Pickl AI

APRIL 21, 2025

Data Science, on the other hand, uses scientific methods and algorithms to analyses this data, extract insights, and inform decisions. Big Data technologies include Hadoop, Spark, and NoSQL databases. Database Knowledge: Like SQL for retrieving data. Machine Learning: Understanding and applying various algorithms.

Big Data 52

Big Data Big Data Data Science Machine Learning 52

Dataconomy

JULY 24, 2023

Concepts such as linear algebra, calculus, probability, and statistical theory are the backbone of many data science algorithms and techniques. Coding skills are essential for tasks such as data cleaning, analysis, visualization, and implementing machine learning algorithms. Specializing can make you stand out from other candidates.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

Data Science Dojo

JULY 24, 2023

Different algorithms and techniques are employed to achieve eventual consistency. Hadoop Distributed File System (HDFS) : HDFS is a distributed file system designed to store vast amounts of data across multiple nodes in a Hadoop cluster. They use redundancy and replication to ensure data availability.

Big Data 195

Big Data Big Data Data Engineer Data Engineering 195

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Smart Data Collective

NOVEMBER 8, 2020

This will enable you to leverage the right algorithms to create good, well structured, and performing software. As such, you should begin by learning the basics of SQL. SQL is an established language used widely in data engineering. Just like programming, SQL has multiple dialects.

Data Engineer 104

Data Engineer Data Engineering Data Engineering Data Engineering 104

Pickl AI

OCTOBER 27, 2024

Enrolling in a Data Science course keeps you updated on the latest advancements, such as machine learning algorithms and data visualisation techniques. Students learn to work with tools like Python, R, SQL, and machine learning frameworks, which are essential for analysing complex datasets and deriving actionable insights1.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Data Science 101

JUNE 10, 2019

Data scientists are the bridge between programming and algorithmic thinking. The power of data science comes from a deep understanding of statistics,algorithms, programming, and communication skills. Hadoop, SQL, Python, R, Excel are some of the tools you’ll need to be familiar using. Data Scientists.

Data Science 96

Data Science Data Scientist Data Analyst Data Engineering 96

IBM Journey to AI blog

SEPTEMBER 19, 2023

Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists. And you should have experience working with big data platforms such as Hadoop or Apache Spark. Those who work in the field of data science are known as data scientists.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Pickl AI

DECEMBER 9, 2024

Familiarise yourself with essential tools like Hadoop and Spark. What are the Main Components of Hadoop? Hadoop consists of the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing data across distributed systems. What is the Role of a NameNode in Hadoop ? What is a DataNode in Hadoop?

Big Data 52

Big Data Big Data Hadoop Apache Kafka 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. ETL Tools: Apache NiFi, Talend, etc.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

JANUARY 13, 2025

Machine Learning Engineer Machine Learning Engineers develop algorithms and models that enable machines to learn from data. Strong understanding of data preprocessing and algorithm development. Proficiency in programming languages like Python and SQL. They explore new algorithms and techniques to improve machine learning models.

AI 52

AI AI Machine Learning Machine Learning 52

IBM Journey to AI blog

SEPTEMBER 25, 2023

This blog takes you on a journey into the world of Uber’s analytics and the critical role that Presto, the open source SQL query engine, plays in driving their success. This allowed them to focus on SQL-based query optimization to the nth degree. What is Presto? It also provides features like indexing and caching.”

Data Lakes 102

Data Lakes Analytics Analytics Clustering 102

Pickl AI

JULY 18, 2023

SQL: Mastering Data Manipulation Structured Query Language (SQL) is a language designed specifically for managing and manipulating databases. While it may not be a traditional programming language, SQL plays a crucial role in Data Science by enabling efficient querying and extraction of data from databases.

Data Science 52

Data Science SQL Data Scientist Python 52

Mlearning.ai

MAY 23, 2023

Familiarity with libraries like pandas, NumPy, and SQL for data handling is important. This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA). Additionally, knowledge of model evaluation, hyperparameter tuning, and model selection is valuable.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 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

IBM Journey to AI blog

JULY 6, 2023

Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention. It’s unnecessary to know SQL, as programs are written in R, Java, SAS and other programming languages.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Dataconomy

JULY 25, 2023

data visualization tools, machine learning algorithms, and statistical models to uncover valuable information hidden within data. Finance: In the financial sector, data science is used for fraud detection, risk assessment, algorithmic trading, and personalized financial advice.

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91

Pickl AI

MAY 29, 2024

These skills encompass proficiency in programming languages, data manipulation, and applying Machine Learning Algorithms , all essential for extracting meaningful insights and making data-driven decisions. Programming Languages (Python, R, SQL) Proficiency in programming languages is crucial.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

APRIL 6, 2023

In-depth knowledge of distributed systems like Hadoop and Spart, along with computing platforms like Azure and AWS. Having a solid understanding of ML principles and practical knowledge of statistics, algorithms, and mathematics. Hands-on experience working with SQLDW and SQL-DB. Knowledge in using Azure Data Factory Volume.

Azure 52

Azure Data Engineer Data Engineering Data Engineering 52

Pickl AI

FEBRUARY 28, 2023

Further, Data Scientists are also responsible for using machine learning algorithms to identify patterns and trends, make predictions, and solve business problems. Effectively, Data Analysts use other tools like SQL, R or Python, Excel, etc., Effectively, they analyse, interpret, and model complex data sets.

Data Analyst 52

Data Analyst Data Scientist Data Science Computer Science 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., Hadoop, Spark).

Data Engineer 40

Data Engineer Data Engineering Data Engineering Data Engineering 40

Women in Big Data

SEPTEMBER 7, 2024

Advances in big data technology like Hadoop, Hive, Spark and Machine Learning algorithms have made it possible to interpret and utilize this variety of data effectively. Examples include Excel files, SQL databases, and data warehouses. Variety Data comes in a myriad of formats including text, images, videos, and more.

Big Data 59

Big Data Big Data Database Machine Learning 59

Pickl AI

DECEMBER 4, 2024

The field has evolved significantly from traditional statistical analysis to include sophisticated Machine Learning algorithms and Big Data technologies. Issues such as algorithmic bias, data privacy, and transparency are becoming critical topics of discussion within the industry.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

ODSC - Open Data Science

JANUARY 7, 2025

Just as a writer needs to know core skills like sentence structure and grammar, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and soon. While knowing Python, R, and SQL is expected, youll need to go beyond that. Employers arent just looking for people who can program.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

Pickl AI

JUNE 19, 2024

Computer Science A computer science background equips you with programming expertise, knowledge of algorithms and data structures, and the ability to design and implement software solutions – all valuable assets for manipulating and analyzing data. Databases and SQL Data doesn’t exist in a vacuum.

Data Science 52

Data Science Data Scientist Hypothesis Testing Natural Language Processing 52

Pickl AI

JULY 24, 2023

Data science is an interdisciplinary field that combines scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. They employ advanced statistical modeling techniques, machine learning algorithms, and data visualization tools to derive meaningful insights.

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

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. Like their counterparts in the machine learning world, engineers need to know a variety of scripted languages such as SQL for database management, Scala, Java, and of course Python.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Pickl AI

JANUARY 28, 2025

The company employs advanced algorithms and BI tools to analyse vast amounts of data generated from user interactions across its 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

JULY 8, 2024

Database Extraction: Retrieval from structured databases using query languages like SQL. However, inefficient data processing algorithms and network congestion can introduce significant delays. API Integration: Accessing data through Application Programming Interfaces (APIs) provided by external services.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Pickl AI

MAY 31, 2023

Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines. To obtain practical expertise, run the algorithms on datasets. You should be skilled in using a variety of tools including SQL and Python libraries like Pandas.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Pickl AI

APRIL 2, 2024

Predictive Analytics: Forecasting future outcomes based on historical data and statistical algorithms. SQL (Structured Query Language): Language for managing and querying relational databases. Hadoop/Spark: Frameworks for distributed storage and processing of big data.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52

DagsHub

OCTOBER 23, 2024

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. This text has a lot of information, but it is not structured.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

FEBRUARY 23, 2025

These tools leverage advanced algorithms and methodologies to process large datasets, uncovering valuable insights that can drive strategic decision-making. Best Big Data Tools Popular tools such as Apache Hadoop, Apache Spark, Apache Kafka, and Apache Storm enable businesses to store, process, and analyse data efficiently.

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52

Dataconomy

MARCH 19, 2025

Predictive analytics utilizes statistical algorithms and machine learning to forecast future outcomes based on historical data. Additionally, biases in algorithms can lead to skewed results, highlighting the need for careful data validation. Roles of data professionals Various professionals contribute to the data science ecosystem.

Data Science 91

Data Science Citizen Data Scientist Data Scientist Machine Learning 91