title
Sharanya Towards Data Scientist at Value Labs | Data Scientist Interview | Applied AI Course Reviews

description
Sharanya Towards Data Scientist at Value Labs | Data Scientist Interview | Applied AI Course Reviews #datascientist #datascienceinterviewquestions #appliedaicourse -------------------------------------------------------------------------------------------------------------------------------------- The AppliedAICourse attempts to teach students/course-participants some of the core ideas in machine learning, data-science and AI that would help the participants go from a real world business problem to a first cut, working and deployable AI solution to the problem. Our primary focus is to help participants build real world AI solutions using the skills they learn in this course. This course will focus on practical knowledge more than mathematical or theoretical rigor. That doesn't mean that we would water down the content. We will try and balance the theory and practice while giving more preference to the practical and applied aspects of AI as the course name suggests. Through the course, we will work on 20+ case studies of real world AI problems and datasets to help students grasp the practical details of building AI solutions. For each idea/algorithm in AI, we would provide examples to provide the intuition and show how the idea to used in the real world. For more information, please visit: https://www.appliedaicourse.com/ For any queries you can either drop a mail to team@appliedaicourse.com or call us at +91 8106-920-029 or +91 6301-939-583 Facebook: https://www.facebook.com/appliedaicourse Soundcloud: https://soundcloud.com/applied-ai-course

detail
{'title': 'Sharanya Towards Data Scientist at Value Labs | Data Scientist Interview | Applied AI Course Reviews', 'heatmap': [{'end': 631.32, 'start': 608.357, 'weight': 1}], 'summary': "Sharanya vanka's journey to a data scientist role at value labs after completing the applied ai course is detailed, along with insights into the interview process and advice for transitioning to data science careers, emphasizing the importance of practical experiences and time management.", 'chapters': [{'end': 301.665, 'segs': [{'end': 39.549, 'src': 'embed', 'start': 10.48, 'weight': 0, 'content': [{'end': 15.222, 'text': 'Today, we have Sharanya Vanka with us, who is one of our course enrolled students for the Applied AI course.', 'start': 10.48, 'duration': 4.742}, {'end': 21.603, 'text': 'And she has successfully made a transition to a data scientist role at Value Labs very recently.', 'start': 16.021, 'duration': 5.582}, {'end': 25.225, 'text': 'I believe you just recently joined Value Labs, right? SHARANYA VANKAVITASURIKARANTHAMI, Correct.', 'start': 21.944, 'duration': 3.281}, {'end': 25.705, 'text': 'Today is my second day.', 'start': 25.245, 'duration': 0.46}, {'end': 26.885, 'text': 'SHARANYA VANKAVITASURIKARANTHAMI, Oh, cool, cool.', 'start': 25.725, 'duration': 1.16}, {'end': 34.127, 'text': 'And prior to this, she has about four years of work experience in IT in companies like Accenture and Uber.', 'start': 27.285, 'duration': 6.842}, {'end': 39.549, 'text': 'So thank you, Sharanya, for taking the time to share your learning experiences and also your interview experiences.', 'start': 34.627, 'duration': 4.922}], 'summary': 'Sharanya successfully transitioned to a data scientist at value labs after completing the applied ai course, with 4 years of prior it experience at companies like accenture and uber.', 'duration': 29.069, 'max_score': 10.48, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g10480.jpg'}, {'end': 120.244, 'src': 'embed', 'start': 93.744, 'weight': 2, 'content': [{'end': 100.773, 'text': 'But, much to my surprise, in the interview, in the actual interview, they were quite focused on.', 'start': 93.744, 'duration': 7.029}, {'end': 104.035, 'text': 'you know, they were quite focused on the crux of like.', 'start': 100.773, 'duration': 3.262}, {'end': 108.037, 'text': 'if you, if you take a business problem, how will be the what will be in your platter?', 'start': 104.035, 'duration': 4.002}, {'end': 112.72, 'text': 'like also that I have worked for a startup earlier before getting this role.', 'start': 108.037, 'duration': 4.683}, {'end': 114.401, 'text': 'I have worked for a startup for six months.', 'start': 112.72, 'duration': 1.681}, {'end': 120.244, 'text': 'So in that startup, I have done a couple of projects, especially mostly on NLP.', 'start': 114.761, 'duration': 5.483}], 'summary': 'Candidate discussed startup experience and nlp projects during interview.', 'duration': 26.5, 'max_score': 93.744, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g93744.jpg'}, {'end': 206.121, 'src': 'embed', 'start': 169.066, 'weight': 1, 'content': [{'end': 174.728, 'text': 'Like I would also consider it more focused on breadth and your understanding of applicative knowledge.', 'start': 169.066, 'duration': 5.662}, {'end': 179.73, 'text': 'But you also mentioned that for logistic regression, they were going into the optimization problem.', 'start': 175.729, 'duration': 4.001}, {'end': 181.911, 'text': 'So they expect some math, but not too much.', 'start': 179.93, 'duration': 1.981}, {'end': 183.052, 'text': 'Correct Correct.', 'start': 182.491, 'duration': 0.561}, {'end': 187.754, 'text': "Because, uh, in my resume also, my plus is maths because I've been very good in mathematics.", 'start': 183.112, 'duration': 4.642}, {'end': 190.975, 'text': 'And that was one of the primary reasons that I took up this course.', 'start': 187.834, 'duration': 3.141}, {'end': 192.938, 'text': 'So I was good with math.', 'start': 191.738, 'duration': 1.2}, {'end': 195.859, 'text': "That's why they just wanted to test more on mathematics side.", 'start': 192.998, 'duration': 2.861}, {'end': 196.719, 'text': 'But that was it.', 'start': 196.119, 'duration': 0.6}, {'end': 202.22, 'text': 'Like hardly, I was asked like 30 seconds about like the optimization equation, not much into it.', 'start': 196.779, 'duration': 5.441}, {'end': 206.121, 'text': 'But again, they tried to focus more on the statistics part.', 'start': 202.6, 'duration': 3.521}], 'summary': 'Interview focused on math and statistics, with minimal optimization questions', 'duration': 37.055, 'max_score': 169.066, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g169066.jpg'}], 'start': 10.48, 'title': "Sharanya vanka's journey to becoming a data scientist", 'summary': "Discusses sharanya vanka's transition to a data scientist role at value labs after completing the applied ai course, with four years of prior it work experience. it also highlights insights into the data scientist interview process, emphasizing the importance of breadth of knowledge, applicative understanding, and practical experience, with a specific focus on mathematics and statistics.", 'chapters': [{'end': 49.517, 'start': 10.48, 'title': 'Transition to data scientist role at value labs', 'summary': "Discusses sharanya vanka's successful transition to a data scientist role at value labs after completing the applied ai course, with four years of prior it work experience at companies like accenture and uber.", 'duration': 39.037, 'highlights': ['Sharanya Vanka recently transitioned to a data scientist role at Value Labs after completing the Applied AI course, with prior work experience at companies like Accenture and Uber.', 'Sharanya Vanka joined Value Labs very recently and has about four years of work experience in IT.', "Sharanya Vanka's interview experiences are shared to help students understand and prepare for similar transitions."]}, {'end': 301.665, 'start': 49.517, 'title': 'Data scientist interview insights', 'summary': 'Highlights the interview process for a data scientist role, including the focus on breadth of knowledge, applicative understanding, and a preference for practical experience over theoretical knowledge, with a specific emphasis on mathematics and statistics.', 'duration': 252.148, 'highlights': ['The interview process focused on breadth of knowledge and applicative understanding, with a preference for practical experience over theoretical knowledge, emphasizing mathematics and statistics. Breadth of knowledge, applicative understanding, preference for practical experience, emphasis on mathematics and statistics', 'The interview process emphasized understanding the crux of algorithms and applicative knowledge, rather than depth of knowledge, with a particular focus on the practical application of knowledge in real-world scenarios. Emphasis on understanding the crux of algorithms, applicative knowledge, practical application of knowledge in real-world scenarios', 'The preference for practical experience over theoretical knowledge was evident, with a specific emphasis on mathematics and statistics, and a focus on understanding the applicative knowledge of different algorithms and their practical usage. Preference for practical experience, emphasis on mathematics and statistics, understanding applicative knowledge of algorithms and practical usage', 'The interview process required an understanding of mathematics and statistics, with a particular emphasis on the practical application of knowledge and the applicative understanding of algorithms in real-world scenarios. Understanding of mathematics and statistics, practical application of knowledge, applicative understanding of algorithms in real-world scenarios', 'The interview process focused on understanding the practical application of algorithms and their applicative knowledge in real-world scenarios, with a specific emphasis on mathematics and statistics. Practical application of algorithms, applicative knowledge in real-world scenarios, emphasis on mathematics and statistics']}], 'duration': 291.185, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g10480.jpg', 'highlights': ['Sharanya Vanka recently transitioned to a data scientist role at Value Labs after completing the Applied AI course, with prior work experience at companies like Accenture and Uber.', 'The interview process focused on breadth of knowledge and applicative understanding, with a preference for practical experience over theoretical knowledge, emphasizing mathematics and statistics.', 'The interview process emphasized understanding the crux of algorithms and applicative knowledge, rather than depth of knowledge, with a particular focus on the practical application of knowledge in real-world scenarios.', 'The preference for practical experience over theoretical knowledge was evident, with a specific emphasis on mathematics and statistics, and a focus on understanding the applicative knowledge of different algorithms and their practical usage.']}, {'end': 649.009, 'segs': [{'end': 351.054, 'src': 'embed', 'start': 320.538, 'weight': 0, 'content': [{'end': 324.824, 'text': 'And again, on deep learning, they wanted to know why deep learning came in the first place.', 'start': 320.538, 'duration': 4.286}, {'end': 327.228, 'text': "Like why couldn't we just go on with machine learning?", 'start': 325.205, 'duration': 2.023}, {'end': 330.392, 'text': 'And in which cases would you go for deep learning?', 'start': 327.809, 'duration': 2.583}, {'end': 334.098, 'text': 'And which case would you again stick back to the classing ML method?', 'start': 330.433, 'duration': 3.665}, {'end': 340.868, 'text': "And then came RNN, CNN, and in deep learning also, there were a few concepts that I didn't know of.", 'start': 334.645, 'duration': 6.223}, {'end': 345.291, 'text': "And I was very, like, I was frank with that, that I didn't know of these concepts.", 'start': 341.228, 'duration': 4.063}, {'end': 351.054, 'text': 'But in the concept that I knew, for example, in that also, they were asking the basic, like, for example, dropout.', 'start': 345.651, 'duration': 5.403}], 'summary': 'Exploring deep learning concepts and its applications, including rnn, cnn, and understanding dropout.', 'duration': 30.516, 'max_score': 320.538, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g320538.jpg'}, {'end': 377.003, 'src': 'embed', 'start': 355.216, 'weight': 2, 'content': [{'end': 363.6, 'text': 'and one of the main things that you told in the notes also that in the course also that dropout rate like what will happen in the train time and test time.', 'start': 355.216, 'duration': 8.384}, {'end': 366.655, 'text': 'So I think that was one of the most important questions.', 'start': 364.153, 'duration': 2.502}, {'end': 371.559, 'text': "Usually, by going through the course, that's just another concept that you write on the notes.", 'start': 368.016, 'duration': 3.543}, {'end': 373.76, 'text': 'But that was us.', 'start': 371.959, 'duration': 1.801}, {'end': 377.003, 'text': 'So nothing beyond the notes came, of course.', 'start': 373.82, 'duration': 3.183}], 'summary': 'Course dropout rate is a key concern during training and testing.', 'duration': 21.787, 'max_score': 355.216, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g355216.jpg'}, {'end': 423.077, 'src': 'embed', 'start': 398.289, 'weight': 1, 'content': [{'end': 403.951, 'text': 'They told me the nature of work and they tried to know how much data analysis do I know in that.', 'start': 398.289, 'duration': 5.662}, {'end': 410.753, 'text': "And also I'd like to add here that in the second round, they also asked me on Pandas and NumPy operations.", 'start': 404.331, 'duration': 6.422}, {'end': 416.475, 'text': 'See, loading a CSV file, we know that because we have it.', 'start': 411.933, 'duration': 4.542}, {'end': 417.575, 'text': 'You can use it.', 'start': 416.955, 'duration': 0.62}, {'end': 419.196, 'text': "It's just a single line of code.", 'start': 418.035, 'duration': 1.161}, {'end': 423.077, 'text': "But they asked me, they tried to know if there's any other way to do it.", 'start': 419.476, 'duration': 3.601}], 'summary': 'Interviewers assessed data analysis skills, focusing on pandas and numpy operations, including csv file loading.', 'duration': 24.788, 'max_score': 398.289, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g398289.jpg'}, {'end': 589.804, 'src': 'embed', 'start': 563.061, 'weight': 4, 'content': [{'end': 569.707, 'text': "And then I'm sure you might have had to spend more time in the first module because you come from a non-programming background.", 'start': 563.061, 'duration': 6.646}, {'end': 578.155, 'text': 'It took me time, but, uh, as you said, the learn to learn approach that I started doing the small, small examples on Python.', 'start': 571.168, 'duration': 6.987}, {'end': 586.302, 'text': "And, uh, I started brushing up my own Python skills because again, the assignments part was a bit scary, because I didn't know it, but slowly, slowly,", 'start': 578.435, 'duration': 7.867}, {'end': 589.804, 'text': "I tried to pick myself up and, uh, Yeah, that's about it.", 'start': 586.302, 'duration': 3.502}], 'summary': 'Transitioning from non-programming background, improved python skills through practice and overcoming initial assignment challenges.', 'duration': 26.743, 'max_score': 563.061, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g563061.jpg'}, {'end': 649.009, 'src': 'heatmap', 'start': 608.357, 'weight': 3, 'content': [{'end': 613.541, 'text': 'So the case studies helped a lot and I think post case studies only I started applying for roles.', 'start': 608.357, 'duration': 5.184}, {'end': 615.102, 'text': 'Okay, got it, got it.', 'start': 613.641, 'duration': 1.461}, {'end': 619.03, 'text': "And that's how I landed the internship at the startup.", 'start': 615.848, 'duration': 3.182}, {'end': 620.411, 'text': 'And then I worked for the startup.', 'start': 619.07, 'duration': 1.341}, {'end': 626.716, 'text': 'And then worked in the startup for about six months and then transitioned to a full-time data scientist role.', 'start': 620.892, 'duration': 5.824}, {'end': 631.32, 'text': 'This is something that we recommend a lot of students, especially who come from non-programming background.', 'start': 626.996, 'duration': 4.324}, {'end': 634.081, 'text': "that it's always good to intern in a startup,", 'start': 632, 'duration': 2.081}, {'end': 642.466, 'text': 'get that six months to nine months to 12 months experience and then jump to a larger company or convert as a full-time role in that startup itself.', 'start': 634.081, 'duration': 8.385}, {'end': 649.009, 'text': "that's a much more natural path for people coming from non-programming backgrounds or people who have a career break.", 'start': 642.466, 'duration': 6.543}], 'summary': 'Case studies led to internship at startup, then transition to full-time data scientist role, recommended for non-programming backgrounds.', 'duration': 49.038, 'max_score': 608.357, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g608357.jpg'}], 'start': 301.705, 'title': 'Interview experiences and work-life balance', 'summary': "Details interview experiences focusing on deep learning, rnn, cnn, and managerial questions on pandas and numpy operations. it also highlights a learner's journey into a data scientist role, emphasizing time management, learning strategies, and successful role transition.", 'chapters': [{'end': 463.153, 'start': 301.705, 'title': 'Interview experiences and focus areas', 'summary': 'Details the interview experiences, including focus areas on deep learning, rnn, cnn, and concepts like dropout, and also touches on the nature of the third round, which was more managerial and included questioning on pandas and numpy operations.', 'duration': 161.448, 'highlights': ["The second interview focused more on deep learning and the candidate's projects, with specific questions on why deep learning came into existence, when to use deep learning versus classic machine learning methods, and concepts like RNN, CNN, and dropout. Focus on deep learning, RNN, CNN, dropout, reasons for using deep learning, comparison of deep learning and classic ML methods", "The third round was more managerial in nature, focusing on the candidate's knowledge of data analysis and also included questions on Pandas and NumPy operations, emphasizing the understanding of their usage and alternative methods beyond basic memory-based operations. Managerial nature of the third round, focus on data analysis, questioning on Pandas and NumPy operations", 'The candidate emphasized the importance of understanding concepts rather than memorizing functions, highlighting the practical approach of learning and referencing your live sessions as an example. Emphasis on understanding concepts, practical approach to learning, referencing live sessions']}, {'end': 649.009, 'start': 463.153, 'title': 'Balancing work and learning', 'summary': 'Highlights the journey of a learner transitioning from a non-programming background to a data scientist role, emphasizing the importance of time management, learning strategies, and case studies in the applied ai course, leading to successful internship and full-time role transition.', 'duration': 185.856, 'highlights': ['The journey from a non-programming background to a data scientist role, emphasizing the importance of time management, learning strategies, and case studies in the Applied AI course, leading to successful internship and full-time role transition.', 'The emphasis on time management and learning strategies, including setting small targets and focusing on basic concepts, especially for learners without a coding background.', 'The significance of case studies in building confidence and practical skills, leading to successful internship and eventual transition to a full-time data scientist role.', 'The recommendation for learners from non-programming backgrounds to intern in a startup, gaining experience before transitioning to a larger company or converting to a full-time role.']}], 'duration': 347.304, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g301705.jpg', 'highlights': ['Focus on deep learning, RNN, CNN, dropout, reasons for using deep learning, comparison of deep learning and classic ML methods', 'Managerial nature of the third round, focus on data analysis, questioning on Pandas and NumPy operations', 'Emphasis on understanding concepts, practical approach to learning, referencing live sessions', 'The journey from a non-programming background to a data scientist role, emphasizing the importance of time management, learning strategies, and case studies in the Applied AI course, leading to successful internship and full-time role transition', 'The emphasis on time management and learning strategies, including setting small targets and focusing on basic concepts, especially for learners without a coding background', 'The significance of case studies in building confidence and practical skills, leading to successful internship and eventual transition to a full-time data scientist role', 'The recommendation for learners from non-programming backgrounds to intern in a startup, gaining experience before transitioning to a larger company or converting to a full-time role']}, {'end': 909.785, 'segs': [{'end': 694.004, 'src': 'embed', 'start': 667.964, 'weight': 0, 'content': [{'end': 672.888, 'text': 'So then I started to taggle a bit like take a small data set and work on it,', 'start': 667.964, 'duration': 4.924}, {'end': 677.111, 'text': 'like how to even load a data frame and then go about the basic operations on it.', 'start': 672.888, 'duration': 4.223}, {'end': 680.514, 'text': 'That will give you a bit confidence that, okay, this is not that hard.', 'start': 677.351, 'duration': 3.163}, {'end': 681.975, 'text': 'You can go forward.', 'start': 680.814, 'duration': 1.161}, {'end': 684.12, 'text': "Yes, that's very valid.", 'start': 682.659, 'duration': 1.461}, {'end': 686.821, 'text': "And that's the purpose of the assignments also to get started.", 'start': 684.18, 'duration': 2.641}, {'end': 692.183, 'text': 'small start with small Python programs keep gradually increasing the complexity of problems you solve.', 'start': 686.821, 'duration': 5.362}, {'end': 694.004, 'text': 'Correct Cool.', 'start': 692.703, 'duration': 1.301}], 'summary': 'Start with small data sets for confidence and gradually increase complexity in python programs.', 'duration': 26.04, 'max_score': 667.964, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g667964.jpg'}, {'end': 750.494, 'src': 'embed', 'start': 712.776, 'weight': 1, 'content': [{'end': 715.057, 'text': 'But I would look at it by module wise,', 'start': 712.776, 'duration': 2.281}, {'end': 722.14, 'text': "like I'd write down my timetable and I'd say that I'd complete this module in a particular timeframe and I would do it.", 'start': 715.057, 'duration': 7.083}, {'end': 728.502, 'text': 'Sometimes it will go one week here and there, but on an average, I would give say 25 to 30 hours per week.', 'start': 722.56, 'duration': 5.942}, {'end': 731.564, 'text': 'Okay, got it, got it, got it.', 'start': 730.263, 'duration': 1.301}, {'end': 738.227, 'text': 'And probably you took slightly more time because you come from completely non-programming background and you had to pick up some of these details.', 'start': 731.824, 'duration': 6.403}, {'end': 739.228, 'text': 'Correct, correct.', 'start': 738.548, 'duration': 0.68}, {'end': 746.672, 'text': 'Cool. So any suggestions you have for students very similar to you with about four years of non-coding experience in IT,', 'start': 739.288, 'duration': 7.384}, {'end': 750.494, 'text': 'who wants to transition to data science careers based on your own experiences?', 'start': 746.672, 'duration': 3.822}], 'summary': 'Plan to dedicate 25-30 hours per week for module-wise study, useful for non-programmers transitioning to data science.', 'duration': 37.718, 'max_score': 712.776, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g712776.jpg'}, {'end': 801.558, 'src': 'embed', 'start': 773.568, 'weight': 3, 'content': [{'end': 777.994, 'text': "Like everybody says it, but it's very hard to follow also, especially if you're working.", 'start': 773.568, 'duration': 4.426}, {'end': 779.095, 'text': 'Hard work is hard.', 'start': 778.294, 'duration': 0.801}, {'end': 782.819, 'text': 'Yeah And so the results will be sweet.', 'start': 779.775, 'duration': 3.044}, {'end': 792.272, 'text': "But I think consistency like even if you take a break for one week and you have a low week and you're not able to, you're stuck at a problem,", 'start': 784.501, 'duration': 7.771}, {'end': 794.274, 'text': 'give it some time and just move forward.', 'start': 792.272, 'duration': 2.002}, {'end': 794.675, 'text': "That's it.", 'start': 794.354, 'duration': 0.321}, {'end': 801.558, 'text': "Don't just get stuck saying that See, I'm not good at coding or I'm not going, I'm not able to do an assignment or not understanding this topic,", 'start': 794.715, 'duration': 6.843}], 'summary': 'Consistency and perseverance lead to sweet results, even after setbacks in work or learning.', 'duration': 27.99, 'max_score': 773.568, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g773568.jpg'}, {'end': 852.074, 'src': 'embed', 'start': 824.851, 'weight': 4, 'content': [{'end': 827.733, 'text': 'There will be roles which will not require that much experience.', 'start': 824.851, 'duration': 2.882}, {'end': 832.877, 'text': "You just don't, you know, you're not exactly knowing the market and you're getting scared.", 'start': 827.973, 'duration': 4.904}, {'end': 840.563, 'text': 'So get to know the market and also like, especially with the course consistency and persistent hard work.', 'start': 833.377, 'duration': 7.186}, {'end': 842.488, 'text': 'And time management, definitely.', 'start': 841.027, 'duration': 1.461}, {'end': 843.969, 'text': 'Like time management helps a lot.', 'start': 842.548, 'duration': 1.421}, {'end': 852.074, 'text': "Like, even if you're not doing, if you take a break of one month or you're working on something at your professional space, like at your work.", 'start': 843.989, 'duration': 8.085}], 'summary': "Some roles don't need much experience. knowing the market, consistency, hard work, and time management are crucial.", 'duration': 27.223, 'max_score': 824.851, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g824851.jpg'}, {'end': 908.48, 'src': 'embed', 'start': 882.088, 'weight': 5, 'content': [{'end': 886.832, 'text': "I'm sure they will help a lot of non-coding background professionals.", 'start': 882.088, 'duration': 4.744}, {'end': 891.576, 'text': 'Think of and not be fearful of a transition to a data science role.', 'start': 887.693, 'duration': 3.883}, {'end': 893.057, 'text': 'Thank you very much for your time.', 'start': 891.916, 'duration': 1.141}, {'end': 894.018, 'text': 'Thanks a lot, sir.', 'start': 893.117, 'duration': 0.901}, {'end': 894.758, 'text': 'Thanks to you.', 'start': 894.058, 'duration': 0.7}, {'end': 902.284, 'text': 'You have helped all of us, especially me, because your explanation has helped me write down a very good elaborate notes.', 'start': 894.878, 'duration': 7.406}, {'end': 904.846, 'text': 'And I only studied that before any interview.', 'start': 902.384, 'duration': 2.462}, {'end': 907.315, 'text': "So I'm very glad and thankful to you, sir.", 'start': 905.287, 'duration': 2.028}, {'end': 908.48, 'text': 'Thank you very much.', 'start': 907.878, 'duration': 0.602}], 'summary': 'Training will benefit non-coding professionals, easing transition to data science. gratitude expressed for helpful explanation.', 'duration': 26.392, 'max_score': 882.088, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g882088.jpg'}], 'start': 649.009, 'title': 'Transitioning to data science careers and roles', 'summary': 'Emphasizes the importance of starting small, putting in an average of 25 to 30 hours per week, and provides advice for transitioning to data science careers, while also discussing the significance of consistency, time management, and practical experiences for those with non-coding backgrounds.', 'chapters': [{'end': 750.494, 'start': 649.009, 'title': 'Transitioning to data science careers', 'summary': 'Highlights the importance of starting small, the average weekly effort put into the course, and advice for students transitioning to data science careers, with an average of 25 to 30 hours per week and suggestions for those with non-coding backgrounds.', 'duration': 101.485, 'highlights': ['The importance of starting small with small Python programs and gradually increasing the complexity of problems to gain confidence. Starting with small Python programs and gradually increasing the complexity of problems can build confidence in tackling data science tasks.', 'An average weekly effort of 25 to 30 hours was put into the course, with a focus on completing modules within a set timeframe. The speaker dedicated an average of 25 to 30 hours per week to the course, focusing on completing modules within a specific timeframe.', 'Advice for students with non-coding backgrounds looking to transition to data science careers, particularly those with about four years of non-coding experience in IT. The speaker provides advice for students with non-coding backgrounds, especially those with about four years of non-coding experience in IT, who are looking to transition to data science careers.']}, {'end': 909.785, 'start': 750.854, 'title': 'Transition to data science role', 'summary': 'Discusses the importance of consistency, time management, and overcoming fear when transitioning to a data science role, emphasizing the value of practical experiences and strategies shared by professionals to help non-coding background individuals succeed.', 'duration': 158.931, 'highlights': ['The importance of consistency in working towards a data science role, despite challenges, is emphasized, with practical advice on managing setbacks and staying motivated. ', 'Overcoming fear and not getting discouraged by the perception of difficulty in transitioning to a data science role is encouraged, with a focus on understanding the market and the value of time management. ', 'The value of practical experiences and strategies shared by professionals is highlighted as beneficial for non-coding background individuals aiming to transition to a data science role. ']}], 'duration': 260.776, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/4xBSI5iL49g/pics/4xBSI5iL49g649009.jpg', 'highlights': ['Starting with small Python programs and gradually increasing the complexity of problems can build confidence in tackling data science tasks.', 'The speaker dedicated an average of 25 to 30 hours per week to the course, focusing on completing modules within a specific timeframe.', 'The speaker provides advice for students with non-coding backgrounds, especially those with about four years of non-coding experience in IT, who are looking to transition to data science careers.', 'The importance of consistency in working towards a data science role, despite challenges, is emphasized, with practical advice on managing setbacks and staying motivated.', 'Overcoming fear and not getting discouraged by the perception of difficulty in transitioning to a data science role is encouraged, with a focus on understanding the market and the value of time management.', 'The value of practical experiences and strategies shared by professionals is highlighted as beneficial for non-coding background individuals aiming to transition to a data science role.']}], 'highlights': ['Sharanya Vanka transitioned to a data scientist role at Value Labs after completing the Applied AI course, with prior work experience at companies like Accenture and Uber.', 'The interview process focused on breadth of knowledge and applicative understanding, with a preference for practical experience over theoretical knowledge, emphasizing mathematics and statistics.', 'The journey from a non-programming background to a data scientist role, emphasizing the importance of time management, learning strategies, and case studies in the Applied AI course, leading to successful internship and full-time role transition', 'Starting with small Python programs and gradually increasing the complexity of problems can build confidence in tackling data science tasks.', 'The speaker dedicated an average of 25 to 30 hours per week to the course, focusing on completing modules within a specific timeframe.']}