Analytics Vidhya

MAY 21, 2023

Introduction Not a single day passes without us getting to hear the word “data.” This is precisely what happens in data analytics. People equipped with the […] The post 10 Best Data Analytics Projects appeared first on Analytics Vidhya. It is almost as if our lives revolve around it. Don’t they?

Analytics 357

Analytics Analytics Power BI Hadoop 357

Alation

FEBRUARY 20, 2020

Architecturally the introduction of Hadoop, a file system designed to store massive amounts of data, radically affected the cost model of data. Organizationally the innovation of self-service analytics, pioneered by Tableau and Qlik, fundamentally transformed the user model for data analysis. Disruptive Trend #1: Hadoop.

Data Lakes 52

Data Lakes Hadoop Tableau Big Data 52

Pickl AI

DECEMBER 25, 2024

Programming languages like Python and R are commonly used for data manipulation, visualization, and statistical modeling. Machine learning algorithms play a central role in building predictive models and enabling systems to learn from data. Data Scientists rely on technical proficiency.

Data Science 52

Data Science Analytics Analytics Data Scientist 52

Data Science 101

JUNE 10, 2019

Among the skills necessary to become a data scientist include an analytical mindset, mathematics, data visualization, and business knowledge, just to name a few. In addition to having the skills, you’ll need to then learn how to use the modern data science tools.

Data Science 96

Data Science Data Scientist Data Analyst Data Engineering 96

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. 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.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Smart Data Collective

SEPTEMBER 25, 2019

Big data has been billed as being the future of business for quite some time. Analysts have found that the market for big data jobs increased 23% between 2014 and 2019. The market for Hadoop jobs increased 58% in that timeframe. The impact of big data is felt across all sectors of the economy. However, the future is now.

Big Data 101

Big Data Big Data Data Scientist Data Science 101

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Pickl AI

AUGUST 9, 2024

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. 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.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Women in Big Data

OCTOBER 9, 2024

Data Processing (Preparation): Ingested data undergoes processing to ensure it’s suitable for storage and analysis. Batch Processing: For large datasets, frameworks like Apache Hadoop MapReduce or Apache Spark are used. Stream Processing: Real-time data is processed using tools like Apache Kafka or Apache Flink.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Mlearning.ai

MAY 23, 2023

A good course to upskill in this area is — Machine Learning Specialization Data Visualization The ability to effectively communicate insights through data visualization is important. Additionally, knowledge of model evaluation, hyperparameter tuning, and model selection is valuable.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Dataconomy

JULY 25, 2023

It combines techniques from mathematics, statistics, computer science, and domain expertise to analyze data, draw conclusions, and forecast future trends. Data scientists use a combination of programming languages (Python, R, etc.), Acquiring and maintaining this breadth of knowledge can be challenging and time-consuming.

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91

Pickl AI

JULY 18, 2023

It is popular for its powerful data visualization and analysis capabilities. Hence, Data Scientists rely on R to perform complex statistical operations. With a wide array of packages like ggplot2 and dplyr, R allows for sophisticated data visualization and efficient data manipulation. Wrapping it up !!!

Data Science 52

Data Science SQL Data Scientist Python 52

Pickl AI

JANUARY 13, 2025

They employ statistical methods and machine learning techniques to interpret data. Key Skills Expertise in statistical analysis and data visualization tools. Data Analyst Data Analysts gather and interpret data to help organisations make informed decisions. Experience with big data technologies (e.g.,

AI 52

AI AI Machine Learning Machine Learning 52

DataSeries

AUGUST 15, 2024

Knowledge of Core Data Engineering Concepts Ensure one possess a strong foundation in core data engineering concepts, which include data structures, algorithms, database management systems, data modeling , data warehousing , ETL (Extract, Transform, Load) processes, and distributed computing frameworks (e.g.,

Data Engineering 40

Data Engineering Data Engineer Data Engineering Data Engineering 40

Pickl AI

SEPTEMBER 24, 2024

Read More: Unlocking the Power of Data Analytics in the Finance Industry Technologies and Tools Used Uber employs a robust technological infrastructure to support its Data Analytics initiatives.By What Technologies Does Uber Use for Data Processing?

Analytics 52

Analytics Analytics Machine Learning Machine Learning 52

Pickl AI

APRIL 18, 2023

Thus, it focuses on providing all the fundamental concepts of Data Science and light concepts of Machine Learning, Artificial Intelligence, programming languages and others. Usually, a Data Science course comprises topics on statistical analysis, data visualization, data mining and data preprocessing.

Data Science 52

Data Science Machine Learning Machine Learning Data Scientist 52

Mlearning.ai

MARCH 15, 2023

Responsibilities of a Data Analyst Data analysts, on the other hand, help businesses and organizations make data-driven decisions through their analytical skills. Their job is mainly to collect, process, analyze, and create detailed reports on data to meet business needs.

Data Science 52

Data Science Analytics Analytics Data Analyst 52

IBM Journey to AI blog

JULY 6, 2023

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

ODSC - Open Data Science

JUNE 1, 2023

It can ingest from batch data sources (such as Hadoop HDFS, Amazon S3, and Google Cloud Storage) as well as stream data sources (such as Apache Kafka and Redpanda). Pinot stores data in tables, each of which must first define a schema.

Analytics 98

Analytics Analytics Apache Kafka Data Science 98

Pickl AI

JANUARY 28, 2025

This layer is critical as it transforms raw data into actionable insights that drive business decisions. Data Visualization Data visualization tools present analyzed data in an easily understandable format. Prescriptive Analytics : Offers recommendations for actions based on predictive models.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

Alation

MARCH 29, 2021

Alation helps connects to any source Alation helps connect to virtually any data source through pre-built connectors. Alation crawls and indexes data assets stored across disparate repositories, including cloud data lakes, databases, Hadoop files, and data visualization tools.

Machine Learning 59

Machine Learning Machine Learning Data Lakes Hadoop 59

Pickl AI

FEBRUARY 28, 2023

Significantly, Data Science experts have a strong foundation in mathematics, statistics, and computer science. Furthermore, they must be highly efficient in programming languages like Python or R and have data visualization tools and database expertise. Who is a Data Analyst?

Data Analyst 52

Data Analyst Data Scientist Data Science Computer Science 52

Pickl AI

JULY 24, 2023

They employ advanced statistical modeling techniques, machine learning algorithms, and data visualization tools to derive meaningful insights. Data Analyst Data analysts focus on collecting, cleaning, and transforming data to discover patterns and trends.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

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

ODSC - Open Data Science

JANUARY 7, 2025

Tools like Apache Airflow are widely used for scheduling and monitoring workflows, while Apache Spark dominates big data pipelines due to its speed and scalability. Hadoop, though less common in new projects, is still crucial for batch processing and distributed storage in large-scale environments.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

Pickl AI

JULY 10, 2023

Packages like stats, car, and survival are commonly used for statistical modeling and analysis. · Data Visualization : R offers several libraries, including ggplot2, plotly, and lattice, that allow for the creation of high-quality visualizations.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

JUNE 19, 2024

Popular libraries for Data Science in Python include NumPy (numerical computing), pandas (data manipulation and analysis), and scikit-learn (machine learning algorithms). R: A powerful language specifically designed for statistical computing and data visualization. Course Focus Data Science is a vast field.

Data Science 52

Data Science Data Scientist Hypothesis Testing Natural Language Processing 52

Pickl AI

JULY 20, 2023

Descriptive Analytics Projects: These projects focus on summarizing historical data to gain insights into past trends and patterns. Examples include generating reports, dashboards, and data visualizations to understand business performance, customer behavior, or operational efficiency.

Analytics 40

Analytics Analytics Big Data Big Data 40

ODSC - Open Data Science

APRIL 26, 2023

As models become more complex and the needs of the organization evolve and demand greater predictive abilities, you’ll also find that machine learning engineers use specialized tools such as Hadoop and Apache Spark for large-scale data processing and distributed computing.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Heartbeat

JULY 31, 2023

These integrations allow users to easily track their machine learning experiments and visualize their results within the Comet platform, without having to write additional code. Comet also integrates with popular data storage and processing tools like Amazon S3, Google Cloud Storage, and Hadoop.

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

Mlearning.ai

AUGUST 18, 2023

Get in touch with us to discuss your needs and wants and bring your ideas to life.

Data Science 52

Data Science Data Scientist Internet of Things Apache Hadoop 52

Data Science Dojo

MAY 24, 2024

Navigate through 6 Popular Python Libraries for Data Science R R is another important language, particularly valued in statistics and data analysis, making it useful for AI applications that require intensive data processing. C++ C++ is essential for AI engineering due to its efficiency and control over system resources.

AI 195

AI AI Deep Learning Deep Learning 195

Data Science Dojo

AUGUST 30, 2023

Big data processing With the increasing volume of data, big data technologies have become indispensable for Applied Data Science. Technologies like Hadoop and Spark enable the processing and analysis of massive datasets in a distributed and parallel manner.

Data Science 195

Data Science Hypothesis Testing Machine Learning Machine Learning 195

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. Cloud Platforms (AWS, Azure, Google Cloud): Infrastructure for scalable and cost-effective data storage and analysis.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 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. Understanding real-time data processing frameworks, such as Apache Kafka, will also enhance your ability to handle dynamic analytics.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

MARCH 19, 2025

It helps streamline data processing tasks and ensures reliable execution. Tableau Tableau is a popular data visualization tool that enables users to create interactive dashboards and reports. It helps organisations understand their data better and make informed decisions.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Dataconomy

MARCH 19, 2025

Essential technical skills Data preparation and mining: Proficiency in cleaning and organizing data effectively. Data visualization and storytelling: The ability to communicate findings clearly and effectively. Roles within a data science team Data engineer: Responsible for building and managing data pipelines.

Data Science 91

Data Science Citizen Data Scientist Data Scientist Machine Learning 91