Microsoft AI Classroom Series was started in September to make students ready with emerging technologies like AI and Cloud Computing. This session of the Microsoft AI Classroom Series will cover data science lifecycle and cognitive services, building machine learning models on Azure, and intelligent conversational AI. Students will also get exclusive Microsoft Official Course (MOC) material for the AI-900 course during the session.
The video session will be delivered by speakers from industry experts from Microsoft India, NASSCOM FutureSkills, IIT Madras, Wipro, and more. This program, however, is only for students enrolled in Indian universities and are residents of India.
After completing the Microsoft AI Classroom Series session, learners will receive an email from taking an online assessment, where they will have to answer 30 multiple-choice questions. On getting a score of 60 percent or more, they will get a “Microsoft AI workshop” certificate of participation. However, aspirants will have a total of 3 attempts to pass and receive the certification from Microsoft and NASSCOM FutureSkills.
The first session starts on 14 December, which also includes a Live Q&A during the event. Although seats are limited per session, you will have an opportunity to choose from any session starting from 14 December to 19 December. Irrespective of which session you attend, the assessment test will be active only from 19 to 30 December. Make sure you clear the examination within three attempts before 30 December to get the certificate.
There are a few prerequisites to attend this session of the Microsoft AI Classroom Series to ensure you make the most out of the upcoming sessions. You can access suggested prerequisite materials of Get started with AI fundamentals, Azure Cognitive Services, Introduction to Azure Machine Learning Services, How to create bots with Azure Bot Service.
There are other requirements like VSCode Jupyter Notebooks setup, GitHub account, and more to simplify the session’s learning process.
FutureSkills Prime is offering a big data foundation course with certification for a limited time. The program is devised by Digital Vidya, which includes videos and projects, and more. According to the course description, it would take 55 hours to complete and get certified by Digital Vidya and NASSCOM. However, you will have only one attempt to complete the course, which is free till 17 May 2021.
Big Data course has three modules — introduction to big data analytics, big data fundamentals and platforms, big data processing, management, and analytics. The course includes video lessons on prominent big data platforms MongoDB, Spark, Map Reduce, and Hadoop. While you might not get to learn all the in-depth techniques of the tools, you can get an overview of big data platforms’ capabilities and implementation.
The course also imparts knowledge about data pipelines, which is an essential part of data management in organizations to streamline the data science workflow, thereby making it a complete foundational course for big data analytics. The course also allows you to download materials that can be handy while working with big data tools for quickly setting up the environment for projects.
In an attempt to democratize digital skills among people, FutureSkills Prime was launched in a joint partnership among the Government of India, Ministry of Electronics and Information Technology, and National Association of Software and Services Companies (NASSCOM). The platform will include courses on emerging technologies like Artificial Intelligence, Augmented and Virtual Reality, Blockchain, Cloud Computing, Cybersecurity, Robotics Process Automation, Web and Mobile development, and more.
Although it is still in the beta phase, learners can leverage the FutureSkills Prime platform to learn from courses available for free created by industry experts. The platform will further include more courses in the future to ensure learners have access to learn cutting-edge technologies.
Register for the FutureSkills Prime Big Data Course for free.
PhonePe’s data science team acts as one of the foundational pillars of its payments service to millions of people across India. Founded in 2015, the company became the most widely used payment service, amassing 40% of the total UPI payments with 925 million transactions overall in October 2020. Being trusted by 250 million users, PhonePe extensively leverages machine learning to ensure customer trust in a highly regulated industry. A strong Data Science culture is a key pillar for the rising popularity and leadership of PhonePe in this highly competitive space.
To peek into the data science culture of the company, Analytics Drift got in touch with Kedar Swadi, head of data sciences at PhonePe. Kedar shared interesting insights into the data science team of the Walmart-owned payments company, as well as shared his opinions on best practices for aspirants to flourish in their careers.
How Is Machine Learning Leveraged At PhonePe?
Machine learning not only helps PhonePe users and partner merchants enhance customer satisfaction but also is leveraged within the organization to optimize business processes for increasing revenue and reducing costs. “Data science is at the core of a lot of decisions that we make at PhonePe; we utilize patterns in users’ payment history to remind them about the right payment at the right time,” says Kedar. It may be a payment to another user or a bill that one should not miss or recharge to keep the continuity of services. Besides, with superior machine learning techniques, PhonePe ensures that it identifies potential fraudulent users as early as possible to maintain a healthy payments ecosystem.
PhonePe’s Data Science Culture
“One of the many great things about data-driven PhonePe is its data science culture. We thrive on taking up hard problems while limiting the risks and experimenting to arriveat better solutions,” explains Kedar. The decision-makers back the data science efforts to enable the team to blaze a trail in various payment aspects. Data scientists at PhonePe are provided with the necessary time to explain the problems, gather information, and extract value from multiple data. With this, PhonePe allows innovation and accepts failures as a part of data science initiatives to keep the team motivated during setbacks.
While following the right set of principles to drive the team is one aspect of thriving in the competitive market, having the right professionals who fit in the culture is equally important. And since it takes years to understand the basics, tools, and techniques for a data science job, PhonePe relies on professionals who have two to three years of experience in the field. However, Kedar believes there are always exceptions, even for someone who wants to change careers but may have to start at the entry-level and build the required skills.
And when asked about the critical skills he seeks in data scientists while hiring, Kedar said that a strong background in algorithms, basic statistics, optimization, and programming skills is a prerequisite. Although some experience in the application of multiple machine learning algorithms helps, he believes new algorithms can always be learned on the job if one has obtained foundational skills. And since data science is also a communication-heavy discipline, Kedar stressed the importance of effectively communicating, whether it is to understand the problem or explain the results. Apart from the aforementioned skills, he evaluates applicants for the cultural fit-aspirants who can work in hierarchy-less organizations like PhonePe.
But how does PhonePe hire the desired data science professionals? Hiring data scientists is a massive challenge for organizations due to the talent gap in the market. As a workaround, PhonePe looks out for good people and then provides them with the support and opportunities to grow and get better. “A lot of emphases is laid on teamwork and supplementing each other’s skills. We look for people who show other valuable traits like curiosity, perseverance, self-motivation, and desire to excel,” says Kedar.
Do Data Scientists With Ph.D. Perform Well Over The Self-Learned?
According to Kedar, a Ph.D. implies a solid five or more years spent understanding a topic and the theory behind the techniques. This helps learners assimilate the strengths and weaknesses of a wide range of machine learning methodologies, enhance the ability to formulate and solve problems independently, and train to deal with failures. On the other hand, self-learning with MOOCs focuses on specific skills in a reasonably short time and in a fast-paced environment. Therefore, a Ph.D. has an advantage, but a few years of on-the-job experience by a self-learner usually reduces the gap.
Advice To Aspiring Data Scientists
Data science is about handling data, drawing insights from it, and communicating in a meaningful and actionable way with the various functions of an organization. To carry out all of these, one needs to have technical competence, domain expertise, and, most importantly, the right attitude.
“When it comes to technical competency, you need to have a strong foundation in algorithms, mathematics, and statistics. Learning new techniques when needed will become easy when you have acquired solid fundamentals. You should also be good at programming, know how to handle large amounts of data efficiently, and create scalable pipelines to work with ever-increasing amounts of data,” mentions Kedar.
“But domain expertise is acquired through experience. For instance, if you do not have sufficient knowledge about radiology, you will struggle to understand the data, problems, and other nuances of the field. Mastering even the sub-domains like banking, finance, insurance, investments and trading under the BFSI sector will take years. Consequently, steadily improving the data science skills in conjunction with domain expertise will be the key to a successful career.”
“Finally, as a data scientist, one needs to have the right attitude to believe in the process and not just implement glamorous machine learning techniques. 80% of data science is manual work of looking, cleaning, and processing data. This requires humility to work on non-glamorous aspects of data science. The tenacity to sift through a massive amount of data, the ability to deal with failures, and the strength to learn and get better are equally essential for data scientists,” concludes Kedar.
Backed by Alibaba, AutoX deploys 25 self-driving cars without safety drivers in Shenzhen, China. However, the service is not available for the general public just yet. In an interview, Xiao, founder of AutoX, said that it could take another two to three years for approval from regulatory authorities to offer fully self-driving cars to the public.
AutoX deploys self-driving cars
Founded in 2016, the company has delivered exceptional results quickly to impress regulators. On June 19, 2019, the company became the second in the world to operate robotaxi after obtaining a permit from California Public Utilities Commission. And in July 2020, the company tested its driverless car in California without a safety driver.
Robotaxi, a self-driving ride-hailing service with safety drivers, has gained prominence in the US and China, where AutoX has been at the forefront. The company’s success in mere four years can also be attributed to the association with its partners like NVIDIA for DRIVE platform and FIAT Chrysler for rolling out fleets in Asia.
In addition, AutoX has a global presence for its technology and market with research and development in Silicon Valley and Beijing, and an operational center in Wuhan and Shanghai. To further ramp up the operation, the company has plans to deploy self-driving cars in three more cities.
Over the years, the race to become a leader in the self-driving cars market has become apparent as more than 60+ companies are testing autonomous vehicles. Although it started late, AutoX is making huge inroads in the industry and giving early starters like Waymo, Cruise, Zoox, and Tesla, a run for their money.
2020 has been the year where self-driving cars promise is materializing as many stakeholders are embracing contactless delivery of food and travel services. After a lot of criticism from experts about autonomous technology, the industry is making people believe that level 5 autonomy is around the corners.
Salesforce, a CRM platform provider, announces the acquisition deal with Slack for $27.7 billion. If the deal doesn’t hit any regulatory roadblock, it will become the biggest ever acquisition by Salesforce. Earlier, in 2019, the company had acquired Tableau for $15.7 billion. Like Tableau, Slack will also be integrated with Salesforce Customer 360.
Since the launch of Slack in 2009, the company has been at the forefront of simplifying communication for developers as well as organizations. Over the years, it integrated tons of services to make it an all-in-one platform for effective communication and process workflows. But, since the launch of Microsoft Teams in 2017, Slack has faced the heat of Microsoft’s deep pocket. And during the pandemic, when employees started working from home, the demand for collaboration rose exponentially, and Slack failed to capture most of the market.
Even though Slack was uniquely positioned to capture the gamut of the market share, the company struggled to compete with Microsoft’s sales workforce, thereby losing its advantage in the market. Although Slack quickly integrated necessary services like video calling of different platforms, Teams increased its ambit quickly in the collaboration tool since it offers office suite to organizations. Teams made a lot of sense for organizations than Slack due to its deep integration with office products.
This is yet another example of the rising monopoly of blue-chip companies that force small companies to get acquired. Google, another tech giant that only realized the importance of collaboration tools, is actively revamping since the pandemic. With meet, chat, and rooms, Google is also tapping into the market for ease of collaboration. Slack could sense Microsoft’s increasing dominance and witness the change in strategy by Google, which could eventually impede its business.
While collaboration tools like Zoom saw a sharp increase in stock price during the pandemic, Slack’s stock was trading below $38 per share, which was the opening trading price when Slack went public on NYSE last June. Slack was struggling when the market for collaboration tools was hot during the early part of the year. In such circumstances, one of the best bets for a company is to get acquired.
And what better partner other than Salesforce would Slack have had agreed to make this acquisition deal? Salesforce has a deep penetration in organizations with a must-have CRM tool. With the integration of Slack with Salesforce, it has the potential to streamline the business process within organizations. Maybe, now, the competition for the market share for collaboration tools is even; Salesforce can fight shoulder to shoulder with Microsoft. At the same time, Google will now not have a straightforward path to penetrate the market. But, people would not complain even if Google comes into the ground zero with Microsoft Teams and Salesforce’s Slack vying for the top spot.
Analytics Drift interacted with Arihant Jain, Lead Data Scientist at ZestMoney, to get his perspective on various data science topics to help freshers make effective career decisions. Arihant is a mechanical engineer by degree and a data scientist by choice. Over the years, he has been actively mentoring students who want to get into the data science field. Currently, Arihant has more than six years of data science experience while working with some prominent organizations like Vodafone, RBL Bank, and Genpact.
AD: According to you, what are some of the mistakes made by freshers?
Arihant Jain: Beginners often consider themselves data scientists after learning Python, Statistics, SQL, and Mathematics. But, the real world is different; one has to have a business understanding to obtain returns on investment for companies. Data scientists are expected to assimilate business challenges and analyze whether it is solvable using machine learning techniques. If problems can be solved, the next step involves collecting the right data from different sources to eventually implement technical learnings. Along with technical skills, they should focus on structural and critical thinking, understanding the domain, converting business problems into analytical problems, and more.
AD: Why do most of the data science projects fail to go into production?
Arihant Jain: There are multiple factors as to why data science projects fail to go into production. One of the reasons projects remain in proof of concept for life is because professionals fail to accurately define the problem statements. This is where business understanding plays a significant role. In addition, failing to communicate the results of the proof of concepts with the decision-makers leads to failure in many data science projects. As a result, effective storytelling skills are vital for data scientists to bring ideas into reality within organizations. Critical thinking, business understanding, and storytelling, although underlooked, are essential for any data scientist to thrive in their careers as these help in delivering value in organizations.
AD: Do you think data scientists need to know about product development?
Arihant Jain: In the last couple of years, there was a demand for talents who could build models, but the curve is shifting, and it is going to move rapidly. Companies now realize that there is no value in hiring people who can only create models in Jupyter Notebook. Relying on software engineers to develop the products might not be the best way forward since they may not implement data science projects in the desired way. This is why MLOps is a prominent trend, where data scientists need to write production-level code and understand the deployment of models.
It might be too much to ask from beginners to learn MLOps, but merely an understanding will differentiate them from the rest. Obviously, they can become proficient as they move ahead in their careers, at least, a basic knowledge like fundamentals of Dockers and wrapping it up to deploy models locally and on the cloud will take them a long way.
Today, data scientists do not need to know some secrets to build models. Anyone can refer to articles and GitHub to create ML models. Now, the core skill which remains is how to design a model, how to design a problem statement, how to sell it through better storytelling, and how to deploy it and measure the effects. Organizations are already considering these skills while hiring, and, in the coming years, the demand for such skills will grow dramatically.
AD: How will the AutoML impact data science jobs?
Arihant Jain: AutoML is another hype created in the last couple of years, but it surely will not eat up the data science jobs. However, I am still optimistic about AutoML solutions’ role in automating redundant jobs in data science workflows. But, identifying and defining a problem is something that can only be done by professionals. Automating trivial tasks is a win-win for data scientists and AutoML providers, as professionals can focus on creative things, thereby bringing value to the organizations. Data scientists still spend a lot of time cleaning and other redundant tasks. Eliminating such practices with AutoML is what everyone wants.
AD: Is there a talent supply and demand gap in the industry?
Arihant Jain: In a way, there is no supply and demand gap because we see many data scientists coming out every day in a month. But, there is a difference between a data scientist who learns in nine months with certification versus having real skills, which is where the gap exists. Data science would not have been popular the way it is only because someone can import a library and run code. Rather it is about the impact data science can bring if implemented with due diligence. Organizations need professionals who can assist in generating revenue in organizations, not just write some algorithms that do not deliver value.
Although data science practitioners are coming out of institutes who are trained on Python and other tools, when it comes to the value they can bring, the industry is struggling to find the right talent. Unfortunately, I think the talent gap will still exist until aspirants start thinking independently because that is what data scientists do; learn the necessary skills while having a strong foundation. However, aspirants get lost in the ocean of information and learn several techniques instead of honing up basics.
AD: Many aspirants pursue data science because of the hype. What would you advise them?
Arihant Jain: If learners want to identify whether data science is for them or are just here because of the hype, they should work for three months on various projects, and if they feel like learning more, then data science is for them. One of the best ways to access oneself is by participating in Kaggle competitions that run for three to six months. After the contest ends, evaluate the leaderboard position and contemplate if they loved the process. If the answer to the latter is no, then one will eventually get frustrated in a few months. Do not enter the field because of the high paycheck. This field requires dedication, commitment, and hard work to solve problems and create impact.
Facebook announces to acquire Kustomer, a CRM solution provider, in an undisclosed deal. However, according to various sources, the deal is a little over $1 billion. Kustomer is an omnichannel CRM tool that brings all the interaction from different sources on its platform to enable organizations to interact with customers effortlessly.
Post the closing of the transaction Brad Birnbaum and Jeremy Suriel, co-founders of Kustomer, and the rest of the team will join Facebook. Started in 2015, the New York-based Kustomer has quickly gained traction among organizations that are providing superior customer experience with chatbots, in-house forums, and social platforms.
The adoption of chatbots due to the pandemic has increased rapidly to automate customer service and support. Organizations can process such data and target users with ads, improve services, and more. With the acquisition of Kustomer, Facebook will try to streamline customer service management by integrating CRM with Facebook products. While the data will not be automatically used from Kustomer for Facebook Ads, organizations will be able to use it for their own marketing campaigns.
Over 175 million people use WhatsApp to interact with organizations for several queries and services. Facebook sees this as a massive opportunity for them to further enhance the offerings and empower organizations to harness the power of unstructured data from web chat, email, and messaging.
“Facebook plans to support Kustomer’s operations by providing the resources it needs to scale its business, improve and innovate its product offering, and delight its customers,” wrote Dan Levy, VP of Ads and Business Products, and Matt Idema, COO of WhatsApp.
Simplilearn, an edtech platform that offers a wide range of tech courses, launches the SkillUp platform to provide over 300 top skills. Deliver by experts from the technology space, the platform has on-demand video lessons not only on data science and machine learning but also DevOps, IT service & architecture, and more. With over 1000+ hours of video lessons, it makes for a good start for beginners.
The idea behind Simplilearn’s SkillUp initiative is to allow aspirants to get started with technologies of their choice. If you are interested in exploring new fields of technology, you can also enroll in different in-demand skills like cybersecurity, software development, among others on the platform for fee.
In addition, to allow beginners to make effective career decisions, Simplilearn also offers free guides on career paths, interview tips, and salaries on Simplilearn’s SkillUp platform. The course and guides are available on both web and mobile platforms, thereby eliminating the form factor barrier for learners.
“We have launched this initiative to benefit millions of learners across the globe who may find it difficult to afford or access quality learning programs. It is our humble effort to democratize online skilling and help our learners to boost their careers and stay ahead of the curve. We will continue to add more topics and content to SkillUp over time, making it the premier destination for free digital-skills education,” said Krishna Kumar, founder and CEO of Simplilearn.
Indian Institute of Science (IISc) sets up an Artificial Intelligence and Robotics Technology Park (ARTPARK) in Bengaluru to innovate and solve problems related to Indian ecosystems. The non-profit ARTPARK is a public-private partnership where the Department of Science and Technology (DST) invested ₹170 crores as seed funding under the National Mission of Inter-disciplinary Cyber-Physical Systems (NM-ICPS). The idea behind NM-ICPS is to establish 25 Technology Innovation Hubs (TIHs) in India at top academic institutions. The mission is not only to perform but also to integrate the new technologies into real-world applications of sectors like agriculture, retail, automobiles, and more.
“While Silicon Valley might be innovating for the first billion, in India, we have the data and the talent to take on the problems of the developing world–the so-called six billion people,” said Umakant Soni, ARTPARK Co-Founder.
Being a public-private partnership, ARTPARK will receive ₹60 crores in a span of five years from the state government of Karnataka. Similar associations of center and state will further gain steam as the country is poised to introduce Science, Technology and Innovation Policy, 2020.
“Indian academia has been carrying out cutting-edge technology research in various domains. However, we have had systemic issues in moving the results of this research from university labs into the outside world. ARTPARK would go a long way in establishing a template for addressing this need,” said Govindan Rangarajan, director of IISc.
ARTPARK will bring researchers, entrepreneurship, and global industry partners to bring diverse perspectives in the innovation to mitigate some of the strenuous challenges witnessed in India.
“Google was born out of Stanford University and it is a trillion-dollar company now. We need to build such companies out of India. We work with industry, academia and entrepreneurs from across the world to make this happen, and I invite them to come and collaborate with us,” said Subhashis Banerjee, Chief Investment Officer, ARTPARK.
“We have also started a venture studio to create such deep tech companies and are also raising a $100M international venture fund along with the support of DST to support these and other AI and robotics companies.”
Google Verse by Verse will assist you in composing poems by predicting verses according to your inputs. In an attempt to integrate creativity with artificial intelligence, Google researchers released Verse by Verse as an experimental product to leverage machine learning techniques in highly creative tasks. Writing poems require your creative juice to make it thought-provoking as well as impactful. To help your creative juices flow, Verse by Verse suggests you with a pool of possible verses.
The researchers leverage two machine learning models to deliver relevant verses. While a generative model is trained on classical poetry, the other is trained to systematically understand the verses best follow the instructions.
With Verse by Verse, you can start composing poems with classical poets like William Cullen Bryant, Emily Dickinson, Sidney Lanier, Henry Wadsworth Longfellow, and more. Select any three poets from the list on the website to get predictions in the style of selected poets that act as your muses. You will then be asked to choose the poetic forms, syllable count, and rhyme (if you choose Quatrain as a poetic form). You are now good to start with the first line based on which the machine learning model provides predictions.
You can either use Google Verse by Verse predicted verses or take inspiration from the generated suggestions to write your own. Below is an example of a composed poem after providing the input as “Poetry flowing through my thoughts.”
“… to be able to suggest relevant verses, the system was trained to have a general semantic understanding of what lines of verse would best follow a previous line of verse. So even if you write on topics not commonly seen in classic poetry, the system will try its best to make suggestions that are relevant,” wrote Dave Uthus, software engineer of Google AI.
