Author Archives: Ajitesh Kumar
Types of Frequency Distribution & Examples
Frequency distributions are an important tool for data scientists, statisticians, and other professionals who work with data. Frequency distributions help to organize and summarize data, making it easier to identify the behavior of the data including patterns and trends. Evaluating frequency distribution is one of the important technique of univariate descriptive statistics. In this article, we’ll take a look at the concepts of the frequency distribution, its different types and provide some examples of each. What is Frequency Distribution? Frequency distribution is a statistical tool used to represent the frequency with which different categories of a qualitative or quantitative variable occur. It provides an overview of the data and allows …
Data Catalog Concepts, Tools & Examples
A data catalog is a comprehensive collection of information about an organization’s data assets, and it serves as the foundation for making informed decisions about how to manage and use data. This includes all types of data, structured or unstructured, spread across multiple sources including databases, websites, stored documents, and more. A good data catalog should provide users with the ability to quickly identify what types of data are available within the organization, where they are located, and who owns them. In this blog, we will learn basic concepts of data catalog along with some examples. What is Data Catalog? A data catalog is a comprehensive inventory of all the …
Most Common Data Pitfalls to Avoid
Working with data can be a powerful tool, but there are some common pitfalls that a data professionals including data analysts & data scientists should always be aware of when gathering, storing, and analyzing data. Good data is essential for any successful analytics project, and understanding the most common data pitfalls will help you avoid them. In this blog, we will take a look at what these mistakes are and how to avoid them. The picture below represents the most common data pitfalls to avoid. Considering Data as the Truth One major data pitfall is when people consider data as absolute truth (reflection of reality) without taking any other factors …
Top ESG Benchmarks / Companies List
ESG (Environmental, Social, and Governance) benchmarking is an important part of any company’s sustainability strategy. But with so many options available, it can be difficult to know which companies to trust. To help you make the right decision for your business, let’s take a look at some of the top ESG benchmarks which is adopted by the companies across the globe in the market today. Dow Jones Sustainability Index (DJSI) The Dow Jones Sustainability Index (DJSI) is an important tool for evaluating how well companies are meeting environmental, social and governance (ESG) goals. The index measures the performance of global sustainability leaders by providing a comprehensive assessment of corporate sustainability …
NoSQL Data Models Types: Concepts & Examples
Not every data set fits neatly into a traditional SQL relational database. To address the need for more flexible databases, NoSQL data models were developed. These models allow for faster development cycles, larger data sets and greater scalability than traditional SQL databases. In this post, we’ll provide an overview of NoSQL data models and some examples of how they are used in real-world applications. NoSQL Data Model Types NoSQL data models can be divided into four main types: document stores, key-value stores, graph databases, and column stores. Each type has its own unique strengths and weaknesses and is best suited to certain types of applications or use cases. Here’s a …
Scaling Techniques for Relational Databases
When it comes to relational databases, scaling can be a difficult process. As data volume increases, the performance of the database can suffer. To ensure that your database continues to perform at its best, you must scale it properly. In this blog post, we’ll explore some of the techniques used to scaling up and scaling out the relational databases for maximum performance. Scaling up Scaling up (vertical scaling) of a relational database is the practice of increasing the capacity of a single server, either by adding more memory, processors, and/or storage to the existing setup. As a matter of fact, this technique can also be used for non-relational databases. This …
Building AI-powered Organization & Cultural Traits
Artificial Intelligence (AI) has become an integral part of many organizations’ operations. From customer service to supply chain management, AI is increasingly being used to automate and streamline processes. However, AI can do more than just help you run your business more efficiently; it can also be used to build organizational culture and foster data-driven decision making in general while leveraging analytical tools & techniques. Let’s take a look at how AI-powered organizational and cultural traits can help improve the workplace. The following picture is a summary of cultural traits in AI-driven organization Be Curious The adoption of artificial intelligence (AI) within an organization can enhance curiosity in several ways. …
Data Science Interview Questions – List
Are you preparing for a data science interview and looking for some common questions that may be asked? Look no further! In this blog post, we will provide a list of potential interview questions for a data science position. These questions cover a range of topics, from technical skills and experience to problem-solving and communication. Whether you are a seasoned data scientist or just starting out in the field, these questions will help you get ready for your upcoming interview and showcase your knowledge and expertise. So let’s dive in and see what’s in store! Here are some of the most popular / potential interview questions that may be asked …
Instance-based vs Model-based Learning: Differences
Machine learning is a field of artificial intelligence that deals with giving machines the ability to learn without being explicitly programmed. In this context, instance-based learning and model-based learning are two different approaches used to create machine learning models. While both approaches can be effective, they also have distinct differences that must be taken into account when building a machine learning system. Let’s explore the differences between these two types of machine learning. What is instance-based learning & how does it work? Instance-based learning (also known as memory-based learning or lazy learning) involves memorizing training data in order to make predictions about future data points. This approach doesn’t require any …
Open Source Web Scraping Tools List
If you’re looking for a cost-effective way to access the data that matters most to your business, then web scraping is the answer. Web scraping is the process of extracting data from websites and can be used to gather valuable insights about market trends, customer behavior, competitor analysis, etc. To make this process easier, there are plenty of open source web scraping tools available. Let’s take a look at some of these tools and how they can help you collect and analyze data with greater efficiency. Beautiful Soup Beautiful Soup is a Python library designed for quick turnaround projects like screen-scraping. This library allows you to parse HTML and XML …
Data-Driven Decision Making: What, Why & How?
Data-driven decision-making is a data-driven approach to making decisions to achieve desired outcome. More precisely, data-driven decision making is an insights-driven approach to drive decisions and related actions. The data can come from internal and external data sources to avoid data biases. Data-driven decision-makers use data in their decision process to validate existing actions or take new actions (predictive or prescriptive analytics). They make decisions based on the actionable insights generated from the data. The goal is to make informed decisions while ensuring trust & transparency across the stakeholders & organization as a whole. It can be noted that data-driven decision making provides great thrust to digital transformation initiatives. In …
Data Analyst, Data Scientist or Data Engineer: What to Become?
There is a lot of confusion surrounding the job designations or titles such as “data analyst,” “data scientist,” and “data engineer“. What do these job titles mean, and what are the differences between them? Before selecting one of these career path, it will be good to get a good understanding about these job titles or designations, related roles & responsibilities and career potential. In this blog post, we will describe each title / designation and discuss the key distinctions between them. By the end of this post, you will have a better understanding of which career path and related designations are right for you! Shall I become a data analyst? …
ETL & Data Quality Test Cases & Tools: Examples
Testing the data that is being processed from Extract, Transform and Load (ETL) processes is a critical step in ensuring the accuracy of data contained in destination systems and databases. This blog post will provide an overview of ETL & Data Quality testing including tools, test cases and examples. What is ETL? ETL stands for extract, transform, and load. ETL is a three-step process that is used to collect data from various sources, prepare the data for analysis, and then load it into a target database. The extract phase involves extracting data from its original source, such as a database or file system. The transform phase involves transforming this data …
Amazon Kinesis vs Kafka: Concepts, Differences
As technology advances, new data streaming solutions emerge to meet the ever-growing demand for real-time analytics. Two popular options are Amazon Kinesis and Apache Kafka. Here, we’ll take a look at these two platforms and compare them in terms of their core concepts and differences. What is Amazon Kinesis? Amazon Kinesis is an AWS serverless streaming service that allows you to collect, process, and analyze streaming data in real time. It is a fully managed service that can capture, store, and analyze hundreds of terabytes of data from millions of sources simultaneously. It is designed to be highly available and scalable so that your streaming data can be reliably processed …
Data Governance Framework Template / Example
Data governance is a framework that provides data management governance. It’s the process of structuring data so it can be governed, managed and used more effectively. Data governance framework forms the key aspect of data analytics strategy. This blog post will discuss key functions of a standard data governance framework and can be taken as a template or example to help you get started with setting up your data governance program. What is Data Governance Framework? Data governance can be defined as enterprise-wide management of data from availability, usability, security and integrity standpoint. The data governance framework is intended to put some structure around how data can be managed and …
ESG Concepts: Reports, Metrics & KPIs
This blog post is geared toward Environmental, Social & Governance (ESG) professionals looking to understand different aspects of ESG and some metrics that can be reported via ESG reports as part of their organization’s ESG reporting (annual reports) in relation to representing the sustainability aspect of their business. An understanding of different aspects of ESG can help you in getting started with ESG initiatives and ESG reporting. ESG initiatives can help companies improve their overall sustainability factor while creating a positive impact on environmental, social, and governance issues. Getting started with ESG-related practices in your organization or department (such as procurement) requires a set of ESG initiatives and related performance …
I found it very helpful. However the differences are not too understandable for me