Even for many data scientists, Probabilistic Programming is a relatively unfamiliar territory. Yet, it is an area fast gaining in importance.
In this post, I explain briefly the exact problem being addressed by Probabilistic Programming
We can think of Probabilistic Programming as a tool for statistical modelling.
Probabilistic Programming has randomization at its core and the goal of Probabilistic Programming is to provide a statistical analysis that explains a phenomenon.
Probabilistic Programming is based on the idea of latent random variables which allow us to model uncertainty in a phenomenon. In turn, statistical inference in this case involves determining the values of these latent variables
A probabilistic programming language is based on a few primitives: we have a set of primitives for drawing random numbers, primitives for computing probabilities and expectations by conditioning and finally primitives for probabilistic inference
A PPL works a bit differently from traditional machine learning languages. The prior distributions are encoded as assumptions in the model. In the inference stage, the posterior distributions of the parameters of the model are computed based on observed data i.e., inference adjusts the prior probabilities based on observed data.
All this sounds a bit abstract. But how do you use it?
Finally, its important to emphasise that probabilistic programming takes a different approach to traditional model building
In traditional CS/ machine learning models, the model is defined by parameters which generate the output. In statistical/ Bayesian programming the parameters are not fixed / predetermined. Instead, we starat with a generative process and the parameters are determined as part of the inference based on the inputs
In subsequent posts, we will expand on these ideas in detail.
Artificial Intelligence (AI) is highly demanded practically in every industry. The greatest example of the successful usage of top-notch technology is the retailers and other e-commerce companies, especially, theirinventory management system. AI provides powerful insights for organizations like trends identified from large volumes of data analyzed so that business owners and their warehouse teams can better manage the daily tasks of inventory management.
Improved decision-making, reduced costs, eliminated risks, optimized warehouse work, increased productivity are just a few benefits of the implementation of AI technology. According to thestatistics, in 2020 about45.1% of companieshave already invested in automation of the warehouse and40.1%in AI solutions.
5 Ways To Use Artificial Intelligence For Inventory Management
It’s estimated that AI can add$1.3 trillion to the global economyin the next twenty years if the technology is used in supply chain and logistics management. It’s because AI can make supply chain management more efficient at all stages.
Nvidia, IBM, Amazon, Facebook, Microsoft, Salesforce, Alteryx, Twilio, Tencent, Alphabet are a few big names among the companies that have already leveraged the benefits of AI. The following are 5 ways that AI is revolutionizing inventory management.
1. Data Mining and Turning It Into Solutions
AI is extremely helpful in data mining. AI solutions have the ability not only to gather but analyze the data to transform it into timely actions. Thus, AI implemented into the inventory management system helps the business to evolve more rapidly and find more effective solutions to a particular situation. By monitoring, gathering, recording, and processing the data and interests of every customer, businesses can understand their customers’ demands to build more effective strategies and pre-plan the needs of the customers and stock products.
2. Dealing with Forecasting, Planning, and Control Issues In The Inventory Management Process
Inventory management is not only about storing and delivering items but it’s about forecasting, planning, and control. By implementing AI solutions, you minimize the risks of overstocking and understocking thanks to the ability of the technology to:
Accurately analyze and correlate demand insights;
Detect and respond to the change in demand for a specific product;
Consider location-specific demand.
AI-based solutions have the flexibility and ability to analyze all the possible factors and situations that are vital for the successful planning, stocking, and scheduling deliveries. Reducing the errors and issues in inventory management, the business can increase customer satisfaction and save costs.
3. Stock Management and Delivery
Planning errors and/or inadequate stock monitoring can result in shortages, delays, and other issues that affect the revenue. AI technology can be pretty helpful in it. The technology can collect the data about customers and analyze it to identify behavior patterns and other crucial factors that help:
Plan the stocking right;
Automate the stocking and fulfillment processes;
Leverage and react to incoming customer demands on time;
Establish efficient transportation and many more.
AI also can streamline deliveriesand increase their efficiency.On-time deliveries and transportation are the fundamentals of supply chain management that have a huge impact on consumer satisfaction. AI analyzes and makes sense of all a company’s telematics data, helps to find the optimal routes to ensure the timely arrival of orders. Besides, the technology can identify any patterns and draw conclusions about the delivery processes of the company so that you can improve it.
4. AI-Powered Robots to Optimize Warehouse Operations
AI-powered robots are not a new thing. Such giants as Amazon have already used them for day-to-day tasks. It’s forecasted that the robot automation market’s value will reach$10+ billionby 2023. There are a number of benefits that put AI-based robots over human staff:
They can work 24/7 tirelessly;
Robots work with more optimal time per action;
They can locate wares and scan their conditions, collecting the needed data for further analysis;
They provide real-time tracking of products;
Robots can select and move orders, reducing manual errors;
They perform inventory optimization, and so on.
All that can save a business a big chunk of the operational budget. Besides that, AI-powered robots used in warehouses free employees so that they can be allocated for more urgent and vital tasks that require human cognition.
5. Logistics Route Optimization
One of the most critical components in logistics is routeoptimization. By implementing AI solutions, companies can reduce time lost in traffic, provide faster delivery times, and in such a way save costs. That’s because AI can help in:
Lowering shipping costsby learning all the possible variants and finding the fastest and most cost-effective ways to deliver the orders to the customers.
Planning the most optimal routes.AI can learn traffic patterns over time, analyze the received data, and consider the different factors while routing. All that enables the drivers to avoid traffic jams more effectively.
Calculating more precise delivery time.Using complex algorithms, AI technology can calculate the delivery time more accurately by taking into account historical and real-time data, optimal routes, and other factors that can affect delivery efficiency.
Final Thoughts
AI has revolutionized and reshaped both inventory management and the way companies stock and store products. The AI solutions implemented to enable the businesses to make the inventory management pre-planned, automated, based on customer demands, and even carried out by robots. AI empowers companies to:
Enhance user experience and consumer satisfaction;
Increase sales;
Reduce costs;
Boost the overall productivity of the company.
AI is the future of the industry. Thus, if you want to stay competitive, you should implement the technology as soon as possible. The results can be outstanding.
Occasionally a novel neural network architecture comes along that enables a truly unique way of solving specific deep learning problems. This has certainly been the case with Generative Adversarial Networks (GANs), originally proposed by Ian Goodfellow et al. in a 2014 paper that has been cited more than 32,000 times since its publication. Among other applications, GANs have become the preferred method for synthetic image generation. The results of using GANs for creating realistic images of people who do not exist have raised many ethical issues along the way.
In this blog post we focus on using GANs to generate synthetic images of skin lesions for medical image analysis in dermatology.
Figure 1 – How a generative adversarial network (GAN) works.
A Quick GAN Lesson
Essentially, GANs consist of two neural network agents/models (called generator and discriminator) that compete with one another in a zero-sum game, where one agent’s gain is another agent’s loss. The generator is used to generate new plausible examples from the problem domain whereas the discriminator is used to classify examples as real (from the domain) or fake (generated). The discriminator is then updated to get better at discriminating real and fake samples in subsequent iterations, and the generator is updated based on how well the generated samples fooled the discriminator (Figure 1).
During its history, numerous architectural variations and improvements over the original GAN idea have been proposed in the literature. Most GANs today are at least loosely based on the DCGAN (Deep Convolutional Generative Adversarial Networks) architecture, formalized by Alec Radford, Luke Metz and Soumith Chintala in their 2015 paper.
You’re likely to see DCGAN, LAPGAN, and PGAN used for unsupervised techniques like image synthesis, and cycleGAN and Pix2Pix used for cross-modality image-to-image translation.
GANs for Medical Images
The use of GANs to create synthetic medical images is motivated by the following aspects:
Medical (imaging) datasets are heavily unbalanced, i.e., they contain many more images of healthy patients than any pathology. The ability to create synthetic images (in different modalities) of specific pathologies could help alleviate the problem and provide more and better samples for a deep learning model to learn from.
Manual annotation of medical images is a costly process (compared to similar tasks for generic everyday images, which could be handled using crowdsourcing or smart image labeling tools). If a GAN-based solution were reliable enough to produce appropriate images requiring minimal labeling/annotation/validation by a medical expert, the time and cost savings would be appealing.
Because the images are synthetically generated, there are no patient data or privacy concerns.
Some of the main challenges for using GANs to create synthetic medical images, however, are:
Domain experts would still be needed to assess quality of synthetic images while the model is being refined, adding significant time to the process before a reliable synthetic medical image generator can be deployed.
Since we are ultimately dealing with patient health, the stakes involved in training (or fine-tuning) predictive models using synthetic images are higher than using similar techniques for non-critical AI applications. Essentially, if models learn from data, we must trust the data that these models are trained on.
The popularity of using GANs for medical applications has been growing at a fast pace in the past few years. In addition to synthetic image generation in a variety of medical domains, specialties, and image modalities, other applications of GANs such as cross-modality image-to-image translation (usually among MRI, PET, CT, and MRA) are also being researched in prominent labs, universities, and research centers worldwide.
In the field of dermatology, unsupervised synthetic image generation methods have been used to create high resolution synthetic skin lesion samples, which have also been successfully used in the training of skin lesion classifiers. State-of-the-art (SOTA) algorithms have been able to synthesize high resolution images of skin lesions which expert dermatologists could not reliably tell apart from real samples. Figure 2 shows examples of synthetic images generated by a recently published solution as well as real images from the training dataset.
Figure 2 – (L) synthetically generated images using state-of-the-art techniques; (R) actual skin lesion images from a typical training dataset.
An example
Here is an example of how to use MATLAB to generate synthetic images of skin lesions.
The training dataset consists of annotated images from the ISIC 2016 challenge, Task 3 (Lesion classification) data set, containing 900 dermoscopic lesion images in JPEG format.
The code is based on an example using a more generic dataset, and then customized for medical images. It highlights MATLAB’s recently added capabilities for handling more complex deep learning tasks, including the ability to:
Process and manage mini-batches of images and using custom mini-batch processing functions.
Evaluate the model gradients for each mini-batch – and update the generator and discriminator parameters accordingly.
The code walks through creating synthetic images using GANs from start (loading and augmenting the dataset) to finish (training the model and generating new images).
One of the nicest features of using MATLAB to create synthetic images is the ability to visualize the generated images and score plots as the networks are trained (and, at the end of training, rewind and watch the entire process in a “movie player” type of interface embedded into the Live Script). Figure 3 shows a screenshot of the process after 600 epochs / 4200 iterations. The total training time for a 2021 M1 Mac mini with 16 GB of RAM and no GPU was close to 10 hours.
Figure 3 – Snapshot of the GAN after training for 600 epochs / 4200 iterations. On the left: 25 randomly selected generated images; on the right, generator (blue) and discriminator (red) curves showing score (between 0 and 1, where 0.5 is best) for each iteration (right).
Figure 4 shows additional examples of 25 randomly selected synthetically generated images after training has completed. The resulting images resemble skin lesions but are not realistic enough to fool a layperson, much less a dermatologist. They indicate that the solution works (notice how the images are very diverse in nature, capturing the diversity of the training set used by the discriminator), but they display several imperfections, among them: a noisy periodic pattern (in what appears to be an 8×8 grid of blocks across the image) and other visible artifacts. It is worth mentioning that the network has also learned a few meaningful artifacts (such as colorful stickers) that are actually present in a significant number of images from the training set.
Figure 4 – Examples of synthetically generated images.
Practical hints and tips
If you choose to go down the path of improving, expanding, and adapting the example to your needs, keep in mind that:
Image synthesis using GANs is a very time-consuming process (just as most deep learning solutions). Be sure to secure as much computational resources as you can.
Some things can go wrong and could be detected by inspecting the training progress, among them: convergence failure (when the generator and discriminator do not reach a balance during training, with one of them overpowering the other) and mode collapse (when the GAN produces a small variety of images with many duplicates and little diversity in the output). Our example doesn’t suffer from either problem.
Your results may not look “great” (contrast Figure 4 with Figure 2), but that is to be expected. After all, in this example we are basically using the standard DCGAN (deep convolutional generative adversarial network) Specialized work in synthetic skin lesion image generation has moved significantly beyond DCGAN; SOTA solutions (such as the one by Bissoto et al. and the one by Baur et al.) use more sophisticated architectures, normalization options, and validation strategies.
Key takeaways
GANs (and their numerous variations) are here to stay. They are, according to Yann LeCun, “the coolest thing since sliced bread.” Many different GAN architectures have been successfully used for generating realistic (i.e., semantically meaningful) synthetic images, which may help training deep learning models in cases where real images are rare, difficult to find, and expensive to annotate.
In this blog post we have used MATLAB to show how to generate synthetic images of skin lesions using a simple DCGAN and training images from the ISIC archive.
Medical image synthesis is a very active research area, and new examples of successful applications of GANs in different medical domains, specialties, and image modalities are likely to emerge in the near future. If you’re interested in learning more about it, check out this review paper and use our example as a starting point for further experimentation.
An AI model building is challenging on three fundamental counts:
Availability of relevant data in good quantity and quality: The less I rant about it, the better.
Need for multiple skills: Building an effective and monetizable AI model is not just the realm of a data scientist alone. It needs data engineering skills and domain knowledge also.
The constant evolution of the ecosystem in terms of new techniques, approaches, methodologies, and tools
There is no easy way out to address the first challenge, at least not so far. So, let us brush that under the carpet for now.
The need for having multiple resources with complementing skills is an area where a no-code AI platform can add tremendous value. The average data scientist spends half of his/her time preparing and cleaning the data needed to build models and the other half fine-tuning the model for optimum performance.No Code AI platforms(such as Subex HyperSense) can step in with automated data engineering and ML programming accelerators that go a long way in alleviating the requirement of having a multi-skilled team. What’s more, it empowers evenCitizen Data Scientistswith the ability to build competent AI models without having the need to know any programming language or having any background in data engineering. Platforms like HyperSense provide advanced automated data exploration, data preparation, and multi-source data integration capabilities using simple drag-and-drop interfaces. It combines this ability with a rich visual representation of the results at every step of the process so that one does not need to wait until the end to realize an error that was done in an early step and have to go back and make changes everywhere.
As I briefly touched upon a while back, getting the data ready is one-half of the battle won. The plethora of options on the other half is still perplexing – Is it a bird? Is it a plane? Oh no, it is Superman! Well, in our context – it would be more like – Is it DBSCAN? Is it a Gaussian Mixture? Oh no, it is K-Means! Feature engineering and experimenting with different algorithms to get the most optimum results is a specialized skill. It requires an in-depth understanding of the data set, domain knowledge, and principles of how various algorithms work. Here again,No Code AI platformslike HyperSense come to the table with significant value adds. With capabilities like autonomous feature engineering and multi-algorithm trial and benchmarking, I daresay that it makes building models almost child’s play. Please do not get me wrong. I am not for a moment suggesting that these platforms will result in the extinction of the technical data scientist role, on the contrary, it will make them more efficient and give them superpowers to solve greater problems in lesser time while managing and guiding teams of citizen data scientists to solve the more mundane, yet, problem statements of existential importance.
So far, so good; and having brushed one challenge under the carpet and discussed the other one, there is one more – The constant evolution of AI techniques, methodologies, tools, and technologies. Today, just being able to build a model which performs well on a pre-defined set of metrics does not cut ice anymore. It is just not enough for a model to be simply accurate. As the AI landscape evolves, the chorus for the Explainability and Accountability in models is reaching a fever pitch. Why did K-Means give you a better result than Gaussian Mixture? Will, you then get the same result if a feature was modified or a new one added? Why did the model predict a similar outcome for most customers belonging to a certain ethnicity? Is the model replicating the bias and vagaries present in the historical data set or the person building the model? If there have been policies and practices in a business where any sort of decision bias crept into day-to-day functioning, it is but natural that the data sets you work on will have those biases and the model you build will continue to persuade you to make decisions with the same biases as before. As an organization that is striving to disrupt and transform your industry, it is pertinent that you identify and weed out such biases sooner than later before your AI models hit scale and it becomes a wild animal out of its cage.
AsNo Code AIplatforms evolve, model explainability is something that is already getting addressed. Platforms like HyperSense give you the option to open up the proverbial ‘black-box’ and peep inside to see why a model behaved the way it did. It provides the analyst or the data scientist with an opportunity to tinker around advanced settings and fine-tune them to meet the objectives. Model accountability and ethics is a whole different ball game altogether. It is not restricted just to technology but also the frailties of human beings as a species. I am sure the evolving AI ecosystem will eventually figure out a way to make the world free of human biases – but hey, where’s the fun then? Human biases do make the world interesting and despicable in equal measure and I believe the holy grail for AI will be to strike a balance between the two.
Until then, let us empower more and more creative and business stakeholders to explore and unleash the true power of AI usingNo Code platformslike HyperSense so that the world can be a better place for all life forms.
The most baleful aspects of the Pandemic seem to be behind us, though the emergence of the Delta variant of the COVID-19 virus is causing companies to question whether it is perhaps too early to shift operations completely back to the office, and months turn into years, the likelihood of a hybrid work model emerging as the dominant approach to work is becoming more and more likely.
This has a major impact upon the shape of work, especially for knowledge workers including data scientists, programmers, designers, and others who work primarily with information systems, as well as those who manage them. As machine learning systems become more integrated into day-to-day activities. Other areas that are also being transformed include education, in all its varied manifestations, entertainment, supply chain management, security, manufacturing, even criminal activity.
As this process plays out, it is forcing a re-evaluation of nearly all aspects of work, including what productivity means in the AI era and whether or not such digital transformations (including Work From Home / Work from Anywhere) is beneficial or harmful to the economy. New DSC Columnist Michael Spencer, editor-in-chief of The Last Futurist, explores this theme in detail in this newsletter, asking whether the digital transformations that we’re seeing will come at the cost of local economies disappearing, especially in the entertainment and service sectors.
The entertainment sector is transforming in ways that would have been unthinkable ten years ago. Salesforce this week announced that they were launching their own business-oriented Streaming Service, even as companies such as Gamestop and AMC are on death watch on Wall Street. We are continuing the process of transforming atoms to bits then making these virtualized atoms transmissible through ever-faster networks. Scarlett Johannson took Disney to court about royalty revenues lost to streaming, which is likely to send shockwaves through the entertainment sector as creators use the opportunity to renegotiate how such creativity is compensated as the traditional movie theater gives way to the virtualization of location. At the same time, Disney’s last major animated project, Raya and the Last Dragon was completed almost completely from the homes of the various animators, editors and other creatives, to the extent that we may not be far from every actor having a green screen room in their house.
Even in the service sector, the skills required (and the demands upon workers) are changing. Delivery has become the next sector to face automation, requiring the coordination of thousands of drivers and fulfillment specialists through the use of highly complex networked systems, often managed through the same kind of tracking tools formerly reserved for large-scale software projects. There is a generation of DIY home manufacturers who are becoming adept at managing such supply chain and distribution issues, and that in turn is shaping how (and where) business gets done.
Ultimately, what is happening is that geolocation is ceasing to be as major a factor as it once was, while at the same time I think that we’ll see the pendulum swinging back towards where local business should be. In my town of Issaquah, here in the Pacific Northwest, the local restaurants along Main Street (or Front Street, in this case) are now seeing more and more patrons, as are the barbers and hair salons, and even a bookstore or two after a few decades of them being destroyed by the large chains (a trend I’m seeing in other sectors as well). I think we’ll find a balance again, but it will be a different equilibrium. We still need that third place, neither home nor work but common ground to re-establish community.
Consumers want fast solutions to their problems. With the help of unprecedented innovation in technology, digital transformation empowers businesses to improve the overall business structure and, most notably, the customer experience. While it always made sense to adopt digitization across companies, but the adaptation of digital transformation has still been slow.
Amid the pandemic, the need for transforming digitally has never been more urgent. Businesses that neglect the transformation will likely be left behind and risk losing their market position.
How can you embrace digital transformation successfully? Consider these ideas:
Switch from being product-focused to being customer-focused mindset
Embracing digital transformation holds special significance for customer experience. The primary focus should not be your product features; instead, more emphasis should be put on understanding and catering to your target customer’s wants and requirements.
If you clearly understand your customers’ problems and extend them a customized experience to resolve them, they will become your loyal customers. The key to earning loyal customers is paved by understanding their problems and offer them a customized experience that can solve their problems.
Scale-up creating innovative digital experiences
With technology pacing forward continuously, customers expect businesses to produce personalized digital content faster and cheap (or even free).
Accordingly, businesses must adapt to this trend and swiftly scale their digital designs, content production, and collaborations to keep their customers engaged, interested, and responsive.
Create your customer journey without depending on technical teams.
We know what kept you wondering, whether it’s even possible to create a digitally customized customer journey without a technical team?
It’s possible! Businesses don’t necessarily have the complete skill sets and teams required to execute the desired action when starting from scratch. However, the market now possesses plenty of new technologies that entrepreneurs and businesses can leverage.
Also, with the boom of low-code or no-code technology, it has become hassle-free to find code-free platforms. For example WordPress and Wix for hosting; Squarespace and Canva for content and website design; Hotjar and Google Analytics for analytics visualization.
Low-code or no-code technologies are designed specifically for entrepreneurs who may not have any technical or design background to efficiently create digital experiences without having to recruit a different team.
Enable remote workforces and automation
Amid the pandemic situation, businesses across the world shifted and aced the remote-work setup. Advancement in technology and adapting digital transformation across the organization thoroughly, employees no longer need to work from offices in a specific place only all the time. Robots can even substitute some responsibilities. For example, in-store robots manage transactional tasks like checking inventory in store aisles and fulfilling small orders.
To enable remote workforces, count on using project management tools, to stay connected with your team with virtual conference platforms.
In addition to an inclinedmindset that understands and implements this new management concept, it is essential to implement an effective strategyand use the right technology.
Digital Strategy:
Digital strategy requires creating a digital culture in the organizations that follow a clear and combined transformation strategy to realize the organization’s digital maturity, which begins with the vision: think digital at all levels stretched across all the departments, from the senior managers to the last employees, that includes clients, middle managers and also external stakeholders or collaborations.
This digital strategy also requires continuous restructuring and rethinking of the business model, thus maintaining a culture that constantly adapts to market trends and the transformation of products and services.
Digital Technology:
ADigitized Companypractices all possible digital technologies to optimize management and satisfy all personalities involved in the business (employees, customers, suppliers, etc.): automation process, digital work stations and mobility, Big data, electronic documentation, sensors, and the Internet of things (IoT), etc.
Moreover, these technologies must be thoroughly integrated and with business management to be truly useful (a factor usually overlooked and causes most digital transformation process failures)
Although this might seem obvious at first, yet the majority of reports and interviews by consultants and experts in digitization usually fail to consider the essentiality of task coordination.
The time for digital transformation is now!
The pandemic was also a wake-up call for businesses to embrace their digital transformation journey. It won’t be easy to transform how you’ve been running your business so far but know that the beginning is always the hardest. With the proper practices we mentioned in this article, you can successfully get ahead in your transformation journey and keep your business striving!
Worldwide the necessity of data science has become very vital in many industries, they are using it to grab valuable insights to stay ahead of the competition. Each industry has a massive amount of data that they don’t know what to do with it. The need for professionals in data science has grown immensely in all industries because only they can understand the data.
People who choose a career path in data science can prove their skills in big data platforms by doing certification programs through several learning institutions that offer certified data scientists both online as well as offline.
If one wants to get certification in data science, then there are many ways they can choose from. In this article, let’s dive deeper and know the best certifications that are in high demand to be an expert in data science.
SAS offers multiple data science certifications that are mainly focused on SAS products. One among them is SAS Certified Big Data Professional Using SAS 9 offers registrants insights into Big Data with the help of a variety of open-source tools and SAS Data Management tools. They will be using intricate ML models to create business recommendations for deploying the models at scale with the help of a robust and flexible SAS environment. To attain a SAS Certified Big Data Professional Using SAS 9 the applicant is required to pass all 5 exams that consist of short-answer, interactive questions, and a mix of multiple-choice. These are the following five exams:
· SAS Certified Big Data Professional:
SAS Big Data Programming and Loading
SAS Big Data Preparation, Statistics and Visual Exploration
· SAS Certified Advanced Analytics Professional:
SAS Advanced Predictive Modeling
Predictive Modeling Using SAS Enterprise Miner 7, 13, or 14
SAS Text Analytics, Time Series, Experimentation and Optimization
DASCA is an industry-recognized certification body, which provides certifications for senior data scientists. These certifications provide professionals best acumen and capabilities to anticipate and appreciate the requirement to deploy the latest Data Science techniques, tools, and concepts to manage as well as to harness Big Data across various verticals, environments, and markets. DASCA tests every person’s ability with the world’s most robust generic data science knowledge framework. The certification programs include a complete range of essential areas of knowledge. It approaches, initiatives and programs work toward developing every professional’s knowledge to address the challenging objectives of Big Data stakeholders globally.
Data Science professionals across 183 countries can take DASCA certification exam. Take these certification programs and study from the most advanced Big Data learning resources ever. Its certifications are based on the renowned and comprehensive Data Science Body of Knowledge (DASCA-DSBoK™) designed around the seminal Data Science Essential Knowledge Framework (DASCA-EKF™).
Being a certified data scientist professional will help you to perfect for reaching horizons of information, specially designed for big data engineers, big data analysts, and data scientists. The following are the certifications: The following certifications are:
· Data Scientist Certifications
DASCA Data Scientist Certifications address credentialing needs of senior, accomplished professionals that specialize in managing and leading big data strategies and programs for firms and have proven competence in leveraging big data technologies for generating mission-critical information for firms and businesses.
The SDS™ credential is a perfect proof that an individual has taken a massive step in mastering the field of data science. The skills and knowledge one can attain by doing this certification will set them ahead of the competition. This credential program has five tracks, which will appeal to various applicants — each track has different prerequisites in terms of degree-level, work experience and requirements to apply.
Principal Data Scientist (PDS™)
This credential consists of three tracks for professionals with 10 or more years of experience in big data. The exam covers basics to advanced data science concepts that include big data best practices, business strategies for data, developing cross-company support, ML, NLP, scholastic modeling and more.
SDS™, PDS™ credentials exam duration is 100–minutes online exam. And a complete exam preparation kit will be offered by DASCA.
The Dell EMC Education Services provides Data Science and Big Data Analytics Certification to evaluate the in-depth knowledge of a person in data analytics. The exam especially lays emphasis on analyzing and exploring data with R, data analytics lifecycle, creating statistical models, choosing accurate data visualization tools, and applying several analytic techniques, Data Science aspects like Natural Language Processing (NLP), random forests, logistic regression. There are no specific pre-requisites to enroll in this certification program.
By doing a certification program is very useful as it ensures to improve the skills and a person can be a valuable asset to the company in which they work in. Certifications are the perfect investment when an individual wants to grow in their respective careers.
Instant Grocery Delivery is the startup hype of the year in Europe. You select a few groceries via the shopping app, pay via Paypal, and 10 minutes later, a bike courier is at your door with your purchases. It’s a business model that spreads magic among the users. A few months after launch, I know friends who do almost half of their shopping this way. It’s a multi-billion dollar idea like Uber. A business model that is so easy to explain and still magical? But there are also apparent problems with highly disruptive business models like this:
Overworked bike couriers going on strike.
Issues with the districts because of noise pollution from warehouses located in the middle of residential areas.
A low margin on products and little price tolerance from customers.
Business growth is occurring geographically from district to district and city to city for companies like Gorillas.
The colossal competition (I count 12 providers in Germany alone by now).
The US company GoPuff, founded in 2013, is considered a pioneer for the startups Gorillas, Flink, Zap, or Getir. GoPuff makes data-driven decisions to minimize the risks mentioned above. To boost these ambitions, GoPuff recently acquired the data science startup RideOS for $115 million. In markets with aggressive pricing, for many direct competitors and existing substitutes building a competitive advantage quickly via technology has proven to make the business model more efficient. A bold but also expensive move by GoPuff. In this article, I will show how to integrate within a day geospatial analytics for an instant grocery delivery use case without spending multi-millions on a startup acquisition.
But how exactly can we think of data-driven decision-making for instant grocery delivery? Assets that are important to optimize are:
Where should I set up warehouses?
What is the optimal size of the drivers fleet?
What are the preferences of target customers in the region?
How big is the market potential overall?
In this article, we ask ourselves the fictitious question, should an instant grocery delivery company go to the outlying Berlin district of Pankow? We do this using external data sources that can scale globally and use the data integration framework of Kuwala (it’s open-source). With Kuwala, we can easily extract scalable and granular behavioral data in entire cities and countries. Below you see activity patterns at grocery shops in Hamburg. We will make use of some of the functionalities to derive insights from the described areas.
[embedded content]
We start our analysis by comparing the data on a neighborhood of Pankow with the neighboring part of PBerg (“Prenzlauer Berg”). The two selected areas are similar in size (square kilometers). Using the Kuwala framework, we first integrate high-resolution demographics data. On a top-level view, they are comparable to each other in total and within subgroups of gender and age.
In the next step, we analyze the current status quo of Point-of-Interests regarding groceries (e.g., supermarkets). We build the data pipeline on OpenStreetMap data and extract categorization and name as well as price level. We combine that data with hourly popularity and visitation frequency at those POIs.
We find that Pankow has significantly fewer supermarkets per square kilometer. In addition, it shows that the price level of grocery stores is much higher in PBerg. Furthermore, we identify that groceries in Pankow are +10% more visited during the evening than PBerg. In summary, we can assume now that people in Pankow…
… travel longer to supermarkets on average.
… often spend more time in the evening hours in supermarkets.
… have a lower price elasticity towards groceries.
Companies can now use that information in a market entry strategy. An aggressive cashback activation convinces people in Pankow to skip the evening shopping in a supermarket for a comfortable way of receiving the purchases right at their door.
We aggregated the high-resolution demographics data on an H3 resolution of 11 (based on raw data representing 30×30 meter areas). By that, we can analyze in-depth the distribution of people in a comparatively small district.
We can spot areas with a high population of the young target demographic and less reachable options for doing groceries.
In addition, we can spot micro-neighborhoods with a low population density, which makes those areas a perfect spot to open a warehouse, close enough to service areas and further away from people who could be disturbed by noise.
In the next part of this article, I will share some more advanced algorithms to identify over- and under-served areas and put everything at scale by comparing entire cities and the popularity of those places. If you want to discuss geospatial topics with us in the meanwhile, I recommend joining our slack community.
Automated messages like “Hi, how may I help you?” are quite familiar when a customer requires some service online. Want to know what’s that? These are the chatbots for businesses that have improved customer service by making service available round the clock, and64% of online users are satisfied with the automated system.
As a fact, chatbots will most likely take care of85% of all customer dealings by early 2022. Almost50% of businesses prefer chatbots to mobile apps, proving it a viable future ofcustomer service. Let’s see what they are!
1. Boost Customer Service
The automated messaging feature of Facebook Chatbots can garner an immediate response from the customers. A well-developedFacebook Messenger Chatbotwith an inbuilt cache of FAQs provides quick answers to customer queries. Moreover, chatbots can offer multiple-choice responses to understand the specific needs of a customer.
The quick response from chatbots permits customers to make their purchase decision faster and lessens the probability of shifting to a competitor.
Vital Statistics show that there are more than300,000active bots on Messenger. This implies that businesses can thrive well in the competition with the help of Facebook Chatbots.
Currently,conversational marketingthrough Facebook has a lead of roughly 70% higher open rate than email marketing.
A use case is Domino’s chatbots on Facebook. The chatbots allow customers to choose their favorite dish from a plethora of items and place orders. The chatbot links the customer’s Facebook account to their Domino’s account. Customers can track their orders, seek support, and do many more things. These digital innovations have helped Domino’s increase their customer base by allowing them to have a good experience on their platform.
2. Offer Personalized Recommendation
Customers can view online catalogues of your store within the Messenger application. For instance, Shopify offers e-commerce stores with The Messenger Sales Channel engineered by chatbots that enables buyers to browse products through Messenger. Once buyers make a purchase decision, they will be automatically redirected to your website. The messenger chatbots via the sales channel let companies send automated notifications regarding their orders. Such chatbots can come as a blessing for small businesses.
Few brands move beyond customers’ expectations and use chatbots to recommend during purchase. Rather than searching many products independently, the customers may ask for suggestions as per their choice of products. Conversational AI plays a significant role in this.
Babylon Health, a renowned British online subscription service, has taken the help of chatbots to provide consultation based on the patient’s medical history and can contact patients via video call from a physician.
3. Collect Feedback Seamlessly
Your Facebook chatbot can effectively conduct a brief survey for customer feedback in a conversational manner, almost like human interactions. Thus, in a few clicks, your company can gather vital information and form an idea of buyers’ response to your brand, products, or services.
Chatbots save your customers’ time, for they just need to click rather than typing out. Prepare a satisfactory scale or few statements for the customers to choose from. A meticulously-designed chatbot boosts the process of getting feedback from your customers.
Take the use case of a typicalsurvey chatbot. The Facebook chatbot asks the buyer if they would like to participate in the survey. Once the buyer gives their consent, the survey starts instantly. The buyers don’t even have to take the pain of typing anything. They can just select from the ‘options’ furnished below the question to progress through the survey. On top of that, GIFs, images, and videos displayed above the questions make the survey fun and less tedious.
WotNotprovides you with some wonderful ways to createFacebook chatbotsfor business. These chatbots are based on conversational AI, and you can deploy them for flawless feedback collection.
4. Makes Scheduling Appointments Easier
You can use Facebook Messenger chatbots to schedule appointments for your customers. Booking a slot for an appointment through a bot lets customers schedule appointments anytime, without the hassle of contacting a customer service representative.
The beauty brand Sephora enables customers to fix appointments using the Facebook Messenger chatbot. By opting for “Book A Service,” the buyer is directed to a trail of questions from the Facebook chatbot. It helps them select the location and services they would like to schedule. Finally, the chatbot generates a scheduling pop-up that lets customers select a particular slot available at the store. Once the time is fixed, Sephora collects the email and name of the user from Facebook to finalize the appointment.
An11% upsurge was seen in in-store booking conversation rates after they introduced the scheduling chatbot. Allowing customers a separate way of scheduling their appointments through Messenger also helps the in-store employees to converse and connect more with customers on the spot personally.
5. Enhance Brand Awareness
Your brand’s Facebook chatbot enables customers to know about what your company does, especially when interacting with people who have forayed into your brand’s ambit of influence. This is an impactful way to capture customer attention, moving them down your sales funnel because marketing via Facebook Messenger has 10-80 times better engagement than email and incurs70-80% open rates on an average.
You can directly present your brand as a part of your chatbot conversation by telling people about your business’s latest event, which might have been an exciting project. In this way, audiences can stick to your brand advertisement.
An interesting use case is the Upbeat Advertising Agency, whose Facebook chatbot allows users to develop awareness about the agency directly as part of its bot conversation. The agency messenger bot gets Facebook users started by letting them know about a recent event or an exciting project that Upbeat has been a part of. Such tactics are likely to capture the audience’s attention.
6. Influence Customers to Visit Your Product Page
Once you warm your audience up via Facebook Messenger, you can start directing them to your product pages. As Facebook messenger bots can be conversational and amiable while communicating with their target audience, the whole interaction looks pretty natural and not like a sales pitch. However, if you do not want to direct people to your product pages in this way, include a shop button to the menu, but a disciplined conversation does help.
Burberry, a luxury brand, has a well-organized bot conversation facility on its Facebook page that provides visitors with the option to browse its products in both the menu and the conversation.
With Facebook messenger providing highly engaging and personal communications,40million businesses have taken to this platform to set up amiable interactions with potential customers and increase their sales. Thus, Facebook Chatbots can play a crucial role insofar as effective customer engagement and conversational marketing is concerned.
Facebook messenger has been prospering exponentially over the past few years and became a well-performing mobile platform rather than simply an app. As a fact, about3,00,000chatbot developers have joined the Facebook messenger app.
7. Enable Shopping Directly Via Facebook Messenger
A “Buy Now” button lets customers enjoy a seamless buying within the Messenger app, cutting short the buyer journey and increasing conversion rates. The Facebook chatbot will fill out the form automatically with users’ data during this quick checkout process.
Beauty Gifter, the Facebook messenger chatbot for L’Oréal, aims to enhance personalization. The messenger chatbot gets to know every buyer’s needs and choices and makes customized product recommendations from 11 L’Oréal brands, integrating with L’Oréal’s e-commerce system for checkout. Beauty Gifter chatbot statistics prove27times better engagement than email, 31% detailed profiling, and 82% buyers loved the experience.
Around$8 billionwill be saved by 2022 from businesses using chatbots, as per IBM. Also, 85% of customer conversations with businesses will occur via chatbots by 2020, as per Gartner, and 53% of customers would heartily text than call a customer care agent.
8. Notify Customers With Broadcasts to Increase Customer Retention
Facebook Messenger chatbots for business can convey your brand’s message by making it effectively engaging, which can compel the target audience to make the right decision. Its high click-through and open rates will bear the right results for your brand’s marketing intent. A bland email template like “Your Cart Is Waiting” might not have what the subscriber wants. Hence, the subscriber will most likely not open the mail. However, crisp and short broadcasts automated by Facebook chatbots using friendly emojis and stickers make them more persuasive.
Observe your organization’s internal style of pitching, customer care terminology, advertising strategy, etc. This will provide you with a strong conscience of your voice of exposition and the attitude to use in your Messenger broadcasts.
A simple use case is a Facebook chatbot forging relationships by sending broadcasts to customers to educate them about your brand. For example, if you own an athletic store, your target customer base must be people who love running a marathon and you aim to sell more and more sneakers.
Use your Facebook chatbot to create a sequence of messages, with each message consisting of an actionable tip to convince them to get started. This implies your sequence gives them insightful information on how to run their first marathon with the expectation that when they need to purchase running shoes, your brand is the first one they would think of buying from.
9. Adding Augmented Reality to Customer Experience
Since customers have been opening up to chatbot-based communication, companies are going one step ahead to include Augmented Reality (AR) and conversational AI to make the customers’ experience more immersive. Companies like POND’S, Sephora, Ikea, etc., incorporate AR and conversational AI in their chatbots to make them more targeted and precise.
Advantages of using Augmented Reality and Artificial Intelligence are their extraordinary selling point, improved experience with a personalized experience to the customers, and the enhanced prospects of earning gravity-defying revenue.
One of the famous use cases is that of Victoria Beckham, who is among the many fashion designers to incorporate Augmented Reality as an integral part of her chatbot, producing impressive results. She owns one of the best Facebook chatbots we have ever seen. She uses her Facebook bot to enable users to use their camera to try on her sunglass collection to see if or not they will suit them. This is an innovative tactic to boost conversions.
10. Generate Leads
Chatbots in Facebook Messenger can add an edge to your sales approach. By communicating with users, you can know their preferences and categorize them and identify your leads. All you need to do is adjust your bot’s situations to your sales funnel and develop a positive buyer experience.
You can use your Facebook chatbots to find out the challenges your potential buyers face regarding a product or a service by asking some multiple-choice questions and then offer valuable suggestions and enable accessible contact. By entertaining consumers through a chatbot, you can engage your customers.
Bots can contribute to your business by nurturing leads. You can send regular automated messages personalized with a follow-up or an interesting new piece of content that can keep your customers hooked on. You can build a formidable bond with potential buyers and increase your leads.
Conclusion
While revising your social media policy, do not forget to includeFacebook Messenger Chatbot for businessin it. Begin with simple FAQs and automated answers to enhance the quality of customer support, and add more options over time, e.g., product recommendations, content distribution, and events. Do not flinch while interacting with your customers in a more engaging discussion, albeit through Facebook chatbots. In this way, you can develop better and more long-lasting relationships with them.
TryWotnotfor creating highly advanced no-code chatbots for business/small business. Wotnot’s state-of-the-art analytics dashboard lets your brand understand customer insights more deeply and use this knowledge to strengthen conversational marketing.
Instant Grocery Delivery is the startup hype of the year in Europe. You select a few groceries via the shopping app, pay via Paypal, and 10 minutes later, a bike courier is at your door with your purchases. It’s a business model that spreads magic among the users. A few months after launch, I know friends who do almost half of their shopping this way. It’s a multi-billion dollar idea like Uber. A business model that is so easy to explain and still magical? But there are also apparent problems with highly disruptive business models like this:
Overworked bike couriers going on strike.
Issues with the districts because of noise pollution from warehouses located in the middle of residential areas.
A low margin on products and little price tolerance from customers.
Business growth is occurring geographically from district to district and city to city for companies like Gorillas.
The colossal competition (I count 12 providers in Germany alone by now).
The US company GoPuff, founded in 2013, is considered a pioneer for the startups Gorillas, Flink, Zap, or Getir. GoPuff makes data-driven decisions to minimize the risks mentioned above. To boost these ambitions, GoPuff recently acquired the data science startup RideOS for $115 million. In markets with aggressive pricing, for many direct competitors and existing substitutes building a competitive advantage quickly via technology has proven to make the business model more efficient. A bold but also expensive move by GoPuff. In this article, I will show how to integrate within a day geospatial analytics for an instant grocery delivery use case without spending multi-millions on a startup acquisition.
But how exactly can we think of data-driven decision-making for instant grocery delivery? Assets that are important to optimize are:
Where should I set up warehouses?
What is the optimal size of the drivers fleet?
What are the preferences of target customers in the region?
How big is the market potential overall?
In this article, we ask ourselves the fictitious question, should an instant grocery delivery company go to the outlying Berlin district of Pankow? We do this using external data sources that can scale globally and use the data integration framework of Kuwala (it’s open-source). With Kuwala, we can easily extract scalable and granular behavioral data in entire cities and countries. Below you see activity patterns at grocery shops in Hamburg. We will make use of some of the functionalities to derive insights from the described areas.
[embedded content]
We start our analysis by comparing the data on a neighborhood of Pankow with the neighboring part of PBerg (“Prenzlauer Berg”). The two selected areas are similar in size (square kilometers). Using the Kuwala framework, we first integrate high-resolution demographics data. On a top-level view, they are comparable to each other in total and within subgroups of gender and age.
In the next step, we analyze the current status quo of Point-of-Interests regarding groceries (e.g., supermarkets). We build the data pipeline on OpenStreetMap data and extract categorization and name as well as price level. We combine that data with hourly popularity and visitation frequency at those POIs.
We find that Pankow has significantly fewer supermarkets per square kilometer. In addition, it shows that the price level of grocery stores is much higher in PBerg. Furthermore, we identify that groceries in Pankow are +10% more visited during the evening than PBerg. In summary, we can assume now that people in Pankow…
… travel longer to supermarkets on average.
… often spend more time in the evening hours in supermarkets.
… have a lower price elasticity towards groceries.
Companies can now use that information in a market entry strategy. An aggressive cashback activation convinces people in Pankow to skip the evening shopping in a supermarket for a comfortable way of receiving the purchases right at their door.
We aggregated the high-resolution demographics data on an H3 resolution of 11 (based on raw data representing 30×30 meter areas). By that, we can analyze in-depth the distribution of people in a comparatively small district.
We can spot areas with a high population of the young target demographic and less reachable options for doing groceries.
In addition, we can spot micro-neighborhoods with a low population density, which makes those areas a perfect spot to open a warehouse, close enough to service areas and further away from people who could be disturbed by noise.
In the next part of this article, I will share some more advanced algorithms to identify over- and under-served areas and put everything at scale by comparing entire cities and the popularity of those places. If you want to discuss geospatial topics with us in the meanwhile, I recommend joining our slack community.
