Author Archives: Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking

Business Problems to Analytics Use Cases: How?

business problems to analytics use cases - Decisions - actions - output

Data is the new oil. It is the lifeblood of modern businesses. It can be used to take informed decisions, measure performance and gain insights into customer behavior. But how do we go from a business problem to an analytics use case? How do we unlock the power of analytics to drive real results? Let’s explore how to make it happen. 1. Identifying Business Objectives – Strategic or Tactical Identifying business objectives (Strategic or Tactical) is an essential part of identifying analytical use cases. When organizations start working on their analytics projects, they need to first define the objectives they want to achieve through their data analysis. There are two …

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Posted in Data, Data analytics. Tagged with .

Data Analysis Types: Concepts & Examples

different types of data analysis

Data analysis plays an important role in understanding the world, discovering trends, and making decisions. Having a good understanding of the different types of data analysis available is essential for anyone looking to make sense of their data. In this blog post, we’ll discuss the six different forms of data analysis and provide examples of each type so you can get a better idea of how they work. The following is a representation of six forms of data analysis. Descriptive Data Analysis One of the most common forms of data analysis is descriptive data analysis. This type of analysis involves analyzing and summarizing the metrics in a dataset including central …

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Procurement Advanced Analytics Use Cases

procurement analytics use cases

The procurement analytics applications are poised to grow exponentially in the next few years. With so much data available and the need for digital transformation across procurement organization, it’s important to know how procurement analytics can help you make better business decisions. This blog will cover procurement analytics and key use cases of advanced analytics that will be useful for business stakeholders such as category managers, sourcing managers, supplier relationship managers, business analysts / product managers, and data scientists implement different use cases using machine learning. Procurement analytics will allow you to use data very effectively in achieving data-driven decision making.  One can get started with procurement analytics with dashboards …

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Posted in Data Science, Machine Learning, Procurement. Tagged with , , .

Data Catalog Concepts, Tools & Examples

data catalog concepts and tools

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 …

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Posted in Data, Data Governance, Data management. Tagged with , , .

Most Common Data Pitfalls to Avoid

most common data pitfalls list

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 …

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Posted in Data, Data analytics, Data Science.

Top ESG Benchmarks / Companies List

ESG benchmarks example

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 …

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NoSQL Data Models Types: Concepts & Examples

graph data model example

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 …

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Posted in Database, NoSQL.

Scaling Techniques for Relational Databases

scaling out technique using read replicas

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 …

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Posted in Data, Database.

Building AI-powered Organization & Cultural Traits

AI powered organization and 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. …

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Data Science Interview Questions – List

interview questions for machine learning

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 …

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Posted in Career Planning, Data Science, Interview questions.

Instance-based vs Model-based Learning: Differences

model based learning example

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 …

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Posted in Data Science, Machine Learning. Tagged with , .

Open Source Web Scraping Tools List

web scraping tool 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 …

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Posted in Data, data engineering. Tagged with .

Data-Driven Decision Making: What, Why & How?

analytics key factor in decision making

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 …

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Posted in Data, Data analytics, Machine Learning. Tagged with .

Data Storytelling Explained with Examples

data storytelling key components

Have you ever told a story to someone, but they just didn’t seem to understand it? They might have been confused about the plot or why the characters acted in certain ways. If this has happened to you before, then you are not alone. Many people struggle with data storytelling because they do not know how to communicate their data effectively. Data storytelling is a powerful tool that can be used to educate, inform or persuade an audience. By using charts, graphs, images and other visuals, data can be made more interesting and engaging. Data storytelling involves taking data and presenting it in a way that is easy to understand and …

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Data Analyst, Data Scientist or Data Engineer: What to Become?

data analysts vs data scientists vs data engineers

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? …

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Posted in Career Planning, Data, Data analytics, data engineering, Data Science.

ETL & Data Quality Test Cases & Tools: Examples

data validation with great expectations

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 …

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Posted in data engineering, Data management. Tagged with .