16
Faculty Sections / Re: AUPF New - 2019
« on: February 20, 2020, 08:03:29 PM »
Nice
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17
Faculty Sections / Re: স্মার্টফোনে যুক্ত হচ্ছে ই-সিম
« on: February 20, 2020, 08:03:02 PM »
Thanks
19
Faculty Sections / Re: 20 websites to get a real data-set for Machine Learning
« on: February 20, 2020, 07:58:20 PM »
Informative
20
Faculty Sections / Re: New Invention Generates Electricity “Out of Thin Air” – Offers Clean Energy 24/7
« on: February 20, 2020, 07:57:49 PM »
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21
Software Engineering / Top 5 must-have Data Science skills for 2020
« on: February 20, 2020, 07:52:08 PM »
Data Science is a competitive field, and people are quickly building more and more skills and experience. This has given rise to the booming job description of Machine Learning Engineer, and therefore, my advice for 2020 is that all Data Scientists need to be developers as well.
To stay competitive, make sure to prepare yourself for new ways of working that come with new tools.
1. Agile
Agile is a method of organizing work that is already much used by dev teams. Data Science roles are filled more and more by people who’s original skillset is pure software development, and this gives rise to the role of Machine Learning Engineer.More and more, Data Scientists/Machine Learning Engineers are managed as developers: continuously making improvements to Machine Learning elements in an existing codebase. For this type of role, Data Scientists have to know the Agile way of working based on the Scrum method. It defines several roles for different people, and this role definition makes sure that continuous improvement and be implemented smoothly.
2. Github
Git and Github are software for developers that are of great help when managing different versions of software. They track all changes that are made to a code base, and in addition, they add real ease in collaboration when multiple developers make changes to the same project at the same time.With the role of Data Scientist becoming more dev-heavy, it becomes key to be able to handle those dev tools. Git is becoming a serious job requirement, and it takes time to get used to best practices for using Git. It is easy to start working on Git when you’re alone or when your co-works are new, but when you join a team with Git experts and you’re still a newbie, you might struggle more than you think.
3. Industrialization
What is also changing in Data Science is the way we think about our projects. The Data Scientist is still the person who answers business questions with machine learning, as it has always been. But Data Science projects are more and more often developed for production systems, for example, as a micro-service in a larger software. At the same time, advanced types of models are getting more and more CPU and RAM intensive to execute, especially when working with Neural Networks and Deep Learning.
In terms of job descriptions of a Data Scientist, it is becoming more important to not only think about the accuracy of your model but also take into account the time of execution or other industrialization aspects of your project.
4. Cloud and Big Data
While industrialization of Machine Learning is becoming a more serious constraint for Data Scientists, it has also become a serious constraint for Data Engineers and IT in general. Where the Data Scientist can work on reducing the time needed by a model, the IT people can contribute by changing to faster compute services that are generally obtained in one or both of the following:
Cloud: moving compute resources to external vendors like AWS, Microsoft Azure, or Google Cloud makes it very easy to set up a very fast Machine Learning environment that can be accessed from a distance. This asks from Data Scientists to have a basic understanding of Cloud functioning, for example: working with servers at distance instead of your computer, or working on Linux rather than on Windows / Mac.
Big Data: a second aspect of faster IT is using Hadoop and Spark, which are tools that allow for the parallelization of tasks on many computers at the same time (worker nodes). This asks for using a different approach to implementing models as a Data Scientist because your code must allow for parallel execution.
5. NLP, Neural Networks, and Deep Learning
Recently, it has still been accepted for a Data Scientist to consider that NLP and image recognition as mere specializations of Data Science that not all have to master. But the use cases for image classification and NLP get more and more frequent even in ‘regular’ business. At current times, it has become unacceptable to not have at least basic knowledge of such models.
Even if you do not have direct applications of such models in your job, a hands-on project is easy to find and will allow you to understand the steps needed in image and text projects.
To stay competitive, make sure to prepare yourself for new ways of working that come with new tools.
1. Agile
Agile is a method of organizing work that is already much used by dev teams. Data Science roles are filled more and more by people who’s original skillset is pure software development, and this gives rise to the role of Machine Learning Engineer.More and more, Data Scientists/Machine Learning Engineers are managed as developers: continuously making improvements to Machine Learning elements in an existing codebase. For this type of role, Data Scientists have to know the Agile way of working based on the Scrum method. It defines several roles for different people, and this role definition makes sure that continuous improvement and be implemented smoothly.
2. Github
Git and Github are software for developers that are of great help when managing different versions of software. They track all changes that are made to a code base, and in addition, they add real ease in collaboration when multiple developers make changes to the same project at the same time.With the role of Data Scientist becoming more dev-heavy, it becomes key to be able to handle those dev tools. Git is becoming a serious job requirement, and it takes time to get used to best practices for using Git. It is easy to start working on Git when you’re alone or when your co-works are new, but when you join a team with Git experts and you’re still a newbie, you might struggle more than you think.
3. Industrialization
What is also changing in Data Science is the way we think about our projects. The Data Scientist is still the person who answers business questions with machine learning, as it has always been. But Data Science projects are more and more often developed for production systems, for example, as a micro-service in a larger software. At the same time, advanced types of models are getting more and more CPU and RAM intensive to execute, especially when working with Neural Networks and Deep Learning.
In terms of job descriptions of a Data Scientist, it is becoming more important to not only think about the accuracy of your model but also take into account the time of execution or other industrialization aspects of your project.
4. Cloud and Big Data
While industrialization of Machine Learning is becoming a more serious constraint for Data Scientists, it has also become a serious constraint for Data Engineers and IT in general. Where the Data Scientist can work on reducing the time needed by a model, the IT people can contribute by changing to faster compute services that are generally obtained in one or both of the following:
Cloud: moving compute resources to external vendors like AWS, Microsoft Azure, or Google Cloud makes it very easy to set up a very fast Machine Learning environment that can be accessed from a distance. This asks from Data Scientists to have a basic understanding of Cloud functioning, for example: working with servers at distance instead of your computer, or working on Linux rather than on Windows / Mac.
Big Data: a second aspect of faster IT is using Hadoop and Spark, which are tools that allow for the parallelization of tasks on many computers at the same time (worker nodes). This asks for using a different approach to implementing models as a Data Scientist because your code must allow for parallel execution.
5. NLP, Neural Networks, and Deep Learning
Recently, it has still been accepted for a Data Scientist to consider that NLP and image recognition as mere specializations of Data Science that not all have to master. But the use cases for image classification and NLP get more and more frequent even in ‘regular’ business. At current times, it has become unacceptable to not have at least basic knowledge of such models.
Even if you do not have direct applications of such models in your job, a hands-on project is easy to find and will allow you to understand the steps needed in image and text projects.
22
Software Engineering / Top 5 Data Science Trends for 2020
« on: February 20, 2020, 04:18:09 PM »
Technology is evolving continuously, and so are we. In the upcoming years, there will be massive growth in the AI and Machine Learning field. There is already a considerable amount of data to be managed, and with new technological advancements, we can utilize big data in many ways. For that, we have to stay up to date with the latest trends in data science.
Data Science is not a single term; it covers a variety of topics and networks, such as the Internet of Things, Deep Learning, AI, etc. In simple terms, we can count data science as a complete blend of data inference, algorithm computation, analysis, and technology that helps in solving multifaceted business problems.
Moreover, it provides businesses with advanced tools and technologies that allow them to automate complicated business processes linked with extracting, analyzing, and presenting raw data. With so much happening in the technical field, and the data being generated at a rapid speed, it is crucial to know about the latest as well as the upcoming trends in data science.
To keep you up-to-date with the trends in data science, we have created a list of top 5 data science trends that are set to push your business to achieve great success.
1. Access to Artificial Intelligence and intelligent apps
AI has become the mainstream technology for both small and large businesses, and it will bloom in the next few years. At present, we are at the initial stage of using artificial intelligence, but in 2020, we will see more advanced applications of AI in all fields. The reason AI is growing rapidly is that it allows enterprises to improve their overall business processes, and provides a better way of handling both customer and client’s data.
Though utilizing AI will still remain a challenge for many, as exploring the advancement of this technology is not that simple. In 2020, we will find more advanced apps developed with AI, Machine learning, and other technologies that can improve the way we work. Another trend that will take over the market is automated machine learning that will be helpful in transforming data science with better data management. So, you might need specialized training for executing deep learning.
2. Rapid growth in the IoT
According to a report by IDC, it is expected that the investment in IoT technology would reach $1 trillion by the end of 2020, which clearly clarifies the growth of smart and connected devices. Even in 2019, we used apps and devices that allow us to control our home appliances like AC, TV, etc. Most of you might not now that it is only possible via IoT.
If you have ever come across smart devices like Google Assistant or Microsoft Cortana that allow us to automate the regular things, then you will get an idea that the Internet of things is grabbing continuous attention from users. Thus, it will encourage businesses to invest in this technology, especially in smartphone development, that uses IoT the most.
3.Evolution of Big data analytics
When it comes to data science, we simply cannot ignore Big Data analysis, which helps businesses gain a competitive edge over data and achieve their objectives. Nowadays, enterprises use different tools and technologies, especially python, to analyze big data. Also, businesses are focused on identifying the reasons behind certain events that take place at present. And that’s where predictive analytics is used; it helps companies identify what can happen in the future.
For instance, predictive analysis helps you identify the interests of your customers by their purchase or browsing history. Depending on that, you will be able to create smarter strategies to target new customers and retain the current one.
4.Edge Computing will be on the rise
At present, edge computing is propelled by sensors. But with the growth of IoT, Edge computing will take over mainstream cloud systems. Edge computing allows businesses to store streaming data close to the data sources so that they can be analyzed in real-time. Moreover, it offers a great alternative to Big Data analytics that require high-end storage devices and higher network bandwidth.
As the number of devices and sensors for collecting data is increasing rapidly, companies are adopting edge computing, as it is capable of resolving issues related to bandwidth, latency, and connectivity. When combined with cloud technology, edge computing can provide a synchronized structure that will be helpful in minimizing the risks involved in data analysis and management.
5. Demand for Data science Security Professionals
The adoption of artificial intelligence and machine learning will give rise to many new roles in the industry. One role that will be highly in demand is data science, security professionals. As both AI and ML entirely depends on data, and to process this data efficiently, data scientists must have expertise in data science as well as command over computer science.
Though the business market already has access to many experts who are proficient in data science and computer science, there is still a need for more professional data security professionals who can process data to customers securely. For that, data security scientists must be well versed with the latest technologies of data science or big data analysis. For example, python is amongst the most used languages in data science and data analysis, so having a clear understanding of python concepts can help you tackle the problems related to data science security.
Conclusion
Data Science has become one of the growing fields in all industries, especially the IT industry. Thus, businesses adopting data science techniques and technologies must stay up-to-date with the latest trends. In this article, we covered five data science trends that will be on the top of the list in 2020. You can take help from these trends to analyze where you need to improve your business processes in order to achieve maximum growth and ROI.
Data Science is not a single term; it covers a variety of topics and networks, such as the Internet of Things, Deep Learning, AI, etc. In simple terms, we can count data science as a complete blend of data inference, algorithm computation, analysis, and technology that helps in solving multifaceted business problems.
Moreover, it provides businesses with advanced tools and technologies that allow them to automate complicated business processes linked with extracting, analyzing, and presenting raw data. With so much happening in the technical field, and the data being generated at a rapid speed, it is crucial to know about the latest as well as the upcoming trends in data science.
To keep you up-to-date with the trends in data science, we have created a list of top 5 data science trends that are set to push your business to achieve great success.
1. Access to Artificial Intelligence and intelligent apps
AI has become the mainstream technology for both small and large businesses, and it will bloom in the next few years. At present, we are at the initial stage of using artificial intelligence, but in 2020, we will see more advanced applications of AI in all fields. The reason AI is growing rapidly is that it allows enterprises to improve their overall business processes, and provides a better way of handling both customer and client’s data.
Though utilizing AI will still remain a challenge for many, as exploring the advancement of this technology is not that simple. In 2020, we will find more advanced apps developed with AI, Machine learning, and other technologies that can improve the way we work. Another trend that will take over the market is automated machine learning that will be helpful in transforming data science with better data management. So, you might need specialized training for executing deep learning.
2. Rapid growth in the IoT
According to a report by IDC, it is expected that the investment in IoT technology would reach $1 trillion by the end of 2020, which clearly clarifies the growth of smart and connected devices. Even in 2019, we used apps and devices that allow us to control our home appliances like AC, TV, etc. Most of you might not now that it is only possible via IoT.
If you have ever come across smart devices like Google Assistant or Microsoft Cortana that allow us to automate the regular things, then you will get an idea that the Internet of things is grabbing continuous attention from users. Thus, it will encourage businesses to invest in this technology, especially in smartphone development, that uses IoT the most.
3.Evolution of Big data analytics
When it comes to data science, we simply cannot ignore Big Data analysis, which helps businesses gain a competitive edge over data and achieve their objectives. Nowadays, enterprises use different tools and technologies, especially python, to analyze big data. Also, businesses are focused on identifying the reasons behind certain events that take place at present. And that’s where predictive analytics is used; it helps companies identify what can happen in the future.
For instance, predictive analysis helps you identify the interests of your customers by their purchase or browsing history. Depending on that, you will be able to create smarter strategies to target new customers and retain the current one.
4.Edge Computing will be on the rise
At present, edge computing is propelled by sensors. But with the growth of IoT, Edge computing will take over mainstream cloud systems. Edge computing allows businesses to store streaming data close to the data sources so that they can be analyzed in real-time. Moreover, it offers a great alternative to Big Data analytics that require high-end storage devices and higher network bandwidth.
As the number of devices and sensors for collecting data is increasing rapidly, companies are adopting edge computing, as it is capable of resolving issues related to bandwidth, latency, and connectivity. When combined with cloud technology, edge computing can provide a synchronized structure that will be helpful in minimizing the risks involved in data analysis and management.
5. Demand for Data science Security Professionals
The adoption of artificial intelligence and machine learning will give rise to many new roles in the industry. One role that will be highly in demand is data science, security professionals. As both AI and ML entirely depends on data, and to process this data efficiently, data scientists must have expertise in data science as well as command over computer science.
Though the business market already has access to many experts who are proficient in data science and computer science, there is still a need for more professional data security professionals who can process data to customers securely. For that, data security scientists must be well versed with the latest technologies of data science or big data analysis. For example, python is amongst the most used languages in data science and data analysis, so having a clear understanding of python concepts can help you tackle the problems related to data science security.
Conclusion
Data Science has become one of the growing fields in all industries, especially the IT industry. Thus, businesses adopting data science techniques and technologies must stay up-to-date with the latest trends. In this article, we covered five data science trends that will be on the top of the list in 2020. You can take help from these trends to analyze where you need to improve your business processes in order to achieve maximum growth and ROI.
23
Software Engineering / Re: “অ্যালান টুরিং”- প্রকৃতির সাথে গণিতের সমন্বয়কারী একজন বিজ্ঞানী
« on: February 20, 2020, 03:58:39 PM »
Good
24
Evening Program (FSIT) / Re: WORKSHOP ON STRUCTURAL EQUATION MODELLING BASICS USING SmartPLS SOFTWARE
« on: July 14, 2019, 07:19:21 PM »
Thanks
25
Evening Program (FSIT) / Re: Take control of Social Media
« on: July 14, 2019, 07:18:48 PM »
Thanks
26
Evening Program (FSIT) / Python Programming-what,where &how to learn
« on: July 14, 2019, 07:17:56 PM »
While Python 3 is the new standard and most companies will want to eventually replace all code by Python 3, a lot of applications are written in Python 2, and I think the language will stay relevant for quite some time.
I'd say you can't really go very wrong choosing to learn one of the two Python versions. There are indeed some difference but it is still the same language and it would be good to learn the differences between the two anyway.
As for tutorials there are these:
- Code Academy Python - this is a very basic course to learn Python syntax and programming principles, but it explains it well
https://www.codecademy.com/en/tracks/python
- Learn Python the Hard Way - a more detailed explanation of Python 2
https://shop.learncodethehardway.org/access/buy/2/
- Also the official Python site has tutorials
https://wiki.python.org/moin/BeginnersGuide
However, I think the best way to learn a language is doing a project. There are lot's of Python meetups (depending on where you live though) - maybe join a group that writes a project in Python?
As for an IDE, I'm a great fan of PyCharm, in my opinion it is by far the best editor for Python. It has a free Community version which will give you most of the great features. I don't know of any other editors that are comparable to PyCharm for Python coding.
I'd say you can't really go very wrong choosing to learn one of the two Python versions. There are indeed some difference but it is still the same language and it would be good to learn the differences between the two anyway.
As for tutorials there are these:
- Code Academy Python - this is a very basic course to learn Python syntax and programming principles, but it explains it well
https://www.codecademy.com/en/tracks/python
- Learn Python the Hard Way - a more detailed explanation of Python 2
https://shop.learncodethehardway.org/access/buy/2/
- Also the official Python site has tutorials
https://wiki.python.org/moin/BeginnersGuide
However, I think the best way to learn a language is doing a project. There are lot's of Python meetups (depending on where you live though) - maybe join a group that writes a project in Python?
As for an IDE, I'm a great fan of PyCharm, in my opinion it is by far the best editor for Python. It has a free Community version which will give you most of the great features. I don't know of any other editors that are comparable to PyCharm for Python coding.
28
Software Engineering / Re: খাবারে ক্যান্সার ঠেকানোর অণু খুঁজবে অ্যাপ
« on: July 14, 2019, 07:05:57 PM »
Thanks
29
Software Engineering / Re: Tomorrow's Cities...
« on: July 14, 2019, 07:05:39 PM »
Thanks for sharing this information
30
Science and Information / Re: MIT Innovation Challenge!!
« on: July 08, 2019, 01:19:30 AM »
Thanks