As year end, one of my reflections is how I have grown professionally. Data is leading the revolution in all industries. The highest paid professionals over the next decade will be data savvy. I started the year with little programming and data science knowledge. Although, I have been trying to build a technology business whose solution was data based, I had limited knowledge about how the solution works. It was a bit struggle for me gaining traction. I was lucky to be enrolled in an academic program, the program covered basics of data science principles. During this time, I had the privilege to visit 4 nations attending fellowships and programs. I can boldly say, the next billionaires will be the ones solving problems using data. I had created a thread on an emerging technology whose value is data centric, the Internet of Things. This is particular to certain industries. However, data science applies to every sector and industry. So this thread will be to educate, share opportunities, learn and engage on the subject matter. It will be a great opportunity for fresh graduate to develop their data skills too
Now and the future is data and I tell you loads of organizations are seeing this. I ended the program well versed in the subject matter, had my business model reviewed and a firm that struggled to gain traction is working with well known brands.
I hope this makes front page. I came across a similar post on Twitter by the way.
https://twitter.com/peero007/status/1078050046988423168?s=21

What is data science?
According to Wikipedia- https://en.m.wikipedia.org/wiki/Data_science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.
There are different methods and programs used in data science but the most popular and widely accepted is Python.
There are a few free online courses you can get started with: https://www.futurelearn.com/courses/data-mining-with-weka/4/register?return=bp20ud7v&utm_campaign=data-mining-with-weka-4&utm_content=GoToCourse&utm_medium=futurelearn_organic_email&utm_source=CRM_email&utm_term=CC_1_Welcome_Now

The ones in udemy and Edx are paid for:
https://www.udemy.com/topic/data-science/

Great thread chief..i love to be a data scientist/analyst and i will love any possible mentorship from you sir

What is Machine Learning?
Machine learning is an provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Some machine learning methods

Machine learning algorithms are often categorized as supervised or unsupervised.

Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiringunlabeled data generally doesn’t require additional resources.
Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

Innopplis university in Russia is currently accepting applications or Masters in Big data, Robotics, Software engineering. Full scholarship are available. Apply at https://apply.innopolis.ru/get-in/en/

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I like the phrase “ how can we use data” in the looking ahead and resolution section.

Following... nice thread.
One of the most popular questions regarding Data Science has been "How/Where Do I Start" considering Data Science is so broad and one would require a large set of skills before you can confidently call yourself a Data scientist. I'd love your opinion on this. Or better still it would be nice if you could share with us how you started

I've been told to just master SQL and PowerBI/Tableau for now. Before venturing fully into python and co

PhilipGallagher:
Following... nice thread.
One of the most popular questions regarding Data Science has been "How/Where Do I Start" considering Data Science is so broad and one would require a large set of skills before you can confidently call yourself a Data scientist. I'd love your opinion on this. Or better still it would be nice if you could share with us how you started

I've been told to just master SQL and PowerBI/Tableau for now. Before venturing fully into python and co

you start by mastering a programming language. I will suggest python. Python is suitable because of the ease in learning and use. It’s the best object oriented langauage in my opinion. Then find contents on data visualization, this will help you understand how to read common comma separated files I.e CSV files and the plot graphs and do statistics. Then find machine learning contents. After this you can master SQL as this is very important in big data. Depending on your domain, Powerbi and Tablaeu are optional. Those two are mostly used for Business Intelligence.

-.. . .-.. . - . -..

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majekdom2:
As year end, one of my reflections is how I have grown professionally. Data is leading the revolution in all industries. The highest paid professionals over the next decade will be data savvy. I started the year with little programming and data science knowledge. Although, I have been trying to build a technology business whose solution was data based, I had limited knowledge about how the solution works. It was a bit struggle for me gaining traction. I was lucky to be enrolled in an academic program, the program covered basics of data science principles. During this time, I had the privilege to visit 4 nations attending fellowships and programs. I can boldly say, the next billionaires will be the ones solving problems using data. I had created a thread on an emerging technology whose value is data centric, the Internet of Things. This is particular to certain industries. However, data science applies to every sector and industry. So this thread will be to educate, share opportunities, learn and engage on the subject matter. It will be a great opportunity for fresh graduate to develop their data skills too
Now and the future is data and I tell you loads of organizations are seeing this. I ended the program well versed in the subject matter, had my business model reviewed and a firm that struggled to gain traction is working with well known brands.
I hope this makes front page. I came across a similar post on Twitter by the way.
https://twitter.com/peero007/status/1078050046988423168?s=21

God bless you for this post bro. Our people really need to wake up to this emerging opportunities in I.T. I am a Data Science newbie myself and I can't wait to excel in this field. I just checked out your profile and would like to get in touch with you personally. Thanks