Nandan works with E.ON Digital Technology at Munich on data engineering & analytics of global learning data of E.ON. Previously, he worked with Allianz, Uniper (an energy generation company) as a Data Engineer dealing with solving the problems of power plants by applying Machine Learning. Having completed his Masters in Data Engineering, applying the concepts learnt in a practical environment is really something he likes to do. Before this, Nandan worked with Microsoft 🇩🇪 as a working student where he also completed his Masters Thesis on Recommender systems. Along with his masters studies in Bremen, he worked as a working student in LittelFuse and joined XING as a Data Engineering Summer School Intern. He also has experience of working in two large consulting companies in India where he solved various client problems ranging from banking to healthcare. While pursuing Bachelors in Electrical Engineering, Nandan also coauthored and published research papers (IEEE & Springer) on application of AI in the field of control systems.

My Mentoring Topics

  • Data Science,
  • Data Engineering,
  • Career Advice,
  • Internship Applications
R.
29.August 2023

"Outstanding data scientist mentor! Their expertise was incredibly helpful. They explained complex concepts in an understandable way and guided me through hands-on projects. Their feedback was specific and actionable, which accelerated my growth. A mentor who truly motivated and empowered me." Thank you sir for your time

D.
26.August 2023

Nandan gave great insights on how to proceed with my transition and learnings to get a data analyst role.

N.
12.April 2023

Nandan is an expert. 30 minutes with him was more insightful than 5 interviews together with other people in the industry that I've met with. He was insightful, to the point, and gave excellent advice on what next steps I should take to break into the Data Analyst industry. Thank you Nandan!

J.
28.March 2023

S.
27.March 2023

Anonymous
13.February 2023

V.
11.October 2022

Nandan really helped to simplify my approach and suggested me clear action points. I am looking forward to speaking to him again!

I.
8.October 2022

Extremely useful. Nanden is very professional and knowledgeable. He is extremely friendly and polite. I learned a lot from him. Thank you!

J.
30.July 2022

This session really helped me to have a direction and narrow down my priorities. This session addressed the limitations I am having in terms of learning and helped me to prioritise my learning skill set.

T.
21.July 2022

The session was really helpful to me. I've gotten many things new to open my mind. The mentor gave me a bigger picture to step in a career as Data Analyst as well as links to upgrade my skills. I'm really appreciated his service😃

I.
14.July 2022

Nandan is the best, he gives you better view about the career and the market.

T.
14.July 2022

Nandan is the best mentor. We had a great session where he gave excellent advice, tips, was very polite, set standards without being rude, and even offered coupons for courses. Highly recommend!

g.
13.July 2022

T.
13.July 2022

We had very exciting mentorship session. As experienced in the data science field, Dandan shared his experiences with me and helped me reshaping my data oriented future career. He also adviced me with the most important data science tools in the indutry. It was really a pleasure speaking with him like a friend of many years.

Anonymous
9.July 2022

T.
12.May 2022

The conversation was excellent. Nandan answered all my questions and identified my strengths and weaknesses. He also advised me on the next steps that are interesting for my career. He proved to be a person with vast knowledge, experienced, and up-to-date in the Data area. I look forward to our next conversations.

M.
3.May 2022

The session with Nandan was very great. He is a very knowledgable and supportive. Also, he has a good insight into job market and job haunting process. I find it very kind that he was willing to give advice and help even after our session on the LinkedIn. In general, the mentoring session with him was very good and recommendable.

P.
30.April 2022

It was a very helpful session for me. He gave me a lot of guidance towards pathways to change my carrier .I will continue keeping in touch with him.

M.
25.March 2022

Yes it was very helpful

A.
22.February 2022

This session was an eye opener for me. He gave me really honest and unbiased opinion for the topics which were raised by me during the session. The session has helped me in de-cluttering my journey.

K.
7.February 2022

My mentoring session with Nandan was extremely helpful. He began by asking questions to find out what stage I am at in my career, what my goals are, and tailored his advice to my situation. He was extremely generous with his time and offered very useful tips on the skills I need to learn, how to learn them, and how I should then approach job applications. After the session, I left feeling encouraged, and had a very clear plan for what my next steps should be in my career. I couldn't recommend him more. Thank you Nandan!

S.
1.February 2022

Hello, Had a meeting with Mr. Nandan. He is very friendly and answered all my doubts. Gave a proper plan of action to take the next step. Shared with me all the resources, sample projects to show case in interviews. Thanks for your time. Regards, Sreenivas

M.
28.January 2022

Very helpful

L.
20.January 2022

The session was really helpful, Nandan was friendly and gave me advice from his real-life experience and helped me see more clearly the path I should go

M.
12.January 2022

In that context it was like water in the desert, Got good insights about how to build a portofolio and the best was about doing something with GA demo data. Keep helping others with things you can do for someone you might be the only one who did that. Xd

S.
11.January 2022

Yes, definitely helpful

A.
4.January 2022

The session was really helpful. My question was answered accurately and I have got insightful advice. I will set another meeting in the future to consult about my decisions.

V.
6.November 2021

The session was really helpful. The discussion with Nandan was very friendly and he did provide me with a couple of options to consider relating to my situation along with a plan. The flow of conversation was very smooth and informative addressing all my questions and providing along with multiple options that I could consider.

M.
2.November 2021

Nandan explained things in great way and explained how to enter the required field. I will recommend everyone to get connected with him.

A.
11.October 2021

Overall a great session. Nandan knows things in his field of work. Would love to have such sessions often.

A.
5.August 2021

Nandan was very helpful throughout the session. He gave feedback on my assignment and also shared some urls, which were helpful. Looking forward for more mentoring sessions with him.

J.
22.July 2021

Yes, I found the session to be beneficial. Nandan is very friendly and easy to talk to. Prior to the session, I had no idea how to begin my career in the data field. The situation has changed, and I now know what to do as a result of the things Nandan shared. Many thanks to Nandan and the mentorship club for giving the platform.

J.
23.June 2021

Yes this session was helpful and he was very much supportive to understand the existing concern and gave good solution to the problem.

P.
1.June 2021

It was great. All my questions with respect to Data Engineering was cleared by the mentor. It was wonderful talking to him.

S.
13.May 2021

It was really very good session. I got to know where to keep my footsteps now and how to plan ahead of my career.

T.
8.April 2021

Thank you for helping me to better navigate myself in this AI/DS complex.

M.
7.April 2021

Intuitive session, appreciate the insight, looking forward to learn more and get experience in the field. Thanks

N.
24.January 2021

Key insights about thesis and job opportunities in Germany

The Hundred-page Machine Learning Book
Andriy Burkov

Key Insights from "The Hundred-page Machine Learning Book" by Andriy Burkov Demystification of Machine Learning: The book simplifies the complex concepts of machine learning, making it accessible for beginners as well as advanced readers. Comprehensive coverage: Despite its brevity, the book covers all the essential aspects of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Practical Implementation: Burkov emphasizes the practical implementation of machine learning algorithms rather than just the theoretical concepts. Real-world examples: The book uses real-world examples to explain abstract concepts, aiding in better understanding. Python Code: The book includes Python code for different machine learning algorithms, making it easier for readers to apply the knowledge practically. Mathematical Foundations: The book provides a clear explanation of the mathematical foundations of machine learning, which are essential for understanding the algorithms. Model Evaluation: Burkov dedicates a significant part of the book to model evaluation, discussing the importance of performance metrics and validation techniques. Feature Engineering: The book highlights the significance of feature engineering in improving the performance of machine learning models. Deep Learning: The book also introduces the concept of deep learning, providing a brief overview of neural networks and their applications. Future of Machine Learning: Burkov concludes the book by discussing the future of machine learning and its potential impact on various industries. Detailed Analysis of "The Hundred-page Machine Learning Book" "The Hundred-page Machine Learning Book" by Andriy Burkov is a concise yet comprehensive guide to machine learning. This makes it an ideal resource for beginners who want to understand the basics of machine learning, as well as for experienced practitioners looking for a quick reference. The book starts by demystifying machine learning, explaining that it is not a complex, inaccessible field reserved for computer scientists or mathematicians. Instead, Burkov argues that machine learning can be understood and applied by anyone with a basic understanding of mathematics and programming. Despite its brevity, the book covers all the essential aspects of machine learning. This includes supervised learning, where the algorithm learns from labeled data; unsupervised learning, where the algorithm learns from unlabeled data; semi-supervised learning, which combines both approaches; and reinforcement learning, where the algorithm learns by interacting with its environment. One of the major strengths of the book is its emphasis on the practical implementation of machine learning algorithms. While many books focus on the theoretical aspects of machine learning, Burkov provides Python code for different algorithms, allowing readers to apply their knowledge practically. This hands-on approach is particularly useful for beginners, who often struggle to bridge the gap between theory and practice. The book also excels in using real-world examples to explain abstract concepts. For instance, Burkov uses the example of a spam filter to explain supervised learning, making it easier for readers to understand the concept. While the book is accessible for beginners, it does not shy away from the mathematical foundations of machine learning. Burkov provides a clear explanation of these foundations, making complex concepts like gradient descent and backpropagation more understandable. This is a valuable resource for readers who want to delve deeper into machine learning. Model evaluation is another significant topic covered in the book. Burkov explains the importance of performance metrics and validation techniques, showing how they can be used to assess the accuracy of a machine learning model. This is crucial for practitioners, as it helps them judge the quality of their models. The book also highlights the importance of feature engineering in improving the performance of machine learning models. Burkov explains how selecting the right features can make a significant difference in the model's performance, providing tips and techniques for effective feature engineering. In the final chapters of the book, Burkov introduces the concept of deep learning, providing a brief overview of neural networks and their applications. This serves as a good introduction to the topic, paving the way for readers to explore more advanced resources. Lastly, Burkov discusses the future of machine learning, looking at how it could impact various industries. This is particularly relevant in today's rapidly changing technological landscape, where machine learning is expected to play a pivotal role. In conclusion, "The Hundred-page Machine Learning Book" by Andriy Burkov is a concise and comprehensive introduction to machine learning. It covers all the essential aspects of machine learning, from the basics to advanced concepts, making it an ideal resource for anyone interested in this exciting field.

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Approaching (Almost) Any Machine Learning Problem
Abhishek Thakur

Key Insights from the Book Practical Approach: The book focuses on practical aspects of machine learning, with an emphasis on real-world applications and problem-solving over theory and math. End-to-End Process: It covers the entire machine learning pipeline, including data gathering, feature engineering, model selection, training, tuning, evaluation, and deployment. Feature Engineering: The book places a lot of importance on feature engineering, which is often overlooked in other machine learning resources. Model Selection: It provides a comprehensive guide on how to choose the right model for your problem and dataset. Model Interpretability: The author emphasizes the importance of model interpretability and how to achieve it, which is crucial for deploying machine learning models in real-world applications. Dealing with Imbalanced Data: The book provides practical strategies for dealing with imbalanced datasets, a common issue in many machine learning problems. Ensembling Techniques: It covers various ensembling techniques, which can greatly improve the performance of your models. Deep Learning: There's a dedicated section on deep learning, including how to design and implement neural networks. Validation Strategies: The book provides insightful guide on how to create robust validation strategies to ensure the generalizability of your models. Competitive Machine Learning: The author shares his experience and tips on competitive machine learning, including Kaggle competitions. In-Depth Analysis and Conclusion "Approaching (Almost) Any Machine Learning Problem" by Abhishek Thakur provides a comprehensive guide to machine learning, focusing on practical problem-solving over theory. The book starts by addressing the end-to-end process of a machine learning project. This is crucial for beginners, as it gives them a concrete roadmap to follow. It also helps experienced practitioners streamline their workflow. The book places a lot of emphasis on feature engineering, an aspect often overlooked in machine learning resources. Feature engineering involves creating new features from the existing data to improve the performance of machine learning models. The author provides numerous practical examples and strategies for feature engineering, which can be applied to a wide range of problems. One of the most valuable sections of the book is on model selection. The author provides a detailed guide on how to choose the right model for your problem, based on the characteristics of your data and the requirements of your application. This is a critical skill, as the choice of model can greatly impact the performance of your machine learning system. The book also covers model interpretability, which is crucial for deploying machine learning models in real-world applications. The author provides various techniques for making your models more interpretable, such as LIME and SHAP. Dealing with imbalanced data is a common issue in machine learning, and the book provides practical strategies for addressing it. These strategies include resampling techniques, cost-sensitive learning, and ensemble methods. The author also covers various ensembling techniques, such as bagging, boosting, and stacking. Ensembling can greatly improve the performance of your models, especially in competitive machine learning. The book includes a section on deep learning, including how to design and implement neural networks. This is a valuable addition, as deep learning is becoming increasingly important in many fields. The book provides insightful guide on how to create robust validation strategies. This is important for ensuring the generalizability of your models, and preventing overfitting. Finally, the author shares his experience and tips on competitive machine learning, including Kaggle competitions. This is a unique aspect of the book, and provides valuable insights for those interested in participating in machine learning competitions. In conclusion, "Approaching (Almost) Any Machine Learning Problem" is a comprehensive and practical guide to machine learning. It covers a wide range of topics, with a focus on real-world applications and problem-solving. Whether you're a beginner or an experienced practitioner, this book will equip you with the knowledge and skills to tackle almost any machine learning problem.

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R for Data Science - Import, Tidy, Transform, Visualize, and Model Data
Hadley Wickham, Garrett Grolemund

Key Facts and Insights The book provides a comprehensive guide to using the R programming language for data science, covering the process from data importation to visualization and modeling. It introduces the 'tidyverse', a collection of R packages designed for data science, and it emphasizes on 'tidy data', a concept that simplifies data analysis. The authors explain data importation in R using the readr package, which enhances efficiency and productivity in data handling. The book extensively covers data transformation using dplyr, which includes filtering, arranging, selecting, mutating, and summarizing data. Hadley Wickham and Garrett Grolemund delve into data visualization using ggplot2, exploring aesthetical mappings, faceting, and geometrical objects. The book elucidates the development of models to understand complex data structures, using the purrr and broom packages. The authors discuss the use of R for programming, data wrangling with stringr and forcats, and the application of functional programming principles. The book also introduces the concept of 'tidy tools' and how they can be used to simplify and automate data analysis in R. The authors explain how to communicate results effectively through reproducible reports using R Markdown and knitr. An In-depth Analysis "R for Data Science" offers a concise yet comprehensive guide to the procedures involved in data science using the R programming language. The authors, Hadley Wickham and Garrett Grolemund, are both highly experienced in the field, and their expertise is evident throughout the book. The book begins by introducing the 'tidyverse', an ecosystem of R packages designed for data science. This includes ggplot2 for data visualization, dplyr for data transformation, tidyr for tidying data, readr for data importation, purrr for functional programming, and tibble for tamed data frames. The tidyverse promotes the concept of 'tidy data', where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. This structure simplifies data analysis and is a recurring theme throughout the book. One of the key strengths of the book is its detailed coverage of data importation in R. Wickham and Grolemund emphasize the use of the readr package, which is part of the tidyverse. This package enhances efficiency and productivity in data handling by simplifying data importation. The authors detail how to read in different data types, including CSV, Excel, and SPSS files, among others. Next, the authors delve into data transformation using dplyr. They discuss various functions for manipulating data sets, including filtering rows, arranging rows, selecting columns, creating new variables with mutate, and summarizing data with summarize. This comprehensive coverage of dplyr provides readers with a powerful toolkit for data manipulation in R. The authors also dedicate a significant section of the book to data visualization using ggplot2, another component of the tidyverse. They explore the concept of aesthetical mappings, which link data to visual properties, such as size, shape, and color. They also discuss faceting, which allows the creation of multiple related plots, and geometrical objects, which represent the data in the plots. This section provides a solid foundation for creating visually appealing and informative graphics in R. Modeling is another crucial aspect of data science that the authors cover in great detail. They discuss the development of models to understand complex data structures, using the purrr and broom packages of the tidyverse. The authors explain how models can help uncover patterns and relationships in data, and they provide examples of both regression and classification models. Furthermore, the book covers programming in R, data wrangling with stringr and forcats, and functional programming principles using purrr. These sections provide readers with the skills to write robust and efficient code in R, which is crucial for data science. The authors also introduce the concept of 'tidy tools', which are designed to work well with the tidy data structure and can be used to simplify and automate data analysis in R. This concept, which is central to the tidyverse philosophy, is a valuable addition to any data scientist's arsenal. Finally, the book emphasizes the importance of communicating results effectively. The authors explain how to create reproducible reports using R Markdown and knitr, which combine code, results, and text into a single document. This allows for transparency and reproducibility in data analysis, which are key principles in the field of data science. In conclusion, "R for Data Science" is a comprehensive guide to performing data science in R. The book is well-structured, covering all the key aspects of data science, from data importation to visualization and modeling. The authors' emphasis on the tidyverse and tidy data is a unique and valuable contribution to the field. Whether you are a beginner or an experienced data scientist, this book provides a wealth of knowledge and practical skills that will undoubtedly enhance your data science journey.

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Steve Jobs
Walter Isaacson

Key Facts or Insights from "Steve Jobs" by Walter Isaacson: Steve Jobs was an adoptee, a fact that greatly influenced his personality and worldview. Jobs was a perfectionist who paid meticulous attention to every detail, a trait that was reflected in the design of Apple’s products. Jobs had an exceptional ability to predict future trends in technology and consumer preferences, often referred to as his 'reality distortion field'. He was often described as a difficult person to work with due to his uncompromising demands and abrasive style of leadership. Jobs' Buddhist beliefs had a profound impact on his life and work, emphasizing simplicity and minimalism. He had a strained relationship with his first daughter, Lisa Brennan-Jobs, whom he initially denied paternity of. Jobs' approach to his cancer diagnosis and treatment reflected his tendency to reject traditional norms and methods. Despite his demanding nature, Jobs was able to inspire loyalty and dedication in his employees. Jobs had a unique approach to business and marketing, often disregarding market research and going with his gut feeling. His departure and return to Apple, known as the 'wilderness years', was a pivotal period in his life and career. Jobs' passion for integrating technology and humanities was a key driving force behind the success of Apple. Analysis of "Steve Jobs" by Walter Isaacson: In "Steve Jobs", Walter Isaacson delivers a masterclass in biography writing by delving into the life and career of one of the most influential figures in technology. As an experienced professor dealing with the topics from the book for many years, I find the insights and lessons contained in this book invaluable. Steve Jobs was an adoptee, a fact that Isaacson contends significantly influenced his personality and worldview. It is suggested that his feelings of abandonment and being 'special' contributed to his relentless drive for success. This is a clear demonstration of how an individual's personal history can shape their attitudes and behaviours in their professional life. Jobs' perfectionism is another key aspect of his personality that is explored in the book. He was known to obsess over even the smallest details of product design, pushing his teams to the brink of exhaustion in pursuit of perfection. This relentless pursuit of excellence is a fundamental element of Apple's DNA, contributing to its reputation for high-quality, innovative products. The book also delves into Jobs' exceptional ability to predict future trends. Often referred to as his 'reality distortion field', this ability allowed him to shape the future of technology, rather than simply reacting to existing trends. This forward-thinking approach is a key attribute of successful leaders in any field. However, Isaacson does not shy away from discussing Jobs' difficult personality. His demanding nature and abrasive leadership style made him a challenging figure to work with. Yet, he was able to inspire loyalty and dedication in his employees. This paradox underscores the complexity of leadership and the fact that there is no one-size-fits-all approach to it. Isaacson also sheds light on Jobs' Buddhist beliefs, which emphasized simplicity and minimalism. These principles are reflected in the design of Apple's products, demonstrating how personal beliefs can influence professional output. The relationship with his first daughter, Lisa, is another pivotal aspect of Jobs' personal life that is explored in the book. His initial denial of paternity and subsequent reconciliation with Lisa provides a glimpse into his personal struggles and his capacity for change. Jobs' approach to his cancer diagnosis and treatment was unconventional, mirroring his tendency to reject traditional norms and methods. This reflects his belief in following his own path, even in the face of life-threatening illness. Jobs' unique approach to business and marketing, disregarding market research and following his intuition, is a notable aspect of his leadership style. This approach, while unconventional, led to the creation of groundbreaking products like the iPhone and iPad. The 'wilderness years', the period of Jobs' departure and return to Apple, is presented as a critical phase in his career. This period, characterized by failure and introspection, ultimately led to his greatest successes, highlighting the importance of resilience and perseverance. Jobs' passion for integrating technology and the humanities was a driving force behind Apple's success. This passion underscores the importance of a multidisciplinary approach in innovation and technology. In conclusion, "Steve Jobs" by Walter Isaacson offers a comprehensive and insightful look into the life and career of Steve Jobs. The book not only serves as a biography of a remarkable individual but also provides invaluable lessons on leadership, innovation, and personal growth.

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