Mar 14, 2023
Unlocking Time’s Secrets: Discover how time series analysis sheds light on historical events, aiding in understanding their causes and effects.
Time Series and AI: Dive into the synergy of time series and artificial intelligence, enabling us to forecast stock prices, weather, and more.
Components Unveiled: Explore the four key components of time series analysis – Trend, Seasonality, Cyclical, and Irregularity, and their roles in deciphering data patterns.
When Not to Use It: Learn when time series analysis is not suitable for static data, small sample sizes, or nonlinear relationships.
Stationarity Matters: Delve into the significance of stationarity in time series, enabling accurate predictions and the use of statistical techniques.
Mar 13, 2023
AI strategy is an important concept for businesses looking to improve their results, better serve their customers, and differentiate themselves from the competition. Deloitte’s piece demonstrates how to build an AI strategy differently by approaching it from a new angle.
Mar 7, 2023
“Machine learning models, like humans, can learn patterns in data in a variety of ways. There are two main methods of learning: supervised and unsupervised learning. These learning methods, similar to humans, may be great for some use cases but may not be as effective when applied to other problems.”
“Supervised Learning: This training method involves feeding labeled data to the machine learning algorithm and allowing it to find patterns in the data. Labeled data is data that has a tag, or, better yet- a description. In essence, the algorithm understands the meaning of the data or its relevance.”
“Unsupervised Learning: Unsupervised learning is the polar opposite of supervised learning. Knowing this, you’ll understand that it entails training machine learning algorithms on unlabeled data. It is unlabeled because it has no tag or description. The goal of unsupervised learning is to find patterns in data and classify it into different sets based on similarities.”
“K-Means Clustering: K-Means clustering is an unsupervised machine learning algorithm that groups similar data points together into clusters based on similarities. The value of K determines the number of clusters. K-Means clustering is a form of partitional clustering, which separates a data set into sets of separate clusters.”
Mar 6, 2023
“Data literacy means more gains with fewer pains. This Precisely blog explains that, in order to achieve data literacy, organizations must overcome barriers to data literacy and engage in a program of data democratization.”
“Data literacy is the ability to read, write, and communicate data in context. This includes understanding data sources, analytical methods and techniques applied, and the ability to describe the use case, application, and resulting value.”
“To achieve data literacy and analytics skills, all employees should be familiar with creating, using, and communicating data across all critical business processes. Data literacy empowers team members to be more successful in their roles and makes cross-functional collaboration easier and more impactful.”
“Data Products partners with organizations to deliver deep expertise in data science, data strategy, data literacy, machine learning, artificial intelligence, and analytics. Our focus is on educating clients on varying aspects of data and modern technology, building up analytics skills, data competencies, and optimization of their business operations.”
Feb 28, 2023
“Data engineering is essential in the field of data science and analytics because it involves the process of transforming raw data into a format that can be easily analyzed and interpreted. Data engineers are in charge of creating and maintaining the data infrastructure on which data scientists and analysts can rely.”
“Difference Between Data Engineering and Data Science: Data science and data engineering are two distinct but interrelated disciplines. Data science involves using statistical and machine learning techniques to analyze and extract insights from data. Data engineering, on the other hand, is concerned with the design and management of the data infrastructure and systems that enable data science and analytics.”
“Data Engineering’s role in Data Science and Analytics: Data engineering is required for data science and analytics to be possible. Data engineers create and maintain the data infrastructure on which data scientists and analysts rely upon.”
“Data Engineering Best Practices: Data engineering requires a set of best practices to ensure data quality, reliability, and security. Collaboration across teams, effective use of tools and technologies, and a focus on data quality and security are characteristics of successful data engineering projects.”
“Data Engineering and the Future of Data Science and Analytics: Data engineering will be essential in the future of data science and analytics. As organizations rely more and more on data to inform their decision-making processes, effective data engineering will be critical to ensuring that data is collected, processed, and stored correctly.”
Feb 27, 2023
“AI Literacy is the ability to understand the basics of what an AI is, how it works, and what the strengths and limitations of the technologies are. The AI market is projected to be 190 billion by 2025, and the average human will interact with at least one AI in their daily life.”
“Building employee skills to eliminate literacy shortfalls can allow brands to achieve their strategic data-driven business goals and undertake AI projects successfully. Artificial intelligence is rapidly accelerating its adoption across industries, and we interact with it often without even realizing it.”
“Artificial intelligence (AI) has the potential to transform how we live, work, and interact with each other. AI has the potential to help tackle some of the toughest global challenges, including displacement, hunger, infectious disease outbreaks, and climate change.”
Feb 21, 2023
“Only 13% of data science projects make it to production, which means that 87% of models built never get deployed. The disconnect between data science and engineering is a major contributor to this.”
“Here’s a scenario to help you understand the importance of model deployment. Yua is an Osaka University student studying Computer Science in her Junior Year. Yua is interested in Machine Learning and Artificial Intelligence, and she decides to build a model that can answer questions when context is provided for her demo project.”
“The final code should look like this: [Code snippet provided]. Finally, save the file as main.py, open your terminal, and run python main.py to launch your flask application.”
“To deploy the app to AWS, you must first create and launch an instance. If you’re not sure how to set up an EC2 instance, you can follow this [step-by-step guide].”
“To keep your server online at all times, set gunicorn to restrart or reboot whenever the EC2 instance is restarted. [Code snippet provided].”
“To receive external requests, you’ll need a webserver like NGINX to accept and route them to Gunicorn. To get started with the NGINX server, [Instructions provided]. After completing these steps, restart NGINX with ‘sudo systemctl restart nginx’, and your application should be operational.”
“You successfully deployed a Hugging Face machine learning model on an AWS EC2 instance using Flask in this article. To learn more about AWS, you can do so by visiting their documentation here.”
Feb 20, 2023
“In the quest for digital transformation, staying true to your vision is paramount. Netflix’s enduring vision to make movie-watching easier and affordable serves as an inspiring example. As you embark on your digital transformation journey, keep this in mind.”
“Digital transformation demands innovation. Don’t limit yourself to conventional best practices; focus on solving modern customer problems. Your competitors may have their strategies, but your unique solutions could outshine them.”
“An open mind is your ally in digital transformation. Unexpected problems and hidden solutions await those who embrace flexibility. Follow Netflix’s lead in serving both the collective and individual needs of customers to build a better, stronger company.”
“Target’s success story emphasizes the importance of employees in digital transformation. Prioritizing employee well-being and work quality can be the key to a successful transformation journey.”
“Customers hold the keys to market insights. Prioritize customer feedback as you undergo digital transformation. Discover what truly matters to them for a more beneficial transformation.”
“Digital transformation is a journey that requires patience. Measure the impact of your actions carefully and make necessary adjustments to realize your vision with technology.”
Feb 13, 2023
“Virtual reality is revolutionizing data visualization, making complex data structures more understandable. Explore how VR can help your data storytelling and decision-making.”
“Virtualitics offers explainable AI solutions that simplify complex data and suggest optimal 3D visualizations. However, the future of VR and AR in data analytics hinges on improved headset resolution and innovative visualizations.”
“The demand for AR/VR data visualization is surging, but much of the world’s data remains untapped. Learn how immersive technology enhances data analysis and transforms decision-making.”
“Immersive data visualization through AR and VR is on the rise. These technologies elevate data research, predict future business outcomes, and foster collaborative exploration.”
“Discover the potential of AR and VR in data analytics. These immersive platforms help users step inside their data, making complex information more accessible and engaging.”
“Traditional 2D data tools struggle with today’s complex datasets. AR and VR technologies simplify data understanding, making it accessible to a broader audience, beyond data scientists.”
Feb 6, 2023
“Robotic process automation is reshaping business operations by automating tasks and reducing staffing costs. Learn how RPA, alongside cognitive technologies, is driving intelligent automation and its exponential market growth.”
“RPA projects are quick to implement, low-risk, and minimize operational errors. Discover how RPA can potentially replace a significant portion of full-time employees, leading to better data quality and change management.”
“Despite initial apprehensions, RPA is proving its worth in business. RPA software performs tasks efficiently, creates jobs, and adapts to business expansion. Plus, its implementation is straightforward and cost-effective.”
