Supriya Jha

Hiring Insights 💡 from a Principal Data Science Manager at Microsoft Read till the end for an amazing opportunity!   Recently, I had a conversation with Siddarth Ranganathan about what entry- and mid- level candidates should do to land a job in the current market.   Siddarth is a seasoned Data Science Leader with experience across Tech, Marketing, E-Commerce and Life Sciences sectors. Currently, he works on leading Product Data Science efforts at Microsoft, helping improve Azure. When recruiting candidates, he prefers candidates to have a strong grasp of metrics and how it ties to the business, ability to solve business problems, and strong skills applying statistical and machine learning concepts.   👉 To increase Callbacks for Product Data Science roles (for entry-/ and mid-) Projects involving A/B testing, rapid experimentation and scaling are attractive in the Product Data Science Domain. In addition, showcasing how projects moved the needle of org OKRs or how your work impacted the business could attract the right attention   👉 To clear interview for Product Data Science roles ⭐ SQL These are “table stakes” and a solid foundation in these cannot be compromised on when interviewing entry-, mid-level candidates   ⭐ Experimentation, Product Sense Build a solid foundation in statistics – A/B testing, Causal Inference being the main ones. A good understanding of product metrics, OKRs will help you succeed.   ⭐ Applied Data Science Skills The ability to solve a problem with data, convert a business problem into scientific formulation/experiments, and ability to derive actionable insights from results to help business   ⭐ Soft Skills Focus on storytelling, cross collaboration and strategic vision, as Product Data Science teams work closely with business/product partners. This is especially important for candidates interviewing for senior/managerial roles. Another unspoken skill the hiring manager often tests for is – can I trust this person to solve an ambiguous problem. So, molding your stories to reflect the situation, challenge, result and impact during behavioral interviews to convey the same can help you have more success. Finally - Siddarth Ranganathan is passionate about mentoring data scientists and aspirants alike and has guided several candidates to uplevel in their career. 💬 Comment 'mentoring' below 👇Repost this post and reach out to 👉 Siddarth Ranganathan if you want to understand how to transition from a data analyst/BIE roles to Data Scientist or if you want to move from a different domain to tech - just like the way he did. 👍 Leave a like to let me know you want to see more of such post! ♻ Repost to help other folks know what it takes to get hired as a Data Scientist 🏃‍♂️ Follow Karun Thankachan for more from this series!

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Thanks Pallav Goyal for sharing these insights. Granting admissions exclusively to female candidates at an institution like the Indian Institute of Management (IIM) in India, while aimed at promoting gender diversity, can indeed be perceived as biased. By reserving admissions only for female, male and non-binary individuals are excluded, which can be seen as discriminatory and contrary to the principle of equal opportunity. Such policies may compromise the merit-based admission system. If admissions are based solely on gender, some qualified candidates might be overlooked, potentially lowering the overall quality of the student body. There is a risk that female candiates admitted under such policies might be perceived as less qualified, leading to a stigma that their admission was not based on merit but on fulfilling a diversity quota. This can undermine the achievements of women and fuel resentment among other students. True diversity encompasses various dimensions including race, socioeconomic status, ethnicity, and sexual orientation. Focusing solely on gender overlooks these other important aspects, potentially leading to a less holistic approach to diversity. While the intention behind gender-specific admissions might be to correct historical gender imbalances and foster an inclusive environment, achieving diversity requires a more balanced approach that promotes equity without creating new forms of discrimination. Implementing comprehensive measures, such as targeted scholarships, mentorship programs, and outreach initiatives, might be more effective in promoting gender diversity without the drawbacks of exclusive admission policies.

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This a foundation course if you want to land up into the world of forecasting. I have done few forecasting projects earlier, but this course has exposed me to some lesser known fact essential if you want be ace time series modeling.

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Shahrukh Khan is recognized for his wit, humor, and charm. In this context, we've fine-tuned LLamav2 to respond briefly, in a crisp, witty, and humorous manner, akin to SRK himself. We've named it SRKGPT. For detailed insights into the fine-tuning process and the creation of the dataset to emulate SRK's style, please refer to the video in my channel for a comprehensive understanding. Video Link: youtu.be/gYPwx0DR7zc  Channel Link: youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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Hi all, I am Priyanka. I work as a Senior Data Engineer at a Non-Banking Financial Company (NBFC). With an MTech in Geoinformatics, I transitioned from Geoinformatics engineering to Data Engineering. In this blog, I’ll share my journey into this field and the resources I found helpful for anyone looking to make a similar switch. Here are some relevant hashtags for a LinkedIn post about Data Engineering and MLOps: #DataEngineering #Python #SQL #AWS #DataWarehousing #CareerTransition #InterviewPreparation

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COVID-19 brought abrupt changes in consumer behavior and impacted the accuracy of forecasting models. Similarly, UPI (Unified Payment Interface) abrupted consumer's spending methods, bringing many of them online. Concept drift happens when the statistical properties of the target variable, which the model is trying to predict, itself change over time. This causes problems because the predictions become less accurate and become unreliable. Here's a short video for Data Science enthusiasts I have created on the topic of Data drift and model monitoring followed by a practical exercise at the end. Like and subscribe for more such interesting concepts. Also, like and share over here for maximum reach. : ) Video Link: youtu.be/tQjRQWfYQ10 YT channel Link:  youtube.com/@datatrek #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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Unlocking Success in the US Job Market: My Insights and Strategies 🚀 🔍 As I mentioned in my previous post - https://bit.ly/44lnZQc 🚀 Most people think that landing into the US job market is difficult, but that’s not the case. Let me explain why. As everyone knows, knowledge is the key to power. If you possess deep knowledge of something you're passionate about, you'll inevitably succeed. That's the design of life. 🧱 Similarly, you need a strong foundational knowledge and basic understanding of technical concepts if you're a beginner, Nothing more is required. No one expects beginners to launch a website or build models for their company on their own. They know how to do it with the help of beginners or intermediates. 📉 People often claim that they've applied to numerous companies over the past six months but haven't landed a job. However, the reality might be different. They may have received interview calls but failed to demonstrate their knowledge or lacked sufficient expertise. 💡 I've observed friends and students applying for roles as software engineers, data scientists, and data engineers simultaneously. As I always advise my friends and acquaintances, before applying for any job, take a moment to assess yourself. Determine what you know, what you don't, and what you aspire to be. Filter all your knowledge and questions into these three categories. 🛠️ Then, start learning what you lack and chart a path towards your desired role. This approach has always been helpful to me. 📌 To be frank, many claim that you must apply to at least 50 jobs a day and need references to break into the market. But that's overwhelming for HR professionals and interviewers. Instead, after identifying your strengths, weaknesses, and aspirations, find a job that aligns perfectly with your knowledge and career path. Tailor your resume accordingly, and you'll surely receive responses. I applied to 12 jobs and received 8 responses. 🔑 Never ask for a template resume from friends or anyone else. Craft your own unique resume, then fine tune further and further; uniqueness is the key to success. 📝 Follow me for my next post on crafting the perfect resume for specific job roles and strategies to secure a job in the USA or anywhere in the world. #JobMarketInsights #CareerAdvice #KnowledgeIsPower #JobSearchTips #CareerDevelopment #TechnicalSkills #SuccessTips #ResumeWriting #JobHunting #CareerStrategy #USAJobs #ProfessionalDevelopment #UniqueResume #InterviewTips #JobSeekers #CareerCoaching #EmploymentTips #CareerPath #LinkedInTips #JobApplicationTips

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Question : Sampling Techniques : What are they❓ As we saw recently in exit polls on Indian elections, choosing samples from a population can have a significant impact on predictions. #AI enthusiasts need to have a solid understanding of statistical sampling techniques. Statistical sampling is essential for AI product managers because it helps them: 1️⃣ Understand the Target Population: Accurate sampling ensures that the data used to train and evaluate AI models is representative of the target population, reducing the risk of biased or skewed results. 2️⃣ Optimize Data Collection: Sampling techniques can help AI product managers determine the appropriate sample size and sampling method, ensuring efficient and cost-effective data collection. 3️⃣ Validate Model Performance: Sampling is crucial for testing and validating AI models, as it allows for the creation of representative test sets that accurately reflect the real-world scenarios the model will encounter. AI enthusiasts should be aware of the different types of sampling methods, including: 🔺 Probabilistic Sampling Techniques: methods where randomisation is used instead of deliberate choice. 👉 Simple Random Sampling: Each member of the population has an equal chance of being selected. 👉Stratified Sampling: The population is divided into subgroups (strata), and samples are drawn from each stratum. 👉Cluster Sampling: The population is divided into clusters, and a random sample of clusters is selected. 👉Systematic Sampling: Every nth member of the population is selected, starting from a random point. 🔺 Non-Probabilistic Sampling Techniques: methods where some selection criteria exists 👉Convenience Sampling: Selecting participants based on their availability and accessibility. 👉Quota Sampling: Selecting participants to match predetermined proportions in the population. 👉Purposive Sampling: Selecting participants based on specific characteristics or criteria. 👉Snowball Sampling: Selecting participants through referrals from initial participants. The choice of sampling method depends on the specific goals of the AI project, the available resources, and the characteristics of the target population. Choosing the wrong sampling technique can lead to biased results, skewed data, and ultimately, the development of AI models that fail to perform well in real-world scenarios. 💡 Quick Tip: When selecting a sampling technique, consider the diversity and representativeness of the target population, the level of precision required, and the resources available for data collection.  #AI #ArtificialIntelligence #productmanagement #productmanager #DataScience 

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𝗜 𝗴𝗼𝘁 𝗳𝗼𝗼𝗹𝗲𝗱 𝗯𝘆 𝗺𝗶𝘀𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝘁𝗿𝗲𝗻𝗱𝘀! Have you ever noticed patterns in data that seem to suggest one thing is causing another, when that's not really the case? Let me explain with an example. In the following chart I tracked two metrics for India from 1990-2020: forest area and mortality rates for kids under 5. Here's what the data shows: 1. Forest area in India has 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 over the years🌳 2. Child mortality rates have 𝗱𝗲𝗰𝗿𝗲𝗮𝘀𝗲𝗱 🏥 At first glance, you might think - more trees must be leading to fewer kids dying! But that doesn't make much sense, does it? The truth is, these two trends are just correlated - they happen to move in opposite directions over time. But having more forests doesn't directly cause lower child deaths. There are likely other major factors involved, like: • Improved healthcare access • Better nutrition • Economic development So while the data shows a strong inverse correlation between forest area and child mortality, it's inaccurate to conclude that one is causing the other. 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗲𝗾𝘂𝗮𝗹 𝗰𝗮𝘂𝘀𝗮𝘁𝗶𝗼𝗻. 𝗧𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆? 𝗔𝗹𝘄𝗮𝘆𝘀 𝘁𝗵𝗶𝗻𝗸 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝗱𝗮𝘁𝗮. Just because two trends co-exist, doesn't necessarily mean one is influencing the other. Dig deeper to understand the real underlying drivers. #dataanalytics #criticalthinking

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I will be speaking  at DES 2024 : 'Lessons from Building a Performant, Efficient, Cost-Saving Data Pipeline with Open Source Technologies'. I will try my best to keep you on the edge of your seats (or at least awake) until the very end :) Mark your calendars, set your alarms, and get ready!! #des2024 #des #dataengineeringsummit #dataengineering

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Question : Why does an AI product manager need to monitor #AI model❓ Once AI model is developed and deployed, the job doesnt end there. Model encodes pattern of the data it has seen during training phase. There may be several patterns or edge cases which are not part of training data. If model encounters any such patterns in testing or production phase, it will produce results which wont make sense without giving any warning about it. 👉 Data drift is a common challenge faced by AI-powered products. Over time, the distribution of input data can shift, leading to a mismatch between the model's training data and the real-world data it encounters. This can result in degraded model performance, unexpected outputs, and even complete model failure. 👉 The consequences of data drift can be severe, ranging from suboptimal user experiences to regulatory compliance issues. Product managers must be vigilant in detecting and addressing these challenges to ensure the continued success of their AI-powered offerings. 👉 Sudden changes in metrics can indicate input data drift. Additionally, monitoring the distribution of model outputs can reveal output drift, which may signal a need to retrain or fine-tune the model. Detecting data drift early is crucial. Product managers can leverage techniques such as A/B testing, anomaly detection, and continuous monitoring to identify and address these issues. Once detected, the product team can take appropriate actions, such as retraining the model, adjusting the data pipeline, or even considering a model architecture change. 💡 Quick Tip: Integrate model monitoring into your product roadmap and allocate dedicated resources to ensure the long-term success of your AI-powered offerings. Establish a comprehensive model monitoring dashboard to gain real-time visibility into the health and performance of your AI models. #artificialintelligence #productmanagement #datascience #productmanager

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From working as a Data Analyst at IBM → Sr. Data Scientist at Fractal! Writing seems so easy, but when I think about my whole professional journey, the whole struggle comes back to my mind. Balancing a full-time job and house errands is one thing, but planning this and your self-learning is another. 😅 Initially, it was really hard for me to pick up the topics and be Consistent. 😰 But thanks to my Seniors and their Guidance, everything became easy. challenging but achievable. Here’s what worked for me: 📍Having a proper time schedule really kept me on my toes. Dedicated early mornings and weekends to study without affecting my job performance. 📍Focused Preparation helped me in the long run. I created a targeted study plan, used online resources like LeetCode and Coursera, and practiced Mock Interviews. 📍When it comes to such a dynamic field like Data Science Staying Updated becomes really important. For this I regularly read industry blogs, attended webinars, and networked with professionals for insights. Doing all these things consistently, really helped me achieve my dream career! 🎉 Remember learning takes time and you need to stay consistent during the process. 💪 Now, if you’re targeting to make a transition like this too and need help with preparation, mentorship or guidance you should check Bosscoder Academy Check here → https://bit.ly/4eAAoV8 Here's what they offer: ✅Structured curriculum to master Machine Learning, Data Visualization, and more. ✅ Personalized guidance from Data Analysts and Scientists working at Google, Samsung, and other top companies. ✅ Multiple projects focused on SQL, Python, and other in-demand skills to build a strong portfolio. To all the working professionals aiming for their next big role, stay consistent and persevere. You’ve got this! 💪📈 #sponsoredpost #DataScience #TechInterviews

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Mumbai's Million Dollar Question: Boom for Who? Imagine Mumbai's hottest new apartment building selling out in just 𝟕𝟐 𝐡𝐨𝐮𝐫𝐬! That's the frenzy gripping India's financial capital, where a record 𝟖𝟔,𝟎𝟎𝟎 homes were snatched up in Q1 2024. But hold on to your dabbawalas! This property boom caters largely to high-flyers. Over half these homes are luxury apartments exceeding a whopping $𝟒𝟖𝟎,𝟎𝟎𝟎! Meanwhile, affordable options (under $54,000) are scarcer than ever, making up a mere 𝟏𝟗% 𝐨𝐟 𝐬𝐚𝐥𝐞𝐬 – the lowest in a DECADE. Why the stark contrast? Mumbai's skyline is transforming. Think revamped streets, trendy districts with swanky stores rubbing shoulders with colonial architecture, and skyscrapers replacing older homes. This is a full-fledged real estate AND infrastructure boom! ️ However, the party isn't for everyone. The 𝐚𝐯𝐞𝐫𝐚𝐠𝐞 𝐈𝐧𝐝𝐢𝐚𝐧 𝐞𝐚𝐫𝐧𝐬 𝐣𝐮𝐬𝐭 $𝟐,𝟏𝟎𝟎 𝐚𝐧𝐧𝐮𝐚𝐥𝐥𝐲, a far cry from those luxury flats. This boom widens the gap between the wealthy and those priced out entirely. So, is Mumbai's property surge a golden opportunity or a gilded cage? For developers like DLF Limited selling out luxury apartments in a flash, it's a roaring success. But it also exposes a growing divide within the city. As Mumbai reaches for the sky, 𝐭𝐡𝐞 𝐦𝐢𝐥𝐥𝐢𝐨𝐧-𝐝𝐨𝐥𝐥𝐚𝐫 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐫𝐞𝐦𝐚𝐢𝐧𝐬: will the dream of owning a home stay grounded for the average citizen? #realestate #explore #luxury #Mumbai #latestupdate #milliondollar #news #foryourpage #nyc

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𝑻𝒊𝒑𝒔 𝒇𝒐𝒓 𝒏𝒆𝒘 𝑫𝒂𝒕𝒂 𝑺𝒄𝒊𝒆𝒏𝒄𝒆 𝑴𝒂𝒏𝒂𝒈𝒆𝒓𝒔 I became a manager in data science early in my career. It has been a journey fraught with failures and some successes. Here are some of my learnings along the way - 📈 𝑹𝒐𝒍𝒆, 𝒓𝒆𝒔𝒑𝒐𝒏𝒔𝒊𝒃𝒊𝒍𝒊𝒕𝒚 𝒂𝒏𝒅 𝒑𝒓𝒊𝒐𝒓𝒊𝒕𝒊𝒆𝒔 Early on, my vision was small which narrowed the value I was able to create. I had two focuses - 1) ship out high quality work, 2) coach & promote my team. After >10 years in leadership roles, here is how I NOW think of the responsibilities of a data science manager in the hierarchy of importance - 🔹 Build authentic relationships with stakeholders and team members 🔹 Identify and align on highest impact work the team can enable and unlock 🔹 Set up systems, processes, and people to execute on these projects 🔹 Manage projects and priorities 🔹 Coach your direct reports on execution Depending on the seniority of the role, the distribution across these may vary but the core buckets remain the same. 🏆 𝑩𝒆 𝒊𝒏 𝒕𝒉𝒆 𝑨𝒓𝒆𝒏𝒂 Don’t be carried away by the business of meetings that make you feel important but don’t create real value. Spend at least 1/3rd of your time towards hands-on work. Don’t get rusty on the technical chops. Be an SME in several areas in the workflow - tech stack/tools, data models, methodologies, algorithms and specific tables. This will build trust with your reports while reducing dependency, increasing team efficiency and productivity. 🔄 𝑻𝒂𝒊𝒍𝒐𝒓 𝑷𝒓𝒐𝒄𝒆𝒔𝒔𝒆𝒔 & 𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒔 Data science lacks standard processes, unlike software engineering, due to its evolving role, inherent variability in methods depending on project goals. Creating processes and systems that will help with the ground reality of the ecosystem and culture at your company, is important. 🎲 𝑼𝒏𝒅𝒆𝒓-𝑷𝒓𝒐𝒎𝒊𝒔𝒆 𝒂𝒏𝒅 𝑶𝒗𝒆𝒓-𝑫𝒆𝒍𝒊𝒗𝒆𝒓 In the unpredictable world of predictive modeling 😉, where factors such as data anomalies, algorithm biases, and unexpected market shifts can disrupt the accuracy of forecasts, it pays to temper expectations and try to exceed them. #datascience #analytics #businessanalytics #leadership #techleadership #careers #techmanagers

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🚚📊 Data Science for Logistics 🌐💡 Data science is revolutionizing the logistics industry, paving the way for enhanced efficiency, cost savings, and improved customer satisfaction. Let's dive deeper into the fascinating intersection of data science and logistics: 🔍 **Insights Driving Optimization**: By harnessing the power of data analytics, logistics companies can unlock valuable insights to optimize route planning, inventory management, and resource allocation. ⏱️ **Real-Time Decision-Making**: Data science enables real-time tracking and monitoring of shipments, allowing for quicker and more informed decision-making to address any unexpected challenges or delays. 🛠️ **Predictive Maintenance**: Through the use of predictive analytics, logistics firms can anticipate maintenance needs for vehicles and equipment, minimizing downtime and keeping operations running smoothly. 🌐 **Global Supply Chain Visibility**: Data science tools provide a comprehensive view of the entire supply chain, facilitating better coordination between different stakeholders and ensuring timely deliveries across the globe. 🚛 **Efficient Last-Mile Delivery**: With the help of data-driven algorithms, companies can streamline the last-mile delivery process, reducing costs and enhancing customer satisfaction through accurate delivery time estimates. 📈 **Performance Analysis**: Data science allows logistics providers to track key performance indicators (KPIs) in real-time, enabling them to identify areas for improvement and drive operational excellence. 🔒 **Risk Mitigation**: By analyzing historical data and external factors, logistics companies can proactively identify and mitigate potential risks such as route disruptions, weather-related delays, and inventory shortages. In the fast-paced world of logistics, embracing data science is not just an option—it's a strategic imperative for staying competitive and meeting the evolving demands of the industry. Let's harness the power of data to drive innovation, efficiency, and sustainability in logistics operations. 💪🌟 #DataScience #Logistics #Innovation #SupplyChain #Analytics #Efficiency #Technology #FutureOfWork

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With only 7 months left for IIT JAM ECONOMICS and GATE ECONOMICS exam, I discuss the strategy one should follow to clear these exams https://lnkd.in/gJZYKCVN

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I didn’t know that returning a pair of shoes is so hard in USA 🤒 Here are the steps as mentioned by Mr. Sundar Pichai in Google I/O 2024👇🏻 ▶️ Search your inbox for the receipt ▶️ Locate the order number from your email ▶️ Fill out the return form ▶️ Schedule the pick up 🤯 In India 🇮🇳, the steps are rather easy👇🏻 ✅ Open the app ✅ Locate the order number ✅ Click on “return” So, they definitely need GenAI to help them with those steps! We absolutely don’t! If someone is thinking of a solution powered by GenAI like below👇🏻 Open the app and say return and boom 💥 Trust me, we can click two buttons. 😎 What are some of the use case your think would make more sense from Indian context? Please let me know your thoughts in the comments 👇🏻

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One thing that data scientists have fooled businesses for years is forecast/predictive accuracy is of so much importance and that too point forecast , point forecast will never be as accurate as your evaluation window accuracy because you have no control over future so what’s the fuss all about ? It’s about predicting confidence intervals accurately and planning best and worst case dynamics ( which actually businesses have followed for years with experience and ballparking these upper and lower limits ) #datascience #AI #supplychainplanning

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I have been asked by many #datascience , #aiml #professional and #enthusiast , What is the benefit of getting a Master Degree from premiere institute. I did my Master from Indian School of Business (AMPBA) and I feel fortunate to be the part of this Journey and I can list out some of the reasons below. 1. Quality of Education: Premier institutes are known for their excellent faculty, infrastructure, and academic resources. They provide high-quality education that equips students with the necessary knowledge, skills, and practical exposure required for their chosen profession. 2. Industry Recognition: Degrees and certifications from premier institutes are highly valued and recognized by employers and organizations. Graduates from such institutes are often sought after due to the rigorous training and qualifications they possess. 3. Networking Opportunities: Premier institutes offer excellent networking opportunities, allowing students to interact with industry professionals, attend seminars, and participate in events. These connections can be invaluable for future career prospects and professional growth. 4. Specialized Expertise: Premier institutes often have specialized programs or concentrations in specific fields, allowing students to gain in-depth knowledge and expertise in their area of interest. This specialized knowledge can give them a competitive edge in the job market. 5. Skill Development: Premier institutes emphasize not only theoretical knowledge but also practical skill development. They provide opportunities for internships, projects, and hands-on training, which help students develop essential skills required in their respective professions. 6. Personal Growth: The challenging academic environment and exposure to diverse perspectives at premier institutes foster personal growth, critical thinking, and problem-solving abilities, which are invaluable assets in any professional field. 7. Career Advancement: A degree or certification from a premier institute can open doors to better job opportunities, higher salaries, and faster career advancement. Employers often recognize the value of education from respected institutions. 8. Reputation and Prestige: Attending a premier institute carries a certain level of prestige and reputation, which can be beneficial for both personal and professional growth. It can enhance an individual's credibility and standing in their chosen field. While the investment in professional education from a premier institute may be substantial, the potential benefits in terms of knowledge, skills, networking opportunities, and career prospects often make it a worthwhile investment for individuals seeking professional growth and success. I am fortunate to meet professors like vijaya marisetty Subodha Kumar Manish Gupta Shailesh Kumar Niyati Chhaya to name a few. Thanks to BNY Mellon for sponsored me. Follow Bijan Dash for great insight and help. ISB Institute of Data Science (IIDS)

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