Did you ever wonder what goes on inside the brain of a data scientist?

A few years ago, PerceptiLabs, a deep tech startup, took on an ambitious goal — to visualize what data scientists see when they are building a machine learning model. In doing so, they reinvented the process of model building, making it simpler and faster for experts and beginners alike, to build, train, and analyze their models, so companies could speed up their innovation process.

It’s not news that AI is transforming the world in which we live. Banks are using AI to identify potential fraud, healthcare providers use AI to assist with diagnosis, grocery stores build algorithms to predict consumer behavior, and much more. Today, as businesses rush to accelerate their digital transformations due to COVID-19, AI is becoming more crucial, penetrating more business-critical functions.

To enable AI to do all these great things, the field has generally relied on experts (highly trained data scientists) to build and train complex mathematical models also called machine learning models. This is a complex time-consuming process, involving thousands of lines of code. To see what the models were doing, the experts have to use their imagination to visualize the models in their heads.

As AI and ML took hold and the experience levels of AI practitioners diversified, efforts to democratize ML materialized into a rich set of open source frameworks like TensorFlow and datasets. Advanced knowledge is still required for many of these offerings, and experts are still relied upon to code end-to-end ML solutions. This can have some advantages when building customized solutions, but can require a large investment in resources, infrastructure, and maintenance.

More recently a variety of AutoML tools have launched, promising end-to-end capabilities, where data is input, parameters are adjusted, and a fully-trained, deployable ML model is generated. The simplicity of this sounds inviting — indeed it’s appropriate in certain scenarios — however, ML models created through AutoML often lack transparency into their performance and can be difficult to interpret (i.e., explain why they produce certain results). As well, AutoML solutions often restrict users to only a few ML techniques.

The next generation of ML modeling

PerceptiLabs has developed a next-generation ML tool with our visual modeler that took the best of all worlds: the flexibility of code, some of the automation in connecting components, generating model architectures as well as tuning settings and hyperparameters, combined with the ease of a drag and drop UI.

This makes model building easier, faster, and accessible to a wider spectrum of users, whether you are an expert or beginner. There is also the ability to create custom models like simple linear regression, or something more complex like a GAN.

We designed our tool as a visual API on top of TensorFlow, which has grown to become the most popular ML framework. This gives developers full access to the low-level TensorFlow API and the freedom to pull in other Python modules.

Most importantly, users have full transparency into how their model is architected and a view into how their model performs. The result is a new visual approach that’s almost as good as seeing inside a data scientist’s brain!

ML modeling approaches at a glance

There are a lot of choices when it comes to building machine learning models, and each approach needs to be carefully evaluated against the resources you have available to see it through.

That’s why here at PerceptiLabs, we think that our new visual way to build machine learning models, strikes just the right balance across a wide spectrum of ML users while offering better explainability, sophistication, and usability. It’s a flexible but comprehensive approach, that lets you choose the way you want to work, depending on your experience and project needs.

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Emerging technologies such as big data and AI can be daunting for the small business owner, but there’s also significant pressure to stay ahead of the curve. Small businesses are always competing to keep costs and overheads low, as they’re less able to sustain themselves through the lean times. However, McKinsey research has shown that implementing big data strategies can significantly impact the bottom line. For example, a retailer could increase operating margins up to 60 percent.

Implementing big data and AI platforms doesn’t need to involve hiring teams of data scientists or analysts. At this point in 2019, small businesses have a wealth of out-of-the-box solutions available to them, covering a variety of use cases.

Chatbots are helping to reduce operational costs by providing a “human” element to first-line customer services and general inquiries. Juniper Research estimates that chatbots will save businesses around $8bn by 2022. Bots can also help with lead generation and conversion, by providing a direct route from the company website to a sales representative for those who choose to engage with the bot online.

Now in 2019, many chatbots operate cross-channel, integrating seamlessly with Facebook Messenger, WhatsApp, Instagram, and more. Liveperson is one example, with a chatbot builder feature that makes it easy for anyone in the organization to build and optimize bots using industry-specific templates. Using Liveperson, a business can automate up to 70 percent of messaging conversations across various platforms.

Predictive Analytics

Predictive analytics platforms have a broad range of applications in companies, including reducing employee turnover and decreasing risks such as cyberattacks. However, the implications for sales and marketing are particularly significant. By understanding which demographics are likely to buy a product, sales and marketing teams can make more efficient use of their budgets.

Endor is one company with a predictive analytics platform that’s as easy to use as a Google search.

The user only needs to type in their question, which could be something as straightforward as “who is most likely to buy x product?” Endor’s data science methodology relies on the “social physics” discipline, to deliver fast and accurate answers based on crowd wisdom. It was developed by a team from MIT, who pioneered the concept of social physics in an academic setting and are now applying it in across a range of industry sectors including retail and finance.

Business Intelligence

Internal business systems are often fragmented, with different software for managing sales, customer services, human resources, and accounting. However, together they generate a vast amount of data that’s greater than the sum of its parts and can be converted into actionable business intelligence. 

This is the goal of Insight Squared. It takes historical data from internal company systems and analyzes it in aggregate to generate recommendations for sales, marketing, and staffing. Users have access to multi-dimensional reports and analytics to help manage pipelines, gain more accurate forecasts of sales or product usage, and clearer visibility into marketing-generated demand.

Recruitment

Recruiting new staff is a considerable cost for any business, taking up a vast amount of management time. Among a 2017 Wasp Barcode survey of 1,100 small business owners, fifty percent stated that their top challenge was hiring new employees.

Developments in AI mean that hiring managers can significantly cut down on the legwork involved in the recruiting process, particularly in the earliest stages of sifting through dozens of applications. Ideal offers a suite of AI-powered tools that will integrate with existing HR software to enable data-backed hiring decisions and make the recruiting process more efficient. Ideal can pre-screen candidates, engage with them online via a chatbot, and automate tedious tasks such as sending out interview requests and rejection letters. 

Visual Analytics

For a bricks-and-mortar business, visual analytics can provide powerful insights. Most stores these days are equipped with security cameras both front and back of house, usually to deter thieves. However, platforms such as Prism can put these cameras to work harvesting all kinds of data to help make better business decisions.

For example, Prism can produce heat maps showing how customers move around a store, helping to ensure optimal placing of merchandise for maximizing revenues. It can also analyze footfall to tell a retailer when the busiest periods will happen so they can arrange adequate staffing. Behind the scenes, the software can also assist with inventory management and help to spot theft.

These are just a few examples of the available AI and analytics tools and platforms on the market today, and none of them require extensive technical expertise to operate. Each of them illustrates how small businesses can start to seamlessly incorporate AI and big data to help reduce business overheads and increase profitability. The point is not to implement technology for its own sake but to find methods of incorporating technology into your business in such a way that it enhances your edge over the competition and helps secure the future of your business

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The amount of data that is getting generated on a day-to-day basis is huge. That is why companies across the globe are turning data into information and are using it to optimise their strategies. But the challenge here is the fact that every company needs a professional with relevant skills to extract insights from the massive data collected — a data scientist who’s now getting a seat at the big table.

Further, with the evolution of data and its increasing use in different types of business, people have started to see data science as an uber-cool job. However, when it comes to becoming a data scientist, we notice a lot of professionals have dozens of MOOC courses and fancy buzzwords on their resumes or LinkedIn profiles. And when a data science neophyte sees these portfolios, they get the impression that data science is not their cup of tea. However, that is not the case all the time — data science is about solving an actual business problem, making the best out of the cluttered data. If you have the relevant knowledge, you can kickstart your data science career without any prior experience.

Steps To Follow

There are many aspirants who want to be a part of the data science community, but they are clueless about how to get started, and there could be several reasons behind it — maybe they didn’t have a data science subject in their formal education, maybe they never attended any data science conference, maybe there are not many faculties who are much aware of the domain, etc.

In this article, we are going to outline some of the important factors to bear in mind and prepare for a data science job without any prior experience.

1. Self-Assessment

This the first and foremost thing to do when you are starting your data science journey and you don’t have any prior experience. Ask yourself these questions: why would a company would hire you? If they are not hiring you, what could be the reason? What do you know about the data science domain? What more do you need to know about the domain? What extra skills do you need to learn to stand out from the crowd?

Further, along with the skills and knowledge a data science professionals should have, learn about the latest industry trends — how corporate works, what are the current job roles that are on demand, what are the latest programming languages etc. Make a list of all the things you know, and you need to know and make a plan for how you should go about them.

2. Skills You Need To Master

Mathematics: It is also considered as one of the vital elements when it comes to data science. It is very important in the field of data science as there are many concepts that help a data scientist with algorithms. Also, concepts like statistics and probability theory are key for algorithms implementation. So, make sure you put in a lot of effort into sharpening your mathematical skills.

Programming: There are many people who would suggest a huge bunch of programming languages to learn if you want to have a career in data science. However, don’t overwhelm yourself with all the hype talks. When it comes to data science Python and R are the two most important programming languages. Put in your complete focus on these two languages at the initial stage. Later, when you gain confidence along with significant confidence, you can move on to the next one (Java could be one of them).

To learn to programme you can always take up short term course or online courses. Also, practice a lot. The more you code, the better coder you become.

Communication & Visualisation: Having an upper hand on all the technicalities is one but to be a successful data scientist you also need to have outstanding communication and presentation skills. You should not just be a data scientist but be a data storyteller too. Why? Once you get the valuable insights from the cluttered data, your next job is to present it, and if you don’t have storytelling skills, how would make others understand what the insights are capable of and the value they would deliver.

3. Practice With Real-Time Problem Statement

Learning and mastering skills are definitely mandatory, but to make the most out of your learning, you need to practice — practice with real-time problem statements you give your data science learning a worth.  The more you solve those problems the more you gain experience as well as confidence and makes the pathway to your dream science job short. There are many hackathons available on the internet — you can always pick one, participate and see where you stand in this ever competitive data science domain.

4.Connect  With Leaders

It is always considered to be a good practice to take advice from someone who has already mastered domain. And for that, you can make the best use of platforms like LinkedIn to connect with some of the leaders from the industry.

Another best ways to make connections is by attending data science conferences, where you not only get to attend talks and masterclasses but also meet a lot of people from the industry who would help you take a right path when you are starting with your data science journey.

5. Accept Reality

It is no surprise that data science is one of the highest paying and reputed jobs right now in the industry. And no company would pay someone a handsome paycheck and give a high-level designation until and unless they prove that s/he is capable of dealing with and some of the complex business problems. So, accept the fact that when you initially start your career, you might not even get the designation as a data scientist (you might get in some exceptional cases). However, if you are determined and learn more and more about the domain, the chance of you getting to a higher position with a significantly high paycheck increases.

Make sure you don’t hesitate to seek help from fellow data scientist when you need. Knowledge and skills are the master keys to success.

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