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Architecture 1

Check out the latest posts
Discover the Different Patterns for Data Transformation
Architecture Data pipeline Data engineering

Discover the Different Patterns for Data Transformation

Data transformation is the process of preparing data for analysis or use by cleaning, filtering, and shaping it into the desired format. There are various patterns for performing data transformation, including batch processing, stream processing, and micro-batching. Cloud services like AWS, Azure, and Google Cloud offer tools and services to implement these patterns in a scalable and cost-effective way.

Best practices 1

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Spark: Best Practices with Examples
Data engineering Spark Best practices

Spark: Best Practices with Examples

Apache Spark is an immensely powerful tool for big data processing, known for its speed, ease of use, and versatility. However, to fully leverage its capabilities, it's crucial to follow certain best practices. In this blog, we’ll explore some key dos and don'ts in Spark, providing a guide to optimize your data processing tasks efficiently.

Clean code 1

Check out the latest posts
Elevate Your Python Skills: Techniques for Writing Production-Ready Code
Python Clean code Programming

Elevate Your Python Skills: Techniques for Writing Production-Ready Code

I have extensive experience in Python, including developing an authentication SDK for WSO2 Identity Server and mentoring an anomaly detection project. In my work, I have implemented standards to ensure that our Python projects meet production-level. I am sharing my key learnings in this article to help other Python developers create stable, readable, and extendable code for production environments.

Data analysis 1

Check out the latest posts
SQL Query Optimization Tips for Data Engineers and Analysts
SQL Data engineering Data analysis Data science

SQL Query Optimization Tips for Data Engineers and Analysts

Learn how to optimize your SQL queries for faster data processing and analysis. From correlated queries to indexing and partitioning, we cover expert tips and techniques to help you improve the performance of your SQL queries.

Data analytics 3

Check out the latest posts
Effective Lead Scoring with Machine Learning
ML Marketing Data analytics

Effective Lead Scoring with Machine Learning

This blog explores lead scoring and its importance in identifying high-quality leads. It discusses features used for scoring, such as behaviors, demographics, and machine learning algorithms, to automate and prioritize leads for better sales performance.

Understand hypothesis testing using a Business Problem
Statistics Data science Data analytics

Understand hypothesis testing using a Business Problem

This article explains the hypothesis testing, using a business problem as an example to guide readers through the four steps of the process and introduce related concepts

Mastering Data Analytics with Window Functions
SQL Window functions Data analytics

Mastering Data Analytics with Window Functions

This blog post discusses the importance and usefulness of window functions in data analytics, including their ability to perform complex calculations, aggregate data, rank data, compare data, and analyze data over a set of rows.

Data engineering 7

Check out the latest posts
Debugging and Optimizing Spark Applications
Spark Data engineering Debugging

Debugging and Optimizing Spark Applications

Apache Spark is a powerful tool for big data processing, offering both speed and flexibility. However, mastering its debugging and optimization can be challenging. In this blog, we'll explore various aspects of debugging and optimizing Spark applications, providing insights and practical tips.

Spark: Best Practices with Examples
Data engineering Spark Best practices

Spark: Best Practices with Examples

Apache Spark is an immensely powerful tool for big data processing, known for its speed, ease of use, and versatility. However, to fully leverage its capabilities, it's crucial to follow certain best practices. In this blog, we’ll explore some key dos and don'ts in Spark, providing a guide to optimize your data processing tasks efficiently.

Unlocking the Potential of Docker for Data Science and Engineering
Docker Data engineering Data science

Unlocking the Potential of Docker for Data Science and Engineering

This blog examines the use of Docker in data science and engineering, including benefits such as reproducible environments and dependency management. It provides examples and reference links for readers to understand and implement Docker in their workflows.

A Data Engineer's Guide to Slowly Changing Dimension
SCD Data engineering Data warehouse

A Data Engineer's Guide to Slowly Changing Dimension

In this blog, we'll take a deep dive into the concept of Slowly Changing Dimension (SCD) with real-world examples of each type and scenarios for implementation to help data professionals understand and implement the right type of SCD for their data warehouse.

SQL Query Optimization Tips for Data Engineers and Analysts
SQL Data engineering Data analysis Data science

SQL Query Optimization Tips for Data Engineers and Analysts

Learn how to optimize your SQL queries for faster data processing and analysis. From correlated queries to indexing and partitioning, we cover expert tips and techniques to help you improve the performance of your SQL queries.

Discover the Different Patterns for Data Transformation
Architecture Data pipeline Data engineering

Discover the Different Patterns for Data Transformation

Data transformation is the process of preparing data for analysis or use by cleaning, filtering, and shaping it into the desired format. There are various patterns for performing data transformation, including batch processing, stream processing, and micro-batching. Cloud services like AWS, Azure, and Google Cloud offer tools and services to implement these patterns in a scalable and cost-effective way.

Moving from Pandas to PySpark: An Easy Transition
Pyspark Spark Data engineering

Moving from Pandas to PySpark: An Easy Transition

In this tutorial, I will show you how easy it is to transition from Pandas to PySpark by providing examples of common operations in both libraries. By following this tutorial, you will be able to quickly and easily migrate your data processing and analysis pipelines from Pandas to PySpark.

Data pipeline 1

Check out the latest posts
Discover the Different Patterns for Data Transformation
Architecture Data pipeline Data engineering

Discover the Different Patterns for Data Transformation

Data transformation is the process of preparing data for analysis or use by cleaning, filtering, and shaping it into the desired format. There are various patterns for performing data transformation, including batch processing, stream processing, and micro-batching. Cloud services like AWS, Azure, and Google Cloud offer tools and services to implement these patterns in a scalable and cost-effective way.

Data science 6

Check out the latest posts
Predicting Loan Defaults: My Journey Through a Coursera Data Science Challenge
Machine learning Data science EDA

Predicting Loan Defaults: My Journey Through a Coursera Data Science Challenge

This project, part of the Coursera Data Science Coding Challenge, aims to predict loan defaults based on various borrower-specific features. Understanding the patterns and correlations in the data allows lenders to better predict and mitigate potential loan defaults, ensuring a healthier portfolio and risk management.

Understanding Neural Networks
Deep learning Data science Neural networks

Understanding Neural Networks

Neural networks are a powerful tool in the field of machine learning, enabling computers to learn and make predictions based on data. At the core of a neural network are individual units called neurons, which mimic the behavior of biological neurons in the human brain. In this article, we will explore the fundamentals of neural networks by examining a single neuron and its role in binary classification tasks. We will also delve into gradient descent, activation functions, and optimizers, essential concepts for training neural networks.

Unlocking the Potential of Docker for Data Science and Engineering
Docker Data engineering Data science

Unlocking the Potential of Docker for Data Science and Engineering

This blog examines the use of Docker in data science and engineering, including benefits such as reproducible environments and dependency management. It provides examples and reference links for readers to understand and implement Docker in their workflows.

SQL Query Optimization Tips for Data Engineers and Analysts
SQL Data engineering Data analysis Data science

SQL Query Optimization Tips for Data Engineers and Analysts

Learn how to optimize your SQL queries for faster data processing and analysis. From correlated queries to indexing and partitioning, we cover expert tips and techniques to help you improve the performance of your SQL queries.

Say Goodbye to Disorganized ML Projects with mlskeleton
Python Machine learning Deep learning Data science

Say Goodbye to Disorganized ML Projects with mlskeleton

Learn how mlskeleton, the open-source python package, can help you create a professional and organized folder structure for your machine learning projects and streamline your workflow.

Understand hypothesis testing using a Business Problem
Statistics Data science Data analytics

Understand hypothesis testing using a Business Problem

This article explains the hypothesis testing, using a business problem as an example to guide readers through the four steps of the process and introduce related concepts

Data warehouse 1

Check out the latest posts
A Data Engineer's Guide to Slowly Changing Dimension
SCD Data engineering Data warehouse

A Data Engineer's Guide to Slowly Changing Dimension

In this blog, we'll take a deep dive into the concept of Slowly Changing Dimension (SCD) with real-world examples of each type and scenarios for implementation to help data professionals understand and implement the right type of SCD for their data warehouse.

Debugging 1

Check out the latest posts
Debugging and Optimizing Spark Applications
Spark Data engineering Debugging

Debugging and Optimizing Spark Applications

Apache Spark is a powerful tool for big data processing, offering both speed and flexibility. However, mastering its debugging and optimization can be challenging. In this blog, we'll explore various aspects of debugging and optimizing Spark applications, providing insights and practical tips.

Deep learning 5

Check out the latest posts
Unmasking Transformers: The Power of Attention in Language Models
Deep learning NLP Neural networks

Unmasking Transformers: The Power of Attention in Language Models

In this engaging third post of my NLP blog series, I delve into the heart of transformers in Natural Language Processing, focusing on the attention layer—its functions, applications, and advantages. We'll unravel how this layer allows models to weigh the importance of different words in a sentence, providing a nuanced understanding of language context.

Understanding the Basics of Language Modeling and its evolution towards Transformers
Deep learning NLP Neural networks

Understanding the Basics of Language Modeling and its evolution towards Transformers

Language modeling, a fundamental task in natural language processing, has witnessed remarkable progress in recent years. This fascinating field has given rise to groundbreaking techniques that enable machines to understand and generate human-like language. In this blog, we embark on a journey through the evolution of language modeling, uncovering key milestones and advancements that have shaped its development.

Understanding Natural Language Processing (NLP) and Word Embeddings
Deep learning NLP Neural networks

Understanding Natural Language Processing (NLP) and Word Embeddings

Natural Language Processing (NLP) is a field of study that focuses on enabling computers to understand, interpret, and generate human language. NLP opens up a world of possibilities by allowing computers to analyze text and generate valuable outputs, such as predictions, summaries, translations, and more. This blog post explores the concept of NLP and delves into the process of encoding text using various techniques. Additionally, we'll discuss the challenges of capturing world knowledge and learning meaningful word embeddings, which are crucial for language understanding in NLP.

Understanding Neural Networks
Deep learning Data science Neural networks

Understanding Neural Networks

Neural networks are a powerful tool in the field of machine learning, enabling computers to learn and make predictions based on data. At the core of a neural network are individual units called neurons, which mimic the behavior of biological neurons in the human brain. In this article, we will explore the fundamentals of neural networks by examining a single neuron and its role in binary classification tasks. We will also delve into gradient descent, activation functions, and optimizers, essential concepts for training neural networks.

Say Goodbye to Disorganized ML Projects with mlskeleton
Python Machine learning Deep learning Data science

Say Goodbye to Disorganized ML Projects with mlskeleton

Learn how mlskeleton, the open-source python package, can help you create a professional and organized folder structure for your machine learning projects and streamline your workflow.

Design pattern 2

Check out the latest posts
Using Modules and Packages to Create Facade in Python
Python Design pattern Facade

Using Modules and Packages to Create Facade in Python

Modules and packages in Python can be used to implement the Facade design pattern, which provides a simplified interface to a complex system. By creating a package or module that serves as a facade, you can expose a simplified interface to the rest of the system, allowing users to access and use the features of the system with ease. This can be useful in situations where you want to provide a simple, easy-to-use interface to a complex system.

How Python Modules and Packages Help You Implement Design Patterns
Python Design pattern Singleton

How Python Modules and Packages Help You Implement Design Patterns

This blog is discussing how to use python modules and packages to structure and optimize the efficiency of your Python projects. It covers the concept of how design patterns can be implemented in python, with a specific focus on the Singleton pattern which is a creational pattern that allows to ensure that only one instance of the class is created throughout the lifetime of the application.

Docker 2

Check out the latest posts
Unlocking the Potential of Docker for Data Science and Engineering
Docker Data engineering Data science

Unlocking the Potential of Docker for Data Science and Engineering

This blog examines the use of Docker in data science and engineering, including benefits such as reproducible environments and dependency management. It provides examples and reference links for readers to understand and implement Docker in their workflows.

Isolate, Organize, and Deploy: The Benefits of Virtual Environments and Docker for Python
Virtual environments Docker Python

Isolate, Organize, and Deploy: The Benefits of Virtual Environments and Docker for Python

A virtual environment is a tool used to isolate specific Python environments on a single machine, allowing you to work on multiple projects with different libraries and dependencies. This is especially useful when working on projects that have conflicting requirements or when you want to test your code in different environments.

EDA 1

Check out the latest posts
Predicting Loan Defaults: My Journey Through a Coursera Data Science Challenge
Machine learning Data science EDA

Predicting Loan Defaults: My Journey Through a Coursera Data Science Challenge

This project, part of the Coursera Data Science Coding Challenge, aims to predict loan defaults based on various borrower-specific features. Understanding the patterns and correlations in the data allows lenders to better predict and mitigate potential loan defaults, ensuring a healthier portfolio and risk management.

Facade 1

Check out the latest posts
Using Modules and Packages to Create Facade in Python
Python Design pattern Facade

Using Modules and Packages to Create Facade in Python

Modules and packages in Python can be used to implement the Facade design pattern, which provides a simplified interface to a complex system. By creating a package or module that serves as a facade, you can expose a simplified interface to the rest of the system, allowing users to access and use the features of the system with ease. This can be useful in situations where you want to provide a simple, easy-to-use interface to a complex system.

Logging 2

Check out the latest posts
Python Best Practices: The Role of Documentation, Version Control, and Code Review
Python Logging Testing

Python Best Practices: The Role of Documentation, Version Control, and Code Review

Documentation, version control, and code review are essential best practices in software development that help ensure the quality and maintainability of code. They provide information about the code, track and manage changes to the code, and involve review by other developers. These practices help ensure that code is reliable and easy to maintain.

Become a Python Pro: Tips for Writing Reliable Code with Testing and Logging
Python Logging Testing

Become a Python Pro: Tips for Writing Reliable Code with Testing and Logging

Writing production-level Python code involves ensuring that the code is not only functional, but also efficient, maintainable, and easy to understand. To achieve these goals, it is important to follow best practices in Python development, such as using logging and testing to ensure the code is correct and easy to debug.

Machine learning 2

Check out the latest posts
Predicting Loan Defaults: My Journey Through a Coursera Data Science Challenge
Machine learning Data science EDA

Predicting Loan Defaults: My Journey Through a Coursera Data Science Challenge

This project, part of the Coursera Data Science Coding Challenge, aims to predict loan defaults based on various borrower-specific features. Understanding the patterns and correlations in the data allows lenders to better predict and mitigate potential loan defaults, ensuring a healthier portfolio and risk management.

Say Goodbye to Disorganized ML Projects with mlskeleton
Python Machine learning Deep learning Data science

Say Goodbye to Disorganized ML Projects with mlskeleton

Learn how mlskeleton, the open-source python package, can help you create a professional and organized folder structure for your machine learning projects and streamline your workflow.

Marketing 1

Check out the latest posts
Effective Lead Scoring with Machine Learning
ML Marketing Data analytics

Effective Lead Scoring with Machine Learning

This blog explores lead scoring and its importance in identifying high-quality leads. It discusses features used for scoring, such as behaviors, demographics, and machine learning algorithms, to automate and prioritize leads for better sales performance.

ML 1

Check out the latest posts
Effective Lead Scoring with Machine Learning
ML Marketing Data analytics

Effective Lead Scoring with Machine Learning

This blog explores lead scoring and its importance in identifying high-quality leads. It discusses features used for scoring, such as behaviors, demographics, and machine learning algorithms, to automate and prioritize leads for better sales performance.

Neural networks 4

Check out the latest posts
Unmasking Transformers: The Power of Attention in Language Models
Deep learning NLP Neural networks

Unmasking Transformers: The Power of Attention in Language Models

In this engaging third post of my NLP blog series, I delve into the heart of transformers in Natural Language Processing, focusing on the attention layer—its functions, applications, and advantages. We'll unravel how this layer allows models to weigh the importance of different words in a sentence, providing a nuanced understanding of language context.

Understanding the Basics of Language Modeling and its evolution towards Transformers
Deep learning NLP Neural networks

Understanding the Basics of Language Modeling and its evolution towards Transformers

Language modeling, a fundamental task in natural language processing, has witnessed remarkable progress in recent years. This fascinating field has given rise to groundbreaking techniques that enable machines to understand and generate human-like language. In this blog, we embark on a journey through the evolution of language modeling, uncovering key milestones and advancements that have shaped its development.

Understanding Natural Language Processing (NLP) and Word Embeddings
Deep learning NLP Neural networks

Understanding Natural Language Processing (NLP) and Word Embeddings

Natural Language Processing (NLP) is a field of study that focuses on enabling computers to understand, interpret, and generate human language. NLP opens up a world of possibilities by allowing computers to analyze text and generate valuable outputs, such as predictions, summaries, translations, and more. This blog post explores the concept of NLP and delves into the process of encoding text using various techniques. Additionally, we'll discuss the challenges of capturing world knowledge and learning meaningful word embeddings, which are crucial for language understanding in NLP.

Understanding Neural Networks
Deep learning Data science Neural networks

Understanding Neural Networks

Neural networks are a powerful tool in the field of machine learning, enabling computers to learn and make predictions based on data. At the core of a neural network are individual units called neurons, which mimic the behavior of biological neurons in the human brain. In this article, we will explore the fundamentals of neural networks by examining a single neuron and its role in binary classification tasks. We will also delve into gradient descent, activation functions, and optimizers, essential concepts for training neural networks.

NLP 3

Check out the latest posts
Unmasking Transformers: The Power of Attention in Language Models
Deep learning NLP Neural networks

Unmasking Transformers: The Power of Attention in Language Models

In this engaging third post of my NLP blog series, I delve into the heart of transformers in Natural Language Processing, focusing on the attention layer—its functions, applications, and advantages. We'll unravel how this layer allows models to weigh the importance of different words in a sentence, providing a nuanced understanding of language context.

Understanding the Basics of Language Modeling and its evolution towards Transformers
Deep learning NLP Neural networks

Understanding the Basics of Language Modeling and its evolution towards Transformers

Language modeling, a fundamental task in natural language processing, has witnessed remarkable progress in recent years. This fascinating field has given rise to groundbreaking techniques that enable machines to understand and generate human-like language. In this blog, we embark on a journey through the evolution of language modeling, uncovering key milestones and advancements that have shaped its development.

Understanding Natural Language Processing (NLP) and Word Embeddings
Deep learning NLP Neural networks

Understanding Natural Language Processing (NLP) and Word Embeddings

Natural Language Processing (NLP) is a field of study that focuses on enabling computers to understand, interpret, and generate human language. NLP opens up a world of possibilities by allowing computers to analyze text and generate valuable outputs, such as predictions, summaries, translations, and more. This blog post explores the concept of NLP and delves into the process of encoding text using various techniques. Additionally, we'll discuss the challenges of capturing world knowledge and learning meaningful word embeddings, which are crucial for language understanding in NLP.

Programming 1

Check out the latest posts
Elevate Your Python Skills: Techniques for Writing Production-Ready Code
Python Clean code Programming

Elevate Your Python Skills: Techniques for Writing Production-Ready Code

I have extensive experience in Python, including developing an authentication SDK for WSO2 Identity Server and mentoring an anomaly detection project. In my work, I have implemented standards to ensure that our Python projects meet production-level. I am sharing my key learnings in this article to help other Python developers create stable, readable, and extendable code for production environments.

Pyspark 1

Check out the latest posts
Moving from Pandas to PySpark: An Easy Transition
Pyspark Spark Data engineering

Moving from Pandas to PySpark: An Easy Transition

In this tutorial, I will show you how easy it is to transition from Pandas to PySpark by providing examples of common operations in both libraries. By following this tutorial, you will be able to quickly and easily migrate your data processing and analysis pipelines from Pandas to PySpark.

Python 7

Check out the latest posts
Say Goodbye to Disorganized ML Projects with mlskeleton
Python Machine learning Deep learning Data science

Say Goodbye to Disorganized ML Projects with mlskeleton

Learn how mlskeleton, the open-source python package, can help you create a professional and organized folder structure for your machine learning projects and streamline your workflow.

Using Modules and Packages to Create Facade in Python
Python Design pattern Facade

Using Modules and Packages to Create Facade in Python

Modules and packages in Python can be used to implement the Facade design pattern, which provides a simplified interface to a complex system. By creating a package or module that serves as a facade, you can expose a simplified interface to the rest of the system, allowing users to access and use the features of the system with ease. This can be useful in situations where you want to provide a simple, easy-to-use interface to a complex system.

How Python Modules and Packages Help You Implement Design Patterns
Python Design pattern Singleton

How Python Modules and Packages Help You Implement Design Patterns

This blog is discussing how to use python modules and packages to structure and optimize the efficiency of your Python projects. It covers the concept of how design patterns can be implemented in python, with a specific focus on the Singleton pattern which is a creational pattern that allows to ensure that only one instance of the class is created throughout the lifetime of the application.

Python Best Practices: The Role of Documentation, Version Control, and Code Review
Python Logging Testing

Python Best Practices: The Role of Documentation, Version Control, and Code Review

Documentation, version control, and code review are essential best practices in software development that help ensure the quality and maintainability of code. They provide information about the code, track and manage changes to the code, and involve review by other developers. These practices help ensure that code is reliable and easy to maintain.

Isolate, Organize, and Deploy: The Benefits of Virtual Environments and Docker for Python
Virtual environments Docker Python

Isolate, Organize, and Deploy: The Benefits of Virtual Environments and Docker for Python

A virtual environment is a tool used to isolate specific Python environments on a single machine, allowing you to work on multiple projects with different libraries and dependencies. This is especially useful when working on projects that have conflicting requirements or when you want to test your code in different environments.

Become a Python Pro: Tips for Writing Reliable Code with Testing and Logging
Python Logging Testing

Become a Python Pro: Tips for Writing Reliable Code with Testing and Logging

Writing production-level Python code involves ensuring that the code is not only functional, but also efficient, maintainable, and easy to understand. To achieve these goals, it is important to follow best practices in Python development, such as using logging and testing to ensure the code is correct and easy to debug.

Elevate Your Python Skills: Techniques for Writing Production-Ready Code
Python Clean code Programming

Elevate Your Python Skills: Techniques for Writing Production-Ready Code

I have extensive experience in Python, including developing an authentication SDK for WSO2 Identity Server and mentoring an anomaly detection project. In my work, I have implemented standards to ensure that our Python projects meet production-level. I am sharing my key learnings in this article to help other Python developers create stable, readable, and extendable code for production environments.

SCD 1

Check out the latest posts
A Data Engineer's Guide to Slowly Changing Dimension
SCD Data engineering Data warehouse

A Data Engineer's Guide to Slowly Changing Dimension

In this blog, we'll take a deep dive into the concept of Slowly Changing Dimension (SCD) with real-world examples of each type and scenarios for implementation to help data professionals understand and implement the right type of SCD for their data warehouse.

Singleton 1

Check out the latest posts
How Python Modules and Packages Help You Implement Design Patterns
Python Design pattern Singleton

How Python Modules and Packages Help You Implement Design Patterns

This blog is discussing how to use python modules and packages to structure and optimize the efficiency of your Python projects. It covers the concept of how design patterns can be implemented in python, with a specific focus on the Singleton pattern which is a creational pattern that allows to ensure that only one instance of the class is created throughout the lifetime of the application.

Spark 3

Check out the latest posts
Debugging and Optimizing Spark Applications
Spark Data engineering Debugging

Debugging and Optimizing Spark Applications

Apache Spark is a powerful tool for big data processing, offering both speed and flexibility. However, mastering its debugging and optimization can be challenging. In this blog, we'll explore various aspects of debugging and optimizing Spark applications, providing insights and practical tips.

Spark: Best Practices with Examples
Data engineering Spark Best practices

Spark: Best Practices with Examples

Apache Spark is an immensely powerful tool for big data processing, known for its speed, ease of use, and versatility. However, to fully leverage its capabilities, it's crucial to follow certain best practices. In this blog, we’ll explore some key dos and don'ts in Spark, providing a guide to optimize your data processing tasks efficiently.

Moving from Pandas to PySpark: An Easy Transition
Pyspark Spark Data engineering

Moving from Pandas to PySpark: An Easy Transition

In this tutorial, I will show you how easy it is to transition from Pandas to PySpark by providing examples of common operations in both libraries. By following this tutorial, you will be able to quickly and easily migrate your data processing and analysis pipelines from Pandas to PySpark.

SQL 2

Check out the latest posts
SQL Query Optimization Tips for Data Engineers and Analysts
SQL Data engineering Data analysis Data science

SQL Query Optimization Tips for Data Engineers and Analysts

Learn how to optimize your SQL queries for faster data processing and analysis. From correlated queries to indexing and partitioning, we cover expert tips and techniques to help you improve the performance of your SQL queries.

Mastering Data Analytics with Window Functions
SQL Window functions Data analytics

Mastering Data Analytics with Window Functions

This blog post discusses the importance and usefulness of window functions in data analytics, including their ability to perform complex calculations, aggregate data, rank data, compare data, and analyze data over a set of rows.

Statistics 1

Check out the latest posts
Understand hypothesis testing using a Business Problem
Statistics Data science Data analytics

Understand hypothesis testing using a Business Problem

This article explains the hypothesis testing, using a business problem as an example to guide readers through the four steps of the process and introduce related concepts

Testing 2

Check out the latest posts
Python Best Practices: The Role of Documentation, Version Control, and Code Review
Python Logging Testing

Python Best Practices: The Role of Documentation, Version Control, and Code Review

Documentation, version control, and code review are essential best practices in software development that help ensure the quality and maintainability of code. They provide information about the code, track and manage changes to the code, and involve review by other developers. These practices help ensure that code is reliable and easy to maintain.

Become a Python Pro: Tips for Writing Reliable Code with Testing and Logging
Python Logging Testing

Become a Python Pro: Tips for Writing Reliable Code with Testing and Logging

Writing production-level Python code involves ensuring that the code is not only functional, but also efficient, maintainable, and easy to understand. To achieve these goals, it is important to follow best practices in Python development, such as using logging and testing to ensure the code is correct and easy to debug.

Virtual environments 1

Check out the latest posts
Isolate, Organize, and Deploy: The Benefits of Virtual Environments and Docker for Python
Virtual environments Docker Python

Isolate, Organize, and Deploy: The Benefits of Virtual Environments and Docker for Python

A virtual environment is a tool used to isolate specific Python environments on a single machine, allowing you to work on multiple projects with different libraries and dependencies. This is especially useful when working on projects that have conflicting requirements or when you want to test your code in different environments.

Window functions 1

Check out the latest posts
Mastering Data Analytics with Window Functions
SQL Window functions Data analytics

Mastering Data Analytics with Window Functions

This blog post discusses the importance and usefulness of window functions in data analytics, including their ability to perform complex calculations, aggregate data, rank data, compare data, and analyze data over a set of rows.

Sathiyakugan Balakrishnan

As a data science and analytics expert, I have experience building and deploying models that deliver value. I am skilled at turning business problems into actionable insights using a range of tools and techniques. I am passionate about using data science and AI to drive innovation

2025 © Kugan's Data Journey.