[FREE LIVE WEBINAR] How To Start Your Career in Data Science- What are The Next Steps?

About the Course

Join this FREE webinar if you are anyone from below:

You are a beginner or a fresher who is aspiring to make a dream career in the field of Data Science.

You want to learn what you need to do to get a job as a data scientist.

You have no prior experience and want to get started with Data Science.

You are not sure if you have the right skillset to make it big in the field of Data Science.

You want to know top mistakes to avoid and gain edge on your competition.

You are looking to gain the knowledge to choose best career path for you.

You will also understand how come there is a shortage of Data Scientists in companies when everybody, even a non-technical person, is trying to become a data scientist today.

Join the Free Webinar To get expert insights on what can make you a better data scientist than other applicants?

Course Objective

How Is Data Science Shaping The Future?

How to Know if Data Science is Right Field for You?

Top 3 Data Science Careers Options for You.

How to Make It in The Data Science Field?

Key Mistakes You Should Avoid.

What Key Skills You Need to Focus On?

What Type of Background Can Give You An Edge?

How to Overcome Gaps in Your Experience & Resume?

Job Market & Career Outlook …and more!

Who is the Target Audience?

Freshers or beginners who wants to make career in Data Science

Basic Knowledge:

No pre-requisites. Just passion to learn

 

Click Here to Continue Free Demo 

15 Reasons Why You Need To Learn Data Science

Data Science has been hailed as the “sexiest job of 21st Century” by Harvard Business Review. But what makes Data Science so important? Why are Data Scientists some of the highly paid professionals? And most importantly, why learn Data Science? In this article, we will walk through some of the main reasons as to why Data Science has become the most sought after job in the market. We will understand the requirements of companies and why they need Data Scientists to boost their performance.

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The topmost IT employment of the moment is Data Scientist. Data science is a very sought after course to learn. Data science has created so much hype in the world of IT sectors that from big to small companies all are now hiring employees who have knowledge regarding this subject. Data science helps an employee to understand data and then synthesize it in a proper way so that they can communicate in a better way which is beneficial for the companies. Data analytic which is a part of data science is taking over the market very quickly. Employers are looking for employees who can work well with data. This makes job hunting easier.
Data scientists are very in demand in the job market. Learning data science will increase your chances of getting a better job.
Salary of data scientists is very good. Their salaries are generally in the six figures digit.
 There are many job titles that are there for a data science professional.
Learning data science does not mean that you will be a data scientist only. There is the option of being Data science manager as well. This post also has lucrative salary options.
Availability of work in any part of the world. Data science professionals can work from anywhere they want. Also, they are employed in various other industries other than IT industry like health care and marketing.
Big data analytics is a part of data science and it is everywhere dominating the job market.
There is a variety of data science undergraduate majors which is useful for the students. And there is the availability of many educational options.
Also, there is very less competition in this field since it is a comparatively new field of study so job opportunities are more.
Data science gives you options for freelancing options.
You can develop your revenue streams.

Reasons to Learn Data Science

There is a shortage of data scientist in the market but the market for them is increasing day by day. This is why learning data science has been very crucial now.  These are the top 15 reasons why you should learn data science.

1. The demand of Data scientists in the job market

Companies have realized that they should hire professionals who have the capacity to gather, interpret and use data in a comprehensive manner so that it is beneficial to the company. The demand for a data scientist is increasing day by day since there are very few professionals in this field. Learning data science gives you the opportunity of getting a very good job in this market where they are very much needed at this moment.

2. The salary of data science professionals is very good

Survives have been done which shows that the salary range of a data scientist in the USA is $ 104,000 to $ 153,750 per year. This salary range variation depends upon the category of a contribution they make to the company. The basic salary of a level one data science contributor is $ 97,000 and that of a level 3 data science contributor is $ 152,000. Apart from these, they also get an additional bonus that starts from $ 10,000 for the level 1 contributor and to a much higher range for the level 3 contributors.
Disclaimer – The above-mentioned salary figures are not the same everywhere as they vary with locations.

3. Data Science can make the World a Better Place

Big Data & Data Science is beyond being a tool of Business Intelligence. Various philanthropic and social organizations are using data to create products for social good. Also, various health-care organizations are using data for helping doctors to have better insights about their patient’s health.

In this section, we will go through various examples where companies are using data for social good. This will help you to develop inspiration to learn Data Science as a tool for enriching the lives of people.

4. Many types of job posts are available when you are good in data science

Studying Data science means that are a number of job options that are available for you. Big data analytics is a part of data science which has a very good market. There are many job titles which are offered by big companies like IBM, Oracle, Opera and many more if you are fluent in this particular field. Your job possibilities increase if you learn data science. These are some of the job posts available for a data science student.
  • Data analyst
  • Data engineer
  • Machine learning engineer
  • Data science generalist

5. An option of being a data science manager

Being a data science student can clear the option of being a data science manager. It has been seen that the salaries of data science managers are almost equal to or more than doctors. Data Science managers who are level 1 earn up to $ 140,000 per year. The level 2 professionals earn $ 190,000 annually whereas level 3 professionals earn $ 250, 00 per year. This amount is more than what a psychiatrist, an internal medicine doctor or a pediatrician earn.

6. Pick Your Job Title

As a data scientist, you won’t just be collecting data. You will be turning unstructured data into structured data, then analyzing it. This is different than what is required from those in traditional data and quantitative management positions.

Possible job titles for a data scientist include:

  • Data Engineer
  • Data Scientist
  • Data Analyst
  • Big Data Engineer
  • Data Architect
  • Business Intelligence Specialist

Some of those sound pretty cool, right?

7. You can work anywhere in the world.

If you become a data science professional then you can work in any part of the world. In the USA, almost 43% of the professionals work on the West coast while almost 28% of them are in the Northeast. These professionals are employed in every region of the country and also aboard. But it has been since that the highest form of salary in this field is in the west coast of USA.
Data scientists apart from working in the technology industry they also get employed in other important industries like healthcare, financial sectors, and marketing. They also impart their wisdom to various consulting firms, CPG industries and also retails.

8. An option of being a big data analytics

The need and use of data analytics in every job sector of industry are growing more and more. Using big data analyst in companies is as important as using computers in workplaces. For companies three things are most important that include brand advertising, customer management, and big data analytics. Every data science student has a great future in the industry since this need will keep on increasing that will increase job opportunities.

 9. Every Organization Needs One

There isn’t a sector that hasn’t been touched by big data and analytics. The demand for data scientists continues to grow because more markets are realizing the value they add to an organization.

Companies continue to use customer data to personalize experiences. The ability to recommend items based on previous purchases and customers’ needs is invaluable to today’s organizations.

10. Variety of Undergraduate majors and education options

Data science is a relatively new subject. This is one subject that has its origin from various other subjects that include statistics or mathematics, computer science and engineering and also natural science. It has been seen that many data scientists have their degree in social science, economics, medical science, and even business.
Also, learning data science does not mean that you have sat in a classroom all day. You can learn this online as well as your own convenient time.

11. The competition in this field is less

There is a shortage of data science professionals in every sector of the industry. Not only this, professionals who come other fields do not want to fill the shoe of a data scientist. It has been from many research studies that employers are in the hunt of finance and accounting employees who have the capability of mining and extracting data, good at data analysis and statistical modeling and also apt in identifying key data trends. But unfortunately, most of the finance and accounting employees do not possess these qualities since generally, people do not tend to learn data science. This is the reason why competition is less in this field making it easier to find a job provided that the salary of these types of jobs are usually pretty handsome.

12. Improve Your Problem-Solving Skills

In order to be a successful data scientist, you should have the desire to dig up a problem, find the questions at its center, and develop testable theories to solve it.

If you have an intense curiosity, chances are you’ll be successful in a career that is based on data science.

13. The option of freelancing opportunities.

In the future, it is quite obvious that a major quantity of workforce will not be tied to only one employer. It is a wise option to find different sources of income and ways of working so that they can have a perfect balance in life. If you are a data science enthusiast that means you will be well known about the trends, number, and data. This will help you in becoming a freelancer or consultant for many big firms. These kinds of jobs pay a good amount. Data science is mainly an IT based job that can be done from anywhere in the world as long as you have a computer in front of you.

14. The opportunity of developing new streams of revenue

When you are learning data science you will have the ability to analyze and use good data information to good use. Also, you will be able to identify unexploited and new streams of revenue generation. If you want to lead a lavish lifestyle by increasing your earning then this is the best way to do so. The competition is not very high right now so this is not an impossible work to do.

15. Job Security

Not only is it easy to get a job as a data scientist with the right skills now, but you won’t have to worry about getting a job in the future.

To choose to be a data scientist is choosing job security. The rise in big data and analytics isn’t going anywhere, and companies will continue to seek professionals who understand it.

Conclusion

Statistics and researches do not lie. It has been seen that data science learning is a growing trend now because this field now offers numerous amount of job positions if you are fluent in this field. Also, data science is a relatively new field in the industry so there is less competition. Less competition means an increase in demand for job facilities. A Job position in this field offers a good amount of salary as well. Learning Data science opens the door of job options in various fields like health care and finance as well. These are the advantages of data science learning.

we learn that Data Science has transformed our society. Data Science gives meaning to data. It converts crude data into meaningful products that can be used by industries to generate insights and recognize market trends. With a dearth in the supply of specialized Data Scientists and a rapid increase in demand, there is a huge income bubble that has made Data Science a lucrative career. Here we conclude that learning Data Science is the hour of need and we must be data-literate to take up jobs of the future. Check how to become a data scientist.

Hope this article helped you to find out the reasons to start learning Data Science. Still any doubt? Drop a comment to us.

Best Machine Learning and Data Science Courses for 2018

Statistics and Data Science in R

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Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.

Let’s parse that.

  • Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.
  • Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.
  • Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.

What’s Covered:

  • Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames
  • Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots
  • Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2
  • Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots
  • Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance

Click here continue to improve your Knowledge

 

Complete iOS 11 Machine Learning Masterclass

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If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging.

In this course, you will:

  • Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
  • Develop an intuitive sense for using Machine Learning in your iOS apps
  • Create 7 projects from scratch in practical code-along tutorials
  • Find pre-trained ML models and make them ready to use in your iOS apps
  • Create your own custom models
  • Add Image Recognition capability to your apps
  • Integrate Live Video Camera Stream Object Recognition to your apps
  • Add Siri Voice speaking feature to your apps
  • Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
  • Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
  • Get FREE unlimited hosting for one year
  • And more!

Click here continue to improve your Knowledge

 

Introduction to Data Science with Python

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This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Basic knowledge
  • This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
What you will learn
  • Writing simple Python scripts to do basic mathematical and logical operations
  • Loading structured data in a Python environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

Click here continue to improve your Knowledge

 

Introduction to Data Science with R

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This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

Basic knowledge
  • This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
What you will learn
  • Writing simple R programs to do basic mathematical and logical operations
  • Loading structured data in a R environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

Click here continue to improve your Knowledge

 

Machine Learning In The Cloud With Azure Machine Learning

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The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies – both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines – we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data.

This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis.

You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example

Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not – based on their credit history, historical loan applications, customers’ data and so on

Or the latest technological revolution from right from science fiction movies – the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior.

Or Amazon’s recommendation engine which recommends products based on buying patterns of millions of consumers.

In all these examples, machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as Predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling.

This progress in the field of machine learning is great news for the tech industry and humanity in general.

But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics.

Well, what if there was an easy to use a web service in the cloud – which could do most of the heavy lifting for us? What if scaled dynamically based on our data volume and velocity?

The answer – is new cloud service from Microsoft called Azure Machine Learning. Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services. It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations.

The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python.

In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.

 

 

Machine Learning Classification Algorithms using MATLAB

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DESCRIPTION

This course is for you If you are being fascinated by the field of Machine Learning?

Basic Course Description 

This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Ouput Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the esesential ideas. The following are the course outlines.

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Sgement 1:  Instructor and Course Introduction

Segment 2:  MATLAB Crash Course

Segment 3:  Grabbing and Importing Dataset

Segment 4:  K-Nearest Neighbor

Segment 5:  Naive Bayes

Segment 6:  Decision Trees

Segment 7:  Discriminant Analysis

Segment 8:  Support Vector Machines

Segment 9:  Error Correcting Ouput Codes

Segment 10: Classification with Ensembles

Segment 11: Validation Methods

Segment 12: Evaluating Performance

At the end of this course, 

  • You can confidently implement machine learning algorithms using MATLAB. 
  • You can perform meaningful analysis on the data.

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Student Testimonials!

★★★★★

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This is the second Simpliv class on Matlab I’ve taken. Already, a couple important concepts have been discussed that weren’t discussed in the previous course. I’m glad the instructor is comparing Matlab to Excel, which is the tool I’ve been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I’m delighted it covers complex numbers, derivatives, and integrals. I’m also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took.

Jeff Philips

★★★★★

This course is really good for a beginner. It will help you to start from ground up and move on to more complicated areas. Though it does not cover Matlab toolboxes etc, it is still a great basic introduction for the platform. I do recommend getting yourself enrolled for this course.Excellent course and instructor. You learn all the fundamentals of using MATLAB.

Lakmal Weerasinghe

★★★★★

Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!

Oamar Kanji

★★★★★

The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!

Josh Nicassio

Student Testimonials! who are also instructors in the MATLAB category

“Concepts are explained very well, Keep it up Sir…!!!”

Engr Muhammad Absar Ul Haq instructor of course “Matlab keystone skills for Mathematics (Matrices & Arrays)

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Your Benefits and Advantages:

  • You receive knowledge from a Ph.D. in Computer science (machine learning) with over 10 years of teaching and reaserch experience, In addition to 15 years of programming experience and another decade of experience in using MATLAB.
  • The instructor has 6 courses on Simpliv on MATLAB including a best seller course.
  • The overall rating in these courses are (4.5/5)
  • If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked!
  • You have lifetime access to the course.
  • You have instant and free access to any updates i add to the course.
  • You have access to all Questions and discussions initiated by other students.
  • You will receive my support regarding any issues related to the course.
  • Check out the curriculum and Freely available lectures for a quick insight.

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It’s time to take Action!

Click the “Take This Course” button at the top right now!

...Time is limited and Every second of every day is valuable

I am excited to see you in the course!

Best Regrads,

Dr. Nouman Azam

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Who is the target audience?

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  • Researchers, Entrepreneurs, Instructors and Teachers, College Students, Engineers, Programmers and Simulators
BASIC KNOWLEDGE
  • Just basic high level math
WHAT YOU WILL LEARN
  • Use machines learning algorithms confidently in MALTAB
  • Build classification learning models and customize them based on the datasets
  • Compare the performance of diffferent classification algorithms
  • Learn the intuition behind classification algorithms
  • Create automatically generated reports for sharing your analysis results with friends and colleague

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Registration Link:

Learn Enterprise WPF with XAML from Scratch

Learn Enterprise WPF with XAML from Scratch 1DESCRIPTION

Learn the WPF and XAML concepts and tools that you will need to build fully functional UI-applications with the modern UI-building framework, Windows Presentation Foundation (WPF). This is the best WPF coursehere on Simpliv.

Teaching Approach

No fluff, no ranting, no beating the air. I esteem your time. The course material is succinct, yet comprehensive. All important concepts are covered. Particularly important topics are covered in-depth. For absolute beginners I offer my help on Skype absolutely free, if requested. Don’t forget that this course has English subtitles, so if you don’t understand my accent, feel free to turn them on.

Take this course and you will be satisfied.

Build a strong foundation with this WPF Tutorial

Learn Enterprise WPF with XAML from Scratch

Today, almost all applications have rich UI, console applications are very specific. Learning the essentials of WPF and XAML puts a powerful and very useful tool at your fingertips. Being familiar with WPF will make it absolutely easy to move to Universal Windows Platform (UWP) if needed, because these technologies rely basically on the same principles and they are both based on XAML.

Content and Overview

This course is primarily aimed at beginner to intermediate developers. It provides solid theoretical base reinforced by tons of practical material.

WPF is a very wide platform and it’s impossible to cover all its features in a single course, or in a single book. That’s why this course includes all the topics needed for the developing of a full-fledged Windows UI-application, sacrificing some advanced topics such as interoperation with Windows Forms, skinning, creating custom markup extensions etc.

The most beneficial aspect of this course is that it gives you the deep understanding of the WPF platform. For example, you will understand how the layout process really works. It helps to compose XAML quicker. Sometimes you’ll find yourself baffled by the events system of WPF without deep understanding of how it really works.

In short, the course covers the following topics:

  • Basic notions of UI-development
  • Controls
  • Layout
  • Data-Binding
  • Core-Types of WPF
  • Events and Dependency Properties
  • Templates
  • Resources
  • Styles
  • User Controls
  • Custom Controls
  • WPF application model (threading model, app life-cycle)

In the end we will recap what you have learned and you will try to understand where you have to go further with intention to master your skills. Here we will have a discussion of different paths you can go on.

Learn Enterprise WPF with XAML from Scratch 5

How long is this course: The course is around 3.5 hours. All are video lectures. You will be able to download all the slides and code samples used in the course.

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Keywords related to the course:

  • Visual Studio WPF
  • WPF beginner tutorial
  • XAML tutorial
  • WPF and XAML tutorial

Who is the target audience?

  • Beginners to quickly start developing Windows apps with rich UI
  • Any experienced WPF-developer who would like to refine their knowledge in the platform
BASIC KNOWLEDGE
  • You should already be familiar with the basics of C#
WHAT YOU WILL LEARN
  • Compose complex layouts
  • Harness the full power of controls
  • Apply data binding
  • Create bindable properties
  • Create and apply custom templates
  • Create and apply resources
  • Make UI looking stylish
  • Develop full-fledged WPF applications

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Registration Link:

Simpliv YouTube course & tutorial : 

How to learn java with programming experience

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DESCRIPTION
  • Taught by a Stanford-educated, ex-Googler, husband-wife team
  • This course will use Java and an Integrated Development Environment (IDE). Never fear, we have a detailed video on how to get this downloaded and set up.
  • Hundreds of lines of source code, and hundreds of lines of comments – just download and open in your IDE!

A Java course for everyone – accessible yet serious, to take you from absolute beginner to an early intermediate level

Let’s parse that.

  • This is a Java course for everyone. Whether you are a complete beginner (a liberal arts major, an accountant, doctor, lawyer) or an engineer with some programming experience but looking to learn Java – this course is right for you.
  • The course is accessible because it assumes absolutely no programming knowledge, and quickly builds up using first principles alone
  • Even so, this is a serious Java programming class – the gradient is quite steep, and you will go from absolute beginner to an early intermediate level
  • The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art – all shown by studies to improve cognition and recall.

What’s Covered:

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  • Programming Basics: What programming is, and a carefully thought-through tour of the basics of any programming. Installing and setting up an IDE and writing your first program
  • The Object-Oriented Paradigm: Classes, Objects, Interfaces, Inheritance; how an OO mindset differs from a functional or imperative programming mindset; the mechanics of OO – access modifiers, dynamic dispatch, abstract base classes v interfaces. The underlying principles of OO: encapsulation, abstraction, polymorphism
  • Threading and Concurrency: A deep and thorough study of both old and new ways of doing threading in Java: Runnables, Callables, Threads, processes, Futures, Executors.
  • Reflection, Annotations: The how, what and why – also the good and bad
  • Lambda Functions: Functional constructs that have made the crossover into the mainstream of Java – lambda functions, aggregate operators.
  • Modern Java constructs: Interface default methods; properties and bindings too. Also detailed coverage of Futures and Callables, as well as of Lambda functions, aggregation operators. JavaFX as contrasted with Swing.
  • Packages and Jars: The plumbing is important to understand too.
  • Language Features: Serialisation; why the Cloneable interface sucks; exception handling; the immutability of Strings; the Object base class; primitive and object reference types; pass-by-value and pass-by-object-reference.
  • Design: The MVC Paradigm, Observer and Command Design Patterns.
  • Swing: Framework basics; JFrames, JPanels and JComponents; Menus and menu handling; Trees and their nuances; File choosers, buttons, browser controls. A very brief introduction to JavaFX.

Programming Drills (code-alongs, with source code included)

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  • Serious stuff:
  • daily stock quote summariser: scrapes the internet, does some calculations, and outputs a nice, formatted Excel spreadsheet.
  • News Curation app to summarise newspaper articles into a concise email snippet using serious Swing programming
  • Simple stuff:
  • Support with choosing a programming environment; downloading and setting up IntelliJ.
  • Simple hello-world style programs in functional, imperative and object-oriented paradigms.
  • Maps, lists, arrays. Creating, instantiating and using objects, interfaces

Who is the target audience?

  • Yep! Folks with zero programming experience – liberal arts majors, doctors, accountants, lawyers
  • Yep! Engineering students from non-CS majors looking to learn fairly serious programming
  • Nope! Experienced Java programmers – this class will be boring for you:)
  • Yep! Computer Science students or software engineers with no experience in Java, but experience in Python, C++ or even C#. You might need to skip over some bits, but in general the class will still have new learning to offer you 🙂
BASIC KNOWLEDGE
  • No prior programming experience needed:)
  • The class will make use of Java and an IDE – never fear, we have a detailed video to walk you through the process of setting this up
WHAT YOU WILL LEAR
Design Patterns in java5
  • Write Java programs of moderate complexity and sophistication (at an early to middling intermediate level)
  • Understand Object-Oriented programming concepts at the level where you can have intelligent design conversations with an experienced software engineer
  • Manage concurrency and threading issues in a multi-threaded environment
  • Create and modify files (including Excel spreadsheets) and download content from the internet using Java
  • Use Reflection, Annotations, Lambda functions and other modern Java language features
  • Build serious UI applications in Swing
  • Understand the Model-View-Controller paradigm, the Observer and Command Design patterns that are at the heart of modern UI programming
  • Gain a superficial understanding of JavaFX and Properties and Bindings
  • Understand the nuances of Java specific constructs in serialisation, exception-handling, cloning, the immutability of strings, primitive and object reference types

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Machine Learning, NLP & Python-Cut to the Chase

Machine Learning2
DESCRIPTION

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today

Let’s parse that.

The course is down-to-earth : it makes everything as simple as possible – but not simpler

The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.

You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won’t tell you what the carburetor is.

The course is very visual : most of the techniques are explained with the help of animations to help you understand better.

This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python.

The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art – all shown by studies to improve cognition and recall.

What’s Covered:

Machine Learning5

Machine Learning: 

Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.

Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff

Natural Language Processing with Python: 

Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-Means

Sentiment Analysis: 

Why it’s useful, Approaches to solving – Rule-Based , ML-Based , Training , Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with Python

Mitigating Overfitting with Ensemble Learning:

Decision trees and decision tree learning, Overfitting in decision trees, Techniques to mitigate overfitting (cross validation, regularization), Ensemble learning and Random forests

Recommendations: Content based filtering, Collaborative filtering and Association Rules learning

Get started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problem

A Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible.

Who is the target audience?

Machine Learning4

  • Yep! Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
  • Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role
BASIC KNOWLEDGE
  • No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.
WHAT YOU WILL LEARN
  • Identify situations that call for the use of Machine Learning
  • Understand which type of Machine learning problem you are solving and choose the appropriate solution
  • Use Machine Learning and Natural Language processing to solve problems like text classification, text summarization in Python

 

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Best Online Guide for Data Structures & Algorithms in Java- Simpliv

Data Structures & Algorithms in Java
DESCRIPTION

This is an animated, visual and spatial way to learn data structures and algorithms

  • Our brains process different types of information differently – evolutionarily we are wired to absorb information best when it is visual and spatial i.e. when we can close our eyes and see it
  • More than most other concepts, Data Structures and Algorithms are best learnt visuallyThese are incredibly easy to learn visually, very hard to understand most other ways
  • This course has been put together by a team with tons of everyday experience in thinking about these concepts and using them at work at Google, Microsoft and Flipkart

What’s Covered:

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We’re super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

  • Yep! Computer Science and Engineering grads who are looking to really visualise data structures, and internalise how they work
  • Yep! Experienced software engineers who are looking to refresh important fundamental concepts
WHAT YOU WILL LEARN
  • Visualise – really vividly imagine – the common data structures, and the algorithms applied to them
  • Pick the correct tool for the job – correctly identify which data structure or algorithm makes sense in a particular situation
  • Calculate the time and space complexity of code – really understand the nuances of the performance aspects of code

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Continue to Improve the Spark for Data Science with Python- Simpliv

Spark for Data Science with Python
DESCRIPTION

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

Get your data to fly using Spark for analytics, machine learning and data science 

Let’s parse that.

What’s Spark? If you are an analyst or a data scientist, you’re used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.

Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease.

Machine Learning and Data Science : Spark’s core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We’ll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

What’s Covered:

Lot’s of cool stuff ..

  • Music Recommendations using Alternating Least Squares and the Audioscrobbler dataset
  • Dataframes and Spark SQL to work with Twitter data
  • Using the PageRank algorithm with Google web graph dataset
  • Using Spark Streaming for stream processing
  • Working with graph data using the  Marvel Social network dataset

.. and of course all the Spark basic and advanced features: 

  • Resilient Distributed Datasets, Transformations (map, filter, flatMap), Actions (reduce, aggregate)
  • Pair RDDs , reduceByKey, combineByKey
  • Broadcast and Accumulator variables
  • Spark for MapReduce
  • The Java API for Spark
  • Spark SQL, Spark Streaming, MLlib and GraphFrames (GraphX for Python)

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We’re super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

  • Yep! Analysts who want to leverage Spark for analyzing interesting datasets
  • Yep! Data Scientists who want a single engine for analyzing and modelling data as well as productionizing it.
  • Yep! Engineers who want to use a distributed computing engine for batch or stream processing or both
BASIC KNOWLEDGE
  • The course assumes knowledge of Python. You can write Python code directly in the PySpark shell. If you already have IPython Notebook installed, we’ll show you how to configure it for Spark
  • For the Java section, we assume basic knowledge of Java. An IDE which supports Maven, like IntelliJ IDEA/Eclipse would be helpful
  • All examples work with or without Hadoop. If you would like to use Spark with Hadoop, you’ll need to have Hadoop installed (either in pseudo-distributed or cluster mode).
WHAT YOU WILL LEARN
  • Use Spark for a variety of analytics and Machine Learning tasks
  • Implement complex algorithms like PageRank or Music Recommendations
  • Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
  • Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX

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Learn By Online : PHP For Dynamic Websites- Simpliv

Learn By Example PHP For Dynamic Websites.jpg
DESCRIPTION

Super-practical PHP: Learn, by example, how to build a smart website with cookies, sessions, login authentication, MySQL integration, Object Oriented PHP and lots more!

Let’s parse that.

  • This course is super-practical: packed with knowledge you can put to use right away, while still giving you a good foundational understanding of web programming, not just PHP.
  • Its about building smart websites: these days, its not OK for a website just to look snappy – login, cookies and sessions are considered necessities, not luxuries anymore
  • In this course, we will learn by example. Each example is self-contained, has its source code attached, and gets across a specific PHP use-case. Each example is simple by itself, but they come together as building blocks to build complex use cases.

What’s included in this course:

  • Installing and setting up a basic web server with PHP
  • Web security basics: validating and sanitizing user input data. Web forms, mitigating XSS and XSRF attacks
  • MySQL Integration and Installation: Connecting to a database, running queries, processing results, prepared statements. Easy integration with MySQL so it’s dead simple to work with databases for permanent data storage
  • Cookies, Sessions and the differences between them, using sessions without cookies
  • End to end login authentication
  • Object oriented PHP, classes, inheritance, polymorphism
  • GET, POST and other superglobals

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We’re super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?

  • YEP! Students who are looking to use the power of programming to build dynamic web sites
  • YEP! Students who are looking to get into the nitty gritty of advanced PHP constructs such as cookies, sessions and object oriented programming
BASIC KNOWLEDGE
  • No prior programming experience needed, this course starts at zero
  • A little basic HTML, CSS and SQL will be helpful for some of the examples – not really required though!
WHAT YOU WILL LEARN
  • Apply advanced constructs such as cookies, sessions and object oriented programming correctly
  • Mitigate basic web security risks by sanitizing and validating user input
  • Build a robust login authentication system using MySQL to allow users to sign up and log into your site
  • Harness the power of programming to build intelligent, interactive and personalized web sites

 

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