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Chan Naseeb

AI Thought Leader at IBM

Munich, Germany

Ranked in Top 10 Thought Leaders


Thought Leader in AI, Data Science, Quantum, Digital Transformation, Cloud, Agile, BlockChain, IoT
Author, Public Speaker, Moderator.

I am an internationally experienced, highly motivated professional and an executive data scientist who can design end-to-end innovative solutions for clients. I lead, plan and realise the strategy by actively contributing to it through thought leadership and my professional experience. I have about 13+ years of experience in design, implementation, and testing of systems using different languages, platforms, and technologies with a particular focus on ML, DL, Data Science, and Python. I am passionate about realising solutions using modern tech stack. I enjoy working with teams and leading them in agile way.

With an impeccable track record of well-honed communication skills, I cultivate relationships with stakeholders and motivate colleagues to implement complex solutions to deliver objectives effectively.

Key Competencies:
-Data Analytics, Data Science, AI, Intelligent Automation, Blockchain, IoT
-Digital Transformation, Data & AI Strategy
-Industry 4.0
-Project and Program Management, Product Development Management, Risk and Change Management.
-Design, Delivery, and Implementation
-Agile and Design Thinking

Technologies and Methodologies:
Data Science, AI, Machine Learning, Cloud Computing, and Block Chain. ML, DL, Watson, AWS, Microsoft Azure, Hadoop, MapReduce, Spark, Kafka, Flume, Python, SPSS Modeler, Alteryx, Kylo, Ethereum, Solidity, HyperLedger, Mist, SQL and NoSQL-based technologies
Scaled Scrum, Scrum Master, Python

Available For: Authoring, Consulting, Influencing, Speaking
Travels From: Munich
Speaking Topics: AI, Quantum, Data Science, Blockchain, IoT, Industry 4.0, Digital Transformation etc

Speaking Fee $8,000

Personal Speaking Website: www.youtube.com
Chan NaseebPoints
Academic20
Author94
Influencer15
Speaker21
Entrepreneur40
Total190

Points based upon Thinkers360 patent-pending algorithm.

Thought Leader Profile

Portfolio Mix

Featured Videos

How to become a data driven business? What makes a Business Data Driven?
November 17, 2020
AI & Analytics Conference: Opening Keynote talk 2019
November 17, 2020
The challenges that keep businesses from becoming data driven
November 17, 2020

Featured Topics

AI Strategy: AI strategy for Enterprises

What is the strategy that you need to make your business an AI business? what do you need to change? How you can leverage the augmented intelligence to make your enterprise a cognitive enterprise? In doing so, what benefits will you achieve etc.

Data Monetisation and a Data culture

Being data-driven is critical to succeeding in today’s world. When an organization exercises a “data-driven” approach, it makes strategic choices based on data analysis and interpretation. Such an approach facilitates companies to experiment and organize their data with the goal of better serving their customers, employees, and improving their operations. Using data to drive its actions, an organization can contextualize and personalize its messaging to its prospects and customers for a more customer-centric strategy.
However, organizations face many challenges in becoming a data-driven organization and building and retaining a data-driven culture. Some of those challenges include their inability to emphasize long term objectives, lack of shared vision, focus on short term RoI gains and ignoring the Return on Value and opportunities that data brings, skills gap, and lack of having the full picture or true understanding of what it would mean for them if they become a DD organization.

Machine Learning g, Deep Learning, IoT, Quantum Computing, Agile etc

There are many other topics as mentioned above where I could deliver a talk, a hands on workshop or a business focused walkthrough.

Company Information

Company Type: Enterprise

Areas of Expertise

5G 30.26
Agile 31.89
AI 38.29
Analytics 30.68
AR/VR 30.22
Autonomous Vehicles 30.52
Big Data 32.46
Blockchain 35.09
Business Continuity
Business Strategy 33.79
Change Management 31.78
Cloud
Culture 31.60
Design Thinking 31.03
Digital Disruption 33.22
Digital Transformation 30.90
Diversity and Inclusion 30.86
Ecosystems 30.20
Emerging Technology 30.75
Entrepreneurship
Fintech
Future of Work 30.48
Innovation
IoT 30.31
Leadership 30.65
Management 30.13
Predictive Analytics 34.97
Quantum Computing 46.38
Renewable Energy
RPA 31.17
Sales 34.36
Social 30.17
Startups
Sustainability 30.18
Health and Safety 30.56
COVID19 31.40
Open Innovation 30.23
Lean Startup 30.49
Health and Wellness 30.38
Proptech 32.33
Public Relations 30.37
ERP 30.23
Healthtech 30.46

Industry Experience

Agriculture & Mining
Federal & Public Sector
Financial Services & Banking
Healthcare
Higher Education & Research
Insurance
Media
Retail
Telecommunications
Travel & Transportation
Utilities

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Publications

1 Adjunct Professor
Dr Chan Naseeb
NA
September 21, 2017
Organise several lectures and workshops on Data Science and its applications

See publication

Tags: AI, Blockchain, Predictive Analytics

31 Article/Blogs
Moving from an intuitive mindset to data driven?
Medium
January 26, 2021
Recently I wrote an article on creating and living a data culture. You can find the first article, which defines what a data culture is? And enlists attributes of such a culture. In the sequel, I focused on those attributes and discussed those pillars of a data culture.

See publication

Tags: AI, Big Data

Understanding Artificial Intelligence, Machine Learning, Deep Learning and Data Science
Medium.com
November 20, 2020
In the article, I have tried to elaborate in very few words what do these terms such as AI, ML, DL and Data Science mean. Right from mimicing the human behavior to applying AI to get the value for the businesses. To read more on human and AI working together, read this article on collaborative intelligence.

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Tags: AI, Digital Transformation

Living a data culture Part 1
Import from medium.com
November 12, 2020
Data-Driven Culture can make your business stand out and become a front runner.Continue reading on Data Driven Investor »

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Tags: AI, Digital Transformation, Business Strategy

Living a Data Culture Part 2
Import from medium.com
November 10, 2020
Data-Driven Culture can make your business stand out and become a front runner.Continue reading on Towards Data Science »

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Tags: AI, Digital Transformation, Business Strategy

AI and ML driving, exponentiating sustainable and quantifiable transformation
IEEE COMPSAC
September 09, 2020
AI is a major transforming technology impacting every sector of life. AI is not a force to deprive humans and take over the control, rather a real enabler and lever for digital transformation. The former view aligns with Hollywood need to be undressed with the latter, which is realistic and becoming tangible over time as more organizations, and communities are leveraging AI’s potential. Developing a practical understanding of AI, its capabilities, the challenges, and opportunities that it brings is fundamental to get the maximum out of its envisaged potential.

The objective of this paper is to highlight how technology and industry have developed, discuss the role of AI in driving intelligent transformation concentrating on an applicable understanding of AI and related technologies. We introduce a new conceptual framework: AI's multi-dimensional role, to highlight its transformative power in multiple aspects and to support that with facts across different industry verticals.

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Tags: AI, Digital Transformation, Digital Disruption

Activity Recognition for locomotion and transportation dataset using deep learning
UbiComp/ISWC 2020
September 01, 2020
Worked on a broad, real-life dataset to classify transport-related activities in a user and location-independent manner. Since deep learning architectures have now received great attention on achieving promising results on time series classification tasks, we focused our experiments on some recent state-of-the-art deep learning architectures such as CNN, Resnet, and InceptionTime. A considerable amount of time was spent on the preprocessing pipeline, which turned out to be a critical phase that impacted most of the results. At the end and after many experiments and hyperparameter tuning, we were able to achieve a 79% F1 score on the validation dataset using InceptionTime architecture. The objective of this paper is to present the technical description of the Machine Learning processing pipeline, the algorithms used, and the results achieved during the development/training phase.

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Tags: AI, Big Data, Predictive Analytics

Becoming a Data scientist: which path to take?
Import from medium.com
June 08, 2020
What are the options out there for you to become a data scientist?Continue reading on Towards Data Science »

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Tags: AI, Big Data

AutoAI: Your buddy (trusted partner) to speed up time to market for your AI and ML Models.
Import from medium.com
June 04, 2020
Automating different steps of the ML LifecycleContinue reading on Towards Data Science »

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Tags: AI, Big Data

Work space Complexity and dealing it with AGILE.
Import from medium.com
May 16, 2020
Agile can help you deal with today’s business complexities.Continue reading on ILLUMINATION »

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Tags: AI, Big Data

Inhibitors to Writing
Import from medium.com
May 15, 2020
Things that keep us from bloggingContinue reading on ILLUMINATION »

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Tags: AI, Big Data

Your stories, Your Publication:Diversified Knowledge
Import from medium.com
May 15, 2020
Do not stop sharing your experience, and your knowledgeContinue reading on Diversified Knowledge »

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Tags: AI, Big Data

Industry 4.0 or Internet 4.0?
Import from medium.com
May 15, 2020
Understanding Industry 4.0Continue reading on Towards Data Science »

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Tags: AI, Big Data

Impact of Covid-19 Lockdown on our planet(Blessings in the disguise)
Import from medium.com
May 11, 2020
What our planet has gained as a result of the lockdown?Continue reading on ILLUMINATION »

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Tags: AI, Big Data

Feature Store:A better way to implement Data Science and AI in and across your organization.
LinkedIn
May 08, 2020
How could a feature store help in accelerating data and AI adoption across the enterprises?

Data Science and AI are great forces to transform your business and everything that you do, however, there is a huge possibility to optimise and automate data science and AI to leverage the fullest of their potential and capabilities. When organisation start their AI journey, they face many challenges and oftentimes it is needed but hard to accelerate its adoption.

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Tags: AI, RPA

Blessings in the disguise. An invitation for Invention and Innovation
Import from medium.com
May 07, 2020
Lockdown, and Seclusion means more personal time and an invitation for invention and innovation: what could we gain?Continue reading on ILLUMINATION »

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Tags: AI, Big Data

World after Covid-19: A lot will change
medium.com
April 21, 2020
The world of new opportunities, and new ways
I think we have changed, the work culture has changed and it will be changed. COVID-19 has changed our world and it will change more than what we had imagined.
While it might be hard for some to predict, for others to conceive, however for many it has surfaced already as they are experiencing it and living through it.

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Tags: Culture, Digital Disruption, Health and Safety

Implications for the Data-Driven Businesses
https://medium.com/towards-artificial-intelligence
April 17, 2020
What do businesses achieve, once they start their journey to become a data-driven enterprise? In this blog, I will discuss what you would obtain if you would become a Data-Driven business. Being data-driven is no longer a choice, rather it is a must-have if you want to stay relevant. Data is the new oil to explore the full potential of an organization and its capabilities. Once the organizations use it to their advantage, they can not only lead with data-driven decision making but also to deliver more digitally enhanced experiences to all the stakeholders and customers.

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Tags: Big Data, Digital Transformation, Future of Work

Live your life… Just do it
https://medium.com/illumination
April 14, 2020
Do not let anyone else take over to steer your life
Through reflection, you can avail the opportunity to appreciate, learn from and find peace with your past, while you take the conscious steps towards your future.
How would you define success?
This is your life, and you have the right to live it nobody else has the right to define the course of your life.

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Tags: Change Management, Culture, Management

Who Can Become a Data Scientist?
Towards AI
April 11, 2020
What does it take to be a data scientist? Questions and Answers
an the following professionals (degree holders) become a data scientist?
Bachelor in Business Administration (BBA) holder
Petroleum engineer,
Psychologist,
Anyone!
Here is my take on this type of questions:

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Tags: AI, Proptech, Public Relations

Data Scientist Types: What kind of data scientists do you come across in the market?
Towards AI
April 11, 2020
I have seen different types of people around carrying out and using the title of a data scientist. I would categorize them in the following types:
1. Self-claimed Data Scientists or the Titalists: These are the ones that started using the titles and critical terms. They go with the hyped, the moment they heard someone saying data scientist is the “sexiest job of the 21st Century”, they jumped over the bandwagon and started declaring them as a data scientist. There would be absolutely no issue, had they learned the required skills, and could do the job. ....

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Tags: Analytics, ERP, Healthtech

What do I do during COVID-19?
Illumination
April 10, 2020
How do I grow myself during this challenging time of pandemic?
What do I want to do during Covid-19?
Undoubtedly, we are all passing through an unprecedented time of COVID-19. However, given that this pandemic has restricted our free movement, changed radically our work and life style- many of us have taken it as something to live with (fear, grabbing everything from supermarket as if they would neever get it again) , and many of us have (or most of us should see) seen this differently considering it as an opportunity to be creative, responsible and finish the things that we have never got the time to do them.

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Tags: COVID19, Digital Disruption, Emerging Technology

Collaborative Intelligence: Humans and AI working Together
Illumination
April 08, 2020
Leveraging strengths of both humans and machines and compensating for their weaknesses.
To truly benefit from tectonic forces such as AI, Blockchain, Quantum etc. and their widespread adoption, a lot has been done and there is much more needed to be done. What are some of those things that you need to embed in your strategy? Read further…
AI and other advance technologies must be considered as revenue generators and not as cost basis.

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Tags: Blockchain, Health and Wellness, RPA

Impact of 5G on our lives: How it will impact our lives and other fields?
Medium
April 06, 2020
5G — Impact of 5G on our lives: Advantages and Disadvantages
How 5G technology will impact our lives and other fields?
In an earlier post, I have discussed the fundamental concepts about 5G (5th generation of mobile networks), how mobile technology works, and how has this technology evolved from 1st generation to 5th generation, over the years. How much speed enhancements will be achieved by 5G.
In this post, I will shed some light on the impact of 5G on our lives and other fields. At the end, I will also discuss advantages of 5G and some limiting factors / disadvantages of 5G (as of now).

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Tags: 5G, AR/VR, Autonomous Vehicles

Becoming a Data Driven Organisation: What changes do you need to implement?
Illumination
April 05, 2020
n this post, I will discuss the changes that organisations need to implement, to truly benefit from data and AI. Part 1 of this series, focused on setting up the stage and discussing the aspects that make a business data-driven, while in Part 2, I have discussed the challenges that keep the businesses away from becoming Data-Driven.
“What are the main challenges for companies to solve in their pursuit to become truly data-driven?”
There are many changes that businesses need to implement, and they vary from business to business. I can spend hours on this, but let me give you some gist of this.

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Tags: Lean Startup, Sustainability, Ecosystems

Build a Simple DL Model Using TensorFlow
Analytics Vidhya
March 30, 2020
Build a very simple Neural Network using TensorFlow.Hello World with Deep Learning: One Layer Model to predict relationship between the input (X) and Output (Y) Values. Although, this is very simple example and perhaps an overkill of using the DL, but we are after to demonstrate the power with a simple and easily comprehendible example.

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Tags: AI, IoT, RPA

Becoming a Data Driven Organisation Part 2
Analytics Vidhya
March 27, 2020
In Part 1, I set up the stage and discussed various aspects which make a business data-driven. In this post, I will discuss the challenges that keep businesses away from becoming Data-Driven (DD hereafter). oday, it is evident that data is the critical asset, and AI is the vehicle that helps businesses thrive. It has made business leaders aware of and come up with the desire to take advantage of the opportunities that data and AI offer. However, contrary to the desire of becoming data driven and infusing data science and AI into their businesses, business leaders still have to deal with many challenges, despite investing their resources toward their data- & AI-related strategic initiatives.

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Tags: Diversity and Inclusion, Leadership, Open Innovation

Coronavirus Statistics and Distributions Part 1
Medium.com
March 25, 2020
How did corona virus develop over time and spread geographically across different countries? In this series, I would like to talk about and analyse the Corona Virus (COVID-19) data. This is part 1 of the series which gives a basic overview about different concepts needed to understand COVID-19, some statistics and some visuals show the spread of the COVID-19.
Our world has witnessed very rapid and extraordinary changes as an aftermath of the Corona virus (COVID-19 hereafter) outbreak.

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Tags: COVID19, Emerging Technology, Future of Work

Becoming a Data Driven Organisation Part 1
Medium.com
March 24, 2020
What are Data Driven Businesses and what do they need to do to become truly data driven? Data and AI are forcing to change the business landscape and this leads the organisations to shift from traditional to a more intelligent and data centric decision making. While doing so, organisations face many challenges; some making to hit the border, other being stuck somewhere in the middle fighting with internal politics and other issues. In this series, I would like to discuss those issues and approaches for businesses to become a data driven entity. However, to start with, I will focus on defining what makes a business to be truly data driven.

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Tags: Big Data, Business Strategy, Digital Disruption

Future Children - Can IoT Devices Help Save the World
IEEE IT Professional
November 08, 2019
Gave a unique perspective of how AI and IoT can be sued to better serve the society at large, reducing the water waste and causing less CO2 emissions.

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Tags: AI, IoT, Social

5G - Fifth Generation of Mobile Networks
LinkedIn
December 04, 2018
We are hearing a lot about 5G these days, but the question is what is it and how it will change our life? 5G is not something that will replace 4G completely at once, and will continue to exist, rather it is organic phenomenon that will evolve and change over time. This should also clarify the perspective that why there are already some efforts going on for 6G. We need to recognise the fact that there are more promising developments to come ahead of this. All smartphone companies are doing their level best to launch 5G enabled devices and connections. These devices will operate in different frequency bands such as from 3 to 300 GHz. Let's put these promising opportunities aside for now and start with actually introducing 5G and how the mobile technology has evolved from 1G to where it stands today. Before, concluding, I will also discuss advantages of 5G and some limiting factors / disadvantages of 5G (as of now).

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Tags: 5G, AI, Digital Transformation

Agile Transformation: What is it?
LinkedIn
September 18, 2016
In the following lines, I will shed some light on my experience w.r.t Agile transformation. This is one of the first few.

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Tags: AI, Digital Transformation, Agile

1 Book
Cognitive Enterprise: An End to End AI Strategy (to be Published)
TBD
July 31, 2020
In this book, I take the readers, the decision makers for a journey of how to transform their enterprises using AI. What are the critical decisions and choices they need to take and what are the steps to be implemented that will lead to a fully transformed and cognitive enterprise.

See publication

Tags: AI, Digital Disruption, Business Strategy

1 Founder
CEO
reminet
January 01, 2018
Developing high quality business strategies and plans ensuring their alignment with short-term and long-term objectives
Leading and motivating RemiNet team to advance employee engagement and develop a high performing managerial team
Overseeing all operations and business activities to ensure they produce the desired results and are consistent with the overall strategy and mission
Act within its powers as Company’s executive in-charge;
Set up and achieve the business targets;
and much more

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Tags: Business Strategy, Leadership, Sales

2 Industry Certifications
Scaled Scrum Master
Scrum.org
May 19, 2016

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Tags: Agile, Change Management, Culture

Scrum Master
Scrum.org
April 29, 2016

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Tags: Agile, Culture, Design Thinking

2 Keynotes
Augmented Human Intelligence: Collaborative Intelligence
EuroEvents, AI & Analytics: The power of AI in Business conference
November 13, 2018
Humans and Machines working together

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Tags: AI, Digital Transformation, Quantum Computing

Data Monetozation
NA
October 18, 2017
How to make your organisation as data monetised enterprise using AI and other modern technologies.

See publication

Tags: AI, Change Management, Emerging Technology

1 Media Interview
Data Driven Future
Dataconomy
November 24, 2018
Data Driven Future
What is holding companies to become truly data and AI driven enterprises?

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Tags: AI, Digital Transformation, Future of Work

1 Panel
Role of Data Science in Transforming the Industry
CUI
March 23, 2018
Discussed the transformation role of AI and Data Science in changing different industry verticals

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Tags: AI, Digital Transformation

Radar

1 Prediction
2021 Predictions for Quantum Computing

Date : October 08, 2020

I think 2021 will be the year when QC becomes more democratized and goes mainstream. While technologies like IoT, hybrid cloud, and data analytics — will still strongly have their position in the business landscape —, I see an expanding usage of AI, fifth-gen networks (5G), and above all, quantum computing. Besides its applications in cybersecurity, drug development, financial modeling, better batteries, traffic optimization, climate change, materials discovery, simulation, AI, etc., it can take our understanding of nature and chemistry to a level that has never been feasible before.

I expect the following quantum trends to accelerate in 2021.
1. Quantum Machine Learning- AI and Machine Learning. As AI has seen many applications already, it is the next realistic step to leverage quantum machine learning for easy and realistic QC usage.
2. Covid-19 and Beyond: QC could play a pivotal role in use cases like vaccine development and identifying and managing the spread of viruses. Potential applications of QC include predicting the evolution of COVID-19 using quantum machine learning. Quantum sensors, including NV-diamond sensors, have the potential to detect COVID-19. Quantum plasmonic sensors with considerably less noise could be utilized in blood protein analysis, chemical detection, and atmospheric sensing.
3. Quantum Algorithms: The development of new Quantum Algorithms and conversion from classical algorithms into quantumized algorithms.
4. Performance improvement and Optimization of Quantum Noise.
5. Development of new (or advancements in) Real-Time architectures for QC.
6. Use Cases: Expanding Industrial Use Cases suitable for Quantum Computing.
7. Conventional cum Quantum: Hybrid computing approach to problem-solving. This new paradigm of computation with a hybrid approach will result in the emergence of novel ways to solve the existing business problems and bring new opportunities, which were inconceivable earlier.
8. Adoption: Quantum computing enterprise adoption at scale, dependent on advances in storage, integrations, deployments, and security of quantum applications.
9. Advances in Quantum cryptography and Quantum-safe cryptography
10. QCaaS: QC as a Service will be a natural choice for organizations to tap into the experiments.


As we move forward with ongoing research and innovation in Quantum technology, the need for products and platforms that support diverse algorithms, frameworks, and hardware with unified development and deployment experience to the developers will start gaining traction. We saw some initial efforts in this area in the 2019–20 that will get a further impulse in 2021. Though the adoption of Quantum at scale might seem to be a faraway reality, the work in the next 1–2 years will determine the speed at which industry will start strategizing, building, and deploying quantum applications.

See Radar

Blog

3 Article/Blogs
Living a data culture
Thinkers360
November 10, 2020
Being data-driven is critical to succeeding in today’s world. When an organization exercises a “data-driven” approach, it makes strategic choices based on data analysis and interpretation. Such an approach facilitates companies to experiment and organize their data with the goal of better serving their customers, employees, and improving their operations. Using data to drive its actions, an organization can contextualize and personalize its messaging to its prospects and customers for a more customer-centric strategy.

However, organizations face many challenges in becoming a data-driven organization and building and retaining a data-driven culture. Some of those challenges include their inability to emphasize long term objectives, lack of shared vision, focus on short term RoI gains and ignoring the Return on Value and opportunities that data brings, skills gap, and lack of having the full picture or true understanding of what it would mean for them if they become a DD organization.

nterprise success in a data-driven context depends upon complete access to data and instantaneous action, among other factors. To carry data to every decision, leaders need not only to tear down silos; rather, they have to manage and work with data where it lives strategically. Such a strategy should entail at least the following:

Identify the technological and cultural obstacles to realizing the full potential of data.
Leverage data organization while reducing friction.
Align their approach to data with overall business strategy.
Brainstorm and plan for data monetization opportunities
“Data-drivenness is about building tools, abilities, and, most crucially, a culture that acts on data.” Carl Anderson

Attributes of a business who has employed the data culture:

A Data-driven organization must display the following attributes:

All Decision making based on the data
Not being victims of their past success
Redesigning their strategy based on collaborative intelligence
Build a data culture
Data monetization
“Without data, you’re just another person with an opinion.” William Edwards Deming

See blog

Tags: Big Data, Change Management, Diversity and Inclusion

Path to Become a Data Scientist?
Thinkers360
November 03, 2020
Whether you have been into the data science field or just entering, I believe you will significantly benefit from this article in many ways. We first outline the importance of the data science field, then we discuss different types of organizations, highlighting the importance of being data-driven. Then we talk about the demand of data scientists, and their skills; emphasizing the dynamicity of the field and the need for continuous learning. Then we dive deep into different routes available for you to become a data scientist or take your skills to the next level.

You must have heard about the fact that Data Science is the sexiest job of the 21st Century and might be wondering how come, it is so overhyped? Why is everyone talking about it? Even after the pandemic, people started to talk more about it and use data science techniques to analyze data. Some got it right, and some got it wrong; that is any way out of scope to our discussion here.
Undoubtedly, data and AI is the fastest growing industry with multi-billion dollar potential. Consequently, every organization is trying to make the most out of it. There are three types of organizations.
1) which have the data and they would like to get insights out of it,
2) the ones who have the skills, and can gain insights and help businesses become data-driven. The ones which are providing data science skills, experts, and consultancy services,
3) the ones who provide specialized platforms to support the organizations in achieving their data-related objectives.

You may see some organizations having a blend of these skills together. However, these are the capabilities that businesses need to become data-driven. Considering the costs and associated ups and downs, some companies may develop their capabilities or outsource them.

Different types of organizations and their dire wish to become data-driven dictates the high and urgent demand for data scientists and Machine Learning engineers. They are the ones, which would in the end crunch the numbers and make sense of them and uncover hidden insights in the data for the businesses.

With all the exciting and wide range of opportunities being available for data scientists, getting yourself skilled and becoming acquainted with data science is a great way to show your competitive edge and prove your value for the business. Data Science is a very complex, dynamic, and continuously evolving field, which makes it challenging as well as exciting. The skills needed, languages that you can leverage to build data science and machine learning pipelines, libraries, frameworks, tools are constantly changing and maturing — this asks for nothing more, nothing less than continuous learning.
Let’s discuss the different options and paths available for you to become a data scientist and (if you are already a data scientist) what to do more to achieve the next level of expertise.
1. Degree
There are several universities providing degrees (Master Level) or postgraduate level courses in data science under different names such as data analytics, machine learning, data science, business analytics, to name a few. There are also various online (remote) alternatives available. One has to evaluate them according to their circumstances, suitability, and affordability. It is up to you to choose a college or an online, in the case of online studies, you do not have to relocate, but costs might still be high, depending on the program that you choose.
2. Data Science Fellowship Programs
There are many institutes, companies, or startups offering several months hands-on fellowships, which give you the possibility to do hands-on assignments focusing on solving a business problem for one of the partners (or sponsor)of the institute/fellowship. It is an excellent opportunity for both the business and fellows to see if they fit for each other in the longer run, and in short-run companies get their problem solved while you as a graduate get the hands-on experience for a real project.
3. Learning through online platforms (MOOCs)
Many online sites are offering courses that you can take to become an expert in the art of data science. I call it art and science because it involves both of them; solving business problems is an art, and solving it using data science, as its name suggests, is science. And you need both; only one would not suffice in today’s job market.
There are many online platforms offering courses in data science; some of the providers include Coursera, Edx, DataCamp, Udacity, Udemy, and many more. Also, many universities are offering online courses and specializations through these platforms. These programs are economically feasible for pretty much everyone, and you can take them at your own pace, which helps the student to grasp the concept and still do the whole course. It is up to you to decide for an individual course, a micromaster or a nano degree program
In addition, there are a few hands-on projects, which help you to see a step by step guideline on how to build a project for a business problem.
4. Doing hands-on projects
This centres around taking it on your own, taking the control in your own hands, and steering it yourself. Although this is true for all other cases but this is more true and much needed in this case. You have to start with one hands-on project and scale it up and sideways for application scenarios, algorithms, etc.
5. Reading books
Especially for those who love reading, to learn a concept, and then implement or master it. It is also an excellent way for many, but maybe not for all; It takes time, and you might be lost when it comes to doing hands-on. I would recommend this only once you have the necessary hands-on experience and know the fundamentals. And in today’s agile and fast-moving world, I would emphasize to start with any source that you like and combine it with others as you go along. There is no one silver bullet.
6. Medium
There are a plethora of articles on almost every subject on Medium. They are usually not very long, and this helps in many ways to keep you focused, to get going by achieving valuable and useful knowledge, and get it implemented and turn it into something that sticks. To be able to use this platform, you can either create a free account or a paid/member account. Then the next step is to look for articles in top publications such as TowardsDataScience and TowardsAI. I usually publish my articles in these publications.

One great way to learn Machine Learning (and anything else, of course) is to write about what you have learned. You could also become a writer on Medium, and submit your stories about Data and AI to this publication here.
7. Kaggle and other competitions
Many people nail down technical concepts by joining competitions like Kaggle and others. It is also a great way to put yourself into a framework of discipline, following the deadlines, being challenged, and achieve something which, in addition to you mastering on concepts, gives you monetary benefits, gets you the recognition that you can claim for your fame. Of course, It does not happen overnight, it takes time, and everything does, so be patient and persistent in whatever you do.
8. Youtube Videos
You can also learn data science by following on YouTube. There are many courses or subject specific, both short and long videos that you can quickly serach and skim through.
Overall you are the one who knows yourself the best, what works for you, and what does not? You know the things about you which you might be reluctant to share with others but would help you to decide which option (or combination of them) would suit you the best. Let me raise some questions here, which might help you to think, plan, and execute your journey to become a data scientist.
Should I consider an online or face to face program?
What are the advantages and disadvantages for each of them in consideration with my particular scenario, life situation, monetary and other aspects?
Can I combine them with my current job, or shall I take a break and join an onsite fellowship?
What are the concepts that I know well, and what do I need to master, or should I start from scratch?
Are there any pre-requisites? Do I need to learn a programming language first, for example?
What is the time commitment required?
What is the required cost contribution on my behalf?
Think about these questions and others and try to answer them for yourself to evaluate the best way for you to learn and master data science.

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Tags: Analytics, Big Data, Digital Transformation

2021 Predictions for Quantum Computing
Thinkers360
October 08, 2020
[Image by Gerd Altmann from Pixabay]

I think 2021 will be the year when QC becomes more democratized and goes mainstream. While technologies like IoT, hybrid cloud, and data analytics — will still strongly have their position in the business landscape —, I see an expanding usage of AI, fifth-gen networks (5G), and above all, quantum computing. Besides its applications in cybersecurity, drug development, financial modeling, better batteries, traffic optimization, climate change, materials discovery, simulation, AI, etc., it can take our understanding of nature and chemistry to a level that has never been feasible before.

I expect the following quantum trends to accelerate in 2021.

1. Quantum Machine Learning- AI and Machine Learning. As AI has seen many applications already, it is the next realistic step to leverage quantum machine learning for easy and realistic QC usage.

2. Covid-19 and Beyond: QC could play a pivotal role in use cases like vaccine development and identifying and managing the spread of viruses. Potential applications of QC include predicting the evolution of COVID-19 using quantum machine learning. Quantum sensors, including NV-diamond sensors, have the potential to detect COVID-19. Quantum plasmonic sensors with considerably less noise could be utilized in blood protein analysis, chemical detection, and atmospheric sensing.

3. Quantum Algorithms: The development of new Quantum Algorithms and conversion from classical algorithms into quantumized algorithms.

4. Performance improvement and Optimization of Quantum Noise.

5. Development of new (or advancements in) Real-Time architectures for QC.

6. Use Cases: Expanding Industrial Use Cases suitable for Quantum Computing.

7. Conventional cum Quantum: Hybrid computing approach to problem-solving. This new paradigm of computation with a hybrid approach will result in the emergence of novel ways to solve the existing business problems and bring new opportunities, which were inconceivable earlier.

8. Adoption: Quantum computing enterprise adoption at scale, dependent on advances in storage, integrations, deployments, and security of quantum applications.

9. Advances in Quantum cryptography and Quantum-safe cryptography

10. QCaaS: QC as a Service will be a natural choice for organizations to tap into the experiments.

As we move forward with ongoing research and innovation in Quantum technology, the need for products and platforms that support diverse algorithms, frameworks, and hardware with unified development and deployment experience to the developers will start gaining traction. We saw some initial efforts in this area in the 2019–20 that will get a further impulse in 2021. Though the adoption of Quantum at scale might seem to be a faraway reality, the work in the next 1–2 years will determine the speed at which industry will start strategizing, building, and deploying quantum applications.

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Tags: Quantum Computing

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AI's transformational role

Location: Virtual, Worldwide    Date Available: December 07th, 2020    Fees: TBD

Submission Date: November 10th, 2020    Service Type: Service Offered

AI's transformational role

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