Businesses in all industries are beginning to capitalize on the vast increase in data and the new big data technologies becoming available for analyzing and gaining value from it.
Demand for skilled data scientists continues to be sky-high, with IBM recently predicting that there will be a 28% increase in the number of employed data scientists in the next two years.
This makes it a great prospect for anyone looking for a well-paid career in an exciting and cutting-edge field.
But it isn’t just those following a traditional academic path – such by studying for one of the best US data science masters degree courses I covered in a recent article – who can benefit.
There are also a large number of free online courses and tutorials which a motivated individual could use as a springboard into a rewarding and lucrative career.
Who could benefit from a free online data science course?
Employers are waking up to the fact that employees with the ability to use data and analytics to solve business problems are increasingly valuable, whatever their background or position in an organization.
A lot of this is because of the proliferation of self-service infrastructure and tools designed to automate many of the technical but repetitive tasks involved with data cleaning, preparation and analytics. This means workers are increasingly able to carry out complex data-driven operations such as predictive modelling and automation without getting their hands dirty coding complex algorithms from scratch.
However, someone with an understanding of the principles will often be in a better position to use these tools productively than someone without! So, if you are looking to enhance your own CV with analytics skills you could do far worse than look at some of these courses. It’s worth noting however that while you can educate yourself with these courses without spending a penny, some of them charge for certification when you’ve finished.
Coursera – Data Science Specialization
Coursera provides one of the longest-established online data science educations, through John Hopkins University. It isn’t completely free – if you can afford it, you are expected to pay a course and certification fee – but this is waived for students who don’t have the financial resources available.
Comprised of 10 courses, the specialization covers statistical programming in R, cluster analysis, natural language processing and practical applications of machine learning. To complete the program, students create a data product which can be used to solve a real-world problem.
Coursera – Data-Driven Decision Making
Also from Coursera, this course is provided by PwC so unsurprisingly focuses more on business applications than theory. It covers the spectrum of tools and techniques which are being adopted by businesses today to tackle data challenges, and the different roles that data specialists can fill in modern organizations. Students are also tutored on selecting the best tools and frameworks for solving problems with data. The four-week course concludes with a task involving deploying a data solution in a simulated business environment,
EdX – Data Science Essentials
This course is provided by Microsoft and forms part of their Professional Program Certificate in Data Science, although it can also be taken as a stand-alone course through EdX. Students are expected to have an “introductory” knowledge of R or Python – the two most popular languages for data science programming at the moment. Subjects covered include probability and statistics, data exploration, visualization, and an introduction to machine learning, using the Microsoft Azure framework. Although all of the course material is free, students can pay ($90 in this case) for an official certificate on completion.
Udacity – Intro to Machine Learning
Machine learning is undoubtedly one of the hot topics in data science right now, and this course aims to give a full overview, from theory to practical application. As well as an introduction to selecting data sources and choosing which algorithms best fit a particular problem the course also forms a part of Udacity’s paid-for “nanodegree” in data analysis.
IBM – Data Science Fundamentals
IBM provides a number of free online courses through its portal formerly known as Big Data University and now rebranded as Cognitive Class. This program covers data science 101, methodology, hands-on applications, programming in R and open source tools. Collectively they should take around 20 hours to complete although those with prior experience of computer science will probably progress more quickly, whereas complete beginners may take a little bit longer.
California Institute of Technology – Learning from Data
This course focuses on machine learning and is delivered as a series of video lectures along with homework assignments and a final exam. As well as an overview of how computers “learn”, it goes into depth with the mathematics (students are expected to have a working knowledge of matrices and calculus, so this one isn’t for complete maths newbies).
Dataquest – Become a Data Scientist
Dataquest is an independent online training provider rather than being affiliated with a university like most of the others here. It offers free access to much of its course materials although you can also pay for premium services which include tutored projects. It offers three paths – data analyst, data scientist and data engineer, and with endorsements from Uber, Amazon and Spotify it looks like a good way to get a feel for whether or not you will enjoy studying data science, without spending money.
KDNuggets – Data Mining Course
KDNuggets is a well-known business and data science website and it has compiled its own free data mining syllabus. There are modules on machine learning, statistical concepts such as decision trees, regression, clustering and classification (see my data science glossary for an introduction to these terms) as well as an introduction to practical implementations of the technology.
The Open Source Data Science Masters
Rather than being offered by an organization or institution, this course is comprised of a collection of open-source materials and resources, available freely online. Subjects covered include natural language processing of the Twitter API using Python, Hadoop MapReduce, SQL and noSQL databases and data visualization. It also includes a grounding in the algebra and statistics needed to understand the fundamentals of data science. Of course there is no certification but the program can be completed at your own speed and works great as a gateway to the wealth of information on data science available online.
'>
Big Data Course Nusantara
Demand for skilled data scientists continues to be sky-high, with IBM recently predicting that there will be a 28% increase in the number of employed data scientists in the next two years.
This makes it a great prospect for anyone looking for a well-paid career in an exciting and cutting-edge field.
Jun 08, 2014 Intended for more realistic picture. Proper sharpness, clearer view and shadows. Initial version. Make sure you run the game in directx 9ex or another render that SweetFX can handle. War Thunder Graphics Settings Guide by kaewon. Some of you may not be so graphics inclined so here is a guide to what each setting does. These are just short simple explanations. If you want more in depth info, just google it. You can also put your mouse over the setting (the scroll bar or checkbox) as some of them have a tooltip. War thunder postfx settings. Jan 17, 2018 War Thunder. All Discussions Screenshots Artwork Broadcasts Videos News Guides Reviews. It appears polynom under postfx settings are not working too good anymore. Can't change night to day with white wash anymore. #3 Showing 1-3 of 3 comments Per page: 15 30 50. Aug 30, 2015 Hey guys, so what I want;)s Lets say, last month I tested really many PostFX settings I found over the internet and I realize, that this simple issue could have really good effect on our game impression more than graphics settings alone. Well, obviously. So, as I said. I tested really a lot of. Jan 26, 2017 I felt that WT on its defult PostFX was a bit 'grey' and lacking in colour so I decided to change mine to get a more colourful and realistic look. Last edited by Katokevin; Jan 26, 2017 @ 2:47pm.
But it isn’t just those following a traditional academic path – such by studying for one of the best US data science masters degree courses I covered in a recent article – who can benefit.
There are also a large number of free online courses and tutorials which a motivated individual could use as a springboard into a rewarding and lucrative career.
Dr seuss horton hears a who full movie download in hindi. Seuss' Horton Hears a Who. ON BLU-RAY, DVD & DIGITAL. Horton the elephant struggles to protect the unseen, infinitesimal creatures of Who-Ville, who.
Who could benefit from a free online data science course?
Employers are waking up to the fact that employees with the ability to use data and analytics to solve business problems are increasingly valuable, whatever their background or position in an organization.
A lot of this is because of the proliferation of self-service infrastructure and tools designed to automate many of the technical but repetitive tasks involved with data cleaning, preparation and analytics. This means workers are increasingly able to carry out complex data-driven operations such as predictive modelling and automation without getting their hands dirty coding complex algorithms from scratch.
However, someone with an understanding of the principles will often be in a better position to use these tools productively than someone without! So, if you are looking to enhance your own CV with analytics skills you could do far worse than look at some of these courses. It’s worth noting however that while you can educate yourself with these courses without spending a penny, some of them charge for certification when you’ve finished.
Coursera – Data Science Specialization
Coursera provides one of the longest-established online data science educations, through John Hopkins University. It isn’t completely free – if you can afford it, you are expected to pay a course and certification fee – but this is waived for students who don’t have the financial resources available.
Comprised of 10 courses, the specialization covers statistical programming in R, cluster analysis, natural language processing and practical applications of machine learning. To complete the program, students create a data product which can be used to solve a real-world problem.
Coursera – Data-Driven Decision Making
Also from Coursera, this course is provided by PwC so unsurprisingly focuses more on business applications than theory. It covers the spectrum of tools and techniques which are being adopted by businesses today to tackle data challenges, and the different roles that data specialists can fill in modern organizations. Students are also tutored on selecting the best tools and frameworks for solving problems with data. The four-week course concludes with a task involving deploying a data solution in a simulated business environment,
EdX – Data Science Essentials
This course is provided by Microsoft and forms part of their Professional Program Certificate in Data Science, although it can also be taken as a stand-alone course through EdX. Students are expected to have an “introductory” knowledge of R or Python – the two most popular languages for data science programming at the moment. Subjects covered include probability and statistics, data exploration, visualization, and an introduction to machine learning, using the Microsoft Azure framework. Although all of the course material is free, students can pay ($90 in this case) for an official certificate on completion.
Udacity – Intro to Machine Learning
Machine learning is undoubtedly one of the hot topics in data science right now, and this course aims to give a full overview, from theory to practical application. As well as an introduction to selecting data sources and choosing which algorithms best fit a particular problem the course also forms a part of Udacity’s paid-for “nanodegree” in data analysis.
IBM – Data Science Fundamentals
IBM provides a number of free online courses through its portal formerly known as Big Data University and now rebranded as Cognitive Class. This program covers data science 101, methodology, hands-on applications, programming in R and open source tools. Collectively they should take around 20 hours to complete although those with prior experience of computer science will probably progress more quickly, whereas complete beginners may take a little bit longer.
California Institute of Technology – Learning from Data
This course focuses on machine learning and is delivered as a series of video lectures along with homework assignments and a final exam. As well as an overview of how computers “learn”, it goes into depth with the mathematics (students are expected to have a working knowledge of matrices and calculus, so this one isn’t for complete maths newbies).
Dataquest – Become a Data Scientist
Dataquest is an independent online training provider rather than being affiliated with a university like most of the others here. It offers free access to much of its course materials although you can also pay for premium services which include tutored projects. It offers three paths – data analyst, data scientist and data engineer, and with endorsements from Uber, Amazon and Spotify it looks like a good way to get a feel for whether or not you will enjoy studying data science, without spending money.
KDNuggets – Data Mining Course
KDNuggets is a well-known business and data science website and it has compiled its own free data mining syllabus. There are modules on machine learning, statistical concepts such as decision trees, regression, clustering and classification (see my data science glossary for an introduction to these terms) as well as an introduction to practical implementations of the technology.
Rather than being offered by an organization or institution, this course is comprised of a collection of open-source materials and resources, available freely online. Subjects covered include natural language processing of the Twitter API using Python, Hadoop MapReduce, SQL and noSQL databases and data visualization. It also includes a grounding in the algebra and statistics needed to understand the fundamentals of data science. Of course there is no certification but the program can be completed at your own speed and works great as a gateway to the wealth of information on data science available online.
Nus Undergraduate Courses
The main objective of this course is to help you understand Complex Architectures of Hadoop and its components, guide you in the right direction to start with, and quickly start working with Hadoop and its components.
It covers everything what you need as a Big Data Beginner. Learn about Big Data market, different job roles, technology trends, history of Hadoop, HDFS, Hadoop Ecosystem, Hive and Pig. In this course, we will see how as a beginner one should start with Hadoop. This course comes with a lot of hands-on examples which will help you learn Hadoop quickly.
The course have 6 sections, and focuses on the following topics:
Big Data at a Glance: Learn about Big Data and different job roles required in Big Data market. Know big data salary trends around the globe. Learn about hottest technologies and their trends in the market.
Getting Started with Hadoop: Understand Hadoop and its complex architecture. Learn Hadoop Ecosystem with simple examples. Know different versions of Hadoop (Hadoop 1.x vs Hadoop 2.x), different Hadoop Vendors in the market and Hadoop on Cloud. Understand how Hadoop uses ELT approach. Learn installing Hadoop on your machine. We will see running HDFS commands from command line to manage HDFS.
Getting Started with Hive: Understand what kind of problem Hive solves in Big Data. Learn its architectural design and working mechanism. Know data models in Hive, different file formats supported by Hive, Hive queries etc. We will see running queries in Hive.
Getting Started with Pig: Understand how Pig solves problems in Big Data. Learn its architectural design and working mechanism. Understand how Pig Latin works in Pig. You will understand the differences between SQL and Pig Latin. Star wars battlefront 2 2019 crash windows 10. Demos on running different queries in Pig.
Use Cases: Real life applications of Hadoop is really important to better understand Hadoop and its components, hence we will be learning by designing a sample Data Pipeline in Hadoop to process big data. Also, understand how companies are adopting modern data architecture i.e. Data Lake in their data infrastructure.
Practice: Practice with huge Data Sets. Learn Design and Optimization Techniques by designing Data Models, Data Pipelines by using real life applications' data sets.
Check out some of our reviews from real students:-
'A nice learning for beginners, the thing which differentiate this course from other similar courses is that it has very 'effective and concise' content, so do even a layman can understand easily. The course shows only 3 hours of on-demand video lecture but one should always give time to each lecture ( by means of bookmarks and pause), then you would able to understand all the basics of Big data and Hadoop.'
'I liked the hands-on approach. very helpful.'
'Overall definitely worth the money for what you get, I learnt so much about Big Data.'
'I absolutely recommend taking this course.'
'Presenter explains in simple terms and any lay person or someone like me who has no background about databases and data can understand. Explaining the business use case application us very helpful in understanding how this can be useful for everyday business.'
'Loved it. Saved lots of time searching information on the internet.'
Nus Training Courses
'Very informative, and the course gave me what I was looking for. Thanks!'
'Big Data introduction can be daunting with several new keywords and components that one needs to understand. But, this course very clearly explains to a beginner about the architecture and different tools that can be leveraged in a big data project. It also has indications on the scope of big data in the industry, different roles one can perform in the big data space and also cover various commercial distributions of big data. Overall, a great course for a beginner to get started on the fundamentals of big data. Use Case is a bonus !'
Comments are closed.
|
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |