Smart Data Collective

MAY 20, 2019

For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. The popular tools, on the other hand, include Power BI, ETL, IBM Db2, and Teradata. Professionals adept at this skill will be desirable by corporations, individuals and government offices alike.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Data Science Dojo

JULY 3, 2024

Tools like Tableau, Power BI, and Python libraries such as Matplotlib and Seaborn are commonly taught. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. R : Often used for statistical analysis and data visualization.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Mlearning.ai

FEBRUARY 22, 2023

This could involve using a distributed file system, such as Hadoop, or a cloud-based storage service, such as Amazon S3. This could involve using tools like Tableau or Power BI to create visualizations and dashboards. This could involve batch processing or real-time streaming, depending on your needs.

Big Data 52

Big Data Big Data Power BI Hadoop 52

Dataconomy

JULY 24, 2023

Tools like Tableau, Matplotlib, Seaborn, or Power BI can be incredibly helpful. Learn relevant tools Familiarize yourself with data science tools and platforms, such as Tableau for data visualization, or Hadoop for big data processing. This is where data visualization comes in.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

Pickl AI

DECEMBER 2, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. Distributed File Systems: Technologies such as Hadoop Distributed File System (HDFS) distribute data across multiple machines to ensure fault tolerance and scalability. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. Distributed File Systems: Technologies such as Hadoop Distributed File System (HDFS) distribute data across multiple machines to ensure fault tolerance and scalability. Data lakes and cloud storage provide scalable solutions for large datasets.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Pickl AI

JANUARY 12, 2025

To confirm seamless integration, you can use tools like Apache Hadoop, Microsoft Power BI, or Snowflake to process structured data and Elasticsearch or AWS for unstructured data. By uniting the strengths of both approaches, organisations can uncover valuable insights and achieve greater efficiency in their processes.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence AI AI 52

Mlearning.ai

MARCH 15, 2023

Experience with visualization tools like; Tableau and Power BI. Knowledge of big data platforms like; Hadoop and Apache Spark. High proficiency in visualization tools like; Tableau, Google Studio, and Power BI. Basic programming knowledge in R or Python.

Data Science 52

Data Science Analytics Analytics Data Analyst 52

Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JULY 25, 2023

With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently. Big Data Technologies: Hadoop, Spark, etc. Big Data Processing: Apache Hadoop, Apache Spark, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

APRIL 26, 2023

Because they are the most likely to communicate data insights, they’ll also need to know SQL, and visualization tools such as Power BI and Tableau as well. Some of the tools and techniques unique to business analysts are pivot tables, financial modeling in Excel, Power BI Dashboards for forecasting, and Tableau for similar purposes.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

DataSeries

AUGUST 15, 2024

Hadoop, Spark). Familiarize with data visualization techniques and tools like Matplotlib, Seaborn, Tableau, or Power BI. Understanding these fundamentals is essential for effective problem-solving in data engineering.

Data Engineering 40

Data Engineering Data Engineering Data Engineer Data Engineering 40

Pickl AI

APRIL 28, 2023

Here is the tabular representation of the same: Technical Skills Non-technical Skills Programming Languages: Python, SQL, R Good written and oral communication Data Analysis: Pandas, Matplotlib, Numpy, Seaborn Ability to work in a team ML Algorithms: Regression Classification, Decision Trees, Regression Analysis Problem-solving capability Big Data: (..)

Data Scientist 52

Data Scientist Data Science Hypothesis Testing Decision Trees 52

Pickl AI

JANUARY 28, 2025

Walmart Walmart has implemented a robust BI architecture to manage data from its extensive network of stores and online platforms. By consolidating data from over 10,000 locations and multiple websites into a single Hadoop cluster, Walmart can analyse customer purchasing trends and optimize inventory management.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Pickl AI

DECEMBER 4, 2024

Gain Experience with Big Data Technologies With the rise of Big Data, familiarity with technologies like Hadoop and Spark is essential. Learn to use tools like Tableau, Power BI, or Matplotlib to create compelling visual representations of data. Additionally, familiarity with cloud platforms (e.g.,

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

APRIL 2, 2024

Tableau/Power BI: Visualization tools for creating interactive and informative data visualizations. Hadoop/Spark: Frameworks for distributed storage and processing of big data. Tools and Technologies Python/R: Popular programming languages for data analysis and machine learning.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52

ODSC - Open Data Science

JANUARY 7, 2025

Hadoop, though less common in new projects, is still crucial for batch processing and distributed storage in large-scale environments. Power BI is surprisingly popular as well, possibly for its focus on business and applications, making it more commonly used by even non-tech-savvy individuals.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

Data Science Blog

MARCH 14, 2023

In der Parallelwelt der ITler wurde das Tool und Ökosystem Apache Hadoop quasi mit Big Data beinahe synonym gesetzt. Big Data tauchte als Buzzword meiner Recherche nach erstmals um das Jahr 2011 relevant in den Medien auf. Big Data wurde zum Business-Sprech der darauffolgenden Jahre.

Big Data 147

Big Data Big Data Apache Hadoop Data Science 147