Weekly Data Science Round-up: March 27th, 2019

5 Ways to Check if Data Science is the Right Career Option for you: Do you love problem-solving? Do numbers excite you? Do you possess the patience of a saint? And lastly, do you love data?

Can Outsourcing Reduce the Jobs Shortage in Data Science?  Outsourcing data can help companies save 50% of their budget as the company do not have to bear office cost, equipment and their maintenance costs.

Why Python is More Relevant in 2019 Than Ever Before: Owing to its versatility, simplicity, flexibility, and a plethora of available libraries Python has the largest community of programmers.

Watch this IPL with a dash of Data Science: ESPNcricinfo will use data science, big data, and cricket intelligence, to produce a set of numbers that will help fans understand and appreciate the game better.

5 Crucial Data Science Skills to Learn in 2019:  Machine learning, statistics, quantitative analysis, mathematics, and programming languages are broad areas in which you’ll need to build expertise. If we were to delve a little deeper in what exactly these skills entail, you’ll come across these tools.

Weekly Artificial Intelligence Roundup: March 27th, 2019

The ethics of artificial intelligence: Teaching computers to be good people: As algorithms become more ‘intelligent’, researchers are working on new ways to regulate the extent to which AI is able to grasp and mimic human intelligence.

Spot and fix supply chain problems with artificial intelligence: Due to the colossal volume of data at the disposal of organisations, deriving insights from data can reduce the error percentage as close to zero as possible.

How Will AI Create More Jobs by 2025?Reports suggest about 9% of the workforce will be employed for roles which do not exist today. These new jobs will be powered by artificial intelligence and machine learning.

AI in Healthcare to Become a Billion Dollar IndustryThe integration of AI in healthcare will automate many processes. Approximately 21% of healthcare facilities have a specific purchase plan of AI tools that is predicted to be executed in the upcoming years in Europe alone.

How Amazon is Dazzling the World with Artificial Intelligence?: Amazon has been investing in AI for more than 2 decades now to predict and mould customer experience. The current state of AI and machine learning promise an exciting future and Amazon is undoubtedly at the forefront.

It would have been impossible to transition to Analytics without guidance and help from Great Learning: Ashwaraj, PGP-BABI Alumnus

Why did you choose the PGP-BABI program?

I come from a mechanical engineering background and after graduation, I worked in the automotive industry for a while. After that, I worked in the education industry and then decided to shift into analytics. I was looking for programs that were available in the market and based on my peers and friends suggestions, I realised that the PGP-BABI program by Great Learning was one of the best programs that I could have gone for.

Why data science?

This happened when I was in the education industry. I used to manage a territory where I had to develop good business outcomes. Basically, the target was to get more business in less time. And practically, I couldn’t find any feasible solution in front of me. The only thing that I had access to was loads and loads of data. When I started crunching the data using Excel, I was unable to get faster outcomes and I realised that maybe I would need other tools that could help me to turn things around faster. And moreover, I also lacked the required knowledge. As I started digging deeper, I realised that I should go for formal training that could help me in turning around the data into goldmines.

What did you like the most about the program?

PGP-BABI program is a perfect blend of both knowledge and skills. Every concept was followed by a practical assignment, case study, or a quiz. The program enabled me to think beyond the simple learning tools of the trade and inculcated the ability to think from a business perspective. Being taught and mentored by some of the best individuals in the industry and academics ensured that my knowledge and skill grew simultaneously. I can humbly say that without the guidance and help from Great Learning, my transition from Education to Analytics would have been impossible.

How did you land your current job?

When I was joining this course, I found a relevant contact in the automotive industry. I also spoke to a few professors who came to Hyderabad. There was a marketing professor, he mentioned that Ford was looking for analysts and I should try for it. When I was about to complete my course, one of the alumni with whom I had worked in Mahindra offered me a job in Ford and the rest, as they say, is history.

How did you manage to learn with a full-time job?

It was a little challenging in the beginning because my job required me to be on the field most of the time. But after proper planning and talking to my company stakeholders, they were cooperative as I was able to show immediate benefits that I was getting out of learning. I also became more organised as time passed. In a way, I was able to learn time management as well with Great Learning.

What advice would you give to the aspirants?

I would say, one should keep three things in mind. First, take the course very seriously because the more serious you are towards the program, the better are the outcomes.

Second thing is to have an open mind when classes are happening, even online classes. Just go through the recordings again and get an understanding of the content.

The third and the most important thing is that when you are given assignments and quizzes, do them deliberately and only for yourself, not for others. These three things will definitely help in churning out the best outcomes from the program.

Weekly Data Science Round-up: March 22, 2019

Top Data Science use cases in Gaming: Data science is making its benefits apparent in game design, monetisation, visual effect and much more.

The difference between Data Science and Machine Learning: Here’s how you spot the biggest differences between machine learning and data science.

How to negotiate your data scientist salary: Building the right skillset, having the right tone and demeanour can go a long way in getting the salary that you want.

Data Science generalists vs. specialists: A “T-shaped” data science professionals with broad general knowledge and specialisation in certain skills is the most valuable.

5 Qualities to Look for When Hiring a Data Scientist: Companies look for candidates with statistical thinking chops and data intuition when hiring, among these other skills.


Weekly Artificial Intelligence Round-up: March 22nd, 2019

Here’s our weekly round-up of the most essential reads on Artificial Intelligence from across the Internet:

Why Every Company Needs An Artificial Intelligence (AI) Strategy For 2019: Companies need to define the specific use cases for AI to leverage this technology to meet their business goals.

Preparing The Human Workforce For The Machine Workforce: The nature of the workforce is changing, and working professionals need to educate themselves to remain relevant in the future.

China: Tech Cold War With U.S. Continues: China is signing laws into place that limit the influence and dependence on American technology.

World’s biggest AI startup exploring self-driving cars: SenseTime is actively seeking out investments in fellow startups that can benefit from its own technology.

How will India contribute to AI innovation in the world?: India is in a unique position; will we leverage it to innovate in AI?

An algorithm that can recognise handwriting: How to build a framework for an AI model that is able to recognise handwritten text.

Data Science Round-up: March 20th, 2019

Here’s our round-up of the most essential reads on Data Science from across the Internet:

Data Science is now bigger than Big Data: Data Science is now commanding the hype that Big data used to have, as organisations are looking for new ways to leverage a large amount of data they have to produce insights.

Why Data Science Teams Need Generalists: Data Science generalists have improved autonomy and are empowered to make better business decisions because they have better knowledge of end-to-end business capabilities.

Europe is better for Data Science compared to the US: Europe’s heavy investments in education and infrastructure and education has paid dividends in better skills.

How Data Science is powering Nvidia’s business: Data Science has become the new driver across the computing landscape, encompassing analytics, AI/machine learning, and inferencing

Secrets of a successful Data Scientist in 2018: A report on the most important skills that successful data scientists have and other key information.

9 Applications of Data Science in E-commerce: Interesting applications of Data Science in e-commerce that are driving results.


Artificial Intelligence Round-up: March 20, 2019

Here’s our round-up of the most essential reads on Artificial Intelligence from across the Internet:

How AI is changing science: Automation algorithms are assuming the role of a scientist and are crunching numbers at a cosmic scale, but they still lack the spirit of scientific enquiry. We still have some way to go before that can discover physics or mathematics that the brightest humans alive are not able to do on their own.

A Robotic Leg Learns to Walk Without Prior Knowledge: Researchers have developed a bio-inspired algorithm that can learn a new walking task by itself after only 5 minutes of unstructured play, and then adapt to other tasks without any additional programming.

AI generated poetry: A retrained version of an older algorithm has finally learned how to make rhyme and meter work to generate poetry based on old classics. The results are middling, but its a promising step forward.

DeepMind and Google; Controlling AI: Google acquired DeepMind for $600 million, and is now looking to monetise the company. But the company’s founder is resisting attempts with a bid to focus on AI research and producing path-breaking technologies.

5 most promising roles for Artificial Intelligence in India: Roles such as AI architect, AI product manager and more are defining the growth of AI, and offering professionals a fulfilling and rewarding career.

Everything you need to run Deep Learning experiments: Learning by doing is the best way to understand Deep Learning better, and you need some serious computing firepower to run experiments. Here is a list of all the components you’ll need to build your rig.


5 most promising roles for Artificial Intelligence in India

There is a massive demand for skilled AI professionals right now in India, and there is a lack of talent that can fill these roles. 76% of companies feel this lack of talent is hindering their ability to implement AI. For working professionals looking to meet this supply-demand gap, it might be confusing to choose the right aspect of AI to specialise in, owing to the broad nature of this domain. So we’ve put together this list of the most impactful, high-demand roles that are available right now. This is by no means an exhaustive list, but a collection of roles that are most likely to be highly impactful on the growth of the AI industry, as well as offering high growth opportunities for professionals engaging in it.

Data Scientist

Data Scientist can be a very broad term that describes a host of functions across a host of different domains, but in this specific instance, a data scientist is responsible for the massive amounts of data that are required to build robust AI models. Their chief responsibility is analysis, that ranges from collecting data to manipulating to provide actionable insights. In most cases, data is unstructured and unsanitized, and the data scientist needs to be able to structure the data to make it processable by the AI algorithm. Any real-world application of AI needs high-quality and relevant data for it to deliver the expected results, and it’s the role of the data scientist to manipulate the data into a machine-readable format.

AI Software Engineer

The role of the software engineer is vital to the functioning and management of AI models and applications. Obvious as it sounds, the role of an AI software engineer is to build the AI models and the applications, where the AI architect will piece them together to form a working solution. Software engineers need to be proficient in the programming language of choice, along with a set of routines and functions, called libraries, that are used frequently.

AI Architect

This is one of the most crucial roles to sustain an AI model, and the lack of AI Architects actually hampers the ability of companies to execute their AI models. An AI Architect is responsible for translating the companies business objectives to efficient and profitable AI implementation, so it’s a really high-impact role in a high-impact industry. They chart out the structure of the AI implementation and design how the different components of AI work together to deliver a solution.

AI Model Trainer

This role would overlap with the Software Engineer or Data Scientist, but as AI systems mature and become more sophisticated, AI trainers will grow into a specialised role where their sole task is to train AI models with preferred data that improves their overall accuracy and effectiveness. They usually do this by manually feeding the training algorithm with relevant data that improves accuracy and performance.

AI Product Manager

It’s crucial to look at AI as a product rather than just as a supplementary service. The function of an AI product manager, as any other product manager is to be the point of contact for all teams, acting as a single point of contact for all stakeholders of the product. They are responsible for charting out the roadmap for the product and are involved in aligning the product development with the business goals of the company. They also take part in activities such as taking the product to market, collecting user feedback and converting them to feature requests, and much more.

If any of these roles interests you and you are looking for a way to launch your career in Artificial Intelligence and Machine learning, you can consider Great Learning’s Post Graduate Program in AI and ML that teachers learners industry-relevant skills through hands-on projects.

I am from a non-technical background but I found it quite easy to follow the videos: Madhan Deverajan, PGP-CC Alumnus

Learning once and applying it for the rest of your life is a long-lost trend. With technologies evolving continuously, ‘Learning for life’ is the new mantra to stay relevant. In the case of Madhan, our PGP-CC alumnus, a professional with 15 years of experience, upskilling was the only way out to excel in his existing job. Read on to find out how Great Learning’s postgraduate program in cloud computing helped him to, in his own words, ‘manage projects effectively’.

Why did you take up the PGP-CC program by Great Learning?

I am an IT project cum service transition manager. I basically manage end to end IT service transitions from various clients and customers to our company. I work for UST Global, which is a service support company. If there is a requirement from a customer for certain IT services like cloud, be it level 1 or level 2 support services, I manage end to end transition right from setting up the process to the transformation from the customer to our company. I ensure that a cloud support team is set up and we are able to deliver support services to the various IT customers. I have over 15 years of experience in handling IT projects. The primary reason for taking up the cloud computing program is that just certifications are very narrowly focussed. I went through the brochure of Great Learning’s PGP-CC program and realized that it touched upon various topics right from development on the cloud to basics of setting up cloud infrastructure. I needed an overall understanding of the cloud so that I would be able to manage projects effectively. Now, I feel much more confident in understanding the problems. I took up this program to perform better in my current role. I had 3-4 choices, but I found this program as more comprehensive than other online programs. So, I opted for this.

How is learning from the program helping in your current role?

All the projects that I did during the program were hands-on projects so that gave me an understanding of the various components of a cloud system and now I know what configurations should be made and what are the checks that need to be performed for running a particular cloud service. Earlier, I used to completely rely on the technical staff or the architects, but now I am able to suggest better options instead of doing something the traditional way.

Are you Azure or AWS certified?

One of my long term plans is to get Azure or AWS certified.

What did you like the most about the program?

1.Curriculum relevance.
2.Assignments, labs and projects.
3.Mr Nirmalya. He was truly amazing during the mentoring sessions that I had attended. I am from a non-technical background but I found it quite easy to connect the dots and follow the videos and sessions with him. His inimitable and fantastic delivery of technical sessions in an understandable way was extremely suitable for an end user like me.

Was the program office cooperative?

Mrs Ekta from the program office had been great. Without her persuasion and mentoring, I would have given up on the course as I was finding it difficult to catch up with the labs and projects midway through the course. She had been open to feedback and was prompt in getting back with answers to our queries and also had been very patient with us. I was given the support and time to catch up with the rest of the group. I am taking some time out to practice the syllabus all over again and possibly plan for further studies in about 6 months with Great Learning.

What advice would you like to give to the aspirants?

I wouldn’t say that the program is very tough, but it is demanding. My advice would be to stick to the deadlines and schedules and work through your practical lab classes. Do not miss even one single session and make sure you watch all the videos. I am able to see the value of the lab sessions in real-life cases now. Even if you are not a very technical person, videos will help you in understanding everything step by step and connect with your peers. Make sure you are not alone that is what even Great Learning’s program office recommends. Connect with fellow learners and get the best out of the program.