Sourav Banerjee successfully completed the online course "Machine Learning Foundations: A Case Study Approach" offered through Coursera by the University of Washington. The certificate confirms his identity and participation in the course, which was taught by Emily Fox and Carlos Guestrin, and can be verified on the Coursera website.
Satya Nadella, Chief Executive Officer of Microsoft, has certified Sourav Banerjee as Microsoft Certified: Azure Fundamentals. Banerjee completed the requirements for this certification on May 01, 2020. His certification number is H412-4812.
Sourav Banerjee successfully completed M001: MongoDB Basics, a course offered by MongoDB, Inc. Grace Francisco, VP of Developer Relations & Education at MongoDB, Inc., confirms this in a course completion confirmation for May 2020.
Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. It allows processing live data streams using the same SQL queries as batch DataFrame/Dataset queries. Structured Streaming queries are continuous and run indefinitely, updating the final result as streaming data arrives.
Delta Lake is an open source storage layer that brings ACID transactions to Apache Spark SQL, unifying streaming and batch data processing. It provides transactional semantics, scalable metadata handling, and works directly with existing data in Apache Spark. Delta Lake tables are ACID-compliant, making them reliable for ETL and analytics workloads.
Sourav Banerjee successfully completed the online course "Machine Learning Foundations: A Case Study Approach" offered through Coursera by the University of Washington. The certificate confirms his identity and participation in the course, which was taught by Emily Fox and Carlos Guestrin, and can be verified on the Coursera website.
Satya Nadella, Chief Executive Officer of Microsoft, has certified Sourav Banerjee as Microsoft Certified: Azure Fundamentals. Banerjee completed the requirements for this certification on May 01, 2020. His certification number is H412-4812.
Sourav Banerjee successfully completed M001: MongoDB Basics, a course offered by MongoDB, Inc. Grace Francisco, VP of Developer Relations & Education at MongoDB, Inc., confirms this in a course completion confirmation for May 2020.
Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. It allows processing live data streams using the same SQL queries as batch DataFrame/Dataset queries. Structured Streaming queries are continuous and run indefinitely, updating the final result as streaming data arrives.
Delta Lake is an open source storage layer that brings ACID transactions to Apache Spark SQL, unifying streaming and batch data processing. It provides transactional semantics, scalable metadata handling, and works directly with existing data in Apache Spark. Delta Lake tables are ACID-compliant, making them reliable for ETL and analytics workloads.
Mlflow managing the machine learning lifecycleSourav Banerjee
MLflow is a platform for managing the machine learning lifecycle including tracking experiments, model registry, and model deployment. It allows users to track metrics and parameters for model training runs, log model artifacts like models and code, and load models for inference in production. MLflow aims to address common problems in ML development like reproducibility, sharing models, and deploying models into production.
Data extraction is the first step in the ETL process. It involves pulling or querying data from various source systems like databases, files and applications and transforming it into a common format. This prepares the data for loading into the data warehouse where it can be cleansed, transformed and loaded for analysis and reporting.
ETL processes involve more than just extracting data. Transformations prepare and cleanse the data for loading. Common transformations include filtering out unnecessary data, converting data types, calculating new fields, and joining data from multiple sources. The transformed data is then loaded into a data warehouse or other destination where it can be analyzed and reported on.
Apache Spark SQL allows querying structured data in Spark. It provides a programming abstraction called DataFrames and can be used to load data from a variety of sources and write queries using SQL or DataFrames API. Spark SQL can also be used to integrate Spark with data sources like Hive, Parquet, and JSON.
The document discusses ETL (Extract, Transform, Load) processes moving to production. It focuses on testing ETL jobs thoroughly before moving to production, monitoring the jobs closely after deployment, and having procedures to rollback changes if any issues arise.
Sourav Banerjee successfully completed a course in Random Forest in October 2018, as evidenced by this Certificate of Achievement. The certificate number is JRJRF2004829M8.
This document appears to be a code or reference containing letters and numbers that may represent an identification, date, name, and number. It provides minimal context to understand its purpose or content beyond these surface level details.
Sourav Banerjee is a software developer with over 4 years of experience in banking, big data, and mainframe development. He has extensive skills in Java, COBOL, SQL, Hadoop, Hive, and Pig. His career includes projects involving data migration, log analysis, statement generation, and developing solutions for requirements from clients like ING Bank and Tata Consultancy Services. Sourav holds certifications in areas like financial markets, big data analytics, and data science.
The document contains a name, Sourav Banerjee, and a date, 26th April 2017. No other information is provided about the person named or context around the date. The short document only lists a name and date without any other details.
This document appears to be a code or reference number along with a date and a person's name. It includes the letters and numbers SIMBHC14-165, the date 6th APR, and the name Sourav Banerjee, as well as the number 7.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Mlflow managing the machine learning lifecycleSourav Banerjee
MLflow is a platform for managing the machine learning lifecycle including tracking experiments, model registry, and model deployment. It allows users to track metrics and parameters for model training runs, log model artifacts like models and code, and load models for inference in production. MLflow aims to address common problems in ML development like reproducibility, sharing models, and deploying models into production.
Data extraction is the first step in the ETL process. It involves pulling or querying data from various source systems like databases, files and applications and transforming it into a common format. This prepares the data for loading into the data warehouse where it can be cleansed, transformed and loaded for analysis and reporting.
ETL processes involve more than just extracting data. Transformations prepare and cleanse the data for loading. Common transformations include filtering out unnecessary data, converting data types, calculating new fields, and joining data from multiple sources. The transformed data is then loaded into a data warehouse or other destination where it can be analyzed and reported on.
Apache Spark SQL allows querying structured data in Spark. It provides a programming abstraction called DataFrames and can be used to load data from a variety of sources and write queries using SQL or DataFrames API. Spark SQL can also be used to integrate Spark with data sources like Hive, Parquet, and JSON.
The document discusses ETL (Extract, Transform, Load) processes moving to production. It focuses on testing ETL jobs thoroughly before moving to production, monitoring the jobs closely after deployment, and having procedures to rollback changes if any issues arise.
Sourav Banerjee successfully completed a course in Random Forest in October 2018, as evidenced by this Certificate of Achievement. The certificate number is JRJRF2004829M8.
This document appears to be a code or reference containing letters and numbers that may represent an identification, date, name, and number. It provides minimal context to understand its purpose or content beyond these surface level details.
Sourav Banerjee is a software developer with over 4 years of experience in banking, big data, and mainframe development. He has extensive skills in Java, COBOL, SQL, Hadoop, Hive, and Pig. His career includes projects involving data migration, log analysis, statement generation, and developing solutions for requirements from clients like ING Bank and Tata Consultancy Services. Sourav holds certifications in areas like financial markets, big data analytics, and data science.
The document contains a name, Sourav Banerjee, and a date, 26th April 2017. No other information is provided about the person named or context around the date. The short document only lists a name and date without any other details.
This document appears to be a code or reference number along with a date and a person's name. It includes the letters and numbers SIMBHC14-165, the date 6th APR, and the name Sourav Banerjee, as well as the number 7.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."