Pickl AI

MAY 30, 2024

Overview of Typical Tasks and Responsibilities in Data Science As a Data Scientist, your daily tasks and responsibilities will encompass many activities. You will collect and clean data from multiple sources, ensuring it is suitable for analysis. Sources of Data Data can come from multiple sources.

Data Analysis 52

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Pickl AI

JUNE 6, 2024

It’s like the heavy-duty cleaning you might do before moving into a new house, where you meticulously scrub floors, remove stains, and ensure everything is spotless. It utilizes sophisticated algorithms and techniques to tackle various data imperfections. Data scrubbing is the knight in shining armour for BI.

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Pickl AI

OCTOBER 18, 2023

It’s essential to ensure that data is not missing critical elements. Consistency Data consistency ensures that data is uniform and coherent across different sources or databases. Timeliness Timeliness relates to the relevance of data at a specific point in time.

Data Quality 52

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Dataconomy

JULY 28, 2023

In the digital age, the abundance of textual information available on the internet, particularly on platforms like Twitter, blogs, and e-commerce websites, has led to an exponential growth in unstructured data. Text data is often unstructured, making it challenging to directly apply machine learning algorithms for sentiment analysis.

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Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Tableau

JULY 28, 2020

With Prep, users can easily and quickly combine, shape, and clean data for analysis with just a few clicks. In this blog, we’ll discuss ways to make your data preparation flow run faster. These tips can be used in any of your Prep flows but will have the most impact on your flows that connect to large database tables.

Data Preparation 52

Data Preparation Tableau Database Clean Data 52

Tableau

JULY 28, 2020

With Prep, users can easily and quickly combine, shape, and clean data for analysis with just a few clicks. In this blog, we’ll discuss ways to make your data preparation flow run faster. These tips can be used in any of your Prep flows but will have the most impact on your flows that connect to large database tables.

Data Preparation 52

Data Preparation Tableau Database Clean Data 52

Pickl AI

MAY 29, 2024

Technical Skills Technical skills form the foundation of a Data Scientist’s toolkit, enabling the analysis, manipulation, and interpretation of complex data sets. SQL is indispensable for database management and querying. Skills in data manipulation and cleaning are necessary to prepare data for analysis.

Data Scientist 52

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Mlearning.ai

JANUARY 26, 2023

Video Presentation of the B3 Project’s Data Cube. Presenters and participants had the opportunity to hear about and evaluate the pros and cons of different back end technologies and data formats for different uses such as web-mapping, data visualization, and the sharing of meta-data. Data Intelligence , 2 (1–2), 199–207.

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Pickl AI

AUGUST 15, 2023

Programming languages such as Python, R, SQL, and others are widely used in Data Analytics. With coding skills, Data Analysts can automate repetitive tasks, develop custom algorithms, and implement complex statistical analyses. Python, known for its simplicity and versatility, is highly favored by Data Analysts.

Analytics 52

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Pickl AI

DECEMBER 3, 2024

Raw data often contains inconsistencies, missing values, and irrelevant features that can adversely affect the performance of Machine Learning models. Proper preprocessing helps in: Improving Model Accuracy: Clean data leads to better predictions. Scikit-learn: For Machine Learning algorithms and preprocessing utilities.

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Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

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Snorkel AI

JULY 12, 2024

Key aspects of model-centric AI include: Algorithm Development: Creating and optimizing algorithms to improve a model’s performance. Data-Centric AI Data-centric AI is an approach to artificial intelligence development that focuses on improving the quality and utility of the data used to train AI models.

AI 52

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Snorkel AI

JULY 12, 2024

Key aspects of model-centric AI include: Algorithm Development: Creating and optimizing algorithms to improve a model’s performance. Data-Centric AI Data-centric AI is an approach to artificial intelligence development that focuses on improving the quality and utility of the data used to train AI models.

AI 52

AI AI Data Lakes Artificial Intelligence 52

Mlearning.ai

FEBRUARY 21, 2023

Raw data is processed to make it easier to analyze and interpret. Because it can swiftly and effectively handle data structures, carry out calculations, and apply algorithms, Python is the perfect language for handling data. data = data.dropna() We can also use the drop_duplicates() method to remove duplicated rows.

Data Wrangling 52

Data Wrangling Python Data Analysis Data Analysis 52

Pickl AI

JULY 8, 2024

Organisations leverage diverse methods to gather data, including: Direct Data Capture: Real-time collection from sensors, devices, or web services. Database Extraction: Retrieval from structured databases using query languages like SQL. Aggregation: Summarising data into meaningful metrics or aggregates.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

DagsHub

OCTOBER 23, 2024

There are 5 stages in unstructured data management: Data collection Data integration Data cleaning Data annotation and labeling Data preprocessing Data Collection The first stage in the unstructured data management workflow is data collection. We get your data RAG-ready.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

APRIL 3, 2025

Key Processes and Techniques in Data Analysis Data Collection: Gathering raw data from various sources (databases, APIs, surveys, sensors, etc.). Data Cleaning & Preparation: This is often the most time-consuming step. to understand the data’s main characteristics, distributions, and relationships.

Data Analysis 52

Data Analysis Data Analysis Data Visualization EDA 52

Mlearning.ai

MAY 23, 2023

Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. What is Data Science? Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. It further performs badly on the test data set.

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Pickl AI

SEPTEMBER 12, 2024

Understand the Data Sources The first step in data standardization is to identify and understand the various data sources that will be standardized. This includes databases, spreadsheets, APIs, and manual records. This could include internal databases, external APIs, and third-party data providers.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Pickl AI

DECEMBER 16, 2024

Summary: AI in Time Series Forecasting revolutionizes predictive analytics by leveraging advanced algorithms to identify patterns and trends in temporal data. Advanced algorithms recognize patterns in temporal data effectively. Step 2: Data Gathering Collect relevant historical data that will be used for forecasting.

AI 52

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Pickl AI

DECEMBER 3, 2024

It provides high-quality, curated data, often with associated tasks and domain-specific challenges, which helps bridge the gap between theoretical ML algorithms and real-world problem-solving. The data can then be explored, cleaned, and processed to be used in Machine Learning models.

Machine Learning 52

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Pickl AI

MAY 15, 2023

The systems are designed to ensure data integrity, concurrency and quick response times for enabling interactive user transactions. In online analytical processing, operations typically consist of major fractions of large databases. The step varies slightly from process to process depending on the source of data being processed.

Machine Learning 52

Machine Learning Machine Learning Data Analysis Data Analysis 52

phData

FEBRUARY 7, 2024

The following example uses a dict containing connection parameters to create a new session: connection_parameters = { "account": " ", "user": " ", "password": " ", "role": " ", # optional "warehouse": " ", # optional "database": " ", # optional "schema": " ", # optional } new_session = Session.builder.configs(connection_parameters).create()

Python 52

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Snorkel AI

JANUARY 16, 2024

I don’t think we would have been able to write a paper about just “vector-database-plus-language-model.” The original paper that coined the term “ large language model ” was a 2007 Google paper where they used an algorithm called “Stupid Backoff.” You need data that’s labeled and curated for your use case.

AI 52

AI AI Supervised Learning Algorithm 52

Snorkel AI

JANUARY 16, 2024

I don’t think we would have been able to write a paper about just “vector-database-plus-language-model.” The original paper that coined the term “ large language model ” was a 2007 Google paper where they used an algorithm called “Stupid Backoff.” You need data that’s labeled and curated for your use case.

Supervised Learning 52

Supervised Learning AI AI Algorithm 52

Snorkel AI

APRIL 28, 2023

Often, it requires you to co-design the algorithm and also the system set. If they’re necessary, how can we create a new algorithm to accommodate it? How can we adapt the model to different scenarios as systematic and data-efficient as possible? In this case, you can also use fairness as an objective for data debugging.

ML 52

ML ML Machine Learning Machine Learning 52

Snorkel AI

APRIL 28, 2023

Often, it requires you to co-design the algorithm and also the system set. If they’re necessary, how can we create a new algorithm to accommodate it? How can we adapt the model to different scenarios as systematic and data-efficient as possible? In this case, you can also use fairness as an objective for data debugging.

ML 52

ML ML Machine Learning Machine Learning 52

Dataconomy

FEBRUARY 26, 2025

These tools leverage complex algorithms and data processing capabilities to enhance operational efficiency. Identifying appropriate data sources. Organizing and cleaning data. It incorporates structured, unstructured, and mixed data to enhance decision-making capabilities.

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