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Data science is one of the hottest industries these days, given the massive amounts of data flowing into companies of all sizes and across all sectors.

From product sales to material usage and employee work hours, among others, the variety of data is immense, and this data could be mined for valuable insights to guide the strategic choices of companies.

Data science enthusiasts had a great year in 2023. The data science industry could do even better in 2023, touching global revenues of USD 274 billion (2023: USD 189 billion) and growing at 13.1% annually.

The fourth industrial revolution will further accelerate the demand for data science professionals by creating millions of new data science jobs in the coming years.

The Demand for Data Science Jobs in 2023

This massive sprawl will bring:

Job opportunities for data scientists, data engineers, data analysts, analytics specialists, consultants, insights analysts, analytics consultants, and more

Lower costs, higher efficiencies, uncovering new markets, and an edge over competitors in the market

The prospects for the data science industry are bright. Every day, our world generates 2.5 quintillion bytes of data. This could add up to 5.2 zettabytes by 2025, which must all be analyzed.

Also read: What Is Forex Trade? 5 Untold Forex Trading Benefits + Expert Tips For Higher Forex Profit

The following trends were seen in big data in 2023

Big data analysis got a great push from artificial intelligence (AI) and machine learning (ML).

Acquisitions in the data space moved to core products instead of only adding features incrementally.

Only those companies received funding who innovated more and generated more value than others.

Producing use cases for big data became simple with frameworks for automation.

A large number of data science jobs points to the increasing demand for big data and analytics professionals, further driving the need for distinctive skill sets.

A report from the Royal Society, an independent scientific academy of the UK and the Commonwealth, said that the demand for workers with data skills grew by 231% over the past five years.

Globally, according to IBM, around 28% of the total jobs in 2023 will be data science jobs.

The US Bureau of Labor Statistics (BLS) believes these jobs will grow by at least 19% by 2026.

The data science industry has been hiring an exponentially larger number of professionals every year, making it very popular. About 400,000 new jobs came up during 2023-2023, in particular for administrators, analysts, architects, and engineers.

Along with this growth in job openings, average salary levels for professionals in big data and analytics went up in the same period by 28%.

But there is a serious shortage of the relevant skill sets, which could well disrupt the disruption dream! According to ‘The Quant Crunch’ report from IBM, machine learning, big data, and data science skills are the most challenging to recruit for, and can potentially create the greatest disruption if not filled.

The current skill development rate for data science professionals stands at a slackened 14%.

On average, a data science job remains vacant for 45 days due to a talent crunch.

In a 2023 survey of 3,000 technology leaders, KPMG found that the “Data Analytics Expert” role was the hardest to fill.

With the ever-widening demand and supply gap, this period is more than likely to extend much further. Already, according to LinkedIn, the total number of big data professionals is 2,186,308 worldwide. Against it, approximately 660,528 job postings were made in 2023.

Looking ahead, the demand for big data and data science skills is likely to continue to rise in 2023. Information technology and services could be picking up 44% of the professionals hired in this period (2023: 36%).

A smaller part, though significant, will go to sectors such as financial services, recruitment, and software.

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Top 10 Highest Paying Data Science Jobs In 2023

These paying data science jobs in 2023

There is a rising interest in data science experts all over the planet. These open positions would keep on flooding past 2023, adding more than 1.5 lakh new positions. This pattern is a characteristic reaction to information being a significant asset for associations in the digital age. A survey recorded the main 10 most lucrative data science occupations in India. Here is the list of the top 10 highest paying data science jobs in 2023:  

What Does Data Science Involve?

Data science includes gathering, manipulating, storing, and analysing data. It works with data-driven approaches for decision-making, thus fostering an environment of continuous growth. Amazon’s online shopping site fills in as a prime example of how data collection can further develop execution. Amazon tweaks the landing page perspectives on clients depending on what they search, buy, and spend. As such, it recalls datasets and gives valuable item proposals to fit client needs.  

Infrastructure Architect

Role: An infrastructure architect oversees the existing business systems to ensure that they support the new technological requirements. Nowadays, organizations also hire cloud infrastructure architects to supervise their cloud strategies. Preferred qualifications: A degree in computer engineering or software development with adequate training in database administration, information system development, and system maintenance. Infrastructure architect has become one of the highest salary data science jobs in India due to its demand. Salary:25, 00,000 INR  

Enterprise Architect

Job: As an enterprise architect, the duties incorporate adjusting the organization’s procedure to innovative solutions. You assist organizations with accomplishing their destinations by recognizing needs and afterward planning architecture design to meet explicit requirements. Preferred qualifications: A bachelor-level education combined with a master’s degree and field instruction in enterprise architecture can assist you with entering the labor force as an enterprise architect. The exorbitant and developing demand makes enterprise architects land on one of the highest salary data science occupations in India. Salary:24,81,452 INR  

Applications Architect

Role: These practitioners track applications, supervising how they are operating within the company and how users are interacting with them. As the job title suggests, their job is to build the architecture of applications, replete with components like the user interface and app infrastructure. In addition to being one of the highest-paid data science jobs in India, this is also a fast-paced one. Preferred qualifications: To qualify for an opening for applications architect, you would generally need a computer science degree, along with industry certifications in programming and architectural design. The excessive & growing demand makes application architects land one of the highest salary data science jobs in India. Salary:24,00,000 INR  

Data Architect

Role: One of the highest-paid data science occupations around the world, a data architect makes new data set frameworks, use performance, and plan examination to further develop the interconnected information biological system inside the organization. The ultimate objective is to make the data effectively available for use by information researchers. It has forever been probably the best datum science occupation in India, and managing cash – yours and others – is the stuff of dreams. Preferred qualifications: To turn into an information modeler, you would require a computer engineering education with adequate control over applied mathematical and statistical ideas. Ideally, you ought to have finished coursework in subjects like data management, programming, big data development, system analytics, technology architecture. Salary:20,06,452 INR  

Data Scientist

Role: It is a more technical position than a data analyst. Data scientists might perform data preparation tasks (cleaning, putting together, etc) that permit companies to make key moves. They handle large datasets and uncover valuable trends and patterns in the data. Preferred qualifications: A master’s degree or progressed capabilities, for example, PhD is alluring for the assignment of a data scientist. Some of the time, organizations look for area subject matter experts (medical care, retail, data innovation, IT, and so on) to fill high-responsibility positions. Active experience is basic for data scientist jobs, aside from having a sound foundation in IT, CS, math, and other such disciplines. Salary: 9,84,488 INR  

Machine Learning Engineer

Role: As an ML engineer, you are liable for making data funnels and conveying programming solutions. Moreover, your occupation would include running tests and trials to screen the framework’s usefulness and execution. Preferred qualifications: Machine learning engineers are relied upon to have solid factual and programming abilities. Computer programmers with adequate ML experience are liked for such jobs. You can brush hypothetical points with online courses and gain viable experience by executing projects. Numerous online certifications with integrated tutoring are additionally accessible on the lookout. Salary:8,41,476  

Business Intelligence Analyst

Role: BI analysts form key designs for organizations while guaranteeing that the necessary data can be used easily. They likewise work with end-user entertainment of the BI tools and applications created by them. Preferred qualifications: The work of BI analysts requires a blend of specialized aptitude with the expertise of business and the board ideas of management. Many candidates hold an MBA with a specialization in analytics. Having business research and project coordination experience can give you an upper hand. Salary: 7,28,541 INR  

Data Analyst

Role: Data analysts change and control huge data sets. They likewise help more significant level chiefs in gathering bits of knowledge from their analytics. Analysts ought to have sufficient knowledge of A/B testing and tracking web analytics. It has forever been perhaps the best datum science occupation in India, and managing cash – yours and others – is the stuff of dreams. Preferred Qualifications: Entry-level openings in this space require at least a four-year certification (with accentuation on science/math/measurements). You ought to show fitness in science and sensible capacity. Normally, those capable in programming—with abilities in SQL, Python, Oracle, and so forth—are given inclination by employing administrators. Salary:7,12,965 INR  

Machine Learning Scientist

Role: As an ML scientist, you are entrusted with exploring new methodologies, like calculations, administration, and solo learning strategies. Associations enlist these experts in places with work titles like research scientist or research engineer. Preferred qualifications: Job postings for this job list the ideal profile as “somebody with a science certificate with fitting postgraduate studies and extensive proven research experience.” Salary:6,71,958 INR  


Role: Statisticians are recruited to gather, examine and decipher the information, in this way helping the leaders with their work. Their everyday responsibilities likewise incorporate imparting discoveries (data relationships and patterns) to partners and adding to setting functional techniques. As well as being one of the most lucrative data science occupations in India, it is additionally a high-speed one. Preferred qualifications: Entry-level openings might accept competitors with a four-year certification. However, most statisticians hold no less than a postgraduate diploma in math, computer science, economics, or other quantitative fields.

Data Science Jobs Are Hot—And With A State

Data Science Jobs Are Hot—and with a State-of-the-Art Building, New Faculty, and a New Major, BU Is Ready Data and mathematical science occupations are projected to grow more than 30 percent by 2030

BU’s 19-story Center for Computing & Data Sciences is scheduled to open in 2023. Founding faculty come from areas as diverse as law, medicine, sociology, theology, and education, as well as computer science and engineering. Photo by Janice Checchio

Data Science

Data Science Jobs Are Hot—and with a State-of-the-Art Building, New Faculty, and a New Major, BU Is Ready Data and mathematical science occupations are projected to grow more than 30 percent by 2030

In game one of the 2023 NBA Eastern Conference semifinal series, the Atlanta Hawks drilled one long-range three-point shot after another against the heavily favored Philadelphia 76ers. So many that the Hawks set a franchise playoff record—20 three-pointers for one game—and upset Philadelphia 128-124.

“We gave up a lot of corner threes,” says Grant Fiddyment, the 76ers manager of research. In the next two games, Philadelphia was ready. The 76ers cut their turnovers and put in an aggressive defensive display. After making 42.6 percent of their attempted three-pointers in game one, the Hawks managed only 36.7 percent in game two; by game three, they were sinking just 26.1 percent. The 76ers edged ahead 2-1 in the series. “We really shut that down, and you could see the defensive difference,” says Fiddyment.

Like many other professional sports teams, the 76ers have a cadre of analysts and data scientists picking over reams—or more accurately, gigabytes— of stats and information, from training schedules to player performance. Fiddyment (MED’16), who has been with the 76ers since 2023, is one of them, using that data to show the team ways it might improve on the court.

Around 2013, he says, the NBA started getting data from cameras mounted in arena ceilings, tracking every player in every moment of the game. Each step, bounce, screen, shot, and block became a piece of data for teams to study. And that’s exactly what he did after that first game three-point frenzy: scrutinized the granular game data and looked for ways to snuff out the Hawks’ threat.

The 76ers shut down the Hawks’ three-point shooting, with the help of aggressive defense—and data science. Photo by Tim Nwachuku/Getty Images

“We can now dissect the game at a much deeper level,” says Fiddyment, who’s also an adjunct professorial lecturer at American University. It’s a long way from coaches reviewing basic shot-attempt numbers—or just going with their gut. “There’s all the events leading up to a shot that we can go back and analyze or everything that happens after. We can probe all these things in between that used to be dead space from a data perspective.”

Data Science: the Liberal Arts of the 21st Century

The data helped force change in areas Graham cared about, swaying decision-makers or giving impetus to activists. After completing an immersive data science course, Graham signed up for a master’s in computer science at BU—with a concentration in data analytics—to strengthen their technical skills. In August, they also joined BU’s staff and now use data science to support research and efforts to make the tech industry antiracist.

Dawn Graham (MET’22) worked in social services and community organizing roles and sees data science as “something that you can use to effect change.” Photo by Jackie Ricciardi

“I didn’t go into data science as an end goal,” says Graham. “It’s a tool, something that you can use to effect change. A lot of my work has been in things related to racial equity, gender equity, just our general well-being as communities and people. For me, the shift to data science was a question of, how can we more effectively take care of these things and address them?”

As indirect as Graham’s route into the field might seem, Bestavros says it’s common for data science to attract people from disparate backgrounds. Before Fiddyment crunched basketball numbers, for example, he worked in neuroscience, helping epilepsy researchers and surgeons build statistical models to break down the phases of a seizure. Bestavros says that given the wide and varied applications of data science, it’s time to view it as more of a foundational program than a purely vocational one.

“I actually don’t know if data science is a science,” says Bestavros. “Data science is more like the liberal arts of the 21st century. It’s a way of thinking, a way of doing—it has all the elements, the critical thinking, that we associate with the liberal arts.”

A Ramp, Not the Destination

That philosophy is informing BU’s approach to the field. In 2023, the University established the Faculty of Computing & Data Sciences (CDS), a degree-granting academic unit not tied to any college or department. The group’s goal is to cut across disciplines, pulling together researchers and students interested in leveraging the power of computing and data-led inquiry. Founding faculty members come from areas as diverse as law, medicine, sociology, theology, and education, as well as computer science and engineering. This year, CDS launched its first undergraduate major in data science—with a minor coming soon.

Bestavros says the goal of the new bachelor’s degree is to provide students with “the substrate, the base on which you build lots of other professions.” It will check off mathematics, algorithmics, and software engineering, but also topics like social impacts, ethics, and bias.

“The job of a data scientist is different from that of a software engineer,” says Chatterjee (MET’19). “It’s also about communicating our work. It’s what differentiates good data scientists: being able to explain, justify processes, get good feedback, and iterate.”

At the 76ers, Fiddyment calls himself the glue between the coaches and the stats people.

“If you can’t communicate the results of the data you’re working with, then your impact could be just stopped in its tracks,” he says. “You can develop the most fantastic, amazing model, but if you can’t convince people of the importance, then maybe nothing happens.”

Efforts to Diversify the Field

At a time when more companies need that expertise, there’s a shortage of people ready to fill data science jobs, according to Bestavros. In August, venerable life insurance company MassMutual donated $1 million to CDS, in part to boost its own access to new data scientists. It uses customer data—age, health, lifestyle—to help refine and underwrite policies, as well as process claims.

“Talent is hard to find,” Adam Fox, MassMutual’s head of data, told BU Today when the donation was announced. “So one of the biggest drivers for us is the talent at both the undergraduate and graduate levels at BU, and gaining access to that talent pipeline for recruiting.”

The gift also supports a professor of the practice position, experiential learning opportunities, research, and efforts to diversify the field. The latter is an especially pressing issue. According to a 2023 study by executive recruitment firm Burtch Works, only 15 percent of data scientists are women—and other underrepresented groups don’t fare even that well.

It’s not enough to be a software programming whiz—data scientists also need to be good communicators to ensure the conclusions they draw have an impact, according to Oindrilla Chatterjee (MET’19), a data scientist at enterprise software company Red Hat. Photo by Jackie Ricciardi

“In my graduate program [at BU], there were very few women, very few people from diverse backgrounds in general,” says Chatterjee. She says her own team at Red Hat has made inclusion a priority, in part by attending conferences and recruitment events that target underrepresented groups. She says those who don’t pay attention to the industry’s lack of diversity are in danger of letting bias creep into their analyses: “Bias and ethics in machine learning models—and the whole data science domain—is a huge concern. You must be more mindful about where you are gathering the data from; if the data you’re gathering is itself biased and flawed, your models cannot be neutral.”

One goal of the Antiracist Tech Initiative that Graham is working on at the Center for Antiracist Research is to increase industry diversity. They and their colleagues are setting up partnerships with tech firms to gain access to the firm’s data and help them tailor their push for racial equity.

“I’ve been able to witness and experience some of the challenges around what it means to be from an underrepresented group in a certain industry,” says Graham. “To be able to bring that experience directly into some of the work we’re doing now, I think it helps guide that work in a way that is really meaningful.”

Bestavros says increasing industry diversity is also high on the list of priorities for CDS. Along with embedding ethics and lessons about bias throughout its programs, he says, a push to reach students who might not have considered—or had a route into—the field before will help “democratize access to data science.”

In addition to addressing its lack of diversity, the field faces another critical issue: closing a trust gap. Many have very legitimate fears about the power of big data, especially biased data, to shape our lives. For all its benefits—whether positive (supporting vaccine research) or relatively benign (shaping the comedies we watch on Netflix)—plenty of people are deeply skeptical. They don’t want firms or governments using their data to manipulate them.

Bestavros argues that data science is and will be a force for good, and he says opening up the field to more diverse groups of people will only enhance its potential for positive change. He draws lessons from the early days of nuclear energy and the internet: many of those behind the world-shaping breakthroughs only thought about their potential for good, not for harm. It’s a mistake he wants to learn from—and teach to those entering the hot data science job market.

“There are a lot of things that happen that make our life much better because of data science,” he says. “But there are better ways to do data science than others. It’s almost like you are training future doctors—it goes beyond just what works for mice and rats. This is about the human in the loop. We are now introducing technology that is changing how we interact with each other.”

Explore Related Topics:

10 Essential Python Libraries For Data Science In 2023

The 10 essential python libraries for data science in 2023, the area of data science and data analytics

The 10 essential python libraries for data science in 2023 in the area of data science and data analytics. Aside from its simplicity of use and broad applications, Python has an incredibly supportive community with millions of potential answers to any problems you may encounter. Python can be used for a variety of uses including server, interface, machine learning, data science, middleware, artificial intelligence, even arithmetic, and deep learning.

List of Python Libraries for Data Science in 2023:

1.  NumPy: Machinery has seen the universe through the lens of multi-dimensional arrays, just as we do in terms of sights, scents, tastes, and touch. As humans, we can only see and sense three dimensions (X-Axis, Y-Axis, and Z-Axis). Multi-dimensional arrays reflect the ability of machines to process and grasp numerous dimensions.


NumPy is used to keep RAM usage to a minimum

In Python, it is used as an option for arrays and lists, and it works well with multi-dimensional arrays

NumPy is used in situations where quicker runtime performance is required

2. SciPy: SciPy is an accessible science and technological computing package. It includes tools for optimization, interpolation, integration, eigenvalue, statistics, linear algebra, and multi-dimensional picture processing, among other things. Interesting fact: SciPy is built on NumPy.


Mathematics! SciPy is used for study and science computation jobs involving mathematical operations such as algebra, calculus, difference equations, and signal processing.

3. Theano: Theano is a Python package based on NumPy that allows you to manipulate and analyze mathematical expressions, particularly matrix-valued expressions.

Computer Vision: Theano is used in computer vision for tasks such as handwritten recognition and patchy coding.

Intensive Learning: Theano, widely regarded as the Grandfather of Python programs, was one of the first to make use of GPU optimization.

4. Pandas: Possibly the most popular program among Data Analysts worldwide. Pandas is a software library that deals with data structures and offer data manipulation and analysis features.

Recommendation Systems: Websites such as Netflix and Spotify use Pandas in the background to efficiently handle huge amounts of data.

Natural Language Processing (NLP): With the aid of tools like Pandas and Scikit Learn, it is now easier to build NLP models that can be used in a variety of applications.

5. Matplotlib: Matplotlib is a Python tool that helps with data analysis and plotting to create static, animated, and live displays.

Matplotlib is useful in data visualization because it can generate a large number of early plots for big datasets.

Given that NumPy is used in the server, matplotlib is heavily used with numerous third-party modules to achieve the quickest outcomes.

6. Plotly: Perhaps is Python’s finest charting and graphing program. Plotly allows users to create low-code applications for building, scaling and deploying data apps in Python.  

Plotly can be used to create an enterprise-grade interface with the dash in the backdrop in a variety of ways.

7. Sea Born: We talked about how matplotlib has a low-level interface. Seaborn is a high-level interface developed on top of matplotlib that provides useful statistical diagrams and draws appealing visualizations.

Seaborn is used in a variety of IDEs to visualize data in a visually appealing manner

8. Ggplot: Ggplot is an abbreviation for Graphics Grammar. Ggplot is a tool designed with R in mind. It is available in Python as part of the plotline module.

A fantastic package for creating fast graphics, regardless of how layered the source data is

9.  Altair: Altair is a declarative statistical visualization tool built on the Vega visualization language.

Altair is used to autonomously display graphs for data sets with fewer than 5,000 rows in a variety of methods

10. Autoviz: A collection can be automatically visualized using Autoviz.

AutoViz can be used to better comprehend data in a variety of areas·       

Top Data Science Training Courses For Beginners To Learn In 2023

Analytics Insight features top data science training courses for beginners in 2023

Data science is thriving in the global tech-driven market owing to its unprecedented potential to help organizations make smarter decisions to yield higher revenue efficiently. Aspiring data scientists are aiming to join popular and reputed global companies to have a successful professional career in data management. But to add value to CVs in this competitive world, these aspiring data scientists should have a strong understanding of concepts and mechanisms of data science. It must be overwhelming to select any one data science course that includes data management and data visualization. Thus, let’s explore some of the top data science training courses for beginners to learn in 2023.  

Top data science training courses for aspiring data scientists Applied Data Science with Python from Michigan University at Coursera

Applied Data Science with Python from Michigan University at Coursera is one of the top data science training courses for aspiring data scientists. They can learn to apply data science methods and techniques by enrolling for free from today. Beginners can conduct an inferential statistical analysis, data visualization, data analysis, and many more. There are five courses for aspiring data scientists to learn data science through Python. There are flexible schedules with approximately five months to complete and earn a valuable certificate. This course consists of hands-on projects for a strong practical understanding of the subject.

Introduction to Data Science using Python at Udemy

Introduction to Data Science using Python at Udemy offers aspiring data scientists to understand the basics of data science and analytics, Python and Scikit learn with online video content, a valuable certificate, and an instructor direct message facility. Udemy is well-known for offering highly-related data science training courses for learning data visualization and effective data management.

Analyze Data with Python at Codeacademy

Analyze Data with Python at Codeacademy offers the fundamentals of data analysis while building Python skills efficiently and effectively in the data science training course. Aspiring data scientists can learn about Python, NumPy, SciPy, and many more to gain Python skills, data management, data visualization, etc. to earn a valuable certificate after completion. There are multiple practical projects to gain a strong understanding of data science such as FetchMaker, A/B Testing, and so on. There are eight courses for aspiring data scientists to get specialized skills and step-by-step guidance to gain sufficient knowledge in a few months.

Data Science Specialization from Johns Hopkins University at Coursera

Data Science Specialization from Johns Hopkins University at Coursera offers a ten-course introduction to data science from eminent teachers. Aspiring data scientists can learn to apply data science methods and techniques by enrolling for free from today. They can also gain knowledge of using R for data management and data visualization, navigating the data science pipeline for data acquisition, and many more. This data science training course provides a flexible schedule for approximately 11 months with seven hours per week. It offers hands-on projects for aspiring data scientists to complete for earning a valuable certificate to add value to the CV.

Programming for Data Science with Python at Udacity

Programming for Data Science with Python at Udacity is a well-known data science training course for beginners. It helps to prepare a data science career with programming tools such as Python, SQL, and git. The estimated time to complete this data science course is three months at ten hours per week. Aspiring data scientists should enroll by November 3, 2023, to solve problems with effective data management and data visualization. There are real-world projects from industry experts with technical mentor support and a flexible learning program.

Data Science for Everyone at DataCamp

Data Science for Everyone at DataCamp is one of the top data science training courses for beginners. It provides an introduction to data science without any involvement in coding. It includes 48 exercises with 15 videos for aspiring data scientists. They can learn about different data scientist roles, foundational topics, and many more. The course curriculum includes the introduction to data science, data collection and storage, data visualization, data preparation, and finally the experimentation and prediction.

The Ultimate Numpy Tutorial For Data Science Beginners

NumPy arrays and Images

NumPy arrays find wide use in storing and manipulating image data. But what is image data really?

Images are made up of pixels that are stored in the form of an array. Each pixel has a value ranging between 0 to 255 – 0 indicating a black pixel and 255 indicating a white pixel. A colored image consists of three 2-D arrays, one for each of the color channels: Red, Green, and Blue, placed back-to-back thus making a 3-D array. Each value in the array constitutes a pixel value. So, the size of the array depends on the number of pixels along each dimension.

Have a look at the image below:

Python can read the image as an array using the scipy.misc.imread() method in the SciPy library. And when we output it, it is simply a 3-D array containing the pixel values:

import numpy as np import matplotlib.pyplot as plt from scipy import misc # read image im = misc.imread('./original.jpg') # image im array([[[115, 106, 67], [113, 104, 65], [112, 103, 64], ..., [160, 138, 37], [160, 138, 37], [160, 138, 37]], [[117, 108, 69], [115, 106, 67], [114, 105, 66], ..., [157, 135, 36], [157, 135, 34], [158, 136, 37]], [[120, 110, 74], [118, 108, 72], [117, 107, 71], ...,

We can check the shape and type of this NumPy array:

print(im.shape) print(type(type)) (561, 997, 3) numpy.ndarray

Now, since an image is just an array, we can easily manipulate it using an array function that we have looked at in the article. Like, we could flip the image horizontally using the np.flip() method:

# flip plt.imshow(np.flip(im, axis=1))

Or you could normalize or change the range of values of the pixels. This is sometimes useful for faster computations.

im/255 array([[[0.45098039, 0.41568627, 0.2627451 ], [0.44313725, 0.40784314, 0.25490196], [0.43921569, 0.40392157, 0.25098039], ..., [0.62745098, 0.54117647, 0.14509804], [0.62745098, 0.54117647, 0.14509804], [0.62745098, 0.54117647, 0.14509804]], [[0.45882353, 0.42352941, 0.27058824], [0.45098039, 0.41568627, 0.2627451 ], [0.44705882, 0.41176471, 0.25882353], ..., [0.61568627, 0.52941176, 0.14117647], [0.61568627, 0.52941176, 0.13333333], [0.61960784, 0.53333333, 0.14509804]], [[0.47058824, 0.43137255, 0.29019608], [0.4627451 , 0.42352941, 0.28235294], [0.45882353, 0.41960784, 0.27843137], ..., [0.6 , 0.52156863, 0.14117647], [0.6 , 0.52156863, 0.13333333], [0.6 , 0.52156863, 0.14117647]], ...,

Remember this is using the same concept of ufuncs and broadcasting that we saw in the article!

There are a lot more things that you could do to manipulate your images that would be useful when you are classifying images using Neural Networks. If you are interested in building your own image classifier, you could head here for an amazing tutorial on the topic!

End Notes

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