher, ask questions

sinxloud — Fri, 23 Nov 2018 06:16:53 +0000

As you start practicing security, you have to make a difficult adjustment in your learning process by asking questions.

What is this Bug?
How does this bug work?
Why does this bug work?

Questioning is pivotal to success in web security and you need to ask the WHY question almost every time (why did this work and why did this not work) and answer them yourself.

If you find a bug; How did I find this bug? Why does this bug exist?

If you don’t find a bug; Why didn’t I find this bug? What am I doing wrong?

You have to know why you couldn’t find that bug. Maybe you were able to find that bug in your practice on the VM but you are not able to find the bug in the actual website.

You have to know Why. Possibly the website you are testing has implemented a good security mechanism. So you have to learn what they doing right. Once you know more about doing things right, you will automatically know how to do things wrong. And next time when you go to test another website you will know what these guys are missing.

The common problem in bug bounty hunters is that once you find a bug, you get over excited. You want to get paid for it quickly, your focus redirects to money and you stop logical thinking at $200 bounty. And as a result, sometimes your bug will have much more impact. Only if you think more and questions, you can make it to $2000 or even $20000 bounty.

Remember, as a web security researcher, you take regular everyday things and think of malicious ways of using those things. So, asking questions will keep you from looking at the website from a bird’s eye view and find low hanging fruit i.e, security vulnerabilities without any serious impact.

You need to know everything that's happening in the web development world.

If you want to be an above average web security researcher, you have to take a closer and deeper look at how the different technologies used by the website come together.

So Asking questions will help you do so

This post was originally published here.

5+ Best Data Science Courses, Ranked by Your Reviews

sinxloud — Mon, 05 Nov 2018 14:07:28 +0000

This post was originally published here.

Fortunately, Data Science popularity has yielded hundreds of articles, videos, webinars, courses, and books catering to beginners and experts who aspire to expand their minds.

If you are new to Data Science, you'll find the recommendations in this article tremendously helpful.

5+ Best Data Science Courses with a Specialization Certificate

Here's curated a list of the best (and Affordable!!!) data science courses with professional specialization certification to upgrade your skills with applicability to real jobs that employers value.

1. Introduction to Data Science in Python (Highly Recommended)

This course is created by the University of Michigan and published on Coursera and has been rated 4.5 of 5, of 7,815 ratings. This course is perfect for self-starters to learn the basics of the Python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating CSV files, and the numpy library.

What you will learn? | Check here

2. Data Science Specialization (Highly Recommended)

Data Science Specialization is a nine-course introduction to data science, created and taught by leading professors at John Hopkins University. This specialization will equip you with the functional knowledge of R Programming, key statistical concepts, reproducible research for scientific claims, and Machine Learning.

What you will learn? | Check here

3. Data Scientist with Python Track

Data Scientist with Python Track is comprised of 22 Courses offered by DataCamp, this learning track covers every major topic to equip students to create data-driven products which can be used to solve real-world problems.

What you will learn? | Check here

4. IBM Data Science Professional Certificate

IBM Data Science Professional Certificate is a specialization comprised of 9 courses, offered on Coursera. This specialization will introduce you to open source tools for performing data science tasks, statistical libraries, cloud services, datasets, Machine Learning algorithms, assignments and projects that will provide you with practical and latest job-ready skills.

What you will learn? | Check here

5. Applied Data Science with Python Specialization

Applied Data Science with Python is a skills-based specialization offered by the University of Michigan. The 5 courses in that introduce learners to data science through Python and equip with functional knowledge of using popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and network to gain insight into their data.

What you will learn? | Check here

6. Become a Data Scientist

Become a data scientist learning path by Dataquest offers a practical approach to introduce learners to data science using interactive coding challenges to make inferences and predictions from data. This learning path will equip you to think and solve problems like a data scientist.

What you will learn? | Check here

7. Data Science at Scale Specialization

Data Science at Scale Specialization covers 4 intermediate courses in data science to help learners gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn the Data Visualization techniques and explore legal and ethical issues that arise in working with big data.

What you will learn? | Check here

Happy Learning

We hope the resources listed in this article puts you in the fast lane and if you liked this article enough, i've got one practical read for you about the Probability and Statistics for Data Science.

You may also be interested in starting with one of the Best Courses in Machine Learning to use as a springboard for a rewarding and lucrative career in the field of Data Science and Machine Learning.

Cheers🍻

Originally published at sinxloud.com

Concise Cheat Sheets of Machine Learning with Python (and Maths)

sinxloud — Mon, 22 Oct 2018 13:29:28 +0000

This post was originally published here.

If you are just getting started with Machine Learning or Data Science, you’ll richly benefit from resources compiled from our recent publications;

Best Machine Learning Cheat Sheets ...

Here's a curated a list of Machine Learning Cheat Sheets and most commonly used MLear Libraries in Python.

1. Scikit-Learn Cheat Sheet: Python Machine Learning

2. Python Cheat Sheet for Scikit-learn

3. Keras Cheat Sheet: Neural Networks in Python

4. Python SciPy Cheat Sheet

5. Theano Cheat Sheet

We will update this collection of Machine Learning Cheat Sheets once every fortnight.

You may also be interested in reading Become a Machine Learning Engineer in 6 months to use as a springboard for a rewarding and lucrative career in the field of Machine Learning with a Job Guarantee.

Cheers

How (and Where) to Learn R for Data Science

sinxloud — Sun, 21 Oct 2018 10:15:15 +0000

This post is part of an article that was originally published here.

Learn R For Data Science

In this post, you will get an overview for the best courses to learn R for Data science which are available today.

Well Here's a Curriculum Guideline 🍠

Here's detailed list of 5 Critical Skills in Python/R for Data Science

And if you want to learn Python for Data Science, check this guide

1. Data Science Specialization 👍

Data Science Specialization is a nine-course introduction to data science, created by John Hopkins University and taught by leading professors. You will learn how to get your data into R, clean and transform data into the most useful structure. You also will gain functional knowledge of statistical concepts, reproducible research for scientific claims, and Machine Learning.

What You Will Learn? | Go To Course

2. Data Science with R ❤️

This course is created by Matthew Ranze who is a Data Science Consultant, Author and has 17 years of professional experience working with tech startups to Fortune 500 companies. The topics covered in this course will help you to understand the practice of data science, R programming language, and how to create Data Visualization and Statistical models.

What You Will Learn? | Go To Course

3. Statistics with R Specialization 😎

Statistics with R Specialization is offered by Duke University on Coursera and will help you to master statistics with R. You will learn key statistical concepts and techniques for performing exploratory data analysis in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of correlation, regression and statistical inference.

What You Will Learn? | Go To Course.

Happy Learning

You may also be interested in finding Best (and Free !!!) Machine Learning Resources or reading So You want to Learn Data Science With Python Track.

Cheers !!!

7+ Best Data Visualization Courses on the Internet [2019 Updated]

sinxloud — Thu, 11 Oct 2018 13:37:45 +0000

In this post, I will give a rundown of some of the Best Data Visualization Courses that will help you develop industry ready Data Visualization Skills.

All these courses are available online, some are available at minimal costs and will help you learn and excel at Data Visualization using Python, Tableau, d3.js, and other programs or tools. These courses are suitable for beginners and intermediate learners.

Best Data Visualization Courses and Specialization 🤞[2018]

This post is part of an article originally published here >> Data Visualization Courses.

1. Data Visualization with Tableau Specialization (Coursera)

This course is created by the University of California - Davis. In this course, Data Visualization Course, you will learn to visualize business data with Tableau by understanding what data visualization is, and how you can use it to better see and understand data.

You'll examine the fundamental concepts of data visualization and explore the Tableau interface, identifying and applying the various tools Tableau has to offer.

By the end of the course, you will be able to prepare and import data into Tableau and explain the relationship between data analytics and data visualization.

What You'll Learn?

Fundamentals of Visualization with Tableau
Essential Design Principles for Tableau
Visual Analytics with Tableau
Creating Dashboards and Storytelling with Tableau
Data Visualization with Tableau Project

You can Sign up here 🔗

2. Data Visualization: A Practical Approach for Absolute Beginners (edX)

This course is created by Microsoft and published on edX. In this course, Data Visualization for Beginners, you will understand Practical Data Visualization techniques, tips, and tricks for the aspiring analyst and people who want to start from the beginning in any field needing this critical 21st-century skill.

You can learn all the four modules in this course for free and by the end of the program, you will become a practitioner of the art.

What You'll Learn?

Visual Literacy: Build the most-used and simplest data visualizations and chart types
Visual Analysis: Understand how interaction, design, and the science of visualization enhances basic data visualization artifacts
Visualizations of Business: Create basic reports and dashboards for applications in work and life
Visualization of Tomorrow: Learn how to avoid common pitfalls, and make the visualization that works.

You can Sign up here 🔗

3. Data Visualization Using Tableau Public (Pluralsight)

This course is created by Robert Spent who worked with Microsoft in Team Foundation Server on the Administration and Version Control team for nearly 10 Years.

This course has 4.8-star ratings on Pluralsight and will help you get on grips with building Data Visualization skills using Tableau Public.

What You'll Learn?

Creating Your First Dashboard
Charting Consumer Spending
The Cost of Winning

You can Sign up here 🔗

4. Data Visualization with Python (Coursera)

This course is created by IBM and has 4.6-star user rating on Coursera. In this course, Data Visualization with Python you will learn how to take the data at first glance that has little meaning and present that data in a form that simply makes sense to people.

You will be learning various techniques already developed for presenting data visually and using several data visualization libraries in Python like Matplotlib, Seaborn, and Folium.

What You'll Learn?

Introduction to Data Visualization Tools
Best and Specialized Visualization Tools
Advanced Visualizations and Geospatial Data

You can Sign up here 🔗

5. Introduction to Data Visualization with Python (DataCamp)

This course is created by DataCamp instructors Dhavide Aruliah who is Data Scientist and Applied Mathematician, and Bryan Van de Ven who is Software Engineer at Anaconda and Developer of Bokeh.

The topics covered in this course include customizing graphics, plotting two-dimensional arrays (e.g., pseudocolor plots, contour plots, images, etc.), statistical graphics (e.g., visualizing distributions & regressions), and working with time series and image data.

What You'll Learn?

Customizing plots
Plotting 2D arrays
Statistical plots with Seaborn
Analyzing time series and images

You can Sign up here

6. Data Visualization for Developers (Pluralsight)

This course is created by Dan Appleman who is CTO of Full Circle Insights and a well-known public speaker and author. This course on Data Visualization for Developers has 4.2-star ratings on Pluralsight and will introduce any developer to every aspect of Data Visualization, from story to numbers, to architecture, to code.

The topics covered in this course include fundamentals of data visualization that every developer needs to know regardless of architecture, platform, tools, or language.

What You'll Learn?

Introduction to Data Visualization
Charting -1 A
Business Intelligence
Visualization Architecture
Hands-On Visualization

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7. Interactive Data Visualization with Bokeh (Datacamp)

Interactive Data Visualization course is a complete guide to mastering Bokeh which is a Python library for building advanced and modern data visualization web applications. Created by Bryan Van de Ven, Software engineer at Anaconda and developer of Bokeh. In this course, you will learn to make use of Bokeh Visualization library for Python (and other languages) that target modern web browsers for presentation and build complex statistical plots quickly and through simple commands.

What You'll Learn?

An in-depth introduction to Basic plotting with Bokeh and its use.
Combine multiple Bokeh plots into layouts, links plots together, use legends and hover tooltips for annotations.
Build interactive apps with Bokeh and integrate Numpy and Pandas.
Use Bokeh built-in widgets alongside plots and connect to python code using the Bokeh server.
Build sophisticated Bokeh data exploration application from the scratch, based on the famous Gapminder data set.

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8. D3.js Data Visualization Fundamentals (Pluralsight)

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This course is created by Ben Sullins who is a certified data geek and author with 15 years of industry experience. This course has a 4.8-star rating on Pluralsight and will you help learn how to use D3.js (data-driven documents) make powerful data visualization.

This course mainly focuses on how to visually encode and present data to an audience once an insight has been found.

What You'll Learn?

The Basics and Charting
Working with Data External Data Sources
Enhancing Your Visualization with Scales and Axis
Enhancing Your Visualization with Interactivity
Mapping and Putting It All Together

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Before You Go 🍻

If you liked this article, please do share it with your friends and also follow this Dev.to Handle to keep up with similar content once every fortnight.

Wishing you the best with your career! Cheers!

Happy Learning !!!

Setup & Install DVWA - Create Your Ethical Hacking Environment

sinxloud — Thu, 11 Oct 2018 12:26:49 +0000

In this tutorial, I'll demonstrate you to setup Damn Vulnerable Web Application (DVWA) along with Apache, MySQL, PHP on localhost. It's always been a concern for newbies that where they should practice and explore the vulnerabilities. If you are one of those guys, DVWA would be for you to figure it out yourself. I will help you create a hacking environment into your Linux distro to practice and test your skills.

If you are new to Web Security and want to get ahead in the field of CyberSecurity. I'd suggest that you read 'Kickstart Your Web Application Security Career', you'll richly benefit from the tips and resources provided.

Attacks Covered in DVWA

Brute Force
Command Execution
CSRF
File Inclusion
SQL Injection
SQL Injection (Blind)
Shell Uploading
XSS ( Reflected )
XSS ( Stored)

Prerequisites:

Virtual Machine: VirtualBox

Linux Distro: Kali Linux, Parrot Security OS or elementaryOS ( or any other Linux Distribution)

I prefer using elementaryOS which is Lightweight Linux based distribution, but you can follow the same instructions for Kali Linux which is aimed at advanced Penetration Testing and Security Auditing. Remember, we need to use a virtual machine and not a connected server because DVWA is really vulnerable and should only be installed on your virtual machine with NAT. Having that said - Let's get started.

Setup & Install DVWA Into Your Linux Distribution

DVWA is made with PHP and MySQL for security professionals or aspiring security professionals to discover as many issues as possible and exploit some of the most commons vulnerabilities of web platforms like SQL injection, Cross Site Scripting (XSS), Cross Site Request Forgery (CSRF), and more.

Step 1. Setup Web server (Install Apache)

To install Apache, Open your Terminal and type the following:

sudo apt install apache2

Once done, type 127.0.0.1 in the browser and you will see the default Apache 2 web page, similar to this:

When you are done looking at this test page, you can remove it by typing the following command:

sudo rm /var/www/html/info.html

Step 2. Download DVWA

We need to download the archive of DVWA from Github.

To install Git, type following command:

sudo apt-get install git

Go to the apache2 folder.

cd /var/www/html/

Clone DVWA from Github, type the following command:

sudo git clone https://github.com/ethicalhack3r/DVWA.git

Once done, type 127.0.0.1/DVWA/ in the browser and you will see the DVWA page, similar to this:

Change permissions for DVWA

sudo chmod -R 777 /var/www/html/DVWA/

Step 3. Install MySQL

The next component for Setting up DVWA is Installing MySQL.

To install MySQL, type the following:

sudo apt install mysql-server

Note that the installation routine may ask you to create a new password for the root MySQL user. Once you have completed all of the required steps, your MySQL installation should be completed. Let's double-check that our new MySQL server is running. Type this command:

mysql -u root -p

Enter the root password you created for MySQL when you installed the software package. Once in, the following to get the server status, version information and more:

status

This is a good way to ensure that you’ve installed MySQL and are ready for further configuration.

Restart Apache Server

sudo service apache2 restart

-Create Database and User

To create a MySQL database and user, follow these steps:

At the command line, type the following:

mysql -u root -p

Type the MySQL root password, and then press Enter.

To create a database, type the following command:

CREATE DATABASE dvwadb;

To create a database user, type the following command. Replace dvwausr with the user you want to create, and replace dvwa@123 with the user's password:

CREATE USER 'dvwausr'@'127.0.0.1' IDENTIFIED BY 'dvwar@123';

Grant permission, type the following command:

GRANT ALL PRIVILEGES ON dvwadb.* TO 'dvwausr'@'localhost' IDENTIFIED BY 'dvwa@123';

Once done, exit the application by typing either of the following commands:

\q (or) exit

Step 4. Install PHP5

For our last component in DVWA Installation, we will setup and install PHP. Installing this on your VM is quite easy.

To install PHP, simply type the following command:

sudo apt install php5 or sudo apt install php5.6

Agree to the installation and PHP 5 will be installed on your Server.

Restart Apache Server

sudo service apache2 restart

Now, let’s take a moment to test the PHP software that you just installed. Move into your public web directory:

cd /var/www/html

Once there, use the text editor to create a file named info.php by typing the following command:

sudo vim info.php

This command will use the command line editor vim to open a new blank file with this name. Inside this file, type the following:

Inside this file, copy paste the following:

<?php phpinfo(); ?>

Save your changes by entering:

:wq!

Once done, open your web browser and type your localhost IP address in the browser.

(Example IP Address) http://127.0.0.1/info.php

You will see the default PHP information page, similar to this:

When you are done looking at this test PHP page, you can remove this file if you want by typing the following command:

sudo rm /var/www/html/info.php

Install MySQL Extension for PHP.

To Install MySQL Extension for PHP Support, type the following:

sudo apt install php5-mysql

Once done, you have completed the PHP installation required for DVWA.

Install PHP-GD

DVWA requires a module for PHP which is not installed into Kali Linux or elementaryOS. So we need to add a Debian source for APT.

sudo add-apt-repository 'http://ftp.de.debian.org/debian sid main'

sudo apt update

sudo apt install php5-gd

Once done, you have completed the PHP installation for DVWA.

Step5. Configure DVWA

Now we are ready to edit the source of PHP config files to make sure your web application connects to the database and has got a working captcha. You can obtain reCaptcha keys from your Google Account by clicking here.

We will use the text editor to edit the configuration typing the following command:

sudo vim /var/www/html/dvwa/config/config.inc.php.dist

Add the database name, user, and password of the MySQL database.

Enter reCaptcha keys.

Here’s a screenshot on how your file needs to be after editing.

Once done, we need to edit the main config (php.ini) file for apache2, which is not correctly overridden for DVWA by default.

sudo vim /etc/php5/apache2/php.ini

Enable Allow_url_fopen
Enable Allow_url_include

This is necessary to exploit the file upload vulnerability. Here's a screenshot for php.ini after making changes.

After saving changes for php.ini, we need to follow few more steps.

Install Iceweasel

sudo apt install iceweasel

Restart Apache

sudo /etc/init.d/apache2 restart

Restart MySQL Service

sudo /etc/init.d/mysql restart

Once done, you have completed the required configuration for DVWA.

Test DVWA Installation

iceweasel http://127.0.0.1/DVWA/setup.php

You will be redirected to the web browser and the page similar to this will be in front of you.

When you are done looking at this DVWA Setup page, you can click on Create / Reset Database button. You will be redirected to the login page.

Use MySQL User and Password to Login

Now, login to change the strength of vulnerabilities by clicking on “DVWA Security”.

DVWA Security Options for Attacks:

Low Level: Low-Level Security gives you the freedom to exploit all known vulnerabilities means there will be no security in a given framework and hence you can try all attacks if you are using it first Time.
Medium Level: Medium security will have all entry-level validations and filtration which can stop any script kiddie to get the benefit of available vulnerabilities.
High Level: High Level is kind of Zero Day environment and if you can breach it then that means you are on the right track to becoming a VAPT Expert.

Start with Low level.

You’re done. 🍻

So, we have setup a simple vulnerable web application on localhost. You can now Explore DVWA interface.

If you encounter any errors during the installation or have questions, Let us know in the comments below!