Data Science Dojo

MAY 18, 2023

Distributed System Design for Data Engineering: This talk will provide an overview of distributed system design principles and their applications in data engineering. Getting Started with SQL Programming: Are you starting your journey in data science?

Data Science 195

Data Science AI AI SQL 195

Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

Smart Data Collective

NOVEMBER 8, 2020

This explains the current surge in demand for data engineers, especially in data-driven companies. That said, if you are determined to be a data engineer , getting to know about big data and careers in big data comes in handy. Similarly, various tools used in data engineering revolve around Scala.

Data Engineering 104

Data Engineering Data Engineering Data Engineer Data Engineering 104

Pickl AI

DECEMBER 25, 2024

Descriptive analytics is a fundamental method that summarizes past data using tools like Excel or SQL to generate reports. Techniques such as data cleansing, aggregation, and trend analysis play a critical role in ensuring data quality and relevance. Data Science, however, uses predictive and prescriptive solutions.

Data Science 52

Data Science Analytics Analytics Data Scientist 52

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. Read more to know.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

Data Warehouse 113

Data Warehouse Machine Learning Machine Learning Cloud Data 113

ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. While knowing Python, R, and SQL are expected, you’ll need to go beyond that.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

Dataconomy

JULY 24, 2023

Concepts such as linear algebra, calculus, probability, and statistical theory are the backbone of many data science algorithms and techniques. Programming skills A proficient data scientist should have strong programming skills, typically in Python or R, which are the most commonly used languages in the field.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

DataSeries

AUGUST 15, 2024

Enrich data engineering skills by building problem-solving ability with real-world projects, teaming with peers, participating in coding challenges, and more. Globally several organizations are hiring data engineers to extract, process and analyze information, which is available in the vast volumes of data sets.

Data Engineering 40

Data Engineering Data Engineer Data Engineering Data Engineering 40

Data Science Dojo

JULY 3, 2024

Data Visualization : Techniques and tools to create visual representations of data to communicate insights effectively. Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Ensure that the bootcamp of your choice covers these specific topics.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Applied Data Science

AUGUST 2, 2021

Team Building the right data science team is complex. With a range of role types available, how do you find the perfect balance of Data Scientists , Data Engineers and Data Analysts to include in your team? The Data Engineer Not everyone working on a data science project is a data scientist.

Data Science 130

Data Science Data Scientist ML ML 130

Data Science 101

JUNE 10, 2019

Data Scientists. Data scientists are the bridge between programming and algorithmic thinking. A data scientist can run a project from end-to-end. They can clean large amounts of data, explore data sets to find trends, build predictive models, and create a story around their findings. Data Engineers.

Data Science 96

Data Science Data Scientist Data Analyst Data Engineering 96

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

ODSC - Open Data Science

FEBRUARY 17, 2023

Cloud Computing, APIs, and Data Engineering NLP experts don’t go straight into conducting sentiment analysis on their personal laptops. NLTK is appreciated for its broader nature, as it’s able to pull the right algorithm for any job. Knowing some SQL is also essential.

Deep Learning 111

Deep Learning Deep Learning Data Science Natural Language Processing 111

ODSC - Open Data Science

MARCH 13, 2023

Machine learning practitioners tend to do more than just create algorithms all day. First, there’s a need for preparing the data, aka data engineering basics. Which of these related skills in machine learning are important to you? As the chart shows, two major themes emerged.

Machine Learning 94

Machine Learning Machine Learning Data Wrangling Data Science 94

Dataconomy

MAY 2, 2023

They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. What do machine learning engineers do? In the context of a business, machine learning engineers are responsible for creating bots that are utilized for chat purposes or data collection.

ML 110

ML ML Machine Learning Machine Learning 110

Data Science Dojo

OCTOBER 10, 2023

These events often showcase how AI is being practically applied across diverse sectors – from enhancing healthcare diagnostics to optimizing financial algorithms and beyond. However, in previous iterations of the summit, speakers have included prominent voices in data engineering and analytics. Link to event -> Live!

AI 243

AI AI Data Observability Artificial Intelligence 243

IBM Journey to AI blog

SEPTEMBER 19, 2023

Business users will also perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

AWS Machine Learning Blog

OCTOBER 9, 2024

Using Amazon CloudWatch for anomaly detection Amazon CloudWatch supports creating anomaly detectors on specific Amazon CloudWatch Log Groups by applying statistical and ML algorithms to CloudWatch metrics. Use AWS Glue Data Quality to understand the anomaly and provide feedback to tune the ML model for accurate detection.

AWS 94

AWS ML ML Data Quality 94

IBM Journey to AI blog

JULY 6, 2023

Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis. 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.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

AWS Machine Learning Blog

APRIL 19, 2023

We use Amazon SageMaker to train a model using the built-in XGBoost algorithm on aggregated features created from historical transactions. In our use case, we show how using SQL for aggregations can enable a data scientist to provide the same code for both batch and streaming. The application is written using Apache Flink SQL.

ML 98

ML ML Apache Kafka SQL 98

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. They employ statistical methods and machine learning techniques to interpret data.

AI 52

AI AI Machine Learning Machine Learning 52

ODSC - Open Data Science

SEPTEMBER 25, 2023

Tapping into these schemas and pulling out machine learning-ready features can be nontrivial as one needs to know where the data entity of interest lives (e.g., customers), what its relations are, and how they’re connected, and then write SQL, python, or other to join and aggregate to a granularity of interest.

Data Preparation 104

Data Preparation Machine Learning Machine Learning ML 104

ODSC - Open Data Science

AUGUST 15, 2023

Build Classification and Regression Models with Spark on AWS Suman Debnath | Principal Developer Advocate, Data Engineering | Amazon Web Services This immersive session will cover optimizing PySpark and best practices for Spark MLlib. Free and paid passes are available now–register here.

Machine Learning 52

Machine Learning Machine Learning Data Science Data Scientist 52

DataRobot

FEBRUARY 22, 2021

Specifically, 10X Academy students use DataRobot to develop the following skills: Problem framing Effectively working with Data Exploring data Modeling setups Feature engineering Developing algorithms Evaluating and understanding models Understanding bias, trust, and ethics Deploying models Time series modeling Working with multi-modal data.

Data Science 52

Data Science Data Scientist Citizen Data Scientist Data Analyst 52

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.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Dataconomy

APRIL 27, 2023

They are also designed to handle concurrent access by multiple users and applications, while ensuring data integrity and transactional consistency. Examples of OLTP databases include Oracle Database, Microsoft SQL Server, and MySQL. Final words Back to our original question: What is an online transaction processing database?

Database 159

Database Data Scientist Data Mining Data Mining 159

ODSC - Open Data Science

APRIL 3, 2023

While a data analyst isn’t expected to know more nuanced skills like deep learning or NLP, a data analyst should know basic data science, machine learning algorithms, automation, and data mining as additional techniques to help further analytics. Cloud Services: Google Cloud Platform, AWS, Azure.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Dataversity

JUNE 3, 2021

First and foremost, what, exactly, is Data Science? Data Science is a multidisciplinary field that uses processes, algorithms, and systems to obtain various insights coming from both structured and unstructured data. It is related to data mining, machine learning, and big data.

Data Science 76

Data Science Data Mining Data Mining Data Mining 76

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.), Ethical considerations: Data scientists must be mindful of the ethical implications of their work.

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91

Pickl AI

AUGUST 15, 2023

Coding Skills for Data Analytics Coding is an essential skill for Data Analysts, as it enables them to manipulate, clean, and analyze data efficiently. Programming languages such as Python, R, SQL, and others are widely used in Data Analytics. Ideal for academic and research-oriented Data Analysis.

Analytics 52

Analytics Analytics Data Analyst Data Analysis 52

ODSC - Open Data Science

APRIL 24, 2023

We use, and recommend, using a query engine in ML pipelines to save time and costs. Here you can find an example SQL query, with parameters we used in our experiments. The distances are then added to a distance matrix which is sent to a clustering algorithm. The algorithm returns clusters — in our example the detected botnets.

Clustering 52

Clustering SQL Algorithm Data Science 52

AWS Machine Learning Blog

JUNE 17, 2024

With SageMaker Processing jobs, you can use a simplified, managed experience to run data preprocessing or postprocessing and model evaluation workloads on the SageMaker platform. Twilio needed to implement an MLOps pipeline that queried data from PrestoDB. For more information on processing jobs, see Process data.

ML 121

ML ML AWS Machine Learning 121

phData

FEBRUARY 7, 2024

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python, Java, and Scala. A DataFrame is like a query that must be evaluated to retrieve data. An action causes the DataFrame to be evaluated and sends the corresponding SQL statement to the server for execution.

Python 52

Python ML ML SQL 52

ODSC - Open Data Science

APRIL 26, 2023

Though scripted languages such as R and Python are at the top of the list of required skills for a data analyst, Excel is still one of the most important tools to be used. 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.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 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.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40