Data Science
We are a leading Data Science and AI consulting company, dedicated to creating cutting-edge AI-driven services that unlock the true potential of your accumulated data. Our expertise in data science and artificial intelligence empowers your company to maximize the value derived from your data assets.
Business benefits
Data Science and Artificial Intelligence
Data Science Consulting Services
Discover the diverse range of Data Science consulting services offered by Datable.
Experience the unique benefits that implementing AI solutions can bring to your business.
Explore how Artificial Intelligence is revolutionizing various industries, including retail, eCommerce, manufacturing, finance, healthcare, marketing, and gaming.
Technologies
Our expert team develops tailor-made AI solutions using cutting-edge technologies.
We leverage advanced techniques such as Computer Vision, Natural Language Processing, Predictive Analytics, Image Recognition, Recommendation Engines, Smart Search Engines, and more.
Benefits
Embrace the self-learning capabilities and scalability of AI for your business advantage.
Leverage AI algorithms to cater to thousands of customers through SaaS applications.
Tap into the potential of AI's personalized recommendation systems, enhancing your marketplace offerings.
Join the majority of leading businesses that invest in AI for a competitive edge.
Our Team
Our dedicated data scientists maintain daily communication with our clients, ensuring your project is a top priority.
Benefit from our data science and AI experts' proficiency in solving complex business challenges with analytic algorithms.
Rely on us to design, build, and deploy predictive and prescriptive models using statistical modeling and optimization.
Trust our structured decision-making approach to deliver successful projects from issue identification to model maintenance in production.
Development process
Data Science as a Service
Your industry isn't here? That’s not a problem!
Your industry isn't here?
That’s not a problem!
Technologies
Tools and frameworks for AI solutions
- Programming Languages
- Database
- Frameworks
Python: Python is the most popular programming language in the Data Science field due to its straightforward and easy-to-read syntax. Its extensive community develops and maintains a wide range of libraries and frameworks specifically for Machine Learning and Artificial Intelligence. Python's versatility makes it a top choice for building ML models, as it caters to programming, data analysis, and visualization needs.
R: Designed specifically for machine learning applications, R comes with built-in statistical functions and an extensive library of data visualization tools. Its interactive console enables Data Scientists to explore data and experiment with various analyses and models in real time. R can be easily integrated with other tools and languages, making it a robust option for Data Science projects.
PySpark: PySpark is utilized for transforming data and enables the running of AI applications on distributed clusters, achieving performance speeds up to 100 times faster than traditional Python applications.
Hadoop: An open-source software framework, Hadoop stores data and runs applications on clusters of commodity hardware. It provides massive storage capacity for various data types and offers substantial processing power to handle numerous concurrent tasks or jobs.
TensorFlow: Developed by Google Brain Team, TensorFlow is an open-source machine learning library used for building and training machine learning models, including deep neural networks. Its high-level APIs, such as Keras, simplify the model-building process and come with visualization tools, pre-built models, and datasets.
PyTorch: Developed by Facebook's AI Research team, PyTorch is an open-source machine learning library that enables Data Scientists to prototype and deploy machine learning models using Python. It finds applications in computer vision, natural language processing, and reinforcement learning, among others.
AutoKeras – AutoKeras is an open-source python package written in the deep learning library Keras. AutoKeras uses a variant of ENAS, an efficient and most recent version of Neural Architecture Search.
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Key benefits
Ways that Data Science can improve your business
FAQ
How Do Big Tech Companies Leverage Data Engineering?
Many e-commerce giants use the power of data to create value for their businesses. Specific data allows you to attract potential customers and thereby significantly increase business profits. Nordstrom’s data engineers have developed a system for monitoring customer habits and behavior using Wi-Fi. The data obtained allowed the company to study the purchasing trends of its customers, which resulted in the optimization of personalized data and overall improved customer service. Data is being used by the company to optimize pricing, advertising, the supply chain, and even to decrease fraud. Amazon personalizes every interaction by using a large amount of client data.
Data Engineering vs. Data Science: What Sets Them Apart?
Data science and data engineering serve distinct roles in the data realm. Data science involves analyzing structured and unstructured data using statistics, applied science, and computer science to derive valuable insights for data-driven decision-making. On the other hand, data engineering is responsible for creating efficient data pipelines or procedures to transport data between instances, ensuring seamless data flow and accessibility for analysis. Together, they empower organizations to unlock the full potential of their data assets.
Is Data Engineering Necessary for Me?
Data engineering consulting is vital for businesses seeking to harness the power of data and improve performance. With experts’ guidance, companies can optimize data systems, derive valuable insights, and make data-driven decisions. Tailored solutions aligned with business objectives ensure streamlined processes and enhanced competitiveness in today’s data-rich landscape.
What is a Data Pipeline?
A data pipeline is a sequence of data processes that facilitate the extraction, processing, and loading of data from one system to another. It comes in two primary types: batch and real-time. Batch data pipelines process large volumes of data in chunks, making them suitable for non-time-sensitive tasks. On the other hand, real-time data pipelines handle data as it arrives, enabling near-instantaneous processing, ideal for time-sensitive applications and real-time decision-making. These data pipelines play a crucial role in managing and optimizing data movement, allowing businesses to leverage their data effectively for valuable insights and improved operations.
What does the future hold for Data Engineering?
The future of Data Engineering is promising, with continuous advancements in technology and increasing data complexities. As businesses gather and utilize more data, the demand for efficient data engineering solutions will grow. We can expect further automation, optimization, and scalability in data pipelines to accommodate large datasets and real-time processing. Additionally, Data Engineering will play a vital role in supporting the development of AI and machine learning applications, driving innovation and business success.
What does a Data Engineer do?
A Data Engineer is responsible for designing, constructing, and maintaining data pipelines and systems that enable the seamless flow of data between different stages of processing. They work with various data sources, ensuring data is extracted, transformed, and loaded efficiently to support data-driven decision-making and analysis. Data Engineers also play a crucial role in data infrastructure design and optimization, ensuring data accessibility and scalability. Their expertise is vital in organizing and managing data, enabling businesses to derive valuable insights and improve overall data operations.
Why is Data Engineering so important?
In the realm of data management, a Data Engineer holds a pivotal role. They are tasked with designing, constructing, and maintaining data pipelines and systems, ensuring a smooth flow of data through various processing stages. Working with diverse data sources, Data Engineers handle data extraction, transformation, and loading to facilitate data-driven decision-making and analysis. Additionally, their expertise extends to optimizing data infrastructure, ensuring accessibility and scalability for efficient data management. With their crucial contributions, businesses can leverage data insights effectively, fostering improved data operations and overall success.
Check out our blog and make sure you are keeping up with the last trends in your industry
Data Science vs. Data Engineering
What are the main tools and programming languages used in data engineering and data science?
Find out the main difference between these two fields.
Who is a Data Engineer?
The profession of Data Engineer was ranked as the fastest-growing tech job in 2019!
With the number of open positions up 50% compared to last year.
Data Lake Architecture
In this article, you can learn more about Data Lake Architecture.
Learn what are the key factors you should keep in mind when planning Data Lake Architecture.
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Addepto on the list of top 10 AI consulting companies by Forbes.
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
Our clients
Glossary
FAQ
How Do Big Tech Companies Leverage Data Engineering?
Many e-commerce giants use the power of data to create value for their businesses. Specific data allows you to attract potential customers and thereby significantly increase business profits. Nordstrom’s data engineers have developed a system for monitoring customer habits and behavior using Wi-Fi. The data obtained allowed the company to study the purchasing trends of its customers, which resulted in the optimization of personalized data and overall improved customer service. Data is being used by the company to optimize pricing, advertising, the supply chain, and even to decrease fraud. Amazon personalizes every interaction by using a large amount of client data.
Data Engineering vs. Data Science: What Sets Them Apart?
Data science and data engineering serve distinct roles in the data realm. Data science involves analyzing structured and unstructured data using statistics, applied science, and computer science to derive valuable insights for data-driven decision-making. On the other hand, data engineering is responsible for creating efficient data pipelines or procedures to transport data between instances, ensuring seamless data flow and accessibility for analysis. Together, they empower organizations to unlock the full potential of their data assets.
Is Data Engineering Necessary for Me?
Data engineering consulting is vital for businesses seeking to harness the power of data and improve performance. With experts’ guidance, companies can optimize data systems, derive valuable insights, and make data-driven decisions. Tailored solutions aligned with business objectives ensure streamlined processes and enhanced competitiveness in today’s data-rich landscape.
What is a Data Pipeline?
A data pipeline is a sequence of data processes that facilitate the extraction, processing, and loading of data from one system to another. It comes in two primary types: batch and real-time. Batch data pipelines process large volumes of data in chunks, making them suitable for non-time-sensitive tasks. On the other hand, real-time data pipelines handle data as it arrives, enabling near-instantaneous processing, ideal for time-sensitive applications and real-time decision-making. These data pipelines play a crucial role in managing and optimizing data movement, allowing businesses to leverage their data effectively for valuable insights and improved operations.
What does the future hold for Data Engineering?
The future of Data Engineering is promising, with continuous advancements in technology and increasing data complexities. As businesses gather and utilize more data, the demand for efficient data engineering solutions will grow. We can expect further automation, optimization, and scalability in data pipelines to accommodate large datasets and real-time processing. Additionally, Data Engineering will play a vital role in supporting the development of AI and machine learning applications, driving innovation and business success.
What does a Data Engineer do?
A Data Engineer is responsible for designing, constructing, and maintaining data pipelines and systems that enable the seamless flow of data between different stages of processing. They work with various data sources, ensuring data is extracted, transformed, and loaded efficiently to support data-driven decision-making and analysis. Data Engineers also play a crucial role in data infrastructure design and optimization, ensuring data accessibility and scalability. Their expertise is vital in organizing and managing data, enabling businesses to derive valuable insights and improve overall data operations.
Why is Data Engineering so important?
In the realm of data management, a Data Engineer holds a pivotal role. They are tasked with designing, constructing, and maintaining data pipelines and systems, ensuring a smooth flow of data through various processing stages. Working with diverse data sources, Data Engineers handle data extraction, transformation, and loading to facilitate data-driven decision-making and analysis. Additionally, their expertise extends to optimizing data infrastructure, ensuring accessibility and scalability for efficient data management. With their crucial contributions, businesses can leverage data insights effectively, fostering improved data operations and overall success.
Check out our blog and make sure you are keeping up with the last trends in your industry
Data Science vs. Data Engineering
What are the main tools and programming languages used in data engineering and data science?
Find out the main difference between these two fields.
Who is a Data Engineer?
The profession of Data Engineer was ranked as the fastest-growing tech job in 2019!
With the number of open positions up 50% compared to last year.
Data Lake Architecture
In this article, you can learn more about Data Lake Architecture.
Learn what are the key factors you should keep in mind when planning Data Lake Architecture.
How Do Big Tech Companies Leverage Data Engineering?
Many e-commerce giants use the power of data to create value for their businesses. Specific data allows you to attract potential customers and thereby significantly increase business profits. Nordstrom’s data engineers have developed a system for monitoring customer habits and behavior using Wi-Fi. The data obtained allowed the company to study the purchasing trends of its customers, which resulted in the optimization of personalized data and overall improved customer service. Data is being used by the company to optimize pricing, advertising, the supply chain, and even to decrease fraud. Amazon personalizes every interaction by using a large amount of client data.
Data Engineering vs. Data Science: What Sets Them Apart?
Data science and data engineering serve distinct roles in the data realm. Data science involves analyzing structured and unstructured data using statistics, applied science, and computer science to derive valuable insights for data-driven decision-making. On the other hand, data engineering is responsible for creating efficient data pipelines or procedures to transport data between instances, ensuring seamless data flow and accessibility for analysis. Together, they empower organizations to unlock the full potential of their data assets.
Is Data Engineering Necessary for Me?
Data engineering consulting is vital for businesses seeking to harness the power of data and improve performance. With experts’ guidance, companies can optimize data systems, derive valuable insights, and make data-driven decisions. Tailored solutions aligned with business objectives ensure streamlined processes and enhanced competitiveness in today’s data-rich landscape.
What is a Data Pipeline?
A data pipeline is a sequence of data processes that facilitate the extraction, processing, and loading of data from one system to another. It comes in two primary types: batch and real-time. Batch data pipelines process large volumes of data in chunks, making them suitable for non-time-sensitive tasks. On the other hand, real-time data pipelines handle data as it arrives, enabling near-instantaneous processing, ideal for time-sensitive applications and real-time decision-making. These data pipelines play a crucial role in managing and optimizing data movement, allowing businesses to leverage their data effectively for valuable insights and improved operations.
What does the future hold for Data Engineering?
The future of Data Engineering is promising, with continuous advancements in technology and increasing data complexities. As businesses gather and utilize more data, the demand for efficient data engineering solutions will grow. We can expect further automation, optimization, and scalability in data pipelines to accommodate large datasets and real-time processing. Additionally, Data Engineering will play a vital role in supporting the development of AI and machine learning applications, driving innovation and business success.
What does a Data Engineer do?
A Data Engineer is responsible for designing, constructing, and maintaining data pipelines and systems that enable the seamless flow of data between different stages of processing. They work with various data sources, ensuring data is extracted, transformed, and loaded efficiently to support data-driven decision-making and analysis. Data Engineers also play a crucial role in data infrastructure design and optimization, ensuring data accessibility and scalability. Their expertise is vital in organizing and managing data, enabling businesses to derive valuable insights and improve overall data operations.
Why is Data Engineering so important?
In the realm of data management, a Data Engineer holds a pivotal role. They are tasked with designing, constructing, and maintaining data pipelines and systems, ensuring a smooth flow of data through various processing stages. Working with diverse data sources, Data Engineers handle data extraction, transformation, and loading to facilitate data-driven decision-making and analysis. Additionally, their expertise extends to optimizing data infrastructure, ensuring accessibility and scalability for efficient data management. With their crucial contributions, businesses can leverage data insights effectively, fostering improved data operations and overall success.
Check out our blog and make sure you are keeping up with the last trends in your industry
Data Science vs. Data Engineering
What are the main tools and programming languages used in data engineering and data science?
Find out the main difference between these two fields.
Who is a Data Engineer?
The profession of Data Engineer was ranked as the fastest-growing tech job in 2019!
With the number of open positions up 50% compared to last year.
Data Lake Architecture
In this article, you can learn more about Data Lake Architecture.
Learn what are the key factors you should keep in mind when planning Data Lake Architecture.
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