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Is Forecasting A Part Of Data Science?

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Forecasting Data Science

Forecasting is the act of predicting or estimating (a future event or trend). Forecasting determines what will happen for businesses and analysts by evaluating what has happened in the past and what is happening today.

In data science, forecasting is the technique of forecasting or estimating future occurrences based on past and present data, most typically through trend analysis.

Data science is a broad phrase that involves anything from processing traditional or large amounts of data to explaining trends and predicting behavior.

You can use traditional approaches such as regression and cluster analysis and unconventional machine learning techniques in data science.

Data Science is a broad area, and we hope you get a better sense of how all-encompassing and entwined it is with human existence after reading this article. Forecasting is an essential part of data science, and we will elaborate on how.

Forecasting, or making predictions, is an important part of every company’s decision-making process if it wants to stay in business.

This is because today’s decisions, based on projections, influence tomorrow’s success.

What are the traditional forecasting methods in Data Science?

Traditional forecasting methods include traditional statistical forecasting approaches such as linear regression analysis, clustering, logistic regression analysis, time series, and factor analysis.

The results of each of them are fed into the more advanced machine learning analytics, but let’s look at them separately first.

Some of these approaches are also referred to as machine learning in the data science business; however, this article focuses on newer, smarter, and superior methods, such as deep learning. To know more about forecasting in data science, you can sign up for online data science courses from Great Learning.

Linear Regression:

The linear regression model is used in data science to quantify causal links among the variables included in the investigation.

As an example, consider the link between housing prices, house size, neighborhood, and year built.

If you have the necessary information, the model will produce coefficients that will allow you to anticipate the price of a new house.

Logistic Regression

Because you cannot express all interactions between variables as linear, data science uses methods such as logistic regression to develop non-linear models.

Logistic regression is based on 0s and 1s. Companies, for example, use logistic regression algorithms to filter job prospects during the screening process.

Cluster Analysis

When the observations present in the data form groups based on certain criteria, this exploratory data science approach is used.

Cluster analysis considers that certain observations have commonalities and promotes the discovery of additional relevant predictors not included in the initial data conceptualization.

You may make a similar contrast between predictive analytics and their methodologies: classical data science approaches vs. Machine Learning. One is concerned with traditional data, while the other is concerned with big data.

What is the difference between forecasting and predictive analysis?

Predictive analytics appears to be a game-changer for many businesses and demand planners.

Others see it as a more advanced variant of standard demand planning. Explanatory data analysis is the foundation of predictive analytics in data science.

Unlike traditional forecasting, which is based on statistics and uses level, trend, and seasonality data to predict results, predictive analytics is based on consumer behavior. It may employ explanatory factors to predict outcomes.

The basic difference between forecasting and predictive analysis is that forecasting is requirements-based, with limited demand factors, and forecasts sales. It also tells you what to order and is used when the relationship between variables is strong, and lastly, it provides output to one specific question.

On the other hand, predictive analysis is opportunity-oriented, with many factors; it predicts drivers and tells us why the consumer buys.

You can also use it to assess relationships between unknown variables, and lastly, it provides multiple insights and feasible solutions for the entire business.

Uses of traditional forecasting methods

The data scientist uses traditional forecasting methods, but it is to be kept in mind that this title also refers to someone who uses machine learning techniques for analytics. Much of the work is transferred from one approach to the next.

On the other hand, a data analyst is a person who conducts sophisticated sorts of studies that explain existing patterns in data and ignores the fundamental element of predictive analytics.

What are the programming languages in data science?

Knowing a programming language allows the data scientist to create programs that can do certain tasks.

The most significant advantage of programming languages is reusing programs designed to do the same activity several times.

When paired with SQL, R, Python, and MATLAB cover most of the tools used when working with traditional data, business intelligence, and traditional data science.

R and Python are the two most popular data science tools across all sub-disciplines. Their main benefit is that they can alter data and are compatible with various data and data science software systems.

They are appropriate for mathematical and statistical computations, but they are also flexible.

Of course, R and Python are used to manage big data in data science, but professionals in this field are typically fluent in other languages such as Java or Scala. These two are quite helpful when merging data from many sources.

Conclusion

One of the difficulties in forecasting is determining the number of recent occurrences you should consider when making predictions.

This also relies on whether you are deciding on the near or distant future. However, for multiple reasons, it is still a favorite method for many data analysts, and they prefer the traditional model of forecasting over newer methods such as predictive analysis.

Predictive analysis is concerned with predicting the outcomes of unobserved data. Forecasting is a sub-discipline of prediction in which we make future predictions based on time-series data.

For more information regarding data science & analytics, I recommend a PGP in data science & business analytics from Great Learning. They provide excellent online courses for the learner’s convenience.

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Google’s Fuchsia OS Set to Integrate with Android for Enhanced Efficiency

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Google's Fuchsia OS Set to Integrate with Android

Google Could Soon Introduce a Simplified Version of Fuchsia OS to Android. Google, known for its flagship operating systems Android and ChromeOS, has been quietly developing another operating system called Fuchsia OS since 2016. Initially launched on the first-generation Nest Hub in 2021, Fuchsia OS is now set to play a more significant role in Google’s ecosystem.

According to a report by Android Authority, Google developers are working on a simplified version of Fuchsia OS that can run on a virtual machine within Android devices. While the specifics of Fuchsia OS functionality in Android remain unclear, it is speculated that this integration could enhance efficiency and flexibility.

Fuchsia OS, an open-source operating system, is distinct from Android as it is not built on the Linux kernel but on Zircon, a microkernel. Google claims that the microkernel architecture can “reduce the amount of trusted code running in the system,” potentially offering a new level of security and stability.

Although it is unlikely that Fuchsia OS will replace Android or ChromeOS, it could serve as an alternative to Microdroid, a lighter version of Android designed for on-device virtual machines. By potentially replacing Microdroid, Fuchsia OS could improve workload performance and security.

In April, Google launched a project called Microfuchsia, aiming to make Fuchsia OS bootable on devices through virtualization. Recent patches submitted to the Android Open Source Project (AOSP) reference the Microfuchsia project, indicating ongoing development and integration efforts.

Google has already demonstrated the capabilities of running virtual machines on Android. The company showcased ChromeOS on a Pixel device, calling it Ferrochrome, to highlight Android 15’s virtualization capabilities.

Although Google later clarified that Ferrochrome was merely a proof of concept, the development of an app called “Ferrochrome launcher” suggests continued experimentation with virtualization on Android, potentially involving Fuchsia OS.

The future deployment of Fuchsia OS and its role in the development of Android and ChromeOS remains to be seen. However, it is evident that Fuchsia OS is poised to impact Google’s future operating system releases significantly.

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WhatsApp Develops Personalized AI Image Generator for User Avatars

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WhatsApp Develops Personalized AI Image Generator

WhatsApp is set to revolutionize user experience with an innovative AI feature that allows users to create AI-generated avatars of themselves in any imagined setting. This exciting development was uncovered by WABetaInfo in the latest WhatsApp Beta for the Android 2.24.14.13 update and has been further detailed by The Verge.

The new feature utilizes a combination of images, text prompts, and Meta’s AI Llama model to generate personalized images. According to a screenshot from WhatsApp, users can take photos of themselves once and then use text prompts such as “Imagine in…” or “@Meta AI imagine me…” to visualize themselves in various settings, from a dense forest to the expanse of outer space.

This feature, integrated into the Meta AI Chat, offers users the ability to create unique, personalized avatars by simply typing a prompt. The AI then uses the user’s likeness to generate the requested image, making for a highly customized and creative user experience.

While the exact release date for the wider rollout of this feature remains unclear, it is reported to be an optional addition to the app. This means users can choose whether or not to utilize the AI image generator.

In addition to this new AI feature, WhatsApp has recently introduced in-app custom stickers, which can be created in seconds, and has been developing its AI chat capabilities for several months. These updates indicate that Meta is committed to enhancing both AI integration and in-app creativity on WhatsApp.

With the upcoming AI image generator, WhatsApp continues to push the boundaries of user interaction and creativity, demonstrating a clear focus on leveraging artificial intelligence to enrich the user experience.

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Steam Introduces New Game Recording Features in Beta Testing

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Steam Introduces New Game Recording Features in Beta Testing

Valve is enhancing the gaming experience on Steam with the introduction of a new background gameplay recording and sharing feature, now available in beta. This update aims to provide gamers with console-like video recording capabilities directly within the Steam platform.

The new features include two recording modes, Background Recording and On-Demand Recording.

Background Recording automatically saves gameplay footage to a designated drive, allowing users to set specific duration and storage limits. On-demand recording gives players the flexibility to start and stop recording at their discretion.

To improve usability, Valve has introduced a timeline feature and the ability to add event markers, which helps players pinpoint significant moments in their gameplay. The system supports automatic marker generation for achievements, screenshots, and specific in-game events in supported titles.

Additionally, Valve has revamped the Recordings & Screenshots interface, providing “lightweight” tools for clipping and editing videos. A simplified sharing option allows players to post videos directly into chat sessions or to broader audiences with a single click. Videos can be shared across devices—from Steam Deck to PC, for instance—or sent as temporary MP4 links via the Steam Mobile App or a QR code.

A standout feature is the new Replay mode, accessible from the Steam Overlay, which lets players review their recent gameplay to analyze mistakes or revisit important game moments.

For developers, Valve offers a new SDK and API to integrate and enhance the recording features within their games.

Steam users interested in testing the beta can enable it by navigating to Settings > Interface > Beta Participation in their Steam client and selecting the Beta option. Game recording settings are adjustable via Settings > Game Recording.

Valve anticipates a public release following the beta phase, once all potential issues are resolved. This feature set represents a significant leap forward in making advanced gameplay recording accessible to the vast community of Steam gamers.

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