Knowledge Hut

JANUARY 24, 2024

Data Sources Tableau Software can access many data sources and servers. The thing about Power BI is that it supports different data sources. The ease of Using Tableau provides some crucial advantages for detailed data exploration and visualization. Power BI provides easy-to-use drag-and-drop functionality.

BI 98

BI Business Intelligence Non-relational Database Data Analysis 98

ProjectPro

DECEMBER 14, 2021

Customer Churn Analysis 2. Product Sales Data Analysis 3. Marketing Campaign Insights Analysis 4. Financial Performance Analysis 5. Healthcare Sales Analysis Intermediate-Level Power BI Project Ideas 6. Loan Application Analysis 10. Airport Authority Data Analysis 12.

BI 52

BI Project Business Intelligence Datasets 52

Knowledge Hut

JANUARY 12, 2024

You can multitask effortlessly, maybe run some data analysis while catching up on emails. The best part is that Linux is practically invincible against viruses and malware, ensuring your data stays safe. Before venturing into the world of Linux for data science , it is pivotal to have a solid foundation in data science itself.

Data Science 52

Data Science R (Programming) Python Data 52

ProjectPro

JANUARY 6, 2022

Table of Contents Why Use Python for Data Visualization? Top 10 Python Data Visualization Libraries 1. Pygal Learning Python for Data Analysis and Visualization FAQs Is matplotlib better than plotly? Which library is best for data visualization in Python? Why Use Python for Data Visualization? Matplotlib 2.

Python 52

Python Datasets Programming Language Data Science 52

ProjectPro

NOVEMBER 30, 2021

Additionally, you will learn how to implement Apriori and Fpgrowth algorithms over the given dataset. In this project, you will build an automated price recommendation system using Mercari’s dataset to suggest prices to their sellers for different products based on the information collected. should be used and interpreted.

Business Analyst 52

Business Analyst Project Retail Datasets 52

ProjectPro

JANUARY 20, 2022

C++ and Java); capacity to work with large, complex datasets; deep knowledge of machine learning evaluation measures; excellent analytical and problem-solving skills; meticulous attention to detail; good writing and verbal communication skills, since machine learning engineers often need to communicate the project details to the client, etc.;

Machine Learning 52

Machine Learning Deep Learning Algorithm Programming Language 52

ProjectPro

AUGUST 11, 2016

When people talk about big data analytics and Hadoop, they think about using technologies like Pig, Hive , and Impala as the core tools for data analysis. R and Hadoop combined together prove to be an incomparable data crunching tool for some serious big data analytics for business.

Hadoop 40

Hadoop Big Data R (Programming) Programming Language 40

ProjectPro

JUNE 17, 2015

“Companies are betting big that harnessing data can play a major role in their competitive plans and is leading to high pay for critical skills.”-said According to Dice.com’s salary survey report of 2014, professionals with cloud and big data skills grabbed the best paychecks in the US. from the previous year.

Cloud Computing 40

Cloud Computing Big Data R (Programming) Big Data Skills 40

ProjectPro

DECEMBER 22, 2016

Prepare for Your Next Big Data Job Interview with Kafka Interview Questions and Answers Robert Half Technology survey of 1400 CIO’s revealed that 53% of the companies were actively collecting data but they lacked sufficient skilled data analysts to access the data and extract insights.

Data Mining 52

Data Mining Data Cleanse Datasets Data Analysis 52

Knowledge Hut

FEBRUARY 7, 2023

A data science platform is software that includes a variety of technologies for machine learning, data science, and other advanced analytics projects. Typically, data science projects involve using an abundance of ls (eg. Therefore, no human intervention is required to access the resources.

Data Science 52

Data Science Google Cloud Programming Language AWS 52

ProjectPro

AUGUST 21, 2015

IBM has a nice, simple explanation for the four critical features of big data: a) Volume –Scale of data b) Velocity –Analysis of streaming data c) Variety – Different forms of data d) Veracity –Uncertainty of data Here is an explanatory video on the four V’s of Big Data 3.

Hadoop 40

Hadoop Big Data Metadata Java 40