Rishanki Jain

Rishanki Jain

Product Analyst Providence

Hola folks, I have made a great community at Mentoring club and have constantly been awarded "Top 10 Female Data Science Award" in the past few months . This has been a huge learning curve for me personally and professionally. Seeing how amazingly our community has grown here, I would continue to take 1:1 sessions 🙌. However, I have moved my business to Topmate. ➡️ Here's my topmate link topmate.io/rishanki_jain You can book a 1:1, query, webinar or a consultation as and when you like based on the availability of slots. I have specials for all new users and even my reoccurring mentees. Looking forward to connecting with you in person! 👋🏼

My Mentoring Topics

  • Product Analytics
  • Transition into Data Analytics
  • Standout in Job hunt
  • Interview Frameworks and Structures
  • SQL expertise
  • Data story telling
  • Resume cleanup
  • Analytics tool kit to solve business problems
M.
25.May 2023

Very patient and very good listener. I had a lot of questions and we got through them even more. It was more than I expected. Thank you

N.
24.May 2023

Rishanki was very calm when listening to my pas experience and my future goals. She briefly explained me about the different options available as per my aspirations. I'd highly recommend her to discuss on product, business and data analytics related queries. She also shared me a document which comprised of all the details to revise for interviews.

A.
18.May 2023

Definitely Rishanki's mentoring is worth 6 stars! She's a great listener and quickly got me all the information and resources I needed. Her mentoring is been so valuable and inspiring and helped me choose a better education path that saved me time and money! I highly recommend a session with her before anything else! thank you so much Rishanki!

e.
6.May 2023

Rishanki is a friendly, kind, and helpful person. It does not matter your English level. She spoke English slowly to help me understand better. She reviewed my resume and gave me lots of advice, and homework until our next meeting. I am glad to meet someone who is good hearted. All the best!

G.
4.May 2023

M.
4.May 2023

she is very nice, helpful and patient and gave me good tips about orientation in starting my career. I'm glad to have these mentoring session with her.

N.
27.April 2023

Rishanki is one of the best mentors, which will be clear in the first 5 minutes. She listened well to my situation and provided excellent resources, advice, and ideas. I asked a lot of questions and she answered them all her insights are very valuable. She was very helpful in getting me scheduled for a session despite my time difference.

B.
22.April 2023

Rishanki is really nice and genuine and shared a lot of her experience as well as others' experience that related to my passion. She gave useful advice on job application and help me sort my thoughts about future career. Also she is an expertise in product analytics with lots of helpful knowledge to share.

N.
15.April 2023

Rishanki has well explained me to how i can break in data , what to learn and make resume Thank you somuch Rishanki !!

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Trustworthy Online Controlled Experiments - A Practical Guide to A/B Testing
Ron Kohavi, Diane Tang, Ya Xu

Key Insights From the Book: Importance of Controlled Experiments: The book highlights the essential role of controlled experiments in the world of online businesses to test and optimize the user experience. Principles of A/B Testing: The authors provide a detailed overview of the principles and methodologies of A/B testing, a popular technique used in online controlled experiments. Statistical Significance: The book underscores the importance of statistical significance in interpreting the results of controlled experiments. Common Mistakes: The authors talk about common mistakes that businesses make in conducting and interpreting online controlled experiments and how to avoid them. Business Decisions: The book demonstrates how data from controlled experiments can be used to inform and drive business decisions. Practical Case Studies: Real-world case studies are presented to illustrate the concepts and methodologies discussed in the book. Advanced Topics: The book delves into more advanced topics, such as multivariate testing and multi-armed bandit algorithms. Organizational Culture: The authors emphasize the importance of fostering an organizational culture that values data-driven decision making. Ethical Considerations: The book also discusses ethical considerations in conducting online controlled experiments. Future of A/B Testing: The authors speculate on the future of A/B testing and controlled experiments in the digital age. In-Depth Summary and Analysis: "Trustworthy Online Controlled Experiments - A Practical Guide to A/B Testing" is a comprehensive guide for anyone seeking to understand and apply the principles of A/B testing and controlled experiments in an online business setting. Importance of Controlled Experiments: The book begins by establishing the importance of controlled experiments. The authors, Ron Kohavi, Diane Tang, and Ya Xu, argue that the digital age, with its vast amounts of data and advanced analytics capabilities, provides unprecedented opportunities for businesses to test, measure, and optimize the user experience. This approach, they argue, results in more informed decision-making and improved business outcomes. Principles of A/B Testing: The book then delves into the principles of A/B testing, a popular technique used in online controlled experiments. A/B testing, the authors explain, involves comparing two versions of a webpage or other online element to see which performs better. A/B testing, they argue, allows businesses to make incremental improvements to their online platforms, driving increased user engagement and business growth. Statistical Significance: The authors underscore the importance of statistical significance in interpreting the results of controlled experiments. They explain that a result is statistically significant if it is unlikely to have occurred by chance. They emphasize that understanding statistical significance is crucial in avoiding false positives and ensuring the validity of experiment results. Common Mistakes: The authors also discuss common mistakes that businesses make in conducting and interpreting online controlled experiments. These include failure to randomize, failure to account for bias, and misinterpretation of results. They offer practical advice on how to avoid these pitfalls. Business Decisions: The book demonstrates how data from controlled experiments can be used to inform and drive business decisions. The authors argue that this data-driven approach leads to better decision-making and improved business outcomes. Practical Case Studies: The authors support their arguments with real-world case studies. These case studies illustrate how the principles and methodologies discussed in the book have been applied in practice, and the positive impact they have had on business performance. Advanced Topics: The book also delves into more advanced topics, such as multivariate testing and multi-armed bandit algorithms. These topics, the authors argue, offer more sophisticated tools for optimizing online experiences. Organizational Culture: The authors emphasize the importance of fostering an organizational culture that values data-driven decision making. They argue that this cultural shift is necessary for businesses to fully realize the benefits of controlled experiments and A/B testing. Ethical Considerations: The book also discusses ethical considerations in conducting online controlled experiments. The authors stress the need for transparency and respect for user privacy. Future of A/B Testing: The authors speculate on the future of A/B testing and controlled experiments in the digital age. They suggest that these methodologies will continue to evolve and become even more integral to online business success. In conclusion, "Trustworthy Online Controlled Experiments - A Practical Guide to A/B Testing" provides a comprehensive and practical guide to conducting and interpreting online controlled experiments. The authors’ expertise and insights make this a must-read for anyone seeking to understand and apply the principles of A/B testing and controlled experiments in an online business setting.

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Python for Data Analysis - Data Wrangling with Pandas, NumPy, and IPython
Wes McKinney

Key Facts and Insights from the Book Python as an Ideal Tool for Data Analysis: The book emphasizes the versatility and strength of Python in handling and analyzing complex data. Introduction to Pandas: McKinney, the creator of Pandas, provides a comprehensive overview of the library, showcasing its capabilities in data handling and manipulation. NumPy and Its Importance in Numerical Computations: The book covers the significance of NumPy in performing efficient numerical operations. Role of IPython in Interactive Computing: The book details how IPython enhances the interactive Python experience, making data analysis more intuitive and convenient. Data Wrangling Techniques: McKinney discusses various techniques to clean, transform, and merge data, which forms the crux of data analysis. Data Visualization with matplotlib: The book provides insights into data visualization using matplotlib, enabling readers to create a variety of plots and charts. Time Series Analysis: The book covers time series data analysis in Python, a critical aspect for many real-world applications. Advanced Pandas: The book provides a deep dive into more complex functions and operations in Pandas, such as group operations, categorical data, and more. Data Loading, Storage, and File Formats: The book discusses how to work with various types of data sources and file formats. Applications to Real-World Datasets: McKinney applies the techniques discussed in the book on actual datasets, giving a practical understanding of its application. High-Performance Pandas: The book covers how to optimize the performance of Pandas for handling large datasets. In-Depth Summary and Analysis Python as an Ideal Tool for Data Analysis - The book begins by highlighting Python's capabilities as a data analysis tool. As someone who has been utilizing Python for data analysis over the years, I can affirm the author's assertion. Python's simplicity, readability, and vast array of libraries make it an excellent choice for data analysis. Introduction to Pandas - McKinney introduces the reader to Pandas, a library he created to enhance Python's data handling capabilities. Pandas introduces two powerful data structures - DataFrame and Series, which are fundamental for data manipulation and analysis. NumPy and Its Importance in Numerical Computations - The book also covers NumPy, another essential library for handling numerical data. NumPy arrays, a core feature of the library, allow efficient storage and manipulation of numerical arrays, a common data type in data analysis. Role of IPython in Interactive Computing - The author introduces IPython, an interactive shell for Python. IPython enhances the Python experience by providing a robust platform for executing, testing, and debugging code, which is critical in data analysis. Data Wrangling Techniques - McKinney provides a broad overview of various data wrangling techniques. These include data cleaning, transformation, and merging. These techniques are essential in preparing data for analysis, and the author provides practical examples to illustrate these concepts. Data Visualization with matplotlib - The book covers data visualization using matplotlib, a powerful library for creating static, animated, and interactive visualizations in Python. Data visualization is a crucial aspect of data analysis as it allows for better understanding and interpretation of data. Time Series Analysis - McKinney dives into time series analysis, a critical aspect of many real-world applications such as finance, economics, and signal processing. The author discusses Pandas' capabilities in handling time-series data, providing practical examples for clarity. Advanced Pandas - The book delves into more complex Pandas operations. These include grouping operations, handling categorical data, and more. These advanced features allow for more sophisticated data manipulation and analysis. Data Loading, Storage, and File Formats - McKinney discusses how to work with various types of data sources and file formats. This is crucial as data can come from a variety of sources and in different formats. Applications to Real-World Datasets - The author applies the techniques discussed throughout the book on actual datasets. This practical approach enhances understanding and shows how these techniques can be applied in real-world scenarios. High-Performance Pandas - Lastly, the book covers how to optimize the performance of Pandas for handling large datasets, an increasingly common scenario in today's data-rich world. Overall, the book provides a comprehensive overview of Python's capabilities in data analysis. By covering the essential libraries and techniques, McKinney provides a solid foundation for anyone interested in learning data analysis with Python.

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