Data Science Dojo

JANUARY 16, 2023

Every individual analysis the data obtained via their experience to generate a final decision. Put more concretely, data analysis involves sifting through data, modeling it, and transforming it to yield information that guides strategic decision-making.

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Data Analysis Data Analysis Data Analyst Big Data 195

Pickl AI

APRIL 10, 2023

In a fast-paced world where organisations collect and generate vast amounts of data for making effective decisions, storing data in different formats makes it challenging. That’s where SQL comes in, enabling data analysts to extract, manipulate and analyse data from multiple sources.

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Data Analyst SQL Data Analysis Data Analysis 52

Pickl AI

MAY 7, 2024

Summary: Struggling to translate data into clear stories? This data visualization tool empowers Data Analysts with drag-and-drop simplicity, interactive dashboards, and a wide range of visualizations. What are The Benefits of Learning Tableau for Data Analysts? Enters: Tableau for Data Analyst.

Data Analyst 52

Data Analyst Tableau Data Science Data Analysis 52

Pickl AI

JANUARY 16, 2024

Summary: The blog delves into the 2024 Data Analyst career landscape, focusing on critical skills like Data Visualisation and statistical analysis. It identifies emerging roles, such as AI Ethicist and Healthcare Data Analyst, reflecting the diverse applications of Data Analysis.

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Data Analyst Data Analysis Data Analysis Analytics 52

Smart Data Collective

MAY 29, 2023

The rate of growth at which world economies are growing and developing thanks to new technologies in information data and analysis means that companies are needing to prepare accordingly. As a result of the benefits of business analytics , the demand for Data analysts is growing quickly.

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Data Analyst SQL Data Scientist Data Analysis 104

Data Science Dojo

MARCH 7, 2023

These skills include programming languages such as Python and R, statistics and probability, machine learning, data visualization, and data modeling. Python is widely used in the data science community, with libraries such as Pandas and NumPy for data manipulation, and Scikit-learn for machine learning.

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Data Scientist Exploratory Data Analysis Data Science Data Visualization 221

Smart Data Collective

DECEMBER 19, 2021

For example, the Impute library package handles the imputation of missing values, MinMaxScaler scales datasets, or uses Autumunge to prepare table data for machine learning algorithms. Besides, Python allows creating data models, systematizing data sets, and developing web services for proficient data processing.

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Python Machine Learning Machine Learning Algorithm 141

Data Science Dojo

FEBRUARY 23, 2024

Since data science GPTs enhance data processing and its subsequent results, they are a fundamental tool for the success of enterprises. A list of best data science GPTs in the GPT store From the GPT store of OpenAI , below is a list of the 10 most popular data science GPTs for you to explore.

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Data Science Data Analysis Data Analysis Data Analyst 195

Data Science Dojo

FEBRUARY 23, 2024

A list of best GPTs for data science in the GPT store From the GPT store of OpenAI , below is a list of the 10 most popular GPTs for data science for you to explore. Data Analyst Data Analyst is a featured GPT in the store that specializes in data analysis and visualization.

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Data Science Data Analysis Data Analysis Machine Learning 195

Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

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Analytics Analytics Big Data Analytics Big Data Analytics 203

Women in Big Data

APRIL 4, 2025

During the Keynote talk Responsible AI @ Kumo AI , Hema Raghavan (Kumo AI Co-Founder & Head of Engineering) showcased platform solutions that make machine learning on relational data simple, performant, and scalable. I cant wait for the next Women in Big Data event!

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Big Data Big Data AI AI 52

Smart Data Collective

NOVEMBER 24, 2021

Based on this assumption, specialists relied on false predictive data models that could only reflect a simplified picture of the possible future. In this paradigm, any minor deviations in data (which, in fact, could predict something) could simply be ignored or perceived as exceptions.

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Analytics Analytics Business Intelligence Business Intelligence 134

IBM Journey to AI blog

SEPTEMBER 19, 2023

Data scientists also rely on data analytics to understand datasets and develop algorithms and machine learning models that benefit research or improve business performance. The dedicated data analyst Virtually any stakeholder of any discipline can analyze data.

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Data Science Analytics Analytics Data Scientist 94

Smart Data Collective

OCTOBER 5, 2020

Some fantastic components of Power BI include: Power Query lets you merge data from different sources Power Pivot aids in data modelling for creating data models Power View constructs interactive charts, graphs and maps. Data Processing, Data Integration, and Data Presenting form the nucleus of Power BI.

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Power BI Business Intelligence Business Intelligence Data Analyst 104

Tableau

MAY 11, 2022

Data refresh failure detection that flags the issue to data users for mitigation and downstream consumers. Data modeling for every data source created in Tableau that shows how to query data in connected database tables and how to include a logical (semantic) layer and a physical layer.

Tableau 98

Tableau Data Warehouse Database Data Analyst 98

Tableau

MAY 11, 2022

Data refresh failure detection that flags the issue to data users for mitigation and downstream consumers. Data modeling for every data source created in Tableau that shows how to query data in connected database tables and how to include a logical (semantic) layer and a physical layer.

Tableau 98

Tableau Data Warehouse Database Data Analyst 98

Alation

NOVEMBER 4, 2021

The value of the data catalog depends on the audience. For data modelers, value arose from spending less time finding data and more time modeling data. For analysts, the catalog relieved them of the growing backlog of requests from end users for data.

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Data Governance Data Quality ML ML 94

Pickl AI

JANUARY 30, 2023

Data Modeling : Using libraries like scikit-learn and Tensorflow, one can build and evaluate predictive models. Data Communication : Communicate insights and results to stakeholders through reports, dashboards, and visualizations using libraries such as Matplotlib, Seaborn, and Plotly.

Python 97

Python Machine Learning Machine Learning Data Science 97

Dataversity

FEBRUARY 19, 2021

Enterprises are modernizing their data platforms and associated tool-sets to serve the fast needs of data practitioners, including data scientists, data analysts, business intelligence and reporting analysts, and self-service-embracing business and technology personnel.

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Data Analyst Data Scientist Business Intelligence Business Intelligence 59

Pickl AI

MARCH 3, 2025

Ensuring data accuracy and consistency through cleansing and validation processes. Data Analysis and Modelling Applying statistical techniques and analytical tools to identify trends, patterns, and anomalies. Developing data models to support analysis and reporting.

Business Intelligence 52

Business Intelligence Business Intelligence Data Analyst Data Visualization 52

Smart Data Collective

SEPTEMBER 4, 2020

Not only this, but they can reduce your costs by ensuring that you can harness your data by yourself, without the need to employ a data analyst. The best data analysis software can help you to collect and organize your data, to construct potential data models, and can also help you to write up reports.

Big Data 106

Big Data Big Data Analytics Analytics 106

DataRobot

FEBRUARY 22, 2021

The founding of the 10X Academy is part of DataRobot’s commitment to developing automation that improves the productivity of data scientists while democratizing access to AI for non-data scientists. Natalie Ecker: “I ran a global change management program.

Data Science 52

Data Science Data Scientist Citizen Data Scientist Data Analyst 52

Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

Pickl AI

AUGUST 27, 2024

Summary: Python simplicity, extensive libraries like Pandas and Scikit-learn, and strong community support make it a powerhouse in Data Analysis. It excels in data cleaning, visualisation, statistical analysis, and Machine Learning, making it a must-know tool for Data Analysts and scientists.

Data Analysis 52

Data Analysis Data Analysis Python Data Analyst 52

DrivenData Labs

MARCH 26, 2024

Meredith is a Cost and Data Analyst with the Business Analytics Team at HunaTek, an Alaskan Native Corporation that provides management consulting and logistics services to various federal agencies. Within Power BI, we made a simple relational data model from the datasets by linking them together along common coordinates.

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Power BI Data Scientist Python Machine Learning 264

phData

MAY 31, 2023

From there, that question is fed into ChatGPT along with dbt data models that provide information about the fields in the various tables. From there, ChatGPT generates a SQL query which is then executed in the Snowflake Data Cloud , and the results are brought back into the application in a table format.

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SQL Data Models Data Modeling Database 52

Alation

OCTOBER 27, 2022

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. Jason: How do you use these models?

Data Analyst 52

Data Analyst Data Scientist Analytics Analytics 52

phData

FEBRUARY 13, 2024

Now that the data is in Snowflake, your organization will also have access to the myriad of AI tools , such as Snowpark , that work within Snowflake. Here are some of the biggest challenges: SAP Infrastructure With over 10,000 tables, all with difficult to understand table and column names, SAP’s data model is extremely hard to work with.

Analytics 59

Analytics Analytics Data Scientist Data Modeling 59

Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Data Modeling: Entity-Relationship (ER) diagrams, data normalization, etc.

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Data Engineering Data Engineering Data Engineer Data Engineering 52

Alation

JULY 19, 2022

Fortunately, just as data catalogs help solve the problems of discovery and exploration for data analysts, they can aid data science teams. The Data Science Workflow. Get the data. Explore the data. Model the data. Communicate and visualize the results. Closing Thoughts.

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Data Scientist Data Quality Data Science Data Analyst 59

ODSC - Open Data Science

APRIL 26, 2023

Data Analyst When people outside of data science think of those who work in data science, the title Data Analyst is what often comes up. What makes this job title unique is the “Swiss army knife” approach to data. But this doesn’t mean they’re off the hook on other programs.

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Data Analyst Machine Learning Machine Learning Power BI 52

Alation

MAY 31, 2023

A data architect is responsible for managing an organization’s data architecture, ensuring accuracy, consistency, and security. They collaborate with IT professionals, business stakeholders, and data analysts to design effective data infrastructure aligned with the organization’s goals.

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Data Governance Data Silos Data Quality Data Modeling 52

IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

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Machine Learning Machine Learning Data Science Big Data 70

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

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Business Intelligence Business Intelligence Data Analysis Data Analysis 70

Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

Business Intelligence 70

Business Intelligence Business Intelligence Data Analysis Data Analysis 70

ODSC - Open Data Science

APRIL 6, 2023

For budding data scientists and data analysts, there are mountains of information about why you should learn R over Python and the other way around. Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL.

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SQL Data Scientist Database Data Science 98

Pickl AI

JANUARY 9, 2025

Knowledge of data modelling, process automation, and predictive analytics further enhances an analyst’s ability to support decision-making and operational efficiency. Soft Skills While technical skills are critical, the ability to communicate complex data insights is equally vital.

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Power BI Machine Learning Machine Learning Tableau 52