Enjoy a complete recording of my Q&A with Amazon Applied Scientist, Krishna Rao!! Krishna is responsible for building state-of-the-art advertising recommendation systems for Amazon! Krishna has had a slightly unconventional path to get to this point. His background is in Civil Engineering and he was first a Data Science consultant before joining Amazon. In this Q&A, he shares the tips and tricks he used to land one of the most coveted roles in FAANG! Don't forget to SUBSCRIBE for more useful DS content! Here is my blog: https://www.madhavthaker.com/ My LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Timestamps: 00:00:00 - Introduction 00:09:08 - Start of Presentation 00:39:03 - As a civil engineering undergrad student, how can machine learning be integrated with my studies? 00:41:26 - What are the best ways data science managers can enable their team to be successful? How intimately should the managers be aware of the technical nature of the data science problems they're working on? 00:43:48 - Do you have any good courses to learn Tensorflow? 00:44:44 - What types of personal projects should I concentrate on and how complex or in-depth should they be for someone seeking entry-level work? 00:47:24 - What's the value of a master's in Data Science? Can you comment on the value of doing a program like this versus starting entry-level data science job without it? 00:49:10 - How do we target companies who look for people who are just getting into data science? 00:50:31 - I know R, SQL, and Python at an intermediate level. Should I focus on just getting better with those or add new languages? 00:52:39 - s it feasible to do some form of ML research or new development without a Ph.D. 00:53:55 - I’ve been a Machine Learning Engineer for about five years now, and recently I've been thinking about getting my masters in computer science or data science part-time. Do you think this would be advantageous? 00:56:32 - How flexible is it for a data science professional to switch to Ai research machine learning, etc, and vice versa. 00:57:37 - When you're building your portfolio, do your projects have to be from various domains, or would you focus on one particular domain? 00:59:28 - When transitioning from engineering to data science what are some things that should be considered when looking for your first job in data science. 01:01:09 - What were the steepest parts of your learning curve when you started working on data science and industry. 01:03:11 - Do you recommend becoming a backend engineer first before transitioning into an MLE role? 01:04:50 - I’ve been considering pursuing the Harvard extension school MS, Please share thoughts or advice that you have regarding your experience with a degree. 01:05:53 - What is your every day on the job like as Amazon applied scientists. 01:07:53 - Any advice or anecdotes for those without Ph.D. degrees trying to break in to research? 01:09:48 - How important is learning scala at this stage is something I shouldn't do later or focus on something else. 01:11:15 - What is a good domain for me to utilize my psychology and business background through data science. 01:12:59 - How did you get through the interview around the Amazon um how did you prepare for the coating rounds. 01:15:15 - Any tips for self-taught programmers data scientists to break into the field, I think you touched on that, but I think we go one more, but this question is interesting any tips for finding a mentor for those who aren't working in the tech field. 01:17:45 - Where do you see the future of recommendation systems? 01:22:37 - What coding platform is best for the Machine Learning industry. 01:23:27 - What are you excited to learn about in the future. 01:25:22 - As a Ph.D. candidate, I was fortunate enough to work on a number of data science-related projects that culminated in a publication of either a book. However, I'm not certain how these fit in my resume our publications worth including in the resume, even if it sends my resume from one page or two pages. 01:26:14 - How can you improve your storytelling skills just by practicing do you guys use any other tools. 01:28:38 - If you're already a data scientist, but not in a top tier or hot company, how do you stand out amongst candidates. 01:30:06 - Do you ever have imposter syndrome? If yes, when did you get over that hump? 01:34:60 - I work, mostly on text, processing, and natural language understanding I'm just getting better at an area or should I generalize

Welcome to my very first resume critique! I'm excited to share my process with you all and I hope my tips help you land your dream job! If you're interested in having me review YOUR resume or LinkedIn, register here: https://forms.gle/WcTj75Em9rpVSb9Q6 Here is my blog: https://www.madhavthaker.com/ Connect with me on LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Timestamps: 00:00 - Intro 00:58 - Candidate Overview 01:56 - Start of Resume Critique 04:37 - Skills 06:25 - Work Experience 09:17 - Interests 09:31 - Work Experience 10:47 - Recap 12:02 - Wrap Up

Tensorflow Hub is a fantastic tool that allows you to gain access to state of the art machine learning models! It is something I’ve used regularly for my personal projects and I’ve even leveraged their sentence embeddings in my production pipelines. In this video, I walk you through how I leverage Tensorflow Hub and how you can start leveraging it for your next project! Link to Tensorflow Hub: https://www.tensorflow.org/hub Here is my blog: https://www.madhavthaker.com/ My LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/

Welcome to my very first resume critique! I'm excited to share my process with you all and I hope my tips help you land your dream job! If you're interested in having me review YOUR resume or LinkedIn, register here: https://forms.gle/WcTj75Em9rpVSb9Q6 Here is my blog: https://www.madhavthaker.com/ Connect with me on LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Timestamps: 00:00 - Intro 01:31 - Candidate Overview 01:56 - Work Experience 05:48 - Skills 07:48 - Project Experience 13:54 - Education 17:03 - Wrap Up

Please enjoy a complete recording of my 1:1 session with Ben Xiao! Definitely connect with him on LinkedIn to follow his data journey! Don't forget to like and subscribe! Fill out this form if you want me to review your LinkedIn! https://forms.gle/fb5Rd4qaoxrpa4hg7 Ben's LinkedIn: https://www.linkedin.com/in/benjaminyxiao/ Timestamps 00:00 - Introduction 01:52 - Backgrounds 05:32 - Start of Resume Review 06:42 - Resume Aesthetics 07:49 - Start of Education Section 10:41 - Should I list my courses? 13:11 - Start of Work Experience 13:31 - Should I include poker experience? 15:52 - How should I talk about my Poker Experience? 17:17 - Should I put a skills section in my resume? 18:47 - What skills should I include? 21:19 - Start of Project Experience 21:28 - Project #1: Classifying Video Game Reviews (NLP) 24:14 - How much technical jargon should I include? 26:54 - Project #2: Monte Carlo Simulation for Portfolio Optimization 29:33 - Project #3: Data Visualization: Displaying Poverty Rates 33:37 - Start of LinkedIn Review 37:13 - Adding a featured section 38:35 - Benefits of writing Medium articles 45:14 - How do you balance work, school, and personal projects? 45:49 - What tools do you use to manage your ideas? 50:01 - How well am I positioned to get an entry-level job? 53:55 - Next Steps

For me, Coursera should be the go-to resource for aspiring data scientists. For the price, you get a wealth of knowledge that you really can’t get anywhere else. In this video, I break down my top 5 favorite Coursera Certificates/Specialization for any aspiring data scientist. Coursera Links: 1. https://www.coursera.org/learn/machine-learning 2. https://www.coursera.org/specializations/machine-learning 3. https://www.coursera.org/specializations/aml 4. https://www.coursera.org/specializations/deep-learning 5. https://www.coursera.org/professional-certificates/tensorflow-in-practice Pythonic version of Andrew Ng's Machine Learning Certificate: https://github.com/dibgerge/ml-coursera-python-assignments My LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Timestamps: 00:00 - Intro 01:04 - Top 5 Coursera Courses 01:24 - Andrew Ng’s Machine Learning 04:41 - Uni of Washington’s Machine Learning Specialization 06:14 - HES’ Advanced Machine Learning Specialization 09:32 - DeepLearning.AI’s Deep Learning Specialization 12:46 - DeepLearning.AI’s Tensorflow Developer Certificate

I know it can be difficult to find your next big project for your portfolio. There are so many options and its hard to know what's a good fit for you. In this video, I walk you through the process I take to find and showcase projects for my portfolio. I even show you examples of how to can put this to use on Kaggle! Here is my blog: https://www.madhavthaker.com/ My LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Timestamps: 00:00 - Intro 01:38 - Framework Overview 04:10 - Finding a topic to work on and a dataset Using Kaggle 07:00 - Thinking about what algorithm to work with 07:51 - Explore our dataset and coming up with a use cases 15:27 - Showcasing your projects 18:35 - Wrap Up

Enjoy a complete recording of my Q&A with Principal Data Scientist, Susan Chang!! Susan is a committee member of Aggregate Intellect, a machine learning platform with 13k+ YouTube subscribers. You can find Susan speaking at conferences such as PyCon Canada, and events like this one! Here is my blog: https://www.madhavthaker.com/ My LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Timestamps 00:39:47 - Is this a valid way to build Data Science experience. What other ways could one build dx data science experience outside of working as one? 00:41:26 - At what point in learning data science c can you start applying for jobs? 00:45:06 - As a home learner. I eventually want to add to my portfolio group projects to show that I could work on a team. How would you suggest finding other data scientists web developers and back-end people to project? 00:47:29 - Do you often work on multiple projects at once? 00:01:46 - Susan’s Presentation 00:49:25 - What kind of projects are employers looking for in a portfolio? 00:52:42 - I'm considering studying DS. Any tips for deciding if this is a thing I might like…I'm afraid of committing to graduate school then later regret on pursuing it. 00:55:22 - Since presenting is a huge part of data science, how can you approve on this? 00:59:33 - What's the difference between Data Analysis and Data Science and are there any other data fields that don't know yet? 01:04:51 - What's the hardest part of your day? 01:07:47 - What are your thoughts on Harvard Extension courses? 01:08:52 - What's your advice for newbies or entry-level data scientist? 01:11:44 - I’m a brand new graduate and a lot of companies want some production level coding experience. How do you work on that without the kind of having a job? How did you get that experience ahead of time? 01:14:23 - Is it better to be a generalist or a specialized in one topic when trying to get your first Data Science position? 01:16:14 - What kind of questions do you ask or have been asked in a technical interview? 01:19:35 - Would you recommend a boot camp or master's program? 01:22:33 - What tactics do you use to make sure that your engagement Data Engineers is seamless and efficient? 01:25:45 - How do I get domain knowledge? 01:27:16 - When can you give an example when you suggested something that didn't scale for deployment or what the hang-up was? 01:30:20 - How do you delegate tasks between data scientists? 01:34:08 - How do you approach an ML problem? 01:37:13 - But how do you like working at Clearbanc? What do you like? what's your experience like there? 01:39:35 - What are your thoughts on data visualization skills like Tableau? 01:41:55 - What about Spark? Should we learn spark before getting our first job? 01:45:37 - What kind of ethical problems have you had to face as a data scientist?

Welcome to my very first resume critique! I'm excited to share my process with you all and I hope my tips help you land your dream job! If you're interested in having me review YOUR resume or LinkedIn, register here: https://forms.gle/WcTj75Em9rpVSb9Q6 Here is my blog: https://www.madhavthaker.com/ Connect with me on LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Timestamps: 00:00 - Intro 01:22 - Education 02:03 - Skills 04:12 - Work Experience 09:19 - Project Experience 15:57 - Wrap Up

In this video, I walk you through how I leverage GPT-2 to generate brand new Netflix content. This video is meant for ALL experience levels! Make sure you look at the timestamps below to find out which section interests you! Don't forget to like and subscribe! Google Collab: https://colab.research.google.com/drive/11XpR6BG2ucAsFbuv5ojswFyvrxeIMyLp?usp=sharing Useful Resources: - Computerphile Language Model Overview: https://www.youtube.com/watch?v=rURRYI66E54&ab_channel=Computerphile - Dataset: https://www.kaggle.com/shivamb/netflix-shows Here is my blog: https://www.madhavthaker.com/ My LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Timestamps: 00:00 - Intro GPT Overview: 02:39 - What is Transfer Learning? 03:55 - What is a Language Model? 05:11: - What is Attention? 07:35 - Data Preparation 08:35 - Comparing Unconstrained and Constrained Text Generation 12:34 - Loading and Tuning the Model 14:56 - Generating New Content 16:05 - Example Netflix Content 16:42 - Wrap Up

Being able to effectively present your Data Science projects is an essential skill you need to be a successful data scientist and yet it is rarely taught. In this video, I walk you through my simple 4 Step framework that I consistently use at work: Step 1: Explaining the Problem Statement Step 2: Highlighting Your Solution's Performance Step 3: Overview of the Modeling Approach Step 4: Next Steps It seems simple but I often find that new data scientists will forget to touch each of these points. I hope you find it useful! Don't forget to subscribe! Google Slides: https://docs.google.com/presentation/d/1nX-qm3TEh5ABMdm6v53Mw117Ipwfpxi6Uqzq2A-Fsyg/edit?usp=sharing Timestamps: 00:00 - Introduction 01:43 - Useful Resources 02:32 - Overview Of My 4 Step Framework 03:26 - Example Overview 04:23 - Step 1: Explaining the Problem Statement 06:20 - Step 2: Highlighting Your Solution's Performance 09:59 - Step 3: Overview of the Modeling Approach 12:50 - Step 4: Next Steps 14:06 - Wrap Up

Enjoy a complete recording of my Q&A with Disney Data Scientist, Daphne Cheung! Daphne will provide a fresh perspective from someone who recently entered the Data Science industry. On top of the Q&A, Daphne shares how she became a Data Scientist at Disney with a non-technical degree and how she's using her background to master the art of "Data Storytelling". Here is my blog: https://www.madhavthaker.com/ My LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Timestamps: 00:00:00 - Intro 00:01:10 - Daphne's Presentation 00:34:00 - Can we apply for internship positions when we're overseas and looking for jobs in the US? 00:36:31 - How much does a graduate degree matter to enter into data science? I’m taking the industrial engineering degree with ops research courses. 00:42:09 - How much mathematical theory and processes do you utilize on an average day? 00:48:56 - What was Daphne’s interview process like? 00:50:57 - What are your professional career goals over the next five years? 00:52:48 - What is your preferredd framework or coding language that you typically use at work? 00:54:21 - How is JIRA/AGILE used at your job? 00:56:45 - Do Data Science jobs require an extensive knowledge of data structures and algorithms like a software developer 00:58:29 - Hi Daphne, What do you think Disney saw in you or what do you think they liked about you that convinced them that you're a good fit for the job? 01:00:11 - Do you still pursue personal projects unrelated to work. Can you share some? 01:01:47 - How do you handle something like burnout? I say this because I have a lot of ideas and have issues following through. 01:04:09 - What did you do at LA Tech4Good? Are they working on any meeting any amazing projects right now. 01:05:52 - How do you learn all the skills to show your competency as a beginner data scientists and the communities apps data camps, etc. 01:07:53 - How would you explain the difference between a DS and a DA? 01:07:53 - Any tips on creating a slide deck executive report present data analysis findings to executives? 01:13:45 - When applying to Data Science roles, how important is it to have a portfolio of projects? 01:15:35 - How do you develop your storytelling skills? 01:19:49 - Where do your business problems come from or the work that you need to do, does it come from other teams asking your team sup your manager dreams up, or does your team get to do exploratory work to find the problem solve. 01:22:57 - Is understanding the model deployment part necessary for beginner level role?

In this video, I walk you through how I built a Spotify Recommendation System from scratch in Python. This video is meant for ALL experience levels! I know this video is long so take a look at the timestamps below to find out which section interests you! Github Repo: https://github.com/madhavthaker/spotify-recommendation-system Here is my blog: https://www.madhavthaker.com/ My LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Outline: 00:00 - Intro 01:05 - Approach Overview 04:56 - Start of Feature Engineering 08:56 - TF-IDF Overview 12:52 - Leveraging Spotify API 16:32 - Creating a Playlist Vector 20:37 - Cosine Similarity Overview 22:25 - Spotify v. Madhav (Results Comparison) 24:59 - Outro

Every Data Science interview is entirely different but there is always one common theme throughout; walking through a specific project(s) on your resume. This is often overlooked when prepping for an interview but is a great way to not only build momentum at the start of an interview and also showcase your strong communication skills! Here is the blog post that goes along with this video: https://www.madhavthaker.com/qaposts/how-to-walk-through-a-project-in-an-interview My initial Data Science interview prep article: https://www.madhavthaker.com/qaposts/how-do-i-prepare-for-interviews My LinkedIn: https://www.linkedin.com/in/madhavthaker/ Join Our LinkedIn Group with other aspiring Data Scientists! https://www.linkedin.com/groups/13913186/ Timestamps: 00:00 - 6 Step Process Explained 06:08 Applying this Process to Madhav's Real World Example 09:07 - Start of Mock Interview using Madhav's Real World Example 15:15 - Conclusion

Enjoy a complete recording of Madhav's Data Science Q&A! This time around we have a special guest, Keith Dowd! Keith is a Lead Data Scientist at Verizon and has a wealth of knowledge of how to break into this space. We did run into slight technical issues and the a portion of his presentation was cut off. For more educational content, visit https://www.madhavthaker.com/ Connect with me on LinkedIn: https://www.linkedin.com/in/madhavthaker Connect with Keith on LinkedIn https://www.linkedin.com/in/keithdowd/ Keith's Presentation: https://docs.google.com/presentation/d/138INGihjgM8nrz_ek6dh6WwBjrT3MBomQPDOlsgJgUU/edit#slide=id.g6c52a2e8d8_0_36 00:00 - Intro 00:47:21 - What books were the most helpful to you in your career progression? How did you land your first job, and how competitive was job-hunting after college? 00:53:00 - I am currently working as an SDE in Amazon in India currently. I wanted to do a Masters and PhD abroad focusing on Theoretical Machine Learning or NLP, but I am worried since PhD takes a long time is it worth it? I came to know that PhD in Europe takes around 3 years which is less than what it takes in US. I needed to ask what are your views on PhD in Europe.Is there any other path that you suggest? 00:55:17 - I’m a Senior Data Analyst at Capital One and I’m trying to transition to a Data Science role. My day to day work consists on doing a lot of analysis on clickstream data with Python and Databricks and working with SQL. I just wanted to ask how I can enhance my statistics skills and what else I can do to make this transition? 00:58:15 - So where do you draw the line (if you even do) between Data Science and Data Engineering? I feel like the bootcamp I teach at focuses way too much on modeling, and not enough on data engineering. How much does a ML Engineer differ from a data scientist, from a data engineer? 01:02:11 - With regard to that retention modeling you mentioned, was that essentially survival analysis? 01:04:12 - How did you use your previous experiences and skills you learned, in your new experiences? 01:06:18 - Throughout your career, how have you invested in your own professional development? 01:09:55 - For the initial data scientist position did you apply with primarily your research work for school or did you have extra side projects? 01:13:12 - Lately there are several posts on reddit about entry level data scientist related positions that ask candidates to have basically every skill in the data science fields and oftentimes with X years of experience. What do you think about these requirement? What would you suggest to the newly graduated student to pursue a data science career? 01:17:52 - During your time at AAMC, when you're tracking students' progress during their first year, did you make use of any knowledge tracing models in order to track student growth over time? If so, what challenges did you face with placing these models in production? 01:23:48 - How are job responsibilities commonly distributed among a data science team? 01:28:10 - What would be the best Plan of Action for someone who's going for an MS in Data Science with a Bachelor's in Pure Math? I'm a freshman right now but I'm keen into getting into the field of Data Science. 01:31:32 - Is an entry level data science role the equivalent to, say, a mid-level to senior level data analyst? If I'm looking to make a career change into data science, is it more realistic to start off somewhere as a data analyst versus applying and seeking junior data science roles? 01:34:03 - Was it hard to transition to Verizon, considering the fact that the domains in which you applied Data Science were more along behavior analysis and financial risk assessment? How do you manage the domain change?