ProjectPro

JUNE 6, 2025

You have probably heard the saying, "data is the new oil". It is extremely important for businesses to process data correctly since the volume and complexity of raw data are rapidly growing. However, the vast volume of data will overwhelm you if you start looking at historical trends. Well, it surely is!

BI 45

BI ETL Tools Retail Healthcare 45

ProjectPro

JUNE 6, 2025

Data Collection According to Forbes, data scientists spend about 80% of their time on data collection and cleaning, highlighting the importance of this step. Data collection is about gathering the raw data needed to train and evaluate the model.

Machine Learning 40

Machine Learning Project Building Raw Data 40

ProjectPro

JUNE 6, 2025

The reason for this growing importance is simple: the world is becoming increasingly data-driven. Learning basic AI concepts , particularly in the beginner-friendly domain of data analysis , will thus become a must-have skill among professionals of different industries. FAQs What is Artificial Intelligence for Data Analysis?

Data Analytics 40

Data Analytics Healthcare Datasets Machine Learning 40

ProjectPro

JUNE 6, 2025

Read this blog to know how various data-specific roles, such as data engineer, data scientist, etc., differ from ETL developer and the additional skills you need to transition from ETL developer to data engineer job roles. Data analysis and visualization have traditionally been a common goal for businesses.

Data Engineer 59

Data Engineer Data Engineering Engineering ETL Tools 59

ProjectPro

JUNE 6, 2025

This blog will provide you with valuable insights, exam preparation tips, and a step-by-step roadmap to ace the AWS Data Analyst Certification exam. So if you are ready to master the world of data analysis with AWS, then keep reading. Organizations are currently dealing with petabyte-scale data that hold valuable insights.

AWS 52

AWS Certification Data Analytics Big Data 52

ProjectPro

JUNE 6, 2025

Similarly, companies with vast reserves of datasets and planning to leverage them must figure out how they will retrieve that data from the reserves. A data engineer a technical job role that falls under the umbrella of jobs related to big data. Handle and source data from different sources according to business requirements.

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Data Engineer Data Engineering Engineering Amazon Web Services 58

ProjectPro

JUNE 6, 2025

In an era where data is abundant, and algorithms are aplenty, the MLops pipeline emerges as the unsung hero, transforming raw data into actionable insights and deploying models with precision. This blog is your key to mastering the vital skill of deploying MLOps pipelines in data science. and prepre.py

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Building Machine Learning Raw Data Data Collection 40

AltexSoft

JUNE 26, 2023

While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection?

Data Collection 52

Data Collection Machine Learning Unstructured Data Electronics 52

ProjectPro

JUNE 6, 2025

The data engineer skill of building data warehousing solutions expects a data engineer to curate data and perform data analysis on that data from multiple sources to support the decision-making process. In such instances, raw data is available in the form of JSON documents, key-value pairs, etc.,

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Data Engineer Data Engineering Engineering Hadoop 49

ProjectPro

JUNE 6, 2025

Million opportunities for remote and on-site data engineering roles. So, have you been wondering what happens to all the data collected from different sources, logs on your machine, data generated from your mobile, data in databases, customer data, and so on? We call this system Data Engineering.

Data Science 40

Data Science Data Engineer Data Engineering Engineering 40

ProjectPro

JUNE 6, 2025

Table of Contents What is Real-Time Data Ingestion? Let us understand the key steps involved in real-time data ingestion into HDFS using Sqoop with the help of a real-world use case where a retail company collects real-time customer purchase data from point-of-sale systems and e-commerce platforms.

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ProjectPro

JUNE 6, 2025

This explosive growth in online content has made web scraping essential for gathering data, but traditional scraping methods face limitations in handling unstructured information. Web scraping typically extracts raw data, which often requires manual cleaning and processing.

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Project Unstructured Data Raw Data Python 40

ProjectPro

JUNE 6, 2025

Data Analytics, situated between Data Science and Data Engineering, primarily concentrates on interpreting historical data to derive actionable insights for decision-making. Let's further explore the distinctions between data science and data engineering in the following section.

Data Science 52

Data Science Portfolio Programming Language Algorithm 52

ProjectPro

JUNE 6, 2025

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 40

Big Data Data Engineer Data Engineering Engineering 40

ProjectPro

JUNE 6, 2025

This is what data engineering does. Data engineering entails creating and developing data collection, storage, and analysis systems. Data engineers create systems that gather, analyze, and transform raw data into useful information. What do employers look for in a data engineer resume?

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Data Engineer Data Engineering Recruitment Engineering 40

ProjectPro

JUNE 6, 2025

This allows independent scaling of both components and native data storage in the open Delta Lake format. Real-Time Analytics Real-Time Analytics excels in processing observational data collected from diverse sources such as apps, IoT devices, and human interactions.

Database-centric 40

Database-centric BI Pipeline-centric Data Lake 40

ProjectPro

JUNE 6, 2025

FAQs How to Start an AI Project: The Prerequisites Implementing AI systems requires a solid understanding of its various subsets, such as Data Analysis , Machine Learning (ML) , Deep Learning (DL) , and Natural Language Processing (NLP). Investing time to understand the data can prevent errors later in AI development.

Project 40

Project Deep Learning Datasets Machine Learning 40

Knowledge Hut

MAY 1, 2024

A data scientist is a person who is trained and experienced in working with data, i.e. data gathering, data cleaning, data preparation, data transformation, and data analysis. These steps will help understand the data, extract hidden patterns and put forward insights about the data.

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Algorithm Raw Data Programming Language Computer Science 98

ProjectPro

JUNE 6, 2025

Businesses employ data scientists, analytical frameworks, datasets , and various tools and techniques to leverage vast amounts of data for their profit. The Bureau of Labor Statistics (BLS) predicts that between 2018 and 2028, demand for data analysts will increase by 26%.

Consulting 40

Consulting Healthcare Programming Language BI 40

ProjectPro

JUNE 6, 2025

Data wrangling is as essential to the data science process as the sun is important for plants to complete the process of photosynthesis. Data wrangling involves extracting the most valuable information from the data per a business's objectives and requirements.

Raw Data 58

Raw Data Programming Language Unstructured Data Datasets 58

ProjectPro

JUNE 6, 2025

Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and helps them extract valuable information from the unstructured and raw data that is regularly collected.

Big Data 40

Big Data Hadoop Relational Database AWS 40

AltexSoft

MAY 12, 2022

Audio data transformation basics to know. Before diving deeper into processing of audio files, we need to introduce specific terms, that you will encounter at almost every step of our journey from sound data collection to getting ML predictions. Audio data analysis steps. Audio data labeling.

Machine Learning 122

Machine Learning Building Deep Learning Healthcare 122

Knowledge Hut

JANUARY 18, 2024

Both data science and software engineering rely largely on programming skills. However, data scientists are primarily concerned with working with massive datasets. Data Science is strongly influenced by the value of accurate estimates, data analysis results, and understanding of those results.

Software Engineering 97

Software Engineering Software Engineer Data Science Engineering 97

ProjectPro

JUNE 6, 2025

Non-Volatile: The data warehouse is also non-volatile, which implies that prior data cannot be erased. It also helps with historical data analysis and knowledge of what and when events occurred. What are the stages in Data warehousing? Data warehouses, on the other hand, simplify every type of business data.

Data Warehouse 40

Data Warehouse Data Mining Recruitment Database 40

ProjectPro

JUNE 6, 2025

Here’s a quick breakdown of the Machine Learning process with reference to a real-world example of how recommender systems implement machine learning- Data Collection And Preprocessing Netflix collects extensive user data daily, including watched content, viewing time, devices used, search history, ratings, and viewing pauses.

Machine Learning 40

Machine Learning Process Entertainment Algorithm 40

Knowledge Hut

JANUARY 18, 2024

Data Science initiatives from an operational standpoint help organizations optimize various aspects of their business, such as supply chain management , inventory segregation, and management, demand forecasting, etc. A data analyst would be a professional who will be able to accomplish all the tasks mentioned in the process of data analysis.

Data Science 98

Data Science Deep Learning Business Analyst Data Mining 98

Knowledge Hut

JULY 4, 2023

Data analysis and Interpretation: It helps in analyzing large and complex datasets by extracting meaningful patterns and structures. By identifying and understanding patterns within the data, valuable insights can be gained, leading to better decision-making, and understanding of underlying relationships.

Machine Learning 98

Machine Learning Medical Algorithm Deep Learning 98

ProjectPro

JUNE 6, 2025

This gap presents a golden opportunity for professionals skilled in data-driven decision making. From optimizing warehouse logistics to minimizing supply risks, data-driven supply chains outperform traditional models. Data Collection & Preprocessing Gather historical sales data, product demand reports, and macroeconomic indicators.

Data Science 40

Data Science Project Management Transportation 40

ProjectPro

JUNE 6, 2025

How do I become a marketing data scientist? Can a marketer become a data scientist? Role of Data Science in Marketing Data science deals with extracting useful information out of raw data and helps marketers determine the most valuable insights. As a result, the data will have "gaps."

Data Science 40

Data Science Data Mining Algorithm Retail 40

ProjectPro

JUNE 6, 2025

FAQs What is Synthetic Data Generation? Synthetic data generation is a technique used to create artificial data that mimics the characteristics and structure of real-world data. Scalability As organizations scale their operations, the need for large volumes of data grows.

Healthcare 40

Healthcare Medical Datasets Machine Learning 40

ProjectPro

JUNE 6, 2025

Key skills include: Data Preprocessing: Cleaning , transforming, and structuring raw data to ensure optimal model performance and accuracy. These agents communicate via defined protocols, passing data and control as needed, creating a modular and scalable workflow.

Medical 64

Medical Healthcare Deep Learning Hospitality 64

Knowledge Hut

DECEMBER 26, 2023

However, as we progressed, data became complicated, more unstructured, or, in most cases, semi-structured. This mainly happened because data that is collected in recent times is vast and the source of collection of such data is varied, for example, data collected from text files, financial documents, multimedia data, sensors, etc.

Data Engineer 98

Data Engineer Data Engineering Engineering Pipeline-centric 98

Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

ProjectPro

JUNE 6, 2025

From working with raw data in various formats to the complex processes of transforming and loading data into a central repository and conducting in-depth data analysis using SQL and advanced techniques, you will explore a wide range of real-world databases and tools.

Data Engineer 45

Data Engineer Data Engineering Cloud Engineering 45

Edureka

AUGUST 2, 2023

Organisations and businesses are flooded with enormous amounts of data in the digital era. Raw data, however, is frequently disorganised, unstructured, and challenging to work with directly. Data processing analysts can be useful in this situation. What does a Data Processing Analysts do ?

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Data Process Process Data Cleanse Data Mining 52

Knowledge Hut

DECEMBER 29, 2023

Learning Outcomes: You will understand the processes and technology necessary to operate large data warehouses. Engineering and problem-solving abilities based on Big Data solutions may also be taught. There are several interrelated professions in the data mining industry, including business analyst and statistician.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Databand.ai

JUNE 8, 2023

Key components of an observability pipeline include: Data collection: Acquiring relevant information from various stages of your data pipelines using monitoring agents or instrumentation libraries. Data storage: Keeping collected metrics and logs in a scalable database or time-series platform.

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