Data Mining refers to discovering knowledge out of big data to infer patterns. It helps to understand customer behavior, increases profits & reduces churn rate
Why outsourcing your data analytics process might be the smart way to go ahead
Find out why R is the best language for Data Sciences – data analysis and statistical analysis among others
In this article, we are going to discuss what is data analysis? Pros of outsourcing data analytics and other aspects related to data analysis. So, first of all, let’s have a look at what is data analysis.
Predictive Analytics emerges as a game changer and tools associated come as a boon to the businesses as they swiftly manage the marketing process. So here in this article, we mention some modern Predictive Analytics tools that made a lot of noise in 2018.
Most large-scale companies have tremendous amounts of data, that can be utilized to comprehend the way the consumers behave and give experiences prompting key choices that can help them to develop. Data Cleaning with AI means an error-prone action that may unpredictably affect data examination.
With organizations proceeding to invest intensely in Big Data Analytics and building the foundation, without considering how to get Big Data insights, at that point, it will accomplish more damage than benefit for your business. Here are 10 signs that you must consider if you need Big data value.
Certainly, we all are fond of numbers. And if mention Big Data as huge so is the industry built around it. If we throw light on the industries that it caters to such as services, security and analytics it can be seen that the future of big data is brights and it has a number of years ahead of it.
Know what is Hadoop and its impact on the analysis of Big Data. A complete guide to understanding Hadoop’s structure, working, and processes. It includes information on MapReduce, HDFS, YARN, and Common. Do you Hadoop’s use cases?
Know the 4 major trends in Big Data innovation that will define the future this technology withholds and the current market landscape. Know the potential impact of Big Data on security, mobility, and cloud.
Know the top 5 big data challenges and the ways to tackle them. Common issues addressed here include security, volume, shortage of skills, insignificant analysis, veracity and data governance etc.
The article covers information about recent libraries on data analysis, advantages of Python over other languages and why you should choose Python for data science, ML, AI and big data projects.
Know about the five data analysis techniques that enable the use of Big Data in marketing and sales. A dive into the world of analytics and use of data science.
SQL vs NoSQL – know the meaning of relational and Non-relational databases, how are they different and which one should be chosen based on individual cases.
Precision vs Recall- Understanding the accuracy paradox in machine learning algorithms. Know how to align ML algorithm with business objectives.
5 ways to use Big Data for price optimization. Know how big data analysis can help you make better pricing decisions to increase profits, sales, and revenue.
What is market mix modelling and how it can be used to predict sales and ROI from marketing activities? Know how big data analytics can help in marketing.
The Ultimate Comparison – R vs Python for data science and statistical analysis. This guide will help you choose between the hottest rivals in this field.
What is Tableau and how is it different from Excel? A Complete overview – benefits, uses, applications, and implementation process. Click to know more.
What is attribution modeling and how it can be used to identify the most productive marketing activities in business? A complete guide to precision marketing.