Science Weapons of Outer Worlds :Where to find them ?

From the Shrink Ray to the Gloop Gun, here’s where to get every one of The Outer Worlds science weapons

When you get bored of just plain shooting people, The Outer Worlds Science Weapons provide some unique and interesting ways of making things dead. Admittedly they might not be the most lethal things in the game but they are the most fun, with a range of unique damage and status effects that are amplified by a high Science skill. There are five Outer Worlds Science Weapons to find in total. Some are easy to find, others a little fiddly but all make The Outer Worlds a much more interesting place. A bit screamy, but definitely more interesting.  

Though the full arsenal of Science Weapons can be tracked via the “Weapons from the Void” quest line in your journal, the particulars of achieving each objective in this multi-layered side mission aren’t always crystal clear. With that in mind, we’ve listed every available Science Weapons available in The Outer Worlds below, alongside information on where to find them, how to get them, what they do, and whether they’re worth keeping in your inventory. 

Science weapons on Ground breaker – Prismatic Hammer

science weapons on ground breaker - prismatic hammer

The Prismatic Hammer is one of the easiest The Outer Worlds Science Weapons to find but – luckily for you – it’s also one of the best. I used mine from the early hours of the game all the way up to the final mission, repeatedly tinkering away at the weapon on workbenches to keep it viable, while continually investing in my character’s Science skills to amplify its Knockdown effect. This melee weapon is capable of discharging  powerful energetic waves with each blow, perfect for area-of-effect attacks that can one-shot entire groups of enemies in a single swoop. In short, it’s a must find. 

To acquire the Prismatic Hammer, you’ll need to gain access to a secret engine room on Ground breaker, found on the West side of the ship (take the second left as you go through customs) and discovered by either lock picking your way through a door sat behind several freight boxes, or discovering a hole in the upper left corner of the crew’s quarters. From there, you’ll need to lock pick through a second door found on the north side of the engine room, and the Prismatic Hammer can be found within a lock box on the desk inside. 

Acquire Phineas Science Weapon – Shrink Ray

acquire phineas science weapon - shrink ray

The Outer Worlds shrink ray does exactly what it sounds like, but is unfortunately pretty useless as a long-term weapon for your spacefaring exploits, as targets are only kept shrunk for as long as you’re holding the trigger, which itself does minimal damage over time. Still, it’s certainly a novelty worth having in your inventory whenever you want to bring a particular tough foe or magnanimous NPC down to size, so here’s what you need to do to get it. 

The logs on The Unreliable’s terminal which active that Weapons from the Void quest will immediately tell you the location of the The Outer Worlds Shrink Ray, which is safely stowed away at Phineas’ orbital lab. You can travel to the lab as soon as you’ve found a power regulator for your ship on Terra 2. Head over to Phineas, and the Shrink Ray can be found situated clearly on the table in front of him. 

Science Weapons on Scylla – Mandibular Rearranger

science weapons on scylla - mandibular rearranger

The second, but much less memorable, melee tool in The Outer Worlds Science Weapons arsenal is the Mandibular Re-arranger; a reinforced baton which inflicts freeze damage upon targets, slowing them down with every hit to the point of complete stasis. Frankly, the Re-arranger has nothing on the Prismatic Hammer’s wide area of effect and high damage capacity, but it can be useful as a means of crowd control, keeping high-level enemies at bay while you whittle down any additional mobs.

To get this weapon, you’ll need to purchase the quest item identified as a Data pad from Gladys on the Ground breaker, at which point your journal will guide you to the a mining outpost on Scylla, an large asteroid which can be travelled to as soon as you’ve left Terra 2, with no need for a Navkey. You’ll need to follow your way point into one of the buildings at the outpost, and the Re-arranger is located within a safe on the floor, the first one you see once you open the doors. 

Science Weapons in the Abandoned Lab – Mind Control Ray

science weapons in abandoned lab - mind control ray

The Outer Worlds Mind Control Ray, like that of the Shrink Ray, live up to its namesake, forcing anyone caught in its crossfire to attack their allies. Unlike that shrink ray, however, the damage this weapon deals is significant, making it just as good as a standard firearm as it is a fun way to pit enemies against each other. Sure, it makes a really annoying jingle whenever your finger is on the trigger, but it’s a small price to pay for the mayhem that can be unleashed with this nifty piece of tech. 

To find this weapon, you’ll need to purchase the data pad quest item from Duncan in Fallbrook, which is a city on the East side of Monarch. You’ll naturally acquire a Navkey which allows your ship to travel to the city of Stellar Bay on this planet during the events of the campaign, and Duncan’s shop is on the right hand side of Fallbrook’s main row of shops as you enter. Acquire this data pad, and your way point will highlight an abandoned lab in the nearby, creature-infested town of Cascadia, at which point you’ll find the Mind Control Ray lying on a table in the final room.

Science Weapon on Monarch – Gloop Gun

science weapon on monarch - gloop gun

The Outer Worlds Gloop Gun vomits out radioactive globules of unidentifiable blue mush from its handheld dispensary, the splatter effect of which both shocks and damages those caught in the crossfire, while levitating them in the air for a short period of time. It’s easily one of the most powerful weapons in the game, and great for scoring some cinematic in-air kills via The Outer Worlds’ tactical time dilation mechanic, so you’re going to want to get your hands on it.

As with the Mind Control Ray, you’ll need to purchase the Data pad Quest Item from Duncan in Fallbrook on Monarch, before your way point will show you the location of the weapon at a nearby research facility on the same planet. Fight your way into the building, log in to the highlighted terminal to unlock access to the Gloop Gun’s chambers, and this high-powered hand cannon is yours for the taking. 

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Dolby Audio vs IMAX ? Which one is better

First of all, we have few sound codecs for cinemas. Let us leave about 2 channel stereo sound, the traditional 5.1 channel sound has been so popular.
Dolby’s AC-3 also known as Dolby Digital and its rival from DTS which is DTS 5.1
Dolby’s AC-3 supports bit-rates upto 640kbps, where as DTS gives 1596kpbs.
And then Dolby came up with a compressed loss less audio format Dolby True HD with bit-rates varying from 15 to 18 Mbps where as DTS’s DTA HD MA gives 18 – 24 Mbps. Both supports upto 7.1 channel sound system.
Coming to our original question, now the companies Barco, Dolby and DTS has come up with their own immersive audio codecs and solutions that are Auro 3D, Atmos and X respectively.

Dolby Audio and Auro 3D

auro 3d

It is designed along three layers of sound (surround, height and overhead ceiling), building on the single horizontal layer used in the 5.1 or 7.1 sound format. Auro-3D creates a spatial sound field by adding a height layer around the audience on top of the traditional 2D surround sound system. This additional layer reveals both localized sounds and height reflections complementing the sounds that exist in the lower surround layer. The height information that is captured during recording is mixed into a standard 5.1 surround PCM carrier, and during playback the Auro-3D decoder extracts the originally recorded height channels from this stream.

AuroMax expands on the basic layout used by Auro 11.1 and Auro 13.1 by dividing the side, rear and ceiling channels into “zones”, to allow for placement of sound at discrete points along the theatre wall or ceiling as well as within the theatre itself. The principle employed is similar to other object based formats such as Dolby Atmos or DTS:X.

The Auro-3D technology consists of the Auro-3D Engine and a Creative Tool Suite. The engine comprises the Auro-Codec and the Auro-Matic upmixing algorithm to convert legacy content into the Auro-3D format. The Creative Tool Suite is a set of plugins that can be used to create native immersive 3D audio content. Auro-3D is fully compatible with all existing production processes and theatre systems, and the format also offers a host of compatibility features such as Single Inventory Distribution (multiple formats are combined in one PCM carrier) and full DCI compliancy.

Coming to, Dolby Atmos and DTS:X are object based/oriented. Dolby Atmos contains 7.1 channel bed + virtually unlimited number of audio objects. DTS:X (32 speaker channels supported as of now) which is the most scalable, purely object oriented flexible audio codec recently been addressed by DTS. DTS:X doesn’t require any specifica speaker configuration. It can be accostomed any other immersive formats including Atmos and Auro.

Dolby Atmos Audio

dolby atmos audio

Dolby Atmos is a surround sound technology developed by Dolby Laboratories. It expands on existing surround sound systems by adding height channels, allowing sounds to be interpreted as three-dimensional objects. Following the release of Atmos for the cinema market, a variety of consumer technologies have been released under the Atmos brand, using in-ceiling and up-firing speakers.

DTS: X Audio

dolby dts x audio

DTS:X allows the “location” (direction from the listener) of “objects” (audio tracks) to be specified as polar coordinates. The audio processor is then responsible for dynamically rendering sound output depending on the number and position of speakers available. Dolby Atmos uses a similar technique, although the speaker layout employed by cinema DTS:X is the sum of Dolby Atmos and Auro-3D. The layout showcased at AMC Burbank theatre number 8 has a standard eight channel base layer, a five channel height layer on top of the base layer (on the front and side walls) and three rows of speakers on the ceiling. The surround arrays are bass managed by woofers suspended from the ceiling.

And now comparing IMAX to Dolby audio

dolby audio vs imax
(PR Newsfoto/ IMAX Corporation)

IMAX uses their own 6 channel sound solution, unlike Dolby’s AC-3 or DTS’s DTS5.1. IMAX says its sound is about 12000@W RMS power.
IMAX was planning to launch its own immersive audio (3D sound).
DTS:X is drawn from SRS lab’s MDA (multi dimentional audio). MDA facilitates mix once use anywhere feature. Where as Dolby Atmos cinemas mix is different from home or Blu-ray mix. But MDA files can be used in cinemas, satellite broadcaseting, Blu-ray discs and even in DVDs.
To prove the above, now it has been shout out that, IMAX uses MDA for its future movies. IMAX’s immersive sound is not equal to DTS:X but DTS:X and IMAX immersive sound is taken from a common core audio codec file that is MDA.
So far now, we can say Dolby Atmos is better sounding environment when compared to current IMAX’s 6channel sound. But once MDA is adopted to IMAX, it will definitely takes on Dolby Atmos.
Finally I can say, DTS:X will be the winner among the other (Auro3D, Dolby Atmos and IMAX immersive audio taken from MDA).
* Immersive audio consists, ear level and height channels.

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One plus 8 pro specifications, features, release date

What is Data Science?

What is Data Science ? A Student’s Guide to Data Science

As the world entered the era of big data, the need for its storage also grew. It was the main challenge and concern for the enterprise industries until 2010. The main focus was on building framework and solutions to store data. Now when Hadoop and other frameworks have successfully solved the problem of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science. Data Science is the future of Artificial Intelligence. Therefore, it is very important to understand what is Data Science and how can it add value to your business.Edureka 2019 Tech Career Guide is out! Hottest job roles, precise learning paths, industry outlook & more in the guide. Download now.

In this blog, I will be covering the following topics.

  • The need for Data Science.
  • What is Data Science?
  • How is it different from Business Intelligence (BI) and Data Analysis?
  • The lifecycle of Data Science with the help of a use case.

By the end of this blog, you will be able to understand what is Data Science and its role in extracting meaningful insights from the complex and large sets of data all around us. To get in-depth knowledge on Data Science, you can enroll for live Data Science online course by Edureka with 24/7 support and lifetime access.

Let’s Understand Why We Need Data Science

  • Traditionally, the data that we had was mostly structured and small in size, which could be analyzed by using the simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. Let’s have a look at the data trends in the image given below which shows that by 2020, more than 80 % of the data will be unstructured.

    This data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are not capable of processing this huge volume and variety of data. This is why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.

This is not the only reason why Data Science has become so popular. Let’s dig deeper and see how Data Science is being used in various domains.

  • How about if you could understand the precise requirements of your customers from the existing data like the customer’s past browsing history, purchase history, age and income. No doubt you had all this data earlier too, but now with the vast amount and variety of data, you can train models more effectively and recommend the product to your customers with more precision. Wouldn’t it be amazing as it will bring more business to your organization?
  • Let’s take a different scenario to understand the role of Data Science in decision making. How about if your car had the intelligence to drive you home? The self-driving cars collect live data from sensors, including radars, cameras and lasers to create a map of its surroundings. Based on this data, it takes decisions like when to speed up, when to speed down, when to overtake, where to take a turn – making use of advanced machine learning algorithms.
  • Let’s see how Data Science can be used in predictive analytics. Let’s take weather forecasting as an example. Data from ships, aircrafts, radars, satellites can be collected and analyzed to build models. These models will not only forecast the weather but also help in predicting the occurrence of any natural calamities. It will help you to take appropriate measures beforehand and save many precious lives.

Let’s have a look at the below infographic to see all the domains where Data Science is creating its impression.

Data Science Use Cases - Edureka

Now that you have understood the need of Data Science, let’s understand what is Data Science.

What is Data Science?

Use of the term Data Science is increasingly common, but what does it exactly mean? What skills do you need to become Data Scientist? What is the difference between BI and Data Science? How are decisions and predictions made in Data Science? These are some of the questions that will be answered further.

First, let’s see what is Data Science. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years?

The answer lies in the difference between explaining and predicting. 

Data Analyst v/s Data Science - Edureka

As you can see from the above image, a Data Analyst usually explains what is going on by processing history of the data. On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.

So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.

  • Predictive causal analytics – If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.
  • Prescriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
    The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up.
  • Machine learning for making predictions — If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.
  • Machine learning for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
    Let’s say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.

Let’s see how the proportion of above-described approaches differ for Data Analysis as well as Data Science. As you can see in the image below, Data Analysis includes descriptive analytics and prediction to a certain extent. On the other hand, Data Science is more about Predictive Causal Analytics and Machine Learning.Powered by Edureka

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Data Science Analytics - Edureka

I am sure you might have heard of Business Intelligence (BI) too. Often Data Science is confused with BI. I will state some concise and clear contrasts between the two which will help you in getting a better understanding. Let’s have a look.

Business Intelligence (BI) vs. Data Science

  • BI basically analyzes the previous data to find hindsight and insight to describe the business trends. BI enables you to take data from external and internal sources, prepare it, run queries on it and create dashboards to answer the questions like quarterly revenue analysis or business problems. BI can evaluate the impact of certain events in the near future.
  • Data Science is a more forward-looking approach, an exploratory way with the focus on analyzing the past or current data and predicting the future outcomes with the aim of making informed decisions. It answers the open-ended questions as to “what” and “how” events occur.

Let’s have a look at some contrasting features.

FeaturesBusiness Intelligence (BI)Data Science
Data Sources Structured
(Usually SQL, often Data Warehouse)
 Both Structured and Unstructured( logs, cloud data, SQL, NoSQL, text)
ApproachStatistics and VisualizationStatistics, Machine Learning, Graph Analysis, Neuro- linguistic Programming (NLP)
FocusPast and PresentPresent and Future
ToolsPentaho, Microsoft BI, QlikView, RRapidMiner, BigML, Weka, R

This was all about what is Data Science, now let’s understand the lifecycle of Data Science.

A common mistake made in Data Science projects is rushing into data collection and analysis, without understanding the requirements or even framing the business problem properly. Therefore, it is very important for you to follow all the phases throughout the lifecycle of Data Science to ensure the smooth functioning of the project.

Lifecycle of Data Science

Here is a brief overview of the main phases of the Data Science Lifecycle:

Lifecycle of Data Science - Edureka
Discovery of Data Science - Edureka

Phase 1—Discovery: 
Before you begin the project, it is important to understand the various specifications, requirements, priorities and required budget. You must possess the ability to ask the right questions. Here, you assess if you have the required resources present in terms of people, technology, time and data to support the project. In this phase, you also need to frame the business problem and formulate initial hypotheses (IH) to test.

Data Science data preparation - Edureka

Phase 2—Data preparation:In this phase, you require analytical sandbox in which you can perform analytics for the entire duration of the project. You need to explore, preprocess and condition data prior to modeling. Further, you will perform ETLT (extract, transform, load and transform) to get data into the sandbox. Let’s have a look at the Statistical Analysis flow below.

You can use R for data cleaning, transformation, and visualization. This will help you to spot the outliers and establish a relationship between the variables. Once you have cleaned and prepared the data, it’s time to do exploratory analytics on it. Let’s see how you can achieve that.

Data Science model planning - Edureka

Phase 3—Model planning:Here, you will determine the methods and techniques to draw the relationships between variables. These relationships will set the base for the algorithms which you will implement in the next phase. You will apply Exploratory Data Analytics (EDA) using various statistical formulas and visualization tools.

  Let’s have a look at various model planning tools.

Model planning tools in Data Science - Edureka
  1. R has a complete set of modeling capabilities and provides a good environment for building interpretive models.
  2. SQL Analysis services can perform in-database analytics using common data mining functions and basic predictive models.
  3. SAS/ACCESS  can be used to access data from Hadoop and is used for creating repeatable and reusable model flow diagrams.

Although, many tools are present in the market but R is the most commonly used tool.

Now that you have got insights into the nature of your data and have decided the algorithms to be used. In the next stage, you will apply the algorithm and build up a model.Powered by Edureka

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Data Science model building - Edureka

Phase 4—Model building: In this phase, you will develop datasets for training and testing purposes. You will consider whether your existing tools will suffice for running the models or it will need a more robust environment (like fast and parallel processing). You will analyze various learning techniques like classification, association and clustering to build the model.

You can achieve model building through the following tools.

Model building tools in Data Science
Data Science operationalize - Edureka

Phase 5—Operationalize:In this phase, you deliver final reports, briefings, code and technical documents. In addition, sometimes a pilot project is also implemented in a real-time production environment. This will provide you a clear picture of the performance and other related constraints on a small scale before full deployment.

Communication in Data Science - Edureka

Phase 6—Communicate results: 
Now it is important to evaluate if you have been able to achieve your goal that you had planned in the first phase. So, in the last phase, you identify all the key findings, communicate to the stakeholders and determine if the results of the project are a success or a failure based on the criteria developed in Phase 1.

Case Study: Diabetes Prevention

What if we could predict the occurrence of diabetes and take appropriate measures beforehand to prevent it?
In this use case, we will predict the occurrence of diabetes making use of the entire lifecycle that we discussed earlier. Let’s go through the various steps.

Step 1:

  • First, we will collect the data based on the medical history of the patient as discussed in Phase 1. You can refer to the sample data below.
Data Science sample data - Edureka
  • As you can see, we have the various attributes as mentioned below.


  1. npreg     –   Number of times pregnant
  2. glucose   –   Plasma glucose concentration
  3. bp          –   Blood pressure
  4. skin        –   Triceps skinfold thickness
  5. bmi        –   Body mass index
  6. ped        –   Diabetes pedigree function
  7. age        –   Age
  8. income   –   Income

Step 2:

  • Now, once we have the data, we need to clean and prepare the data for data analysis.
  • This data has a lot of inconsistencies like missing values, blank columns, abrupt values and incorrect data format which need to be cleaned.
  • Here, we have organized the data into a single table under different attributes – making it look more structured.
  • Let’s have a look at the sample data below.
Data Science inconsistent data - Edureka

This data has a lot of inconsistencies.

  1. In the column npreg, “one” is written in words, whereas it should be in the numeric form like 1.
  2. In column bp one of the values is 6600 which is impossible (at least for humans) as bp cannot go up to such huge value.
  3. As you can see the Income column is blank and also makes no sense in predicting diabetes. Therefore, it is redundant to have it here and should be removed from the table.
  • So, we will clean and pre-process this data by removing the outliers, filling up the null values and normalizing the data type. If you remember, this is our second phase which is data pre-processing.
  • Finally, we get the clean data as shown below which can be used for analysis.
Data Science consistent data - Edureka

Step 3:

Now let’s do some analysis as discussed earlier in Phase 3.

  • First, we will load the data into the analytical sandbox and apply various statistical functions on it. For example, R has functions like describe which gives us the number of missing values and unique values. We can also use the summary function which will give us statistical information like mean, median, range, min and max values.
  • Then, we use visualization techniques like histograms, line graphs, box plots to get a fair idea of the distribution of data.
Data Science visualization - Edureka

Step 4:

Now, based on insights derived from the previous step, the best fit for this kind of problem is the decision tree. Let’s see how?

  • Since, we already have the major attributes for analysis like npreg, bmi, etc., so we will use supervised learning technique to build a model here.
  • Further, we have particularly used decision tree because it takes all attributes into consideration in one go, like the ones which have a linear relationship as well as those which have a non-linear relationship. In our case, we have a linear relationship between npreg and age, whereas the nonlinear relationship between npreg and ped.
  • Decision tree models are also very robust as we can use the different combination of attributes to make various trees and then finally implement the one with the maximum efficiency.

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Here, the most important parameter is the level of glucose, so it is our root node. Now, the current node and its value determine the next important parameter to be taken. It goes on until we get the result in terms of pos or neg. Pos means the tendency of having diabetes is positive and neg means the tendency of having diabetes is negative.

If you want to learn more about the implementation of the decision tree, refer this blog How To Create A Perfect Decision Tree

Step 5:

In this phase, we will run a small pilot project to check if our results are appropriate. We will also look for performance constraints if any. If the results are not accurate, then we need to replan and rebuild the model.

Step 6:

Once we have executed the project successfully, we will share the output for full deployment.

Being a Data Scientist is easier said than done. So, let’s see what all you need to be a Data Scientist.  A Data Scientist requires skills basically from three major areas as shown below.

Data Science skills - Edureka

As you can see in the above image, you need to acquire various hard skills and soft skills. You need to be good at statistics and mathematics to analyze and visualize data. Needless to say, Machine Learning forms the heart of Data Science and requires you to be good at it. Also, you need to have a solid understanding of the domain you are working in to understand the business problems clearly. Your task does not end here. You should be capable of implementing various algorithms which require good coding skills. Finally, once you have made certain key decisions, it is important for you to deliver them to the stakeholders. So, good communication will definitely add brownie points to your skills.

l hope you enjoyed reading this blog and understood what is Data Science. Check out this Data Science certification training here, that comes with instructor-led live training and real-life project experience.

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One Plus 8 pro

One Plus 8 Pro Specifications, Features, Release Date

One Plus closed out its 2019 smartphone releases with the One Plus 7T and One Plus 7T Pro. However, we’re already hearing murmurings of the company’s next flagship. Yes, you are thinking right!! It’s all new One Plus 8 Pro.

one plus 8 pro design
Credits: 91 Mobiles

One Plus closed out its 2019 smartphone releases with the One Plus 7T and One Plus 7T Pro. However, we’re already hearing murmurings of the company’s next flagship. Yes, you are thinking right!! It’s all new One Plus 8 Pro.

This Article includes all the expected specifications and features of this phone and what will be the price of One Plus 8 Pro.

One Plus 8 Pro Name and Release Date

one plus 8 pro first look
Credits: 91 Mobiles

Given One Plus’ naming convention for its smartphones, we have every reason to believe the company’s flagship will be called the One Plus 8 Pro. The name lends some space for a regular One Plus 8, since we’ve seen “Pro” versions of the One Plus 7 and One Plus 7T.

As for a release date, One Plus normally announces its non-T phones during the springtime. The most recent non-T variant, the One Plus 7 Pro, launched in May 2019. As such, the One Plus 8 Pro could be poised for a spring 2020 launch.

One Plus 8 Pro Design


According to a leak from OnLeaks and 91Mobiles, the OnePlus 8 Pro won’t look too dissimilar from its predecessor. One main difference is with the punch-hole display, a departure from the One Plus 7 Pro and 7T Pro’s pop-up selfie camera.

Also new is the alleged addition of a 3D Time-of-Flight (ToF) sensor. We’ve seen the sensor on phones like the Huawei Mate 30 series, LG G8 ThinQHuawei P30 ProSamsung Galaxy S10 5G, and more. The sensor is used to calculate depth and distance, as well as help with 3D imaging and AR.

The leak also showed a triple camera setup, a speaker grille next to the USB-C port, and an LED flash below the rear cameras.

Finally Specifications and features

one plus 8 pro front view

According to a cryptic tweet from tipster Max J., the One Plus 8 Pro’s display will have a 120Hz refresh rate. Up until now, One Plus smartphones since the One Plus 7 Pro had 90Hz refresh rates.

We haven’t heard much else to substantiate Max J.’s claims, though 120Hz panels are becoming increasingly common among gaming smartphones. We could see a battery increase to help power the display, but we haven’t heard much about of its specs.

That’s all we know about the OnePlus 8 Pro so far. Let us know in the comments below if we missed anything or what you’re looking forward to the most from One Plus next smartphone!