ProjectPro

AUGUST 9, 2021

Table of Contents Why Learn Python for Data Science? Top 20 Python Projects for Data Science Getting Started with Python for Data Science FAQs about data science projects Why Learn Python for Data Science? Python has come to command a celebrity status in data science over the years.

Data Science 52

Data Science Python Project Datasets 52

ProjectPro

AUGUST 24, 2021

Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project. And, out of these professions, this blog will discuss the data engineering job role.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Knowledge Hut

MAY 1, 2024

It is important to make use of this big data by processing it into something useful so that the organizations can use advanced analytics and insights to their advant age (generating better profits, more customer-reach, and so on). These steps will help understand the data, extract hidden patterns and put forward insights about the data.

Algorithm 98

Algorithm Data Science Raw Data Computer Science 98

Knowledge Hut

JUNE 4, 2024

Start a Data Analytics Blog If you are thinking about startup ideas for data science, starting a data analytics blog could be a great business idea if you are passionate about data analytics and enjoy sharing your insights with others. Many business projects use data science to be successful.

Data Science 52

Data Science Data Mining Media Recruitment 52

U-Next

SEPTEMBER 17, 2022

Planning a data mining project can be structured using the CRISP-DM model and methodology. An understanding of the project’s objectives and requirements forms the basis of the Business Understanding phase. Develop a project plan: Plan each project phase by selecting the necessary technologies and tools. .

Data Mining 40

Data Mining Data Preparation Raw Data Data Science 40

AltexSoft

FEBRUARY 21, 2023

For machine learning algorithms to predict prices accurately, people who do the data preparation must consider these factors and gather all this information to train the model. For example, if a hotel is new, there may not be enough historical data to train accurate machine learning models.

Hospitality 52

Hospitality Algorithm Datasets Machine Learning 52

Knowledge Hut

DECEMBER 7, 2023

However, the journey from conceptualizing an AI project to putting it into production is not straightforward. In this blog, I'll define the AI project life cycle and walk you through the steps, tools, and significance of the AI model lifecycle management process. Design The design stage is where the AI project takes shape.

Machine Learning 52

Machine Learning Algorithm Medical Government 52

ProjectPro

JANUARY 31, 2023

Data scientists and machine learning engineers often come across this scenario where the data for their project is not sufficient for training a machine learning model, often resulting in poor performance. This is particularly true when working with complex deep-learning models that require large amounts of data to perform well.

Deep Learning 52

Deep Learning Machine Learning Datasets Data 52

ProjectPro

SEPTEMBER 26, 2021

Big Data Engineers are professionals who handle large volumes of structured and unstructured data effectively. They are responsible for changing the design, development, and management of data pipelines while also managing the data sources for effective data collection.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

ProjectPro

DECEMBER 16, 2021

If you look at the machine learning project lifecycle , the initial data preparation is done by a Data Scientist and becomes the input for machine learning engineers. Later in the lifecycle of a machine learning project, it may come back to the Data Scientist to troubleshoot or suggest some improvements if needed.

Machine Learning 52

Machine Learning Engineering Pipeline-centric Database-centric 52

U-Next

AUGUST 11, 2022

Data Scientist: A Data Scientist studies data in depth to automate the data collection and analysis process and thereby find trends or patterns that are useful for further actions. Data Analysts: With the growing scope of data and its utility in economics and research, the role of data analysts has risen.

Medical 52

Medical Computer Science Machine Learning Scala 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. The end of a data block points to the location of the next chunk of data blocks.

Big Data 52

Big Data Hadoop Relational Database AWS 52

Knowledge Hut

JUNE 20, 2023

Additionally, they create and test the systems necessary to gather and process data for predictive modelling. Data engineers play three important roles: Generalist: With a key focus, data engineers often serve in small teams to complete end-to-end data collection, intake, and processing.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

AltexSoft

JUNE 21, 2023

Alexander Konduforov , who served as a Data Science Competence Lead on this project, underscores the importance of competitor pricing knowledge: “By analyzing your competitors’ rates, you can understand how much more expensive or cheaper you are compared to them. Data shortage and poor quality.

Hospitality 52

Hospitality Management Machine Learning Datasets 52

U-Next

JUNE 29, 2022

Preparing data for analysis is known as extract, transform and load (ETL). While the ETL workflow is becoming obsolete, it still serves as a common word for the data preparation layers in a big data ecosystem. Working with large amounts of data necessitates more preparation than working with less data.

Big Data 40

Big Data Big Data Ecosystem Data Lake Raw Data 40

U-Next

SEPTEMBER 28, 2022

HR Analytics collects and analyzes data that may help firms get essential insight into their operations. Data Collection . One of the first tasks in HR Analytics is to collect relevant data. Generally, the data needed to perform HR Analytics originates from the existing HR systems. How Do They work?

Recruitment 52

Recruitment Algorithm Machine Learning Utilities 52

Rockset

APRIL 12, 2021

Construction projects are hives of constant activity, sustained by steady incoming streams of building materials. Yet for every physical delivery made, many more exchanges of data occur in the background in order to seamlessly orchestrate supply chain operations.

NoSQL 40

NoSQL Transportation Electronics Data Preparation 40

Monte Carlo

MAY 25, 2023

Some of the value companies can generate from data orchestration tools include: Faster time-to-insights. Automated data orchestration removes data bottlenecks by eliminating the need for manual data preparation, enabling analysts to both extract and activate data in real-time. Improved data governance.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

Knowledge Hut

MAY 9, 2024

Skills Required Enterprise architects require project management capabilities, an understanding of business models, strong knowledge of IT processes, strong leadership skills, clear written and verbal communication, and analytical thinking and problem-solving skills.

Data Science 52

Data Science Data Architect Data Mining Programming Language 52

AltexSoft

MAY 14, 2021

Besides, it is not just business users and analysts who can use this data for advanced analytics but also data science teams that can apply Big Data to build predictive ML projects. Big Data analytics processes and tools. Data ingestion. Apache Kafka.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

JUNE 2, 2015

In most of the big data companies, it is not that data is not available; it is that data is not complete, organized, stored and blended right in a manner that it can be consumed directly for big data analysis. of marketers believe that they have the right big data talent.

Big Data 40

Big Data Hadoop Banking Data Analytics 40

Knowledge Hut

JUNE 28, 2023

Business Intelligence: Business Intelligence primarily focuses on analyzing historical and current data to gain insights into past performance. However, it can also incorporate predictive analytics to some extent, allowing for future projections and scenario analysis.

Data Mining 52

Data Mining Business Intelligence BI Structured Data 52

ProjectPro

FEBRUARY 21, 2023

Due to the enormous amount of data being generated and used in recent years, there is a high demand for data professionals, such as data engineers, who can perform tasks such as data management, data analysis, data preparation, etc.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

AltexSoft

DECEMBER 21, 2021

Not only is it hard to get lots of data (particularly for the cases of highly specialized niches such as healthcare), but manually adding tags for each item of data is also a difficult, time-consuming task requiring the work of human labelers. Data labeling approaches. There are different ways to perform data annotation.

Machine Learning 52

Machine Learning Process Raw Data Datasets 52

Knowledge Hut

FEBRUARY 1, 2024

You cannot expect your analysis to be accurate unless you are sure that the data on which you have performed the analysis is free from any kind of incorrectness. Data cleaning in data science plays a pivotal role in your analysis. It’s a fundamental aspect of the data preparation stages of a machine learning cycle.

Data Science 52

Data Science Process Data Cleanse Datasets 52

U-Next

SEPTEMBER 22, 2022

Data from the past is commonly used in predictive analytics models and variables. Predictive modeling projects require historical data to identify patterns and trends. A data science team may not be able to share data freely with some lines of business because they feel that their data belongs to them. .

Hospitality 40

Hospitality Algorithm Machine Learning Banking 40

ProjectPro

MAY 31, 2021

Ace your big data interview by adding some unique and exciting Big Data projects to your portfolio. This blog lists over 20 big data projects you can work on to showcase your big data skills and gain hands-on experience in big data tools and technologies. Table of Contents What is a Big Data Project?

Big Data 52

Big Data Coding Project Hadoop 52

U-Next

SEPTEMBER 23, 2022

The fast development of digital technologies, IoT goods and connectivity platforms, social networking apps, video, audio, and geolocation services has created the potential for massive amounts of data to be collected/accumulated. Components of Database of the Big Data Ecosystem . This is required for real-time data analysis.

Big Data Ecosystem 40

Big Data Ecosystem Big Data Healthcare Data Lake 40

Knowledge Hut

DECEMBER 26, 2023

The essential manual for optimizing your workforce with the resources already at your disposal is People Analytics in the Era of Big Data. This phase aims to determine which of your company's data are important to important business questions and which aren't.

Big Data 75

Big Data Data Mining Business Intelligence Machine Learning 75

Knowledge Hut

NOVEMBER 27, 2023

Through the article, we will learn what data scientists do, and how to transits to a data science career path. What Do Data Scientists Do? A data scientist needs to be well-versed with all aspects of a project and needs to have an in-depth knowledge of what’s happening.

Data Science 52

Data Science Datasets Machine Learning Portfolio 52

ProjectPro

OCTOBER 20, 2021

If you are unsure, be vocal about your thought process and the way you are thinking – take inspiration from the examples below and explain the answer to the interviewer through your learnings and experiences from data science and machine learning projects. How future-proof are the project and the platform?

Machine Learning 52

Machine Learning Algorithm Data Science Government 52

Edureka

JANUARY 29, 2025

Key steps include: Identify the location of the data e.g., Excel files, databases, cloud services, or web APIs, and confirm accessibility and permissions. Data Sources Identification: Ensure that the data is properly formatted (for instance, in tables) and does not contain erroneous values such as nulls or duplicates.

BI 52

BI Datasets Certification Data Analysis 52