Knowledge Hut

MAY 9, 2024

Data Science, with its interdisciplinary approach, combines statistics, computer science, and domain knowledge and has opened up a world of exciting and lucrative career opportunities for professionals with the right skills and expertise. The market is flooding with the highest paying data science jobs.

Data Science 52

Data Science Data Architect Data Mining Programming Language 52

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? Dataset Description The dataset contains the metadata for users and songs.

Data Science 52

Data Science Python Project Datasets 52

ProjectPro

JANUARY 15, 2021

Undoubtedly, everyone knows that the only best way to learn data science and machine learning is to learn them by doing diverse projects. But yes, there is definitely no other alternative to data science and machine learning projects. Table of Contents What is a dataset in machine learning?

Machine Learning 40

Machine Learning Datasets Retail Banking 40

Towards Data Science

MAY 22, 2023

Solving data preparation tasks with ChatGPT Photo by Ricardo Gomez Angel on Unsplash Data engineering makes up a large part of the data science process. In CRISP-DM this process stage is called “data preparation”. In our article we solve typical data engineering tasks using ChatGPT and Python.

Data Engineer 90

Data Engineer Data Engineering Engineering Data Science 90

ProjectPro

FEBRUARY 5, 2021

Nonetheless, it is an exciting and growing field and there can't be a better way to learn the basics of image classification than to classify images in the MNIST dataset. Table of Contents What is the MNIST dataset? Test the Trained Neural Network Visualizing the Test Results Ending Notes What is the MNIST dataset?

Datasets 40

Datasets Deep Learning Medical Algorithm 40

Towards Data Science

JUNE 27, 2023

The Top 5 Features for Efficient Data Manipulation This April, pandas 2.0.0 was officially launched , making huge waves across the data science community. Due to its extensive functionality and versatility, pandas has secured a place in every data scientist’s heart. A Game-Changer for Data Scientists?

Pipeline-centric 98

Pipeline-centric Data Science Machine Learning Datasets 98

Knowledge Hut

JANUARY 25, 2024

In the world of data science, keeping our data clean is a bit like keeping our rooms tidy. Just as a messy room can make it hard to find things, messy data can make it tough to get valuable insights. That's why data cleaning techniques and best practices are super important. What is Data Cleaning?

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Towards Data Science

OCTOBER 12, 2023

Construction engineer investigating his work — Stable diffusion Introduction In our previous publication, From Data Engineering to Prompt Engineering , we demonstrated how to utilize ChatGPT to solve data preparation tasks. We will start out propagating the Database Schema and some example data to ChatGPT.

Data Integration 67

Data Integration Systems Engineering Datasets 67

AltexSoft

MAY 12, 2022

Particularly, we’ll explain how to obtain audio data, prepare it for analysis, and choose the right ML model to achieve the highest prediction accuracy. But first, let’s go over the basics: What is the audio analysis, and what makes audio data so challenging to deal with. Labeling of audio data in Audacity.

Machine Learning 122

Machine Learning Building Deep Learning Datasets 122

AltexSoft

OCTOBER 30, 2021

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering.

Data Engineer 98

Data Engineer Data Engineering Engineering Machine Learning 98

RandomTrees

AUGUST 21, 2024

Advanced Data Cleaning and Transformation : Scenario : A financial institution needs to clean and preprocess large datasets with complex transformations. Solution : Utilize Python’s Pandas library to perform data wrangling tasks such as handling missing values, merging datasets, and applying complex transformations.

BI 52

BI Python Datasets Machine Learning 52

Knowledge Hut

NOVEMBER 17, 2023

You have a large dataset of labeled cat images, but you’re worried that it’s not enough. What if your model encounters a cat in the wild that’s sitting in a strange position or has a different fur color than anything in your dataset? Data augmentation in Python enhances dataset diversity for robust machine learning.

Datasets 52

Datasets Machine Learning Deep Learning Data 52

Precisely

DECEMBER 4, 2023

For example, location-based insights can be delivered through web GIS (geographic information systems) applications or data science models. They also need a strong foundation of data science to underpin those efforts. Collecting, organizing, and cleaning datasets consumes 45-60% of DS time.

Telecommunication 64

Telecommunication Data Science Data Integration Data Preparation 64

Knowledge Hut

JANUARY 29, 2024

Data Wrangler: Another data cleaning and transformation tool, offering flexibility in data preparation. Examples of Data Wrangling Data wrangling can be applied in various scenarios, making it a versatile and valuable process.

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

Cloudera

DECEMBER 17, 2020

When many businesses start their journey into ML and AI, it’s common to place a lot of energy and focus on the coding and data science algorithms themselves. In August 2020 we released CDP Data Engineering (DE) — our answer to enabling fast, optimized, and automated data engineering for analytic workloads.

Machine Learning 82

Machine Learning Data Science Data Pipeline Raw Data 82

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. Data relevance. Data sources In developing hotel price prediction models, gathering extensive data from different sources is crucial.

Hospitality 52

Hospitality Datasets Algorithm Machine Learning 52

ProjectPro

JANUARY 24, 2023

Time-saving: SageMaker automates many of the tasks, by creating a pipeline starting from data preparation and ML model training, which saves time and resources. Analyze – Data Wrangler allows you to analyze the features in your dataset at any stage of the data preparation process.

Building 52

Building Algorithm Machine Learning AWS 52

Edureka

JULY 16, 2024

SageMaker, on the other hand, works well with other AWS services and provides a sound foundation to deal with large datasets and computations effectively. For data storage and warehousing, users can use Amazon S3 service, while for cataloging the data, users can use Amazon Glue and perform ETL operations.

AWS 52

AWS Algorithm Machine Learning Amazon Web Services 52

ProjectPro

AUGUST 30, 2022

This article will walk you through how one can start by exploring a loan prediction system as a data science and machine learning problem and build a system/application for loan prediction using your own machine learning project. Despite using data science in this process, there is still a large amount of manual work involved.

Machine Learning 52

Machine Learning Coding Project Datasets 52

AltexSoft

AUGUST 25, 2021

There are two main steps for preparing data for the machine to understand. Any ML project starts with data preparation. You can’t simply feed the system your whole dataset of emails and expect it to understand what you want from it. What should it be like and how to prepare a great one? MLaaS APIs.

Process 139

Process Deep Learning Datasets Machine Learning 139

ProjectPro

JANUARY 31, 2023

Ultimately, the most important countermeasure against overfitting is adding more and better quality data to the training dataset. One solution to such problems is data augmentation , a technique for creating new training samples from existing ones. Table of Contents What is Data Augmentation in Deep Learning?

Deep Learning 52

Deep Learning Datasets Machine Learning Data 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

Knowledge Hut

SEPTEMBER 26, 2023

This not only speeds up data retrieval but also reduces the need for manual query optimization, allowing data teams to focus on insights rather than performance tuning. Databricks is a unified analytics platform that has rapidly emerged as a powerhouse in the world of data science, engineering, and analytics.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Knowledge Hut

JANUARY 24, 2024

You should be data-driven if you want to pursue your career in Business Intelligence, Analytics, or Data Science. Performance Aspect Power BI Tableau Speed of Data Rendering In my experience, Power BI exhibits commendable speed in rendering visualizations, particularly with smaller datasets.

BI 98

BI Business Intelligence Non-relational Database Data Analysis 98

Knowledge Hut

OCTOBER 27, 2023

Microsoft created Power BI, a business analytics tool that enables users to visualize and analyze data from various sources quickly and interactively. It provides a wide range of features and functionalities, including data preparation, data modeling, data visualization, and collaboration tools.

BI 52

BI Project Datasets Healthcare 52

Edureka

AUGUST 27, 2024

There is no need for BI teams to build data models on data sets to enable this capability. It is built-in with Amazon Quicksight and enables anyone to make sense of the data even if they don’t have any background or experience in data science.

BI 52

BI Business Intelligence AWS Amazon Web Services 52

AltexSoft

NOVEMBER 15, 2022

Dataset preparation and construction. The starting point of any machine learning task is data. A lot of data, to be exact. A lot of quality data, to be even more exact. To learn the basics, you can read our dedicated article on how data is prepared for machine learning or watch a short video.

Hospitality 52

Hospitality Building Datasets Machine Learning 52

Knowledge Hut

JUNE 20, 2023

Many people are learning data science for the first time and need help comprehending the two job positions. Data science is relatively new, and roles and job titles occasionally change. Therefore, keeping up with the latest trends and frameworks and taking online courses like Data Science course review is important.

Machine Learning 52

Machine Learning Data Engineer Data Engineering Engineering 52

ProjectPro

JANUARY 31, 2023

MapReduce is a Hadoop framework used for processing large datasets. Another name for it is a programming model that enables us to process big datasets across computer clusters. This program allows for distributed data storage, simplifying complex processing and vast amounts of data. Explain the data preparation process.

Big Data 52

Big Data Hadoop Relational Database AWS 52

Advancing Analytics: Data Engineering

JULY 2, 2019

But I don’t agree; I think there was a very specific function that was heavily tied into data science that has evolved in the past two years into something new. I’m going to refer to this role as the Data Science Engineer to differentiate from its current state.

Business Intelligence 52

Business Intelligence Data Engineer Data Engineering Engineering 52

Knowledge Hut

JANUARY 30, 2024

In today's world, where data rules the roost, data extraction is the key to unlocking its hidden treasures. As someone deeply immersed in the world of data science, I know that raw data is the lifeblood of innovation, decision-making, and business progress. What is the purpose of extracting data?

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

Snowflake

JUNE 28, 2023

Snowpark is our secure deployment and processing of non-SQL code, consisting of two layers: Familiar Client Side Libraries – Snowpark brings deeply integrated, DataFrame-style programming and OSS compatible APIs to the languages data practitioners like to use.

Python 52

Python Accessibility Accessible Pipeline-centric 52

AltexSoft

DECEMBER 21, 2021

A label or a tag is a descriptive element that tells a model what an individual data piece is so it can learn by example. In this case, the training dataset will consist of multiple songs with labels showing genres like pop, jazz, rock, etc. So, what challenges does data labeling involve? Data labeling challenges.

Machine Learning 52

Machine Learning Process Raw Data Datasets 52

Knowledge Hut

DECEMBER 27, 2023

Roles & Responsibilities Data analysis: Analyzing data to gain insights and make recommendations. Data preparation: Preparing data so that it can be used by other analysts and decision-makers. Data visualization: Visualizing data in a way that makes it easy to understand and use.

Business Analyst 52

Business Analyst Computer Science Finance Data Analysis 52

ProjectPro

JANUARY 31, 2022

Table of Contents Snowflake Overview and Architecture What is Snowflake Data Warehouse? Snowflake Features that Make Data Science Easier Building Data Applications with Snowflake Data Warehouse Snowflake Data Warehouse Architecture How Does Snowflake Store Data Internally? What Does Snowflake Do?

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 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

Knowledge Hut

OCTOBER 30, 2023

Define data architecture standards and best practices. Advanced Data Modeling: Create and maintain advanced data models that support complex reporting requirements, including handling large datasets and optimizing performance. Mentor team members in data modeling techniques.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52