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95 Blog Posts to Learn Data Analytics

No matter the project, data analytics are a must.

1. How Data Analytics Can Revolutionize Your Business Strategy and Unlock Success

Nowadays analyzing big data has become a significant business tool. It’s constantly changing the way brands operate and market their products across various fields and industries. In fact, every company today, regardless of size, is considered to be a data business in some measure.

2. Tips On How To Maximize Your Data Science Team’s ROI

The value of using data to make rational and accurate decisions is not new. From generals to great fictional detectives, people have relied on data to make the right decision and find the answer. Just in the same way businesses have been doing it for the past century.

3. The Importance of Sports Analytics

You’re probably familiar with the movie Moneyball (if not, watch it!). It’s the story of Billy Beane, the former MLB player and manager of the Oakland A’s, a struggling team with one of the smallest budgets in the league. Using statistical analysis methods, he ditched all traditional advice and based recruitment purely on data. The result? The A’s won 20 consecutive games, the first team in over a century to do so.

4. Data Playgrounds are The Cure for Slow and Inefficient DataOps

Companies struggle with their DataOps due to a flawed, code-centric, and linear workflow. To succeed, they must build data playgrounds, not mere pipelines.

5. 3 Ways You Can Build and Update Websites Using Data Pushes

Data is getting more and more accessible and is increasingly being used to inform the way businesses operate.

6. Why AI is the Future of Restaurant Sales

Think about all of the things you could do with unlimited data and insights about your sales. Now, think about all of the things you could do with future data and insights about your sales?

7. Polygon data: What it is and how can it be used?

This blog explains about polygon data, its benefits and how it is widely used in geomarketing, indoor mapping, and mobility analysis for orgnaizations.

8. High-Utility DeFi Data Analytics Tools For Crypto Investors

These four growing platforms will give investors the tools they need to make smarter decisions

9. Data Lakehouses: The New Data Storage Model

Data lakehouses are quickly replacing old storage options like data lakes and warehouses. Read on for the history and benefits of data lakehouses.

10. Use Up-Sampling and Weights to Address Imbalance Data Problem

Have you worked on machine learning classification problem in the real world? If so, you probably have some experience with imbalance data problem. Imbalance data means the classes we want to predict are disproportional. Classes that make up a large proportion of the data are called majority classes. Those that make up a smaller portion are minority classes. For example, we want to use machine learning models to capture credit card fraud, and fraudulent activities happens approximately 0.1% out of millions of transactions. The majority of regular transactions will impede the machine learning algorithm to identify patterns for the fraudulent activities.

11. Why Businesses Need Data Governance

Governance is the Gordian Knot to all Your Business Problems.

12. How to Create a Simple Web Dashboard for Efficient Data Analytics

Dashboard with different visualizations allows you to compare data and show changes and tendencies. In this tutorial I wil explain why and how to build one.

13. 7 Ways to Beat Zoom Fatigue and Improve Your Virtual Meetings

Zoom fatigue is plaguing productivity. Explore fun and data-driven ways to overcome the challenge and to add some personality to your next zoom call.

14. 7 Data Analytics Tools to Boost Your Business

15. Restructure or Recycle: Making the Right Data-driven Decisions

Understanding the difference between restructuring and recycling data allows analysts to make better-educated decisions.

16. Get Started With Big Data Analytics For Your Business.

Everything we do generates Data, therefore we are Data Agents. The question is: how we can benefit from this huge amount of data generated every day?.

17. How to See Areas in Your Organization Where Data can Make a Difference

What is the first thing that you do when you start a new data science or analytics role?

18. Top 7 Use Cases of Predictive Analytics in Healthcare

“I’ve never been able to predict the future of anything”, said Bob Edwards, one of the most accomplished American journalists.

19. Is The Modern Data Warehouse Dead?

Do we need a radical new approach to data warehouse technology? An immutable data warehouse starts with the data consumer SLAs and pipes data in pre-modeled.

20. 4 Ways Cities Are Utilizing Data for Public Safety

Cities have been using data for public safety for years. What new technology is emerging in public safety, and how does it affect you?

21. My Favorite Free Excel Courses for Programmers, Data Analysts, and IT Professionals

If you want to learn Microsoft Excel, a productivity tool for IT professionals, and looking for free online courses, then you have come to the right place.

22. Database Vs Data Warehouse Vs Data Lake: A Simple Explanation

A data lake is totally different from a data warehouse in terms of structure and function. Here is a truly quick explanation of "Data Lake vs Data Warehouse".

23. Big Data Analytics: Why Companies Use It?

Big Data Defined
Real-time big data is data that’s processed as it arrives. Once it arrives either users get consumable insights without exceeding the time period allocated for decision making or analytical systems that trigger a notification or action.

24. How Advanced Data Analytics is Impacting B2B Sales

Machine Learning and data analytics have shown a pronounced effect on various aspects of the commercial world and industries. Enterprises are using innovation in the field of data analytics and machine learning to design better marketing campaigns. It also helps generate pricing and customer-centric recommendations and even plan more effective financial budgets.

25. Why Python Is Leading the Charge in Data Analytics

Python is one of the oldest mainstream programming languages, which is now gaining even more ground with a growing demand for big data analytics. Enterprises continue to recognize the importance of big data, and $189.1 billion generated by big data and business analytics in 2019 proves it right.

26. 7 Data Analysis Steps You Should Know

To analyze data adequately requires practical knowledge of the different forms of data analysis.

27. How to Build Machine Learning Algorithms that Actually Work

Applying machine learning models at scale in production can be hard. Here's the four biggest challenges data teams face and how to solve them.

28. Spotify Audio Features Time Series in Additive Spotify Analyzer

There are many articles on analyzing Spotify data and many applications as well. Some are a one-time analysis on individual's music library and some are an app for a specific purpose. This app is different in that it does not do one thing. It is meant to grow and provide a place to add more analysis. This article is about how the audio features time series was created.

29. Top 6 Mobile Analytics Tools of 2020

Data has become an increasingly important factor when it comes to the health of any app or website. Having all of your important numbers such as the number of downloads, amount of money generated from downloads and even the most recent feedback is the key to continued success.

30. Tableau Vs. Power BI: The Complete Comparison

The world of analytics is continually evolving, introducing new goods and adjustments to the modern market. New companies are entering the market and well-know

31. Data Analytics is a Journey

It is 2020 and the data analytics has gained so much attention even outside of the tech community. "Data is gold", they say - no one wants to be left behind. However, getting the right strategy is neither a straightforward nor static process.

32. What Are the Most Common Mistakes Made by Aspiring Programmers?

I wasted A LOT of my time teaching myself the basics of coding, machine learning, and stats.

33. 6 Reasons to Use Amazon Redshift

A quick guide to Amazon Redshift's benefits and use cases. Learn why your team might want to make the SHIFT to Amazon Redshift.

34. The Role of AI and ML in Enhancing The Ability Of Multiplying Wealth

Landing a good job is generally considered the purpose of education today.

35. A Step By Step Guide To Data Visualization With Power BI

Power BI is the collective name for an assortment of cloud-based apps and services that help organizations collate, manage and analyze data from various sources

36. What You Need to Consider When Hiring a Data Scientist

Dubbed “the sexiest job of the 21st century” by the Harvard Business Review, the demand for data scientists has grown dramatically. The number of job postings for this career increased by 31% from December 2017 to December 2018. And over the course of the last 6½ years, postings have surged by a staggering 256%.

37. Mean Reversion Trading Systems and Cryptocurrency Trading [A Deep Dive]

Prices move in a wave like fashion, moving back and forth following a broader trend. While doing so, it often revolves around a mean. It might move across or bounce off the mean. Mean reversion systems are designed to exploit this tendency.

38. Eliminating Difference Between Business Intelligence analysts, Data Analysts or Data Scientists 🚀

There was a time when the data analyst on the team was the person driving digitalization in an adventurous data quest...and then the engineers took over.

39. Amazon Is Losing Market Share Across Several Segments to Competitors [A Numbers Game]

You’ve probably read about how Amazon has put a stop to its paid acquisition. We’ve covered the topic extensively already over the past few weeks, and yet, what we’ve recently discovered sheds some light on the magnitude of this move.

40. How Drones Are Transforming Big Data Analytics

The world is transforming right before our eyes. We’ve heard about drones for a long time now, especially with big companies like Amazon using them for more efficient package delivery, a major trend in modern e-commerce. Instead of your local delivery man, a drone may drop a package right on your doorstep. The true power of drones goes well beyond that, though. They provide businesses with data that’s difficult to collect otherwise. In addition to taking aerial photos and videos, drones can collect information about everything from the health of crops to thermal leaks in buildings.

41. Beyond Artificial Intelligence: Providing Insights to Your Customers

42. 9 Best Data Integration Software in 2022

Every business needs to collect, manage, integrate, and analyze data collected from various sources. Data integration software can help!

43. Promoting Indigenous Data Sovereignty through Blockchain in Canada

How can Indigenous Sovereignty be upheld with modern technology?

44. Analyzing Data From U.S. Road Accidents With Data Visualization

In this article, we would be analyzing data related to US road accidents, which can be utilized to study accident-prone locations and influential factors.

45. 4 Tips To Become A Successful Entry-Level Data Analyst

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand.

46. 2020: Our Meatless, Cashless, City-less Future

Happy New Year! 2019 has come and gone like Kylo Ren’s reign in The Rise of Skywalker, and so it's time for my annual prediction piece.

47. Don't Be Data-Driven. Become Purpose-Driven and Data-Assisted.

48. AI and Machine Learning for Manufacturing Industry: Use Cases

Artificial Intelligence(AI) has already proven to solve some of the complex problems across the wide array of industries like automobile, education, healthcare, e-commerce, agriculture etc. and yield greater productivity, smart solutions, improved security and care, business intelligence with the aid of predictive, prescriptive and descriptive analytics. So what can AI do for Manufacturing Industry?

49. 5 Upcoming Online Machine Learning Conferences in 2020

Machine learning conferences have always played an important role in the world of data science. They're a place to announce new research, discuss current issues, and connect with the community. They also help to promote new areas of research and development through Q&A sessions, workshops, and tutorials.

50. Future of Marketing: How Data Science Predicts Consumer Behavior

Gradually, as the post-pandemic phase arrived, one thing that helped marketers predict their consumer behavior was Data Science.

51. The Operational Analytics Loop: From Raw Data to Models to Apps, and Back Again

Over the next decade or so, we’ll see an incredible transformation in how companies collect, process, transform and use data. Though it’s tired to trot out Marc Andreessen’s “software will eat the world” quote, I have always believed in the corollary: “Software practices will eat the business.” This is starting with data practices.

52. My Weird Career Transition From MBA to Data Science

Yes you read it correctly! I am calling my transition from being an MBA to being the Analytics Manager in a well known consumer retail brand a "WEIRD" one. And why do I say that? Because during my 5 year journey in data science, I have had the opportunity to work with a lot of business stakeholders like marketing head, brand managers, sales heads etc. and many a times they have asked me about my educational background. I would like to think that they asked this because of my ability to present the solutions keeping the business context and execution feasibility in mind. Well, the reason for asking this might be different for every individual, when I tell them that I am an MBA, their reply has always been the same, which is "What made you choose a technical career path after pursuing MBA?" And hence I decided to write this post to share my thoughts over 2 things:

53. The Art of Data Storytelling: How to Make Your Data Impactful

Data is everywhere: whether you choose a new location for your business or decide on the color to use in an ad, data is an invisible advisor that helps make impactful decisions. With quite a number of resources to choose from, data is becoming more accessible, day by day. But as soon as it has been collected, one inevitable question arises: how do I turn this data into insights that can be acted upon?

54. An Introduction to Data Connectors: Your First Step to Data Analytics

This post explains what a data connector is and provides a framework for building connectors that replicate data from different sources into your data warehouse

55. A Quick Guide To Business Data Analytics

For many businesses the lack of data isn’t an issue. Actually, it’s the contrary, there’s usually too much data accessible to make an obvious decision. With that much data to sort, you need additional information from your data.

56. The Rise of Reusable SQL-based Data Modeling Tools and DataOps services

The resurgence of SQL-based RDBMS

57. What is the Future of the Data Engineer? - 6 Industry Drivers

Is the data engineer still the "worst seat at the table?" Maxime Beauchemin, creator of Apache Airflow and Apache Superset, weighs in.

58. Creativity in Data Analytics is About More than Data Visualization

I recently attended a networking event where I spoke to a range of graduates who were looking at prospective careers in the data science and adjacent spaces.

59. Compete on Data Analytics using Spring Cloud Data Flow

Data Driven

60. Applications of Predictive Analytics in your Recruitment Journey

Elanor is an HR executive at Unicorn marketer. She’s been involved in the recruitment process for six years now. Every year they do a campus drive at the most prestigious college in Chicago. They’re always on the look for a promising candidate for a challenging role as a Digital Marketer. Elanor has been maintaining a spreadsheet of rejected candidates for the same post and logging the reasons for rejection as well.

61. Evolution of The Data Production Paradigm in AI

The long-term success of an AI-based product relies on having the infrastructure for scalable, flexible, and cost-effective data labeling for its learning.

62. Data and Analytics Predictions for 2020 [A Top 5 List]

It would be no exaggeration to say that the capacity of technology to advance itself is proceeding at a faster rate than our ability to process these changes all at the same time. This is both amazing and alarming in the same breath.

63. Data-Driven Approach for Software Engineering: How to Avoid Common Problems

In today’s digital world, data is constantly being generated, evaluated, and updated. It also plays an important role in the work of software engineers by providing accurate, actionable feedback that helps engineers understand where and how to make improvements to a product or process.

64. 6 Places to Start a Career in Data Science in 2022

How to become a data scientist?
Want to become a Data Scientist? Here are the resources.
Resources to Become a Data Scientist

65. Machine-Learning Neural Spatiotemporal Signal Processing with PyTorch Geometric Temporal

PyTorch Geometric Temporal is a deep learning library for neural spatiotemporal signal processing.

66. Software Development for the Nuclear Industry

One of the biggest problems facing leaders in the nuclear energy industry is the aging infrastructure in the United States and abroad.

67. COVID-19: We Need More Than Data, We Need Insights!

TL;DR We are managing the pandemic situation only with part of the data and not necessarily representative of reality. We must take a census of the number of positive and negative cases within a population. The officially reported positive cases contain a bias: they are cases that already manifest the disease in a more or less serious way. In the long term, the strategy of aggressive testing (South Korea model) is the only viable and sustainable to manage coexistence between the virus and the human beings until a vaccine will be available.

68. Which Database Is Right For You?Graph Database vs. Relational Database

Learn about the main differences between graph and relational databases. What kind of use-cases are best suited for each type, their strengths, and weaknesses.

69. 5 Best Data Curation Tools for Computer Vision in 2021

In this article, we’ll dive into the importance of data curation for computer vision, as well as review the top data curation tools on the market.

70. 23andMe and Other Sites are Selling Users' Genetic Data: How Safe is Your DNA?

How genetic information from sites like 23andMe and is being shared and sold.

71. Getting to Know Google Analytics 4: Four Smart Features You Don’t Know About

Let’s take a deeper look into Google Analytics 4 and explore some of its key features that you might not yet know about.

72. How to use Redis HyperLogLog

How to use Redis HyperLogLog data structure to store millions of unique items.

73. Top 40+ Data Science Product Interview Questions

Find the top 40+ product interview questions you must prepare for your next data science interview.

74. 5 Best Practices for Tracking In-app Event Data

This is the era of mobile apps. We get everything - from critical business information to entertaining videos and games - on our mobile devices. Information is right at our fingertips, and we are always striving to catch up with the outside world. As per App Annie, an average smartphone user has 80 apps installed.

75. 8-Ways Data Mining Can Improve your Business

If your company is trying to make sense of the customer data, here’s a not-so-surprising fact for you. You aren’t alone. Far too many companies want to understand data and gain an in-depth insight into the information they are sitting on. Let’s be clear that today, the success of a business lies in how efficient their data mining process is. Their expertise to process the available data as this can help them to decipher age-old questions that make or break them:

76. How to Track User Navigation Events in a React Application

A scalable and maintainable strategy for tracking page navigation events in a React application.

77. How to Think Like a Data Scientist or Data Analyst

Data science is a new and maturing field, with a variety of job functions emerging, from data engineering and data analysis to machine and deep learning. A data scientist must combine scientific, creative and investigative thinking to extract meaning from a range of datasets, and to address the underlying challenge faced by the client.

78. How To Deploy Metabase on Google Cloud Platform (GCP)?

Metabase is a business intelligence tool for your organisation that plugs in various data-sources so you can explore data and build dashboards. I'll aim to provide a series of articles on provisioning and building this out for your organisation. This article is about getting up and running quickly.

79. How GPUs are Beginning to Displace Clusters for Big Data & Data Science

More recently on my data science journey I have been using a low grade consumer GPU (NVIDIA GeForce 1060) to accomplish things that were previously only realistically capable on a cluster - here is why I think this is the direction data science will go in the next 5 years.

80. Customer Data Platform (CDP) Vs Data Warehouse, CRM, and Data Management Platform

In this post, we highlight some key differences between a Customer Data Platform (CDP) and other tools generally used in a marketing tech stack. We also tackle the all-important question on many companies’ minds: “should I build or buy a CDP?.”

81. Top Tableau Consulting Companies on the Market in 2020

Business intelligence has become an indispensable part of successful businesses, and the sooner executives recognize data as a crucial component of decision-making, the sooner they will be able to improve their operational processes.

82. Deep Dive Into Open Source BI Tool Helical Insight

When Helical Insight first announced a couple of years ago that they were releasing an Open Source Business Intelligence (BI) tool, it really caught my interest and I reached out to founder Nikhilesh Tiwari to find out more about what he was doing. I spent a little time with the product and really liked where it was going and was determined to do more of a deep dive in the future, and with their release of version 3.0, that time is now.

83. What is Data Analytics and How It Can Be Used


84. Analyzing Dogecoin Tweet Sentiment in Real Time

How to analyze Dogecoin tweet sentiment in real-time with a new managed Kafka platform.

85. Scraping Data With Selenium: Upwork Series #2

Hi Devs!

86. Data Science From Scratch

Data Science, which is also known as the sexiest job of the century, has become a dream job for many of us. But for some, it looks like a challenging maze and they don’t know where to start. If you are one of them, then continue reading.

87. Planning for Your Startup: The Data Team's Guide to 2021

Planning in a startup can feel like an exercise in futility — especially when it comes to data — especially when your data team is small and scrappy.

88. R and Javascript : Execution, Libraries, Integration

In today’s date, R is the megastar language for big data analytics. In this article, I will talk about on coordination, visualization and execution of R and JavaScript. However, you may ask the question for what reason somebody might want to incorporate R into web applications?

89. Creating an Interactive Word Tree Chart with JavaScript

Learn how to create beautiful interactive JavaScript Word Trees and check out an awesome Word Tree chart visualizing the text of The Little Prince.

90. How to Consolidate Real-Time Analytics From Multiple Databases

Have you ever waited overnight for that report from yesterday’s sales? Or maybe you longed for the updated demand forecast that predicts inventory requirements from real-time point-of-sale and order management data. We are always waiting for our analytics. And worse yet, it usually takes weeks to request changes to our reports. To add insult to injury, you keep getting taxed for the increasing costs of the specialized analytics database.

91. So You Just Became a Data Science Manager... Now What?

With the rise of data science there has been the rise of data science managers. So what do you need to keep in mind if you wish to join these data translators that are acting as a conduit between the business and technical data teams? Going from a practitioner to a manager — your job now is to make sure that data resources are being used optimally so how do you go about doing this effectively?

92. Building a Data Management Strategy: Importance, Principles, Roadmap

Already routinely called the currency, the lifeblood, and the new oil of the modern business world, data promises organizations unbeatable competitive advantages.

93. 5 Big Data Problems and How to Solve Them

“Big Data has arrived, but big insights have not.” ―Tim Harford, an English columnist and economist

94. 3 Best Ways To Import JSON To Google Sheets [Ultimate Guide]

3 ways to pull JSON data into a Google Spreadsheet

95. 3 Best Ways To Import External Data Into Google Sheets [Automatically]

Google Sheets is a great tool to use for business intelligence and data analysis. If you want to eliminate manual data imports and save time, then let me will show you how you can automatically connect and import data from external sources into Google Sheets.


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