How can we increase the diversity in AI talent?

How can we increase the diversity in AI talent?

This is a subject close to my heart

Despite being quite well known in AI (for our pioneering work and teaching at the #universityofoxford for #AI and #edge) and also being the Chief AI Officer of a venture funded company in Germany, I would not pass many of the current recruitment tests in companies because I am neurodiverse  (on the autism spectrum)

Specifically, the problems with tests like leetcode are well documented for example as per

Tech’s diversity problem because of toxic leetcode

It is pretty common to see a lot of companies relying on Leetcode or puzzles to benchmark engineers. If you solve a question in X time with leanest code then you are in or else you are out. It is more for elimination than selection I guess. But this is setting a very bad trend and bad engineering culture. Engineers who do “real engineering” work are required to work with other Engineers not just computers.

I believe that many of the assessment methods like leetcode are discriminatory  and also narrow the talent pool in only one direction. It is important to include people with multidimensional skills in AI.

So, here are some ideas to consider in recruitment to expand diversity

1)  Consider the Elon musk interview strategy

Elon Musk has a very specific approach to interview questions

He asks each candidate he interviews the same question: “Tell me about some of the most difficult problems you worked on and how you solved them.” Because “the people who really solved the problem know exactly how they solved it,” he said. “They know and can describe the little details.” Musk’s method hinges on the idea that someone making a false claim will lack the ability to back it up convincingly, so he wants to hear them talk about how they worked through a thorny issue, step by step.

This approach works because it is both subjective and  analytical

2) Consider the difference between an Engineer vs scientist

Think of the difference between an Engineer and a Scientist. Scientists do fundamental work and engineers do applied work. Most people mix the two. Most companies need engineers and not scientists. 

If a person is asking, “why does this happen?” they are a scientist. Thus, no matter where on the spectrum they stand, they are looking toward fundamental issues. If a person is asking, “How do I make this work?” they are an engineer, and are looking toward the applied end. source northwestern Uni

So, the meta question you should be thinking of is: Do we need a scientist or do we need an engineer? or conversely, as a candidate, Am I comfortable as a scientist or as an engineer?

Most companies need engineers. knowing the distinction helps a lot

3) MLOps may make it easier to retrain software engineers to machine learning

related to 2 , MLOps may mean that you could retrain software engineers especially if you are using a cloud platform like AWS, Azure, GCP etc.  In an MLOps world, we have three jobs (data engineer, data scientist and devops) and in theory, you could start in one and transition to the others.

Hence, if companies change their approach a little, they could reduce recruitment costs and increase diversity

Image source: shutterstock

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Learn More On Advance Pandas and NumPy for Data Science (Part -II)

Learn More On Advance Pandas and NumPy for Data Science (Part -II) Cont…….

Welcome Back to Part II article. Hope you all enjoyed the content coverage there, here will continue with the same rhythm and learn more in that space.

We have covered Reshaping DataFrames and Combining DataFrames in Part I. 

Working with Categorical data: 

First, try to understand what is Categorical variables in a given dataset. 

Categorical variables are a group of values and it can be labeled easily and it contains definite possible values. It would be Numerical or String. Let’s say Location, Designation, Grade of the students, Age, Sex, and many more examples.

Still, we could divide the categorical data into Ordinal Data and Nominal Data.

1. Ordinal Data
   • These categorical variables have proper inherent order (like Grade, Designation)
2. Nominal Data
   • This is just opposite to Ordinal Data, so we can’t expect an inherent order 🙂
3. Continuous Data
   • Continuous variables/features have an infinite number of values with certain boundaries, always it can be numeric or date/time data type.

Actually speaking we’re going to do “Encoding” them and take it for further analysis in the Data Science/Machine Learning life cycle process.

As we know that 70 – 80% of effort would be channelized for the EDA process in the Data Science field. Because cleaning and preparing the data is the major task, then only we could prepare the stable model selection and finalize it.

In Which the process of converting categorical data into numerical data is an unavoidable and necessary activity, this activity is called Encoding.

Encoding Techniques

• One Hot Encoding
• Dummy Encoding
• Label Encoding
• Ordinal Encoding
• Target Encoding
• Mean Encoding

• One-Hot Encoding
  • As we know that FE is transforming the given data into a reasonable form. which is easier to interpret and making data more transparent to helping to build the model.
  • And the same time creating new features to enhance the model, in that aspects the “One-Hot Encoding” methodology coming into the picture.
  • This technique can be used when the features are nominal.
  • In one hot encoding, for each level of a categorical feature, we create a new variable. (Feature/Column)
  • The category can be mapped with a binary variable 0 or 1. based on presence or absence.
• Dummy Encoding
  • This scheme is similar to one-hot encoding.
  • The categorical encoding methodology transforms the categorical variable into binary variables (also known as dummy variables).
  • The dummy encoding method is an improved version of over one-hot-encoding.
  • It uses N-1 features to represent N labels/categories.

         One-Hot Encoding

Code

data=pd.DataFrame({“Fruits”:”Apple”,”Banana”,”Cherries”,”Grapes”,”Mango”,”Banana”,”Cherries”,”Grapes”,“Mango”,”Apple”]})
data

#Create object for one-hot encoding
import category_encoders as ce
encoder=ce.OneHotEncoder(cols=’Fruits’,handle_unknown=’return_nan’,return_df=True,use_cat_names=True)
data_encoded = encoder.fit_transform(data)
data_encoded

Playing with DateTime Data

Whenever you’re dealing with data and time datatype, we can use the DateTime library which is coming along with pandas as Datetime objects. On top of the to_datetime() function help us to convert multiple DataFrame columns into a single DateTime object.

       List of Python datetime Classes

• datetime – To manipulate dates and times – month, day, year, hour, second, microsecond.
• date – To manipulate dates alone – month, day, year.
• time – To manipulate time – hour, minute, second, microsecond.
• timedelta— Dates and Time measuring.
• tzinfo— Dealing with time zones.

Converting data types

As we know that converting data type is common across all programming languages. Python not exceptional for this. Python provided type conversion functions to convert one data type to another.

Type Conversion in Python:

• Explicit Type Conversion: During the development, the developer will write the code to change the type of data as per their requirement in the flow of the program. 
• Implicit Type Conversion: Python has the capability to convert type automatically without any manual involvement.

Explicit Type Conversion

Code

# Type conversion in Python
strA = “1999” #Sting type

# printing string value converting to int
intA = int(strA,10)
print (“Into integer : “, intA)

# printing string converting to float
floatA = float(strA)
print (“into float : “, floatA)

Output

Into integer : 1999
into float : 1999.0

# Type conversion in Python

# initializing string
strA = “Welcome”

ListA = list(strA)
print (“Converting string to list :”,(ListA))

tupleA = tuple(strA)
print (“Converting string to list :”,(tupleA))

Output

Converting string to list : [‘W’, ‘e’, ‘l’, ‘c’, ‘o’, ‘m’, ‘e’]
Converting string to list : (‘W’, ‘e’, ‘l’, ‘c’, ‘o’, ‘m’, ‘e’)

Few other function 

dict() : Used to convert a tuple  into a dictionary.
str() : Used to convert integer into a string.

Implicit Type Conversion

a = 100
print(“a is of type:”,type(a))
b = 100.6
print(“b is of type:”,type(b))
c = a + b
print(c)
print(“c is of type:”,type(c))

Output

a is of type: <class ‘int’>
b is of type: <class ‘float’>
200.6
c is of type: <class ‘float’>

Access Modifiers in Python: As we know that Python supports Oops. So certainly we Public, Private and Protected has to be there, Yes Of course! 

Python access modifications are used to restrict the variables and methods of the class. In Python, we have to use UNDERSCORE  ‘_’ symbol to determine the access control for a data member and/or methods of a class.

• Public Access Modifier:
  • By default, all data member and member functions are public, and accessible from anywhere in the program file.
• Protected Access Modifier:
  • If we wanted to declare the protected data member or member functions prefix with a single underscore ‘_’ symbol
• Private Access Modifier:
  • If we wanted to declare the private data member or member functions prefix with a double underscore ‘__’ symbol.

Public Access Example

class Employee:
def __init__(self, name, age):

# public data mambers
self.EmpName = name
self.EmpAge = age

# public memeber function displayEmpAge
def displayEmpAge(self):

# accessing public data member
print(“Age: “, self.EmpAge)

# creating object of the class from Employee Class
objEmp = Employee(“John”, 40)

# accessing public data member
print(“Name: Mr.”, objEmp.EmpName)

# calling public member function of the class
objEmp.displayEmpAge()

OUTPUT

Name: Mr. John
Age: 40

Protected Access Example

Creating super class and derived class. Accessing private and public member function.

# super class
class Employee:

# protected data members
_name = None
_designation = None
_salary = None

# constructor
def __init__(self, name, designation, salary):
self._name = name
self._designation = designation
self._salary = salary

# protected member function
def _displayotherdetails(self):

# accessing protected data members
print(“Designation: “, self._designation)
print(“Salary: “, self._salary)

# derived class
class Employee_A(Employee):

# constructor
def __init__(self, name, designation, salary):
Employee.__init__(self, name, designation, salary)

# public member function
def displayDetails(self):

# accessing protected data members of super class
print(“Name: “, self._name)

# accessing protected member functions of superclass
self._displayotherdetails()

# creating objects of the derived class
obj = Employee_A(“David”, “Data Scientist “, 5000)

# calling public member functions of the class
obj.displayDetails()

OUTPUT

Name: David
Designation: Data Scientist
Salary: 5000

Hope we learned so many things, very useful for you everyone, and have to cover Numpy, Here I am passing and continue in my next article(s). Thanks for your time and reading this, Kindly leave your comments. Will connect shortly!

Until then Bye and see you soon – Cheers! Shanthababu.

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Data Apps and the Natural Maturation of AI

Figure 1: How Data and Analytics Mastery is Transforming The S&P 500

Artificial Intelligence (AI) has proven its ability to re-invent key business processes, dis-intermediate customer relationships, and transform industry value chains.  We only need to check out the market capitalization of the world’s leading data monetization companies in Figure 1 – and their accelerating growth of intangible intelligence assets – to understand that this AI Revolution is truly a game-changer!

Unfortunately, this AI revolution has only occurred for the high priesthood of Innovator and Early Adopter organizations that can afford to invest in expensive AI and Big Data Engineers who can “roll their own” AI-infused business solutions.

Technology vendors have a unique opportunity to transform how they serve their customers.  They can leverage AI / ML to transition from product-centric vendor relationships, to value-based relationships where they own more and more of their customers’ business and operational success… and can participate in (and profit from) those successes.

Now this transition isn’t something unique. History has shown that there is a natural maturation whenever a new technology capability is introduced.  This natural maturation is from hand-built solutions that can only be afforded by the largest companies, to packaged solutions that democratizes that technology for the masses.

A History Lesson on Economic-driven Business Transformation

Contrary to popular opinion, new technologies don’t disrupt business models and industry value creation processes. It is what organizations do with the technology that disrupt business models and industry value creation processes.  Figure 2 shows a few history lessons where technology innovation changed the economics and created new business opportunities.

Figure 2: History Lesson on Economic-driven Business Transformation

Note: see the blog “A History Lesson on Economic-driven Business Transformation” for a more detailed analysis of the technology-driven business transformation.

And the major lesson from the history lessons in Figure 2?

It’s not the technology that causes the business disruption; it’s how organizations use the technology to attack current business models and formulate (re-invent) new ones that causes the disruptions and creates new economic value creation opportunities.

Welcome to the potential of Data Apps!

What are Data Apps?

The largest organizations can afford the data science and ML engineering skills to build their data and analytic assets.  Unfortunately, the majority market lacks these resources.  This is creating a market opportunity for Data Apps.

Data apps are a category of domain-infused, AI-powered apps designed to help non-technical users manage data-intensive operations to achieve specific business outcomes.  Data apps use AI to mine a diverse set of customer and operational data, identify patterns, trends, and relationships buried in the data, and make timely predictions and recommendations. Data apps track the effectiveness of those recommendations to continuously refine AI model effectiveness.

Increasing revenues, reducing costs, optimizing asset utilization, and mitigating compliance and regulatory risks are all domains for which we should expect to see Data Apps.  However, the Data Apps won’t do anyone any good if they are not easy to use and the analytic insights easy to consume.

Data Apps vendors must master “as-a-service” business models and adopt a more holistic customer-centric “product development” and “engineering” mindset:

“When you engineer and sell a capability as a product, then it’s the user’s responsibility to figure out how best to use that product. But when your design a capability as a service or solution, then it’s up to the technology vendor to ensure that the service is capable of being used effectively by the user.”

Vendors must invest time to understand their customers, their jobs-to-be-done, gains, and pains.  Vendors need to invest to understand the totality of their customers’ journeys so that they can provide a holistic solution that is easy to use and consume, and delivers meaningful, relevant, and quantifiable business and operational outcomes (see Figure 3).

Figure 3:  Learning the Language of Your Customer

CIPIO

We will start to see a movement to Data Apps to address high-value business and operational use cases such as customer retention, customer cross-sell/up-sell, customer acquisition, campaign effectiveness, operational excellence, inventory optimization, predictive maintenance, shrinkage, and fraud reduction.

I am excited to note that I have recently become an early investor in CIPIO, a data apps company that is focused on the Fitness Industry by addressing their critical business use cases including customer retention, campaign effectiveness, and customer acquisition.  I will also serve on their Board of Advisors.

Figure 4 is a screen shot of their Retention analytics.  This is a great example of the “Human in charge” approach of data apps; they are creating prescriptive recommendations based upon the individual’s predicted propensities, but it is still up to the business user to select the most appropriate action given the situation.

Figure 4: CIPIO Retention Screenshot

“Human in control” is a critical concept if we want our business stakeholders to feel comfortable working with these data apps.  Data Apps aren’t removing humans from the process; they augment the human intelligence and human instincts based upon the predictive propensities found in the data.

I was drawn to the CIPIO opportunity because I believe that CIPIO and data apps represents the natural maturation of the AI technology.  And if we remember our history lessons, when it comes to new technologies like AI…

It’s not the technology that causes the business disruption; it’s how organizations use the technology to attack current business models and formulate (re-invent) new ones that causes the disruptions and creates new economic value creation opportunities.

Watch this space as I share more about my journey with CIPIO.  Lots to learn!

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Fascinating Facts About Complex Random Variables and the Riemann Hypothesis

Orbit of the Riemann zeta function in the complex plane (see also here)

Despite my long statistical and machine learning career both in academia and in the industry, I never heard of complex random variables until recently, when I stumbled upon them by chance while working on some number theory problem. However, I learned that they are used in several applications, including signal processing, quadrature amplitude modulation, information theory and actuarial sciences. See here and here. 

In this article, I provide a short overview of the topic, with application to understanding why the Riemann hypothesis (arguably the most famous unsolved mathematical conjecture of all times) might be true, using probabilistic arguments. Stat-of-the-art, recent developments about this conjectured are discussed in a way that most machine learning professionals can understand. The style of my presentation is very compact, with numerous references provided as needed. It is my hope that this will broaden the horizon of the reader, offering new modeling tools to her arsenal, and an off-the-beaten-path reading. The level of mathematics is rather simple and you need to know very little (if anything) about complex numbers. After all, these random variables can be understood as bivariate vectors (X, Y) with X representing the real part and Y the imaginary part. They are typically denoted as Z = X + iY, where the complex number i (whose square is equal to -1) is the imaginary unit. There are some subtle differences with bivariate real variables, and the interested reader can find more details here. The complex Gaussian variable (see here) is of course the most popular case.

1. Illustration with damped complex random walks

Let (Zk) be an infinite sequence of identically and independently distributed random variables, with P(Zk = 1) = P(Zk = -1) = 1/2. We define the damped sequence as 

The originality here is that s = σ + it is a complex number. The above sequence clearly convergences if the real part of s (the real number σ) is strictly above 1. The computation of the variance (first for the real part of Z(s), then for the imaginary part, then the full variance) yields:

Here ζ is the Riemann zeta function. See also here. So we are dealing with a Riemann-zeta type of distribution; other examples of such distributions are found in one of my previous articles, here. The core result is that the damped sequence not only converges if σ  >  1 as announced earlier, but even if σ  > 1/2 when you look at the variance: σ  > 1/2 keeps the variance of the infinite sum Z(s), finite. This result, due to the fact that we are manipulating complex rather than real numbers, will be of crucial importance in the next section, focusing on an application. 

It is possible to plot the distribution of Z(s) depending on the complex parameter s (or equivalently, depending on two real parameters σ and t), using simulations. You can also compute its distribution numerically, using the inverse Fourier transform of its characteristic function. The characteristic function computed for τ being a real number, is given by the following surprising product:

1.2. Smoothed random walks and distribution of runs

This sub-section is useful for the application discussed in section 2, and also for its own sake. If you don’t have much time, you can skip it, and come back to it later.

The sum of the first n terms of the series defining Z(s) represents a random walk (assuming n represents the time), with zero mean and variance equal to n (thus growing indefinitely with n) if s = 0; it can take on positive or negative values, and can stay positive (or negative) for a very long time, though it will eventually oscillate infinitely many times between positive and negative values (see here) if s = 0. The case s = 0 corresponds to the classic random walk. We define the smoothed version Z*(s) as follows:

A run of length m is defined as a maximum subsequence Zk+1, …, Zk+m all having the same sign: that is, m consecutive values all equal to +1, or all equal to -1. The probability for a run to be of length m  >  0, in the original sequence (Zk), is equal to 1 / 2^m. Here 2^m means 2 at power m. In the smoothed sequenced (Z*k), that probability is now 2 / 3^m.  While by construction the Zk‘s are independent, note that the Z*k‘s are not independent anymore. After removing all the zeroes (representing 50% of the Z*k‘s), the runs in the sequence (Z*k) tend to be much shorter than those in (Zk). This implies that the associated random walk (now actually less random) based on the Z*k‘s is better controlled, and can’t go up and up (or down and down) for so long, unlike in the original random walk based on the Zk‘s. A classic result, known as the law of the iterated logarithm, states that

almost surely (that is, with probability 1). The definition of “lim sup” can be found here. Of course, this is no longer true for the sequence (Z*k) even after removing the zeroes.

2. Application: heuristic proof of the Riemann hypothesis

The Riemann hypothesis, one of the most famous unsolved mathematical problems, is discussed here, and in the DSC article entitled Will big data solved the Riemann hypothesis. We approach this problem using a function L(s) that behaves (to some extent) like the Z(s) defined in section 1. We start with the following definitions:

where

• Ω(k) is the prime omega function, counting the number of primes (including multiplicity) dividing k,
• λ(k) is the Liouville function,
• p1, p2, and so on (with p1 = 2) are the prime numbers.

Note that L(s, 1) = ζ(s) is the Riemann zeta function, and L(s) = ζ(2s) / ζ(s). Again, s = σ + it is a complex number. We also define Ln = Ln(0) and ρ = L(0, 1/2). We have L(1) = 0. The series for L(s) converges for sure if σ  >  1.

2.1. How to prove the Riemann hypothesis?

Any of the following conjectures, if proven, would make the Riemann hypothesis true:

• The series for L(s) also converges if  σ  >  1/2 (this is what we investigate in section 2.2)
• The number ρ is a normal number in base 2 (this would prove the much stronger Chowla conjecture, see here)
• The sequence (λ(k)) is ergodic (this would also prove the much stronger Chowla conjecture, see here)
• The sequence x(n+1) = 2x(n) – INT(2x(n)), with x(0) = (1 + ρ) / 2, is ergodic. This is equivalent to the previous statement. Here INT stands for the integer part function, and the x(n)’s are iterates of the Bernoulli map, one of the simple chaotic discrete dynamical systems (see Update 2 in this post) with its main invariant distribution being uniform on [0, 1]
• The function 1 / L(s) = ζ(s) / ζ(2s) has no root if 1/2  <  σ  <  1
• The numbers λ(k)’s behave in a way that is random enough, so that for any ε  >  0, we have: (see here)

Note that the last statement is weaker than the law of the iterated logarithm mentioned in section 1.2. The coefficient λ(k) plays the same role as Zk in section 1, however because λ(mn) = λ(m)λ(n), they can’t be independent, not even asymptotically independent, unlike the Zk‘s. Clearly, the sequence (λ(k)) has weak dependencies. That in itself does not prevent the law of the iterated logarithm to apply (see examples here) nor does it prevent ρ from being a normal number (see here why). But it is conjectured that the law of the iterated logarithm does not apply to the sequence (λ(k)), due to another conjecture by Gonek (see here).

2.2. Probabilistic arguments in favor of the Riemann hypothesis

The deterministic sequence (λ(k)), consisting of +1 and -1 in a ratio 50/50, appears to behave rather randomly (if you look at its limiting empirical distribution), just like the sequence (Zk) in section 1 behaves perfectly randomly. Thus, one might think that the series defining L(s) would also converge for σ  >  1/2, not just for σ  >  1. Why this could be true is because the same thing happens to Z(s) in section 1, for the same reason. And if it is true, then the Riemann hypothesis is true, because of the first statement in the bullet list in section 2.1. Remember, s = σ + it, or in other words, σ is the real part of the complex number s. 

However, there is a big caveat, that maybe could be addressed to make the arguments more convincing. This is the purpose of this section. As noted at the bottom of section 2.1, the sequence (λ(k)), even though it passes all the randomness tests that I have tried, is much less random than it appears to be. It is obvious that it has weak dependencies since the function λ is multiplicative: λ(mn) = λ(m)λ(n). This is related to the fact that prime numbers are not perfectly randomly distributed. Another disturbing fact is that Ln, the equivalent of the random walk defined in section 1, seems biased towards negative values. For instance, (except for n = 1), it is negative up to n = 906,150,257, a fact proved in 1980, and thus disproving Polya’s conjecture (see here). One way to address this is to work with Rademacher multiplicative random functions: see here for an example that would make the last item in the bullet list in section 2.1, be true. Or see here for an example that preserves the law of the iterated logarithm. 

Finally, working with a smoothed version of L(s) or Ln using the smoothing technique described in section 1.1, may  lead to results easier to obtain, with a possibility that it would bring new insights for the original series L(s).

To receive a weekly digest of our new articles, subscribe to our newsletter, here.

About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). He recently opened Paris Restaurant, in Anacortes. You can access Vincent’s articles and books, here.

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How to Grow Your Small Business Using Artificial Intelligence

Image source: pixbay.com

Artificial Intelligence (AI) has numerous applications for small businesses and is not something meant just for large corporations. No one can deny the benefits of AI for any industry. The biggest myth surrounding the use of AI for businesses is that it is expensive.

The truth is:

AI may seem expensive in the beginning but it is actually very cost-effective in the long run. Yes, there is a bit of an initial investment, but it more than pays for itself by delivering exceptional value over time.

So, if you are contemplating whether to invest in AI for your small business or not, read this post. In this post, you will find six of the best ways in which you can use AI to grow your small business.

Excited to learn more?

Let’s get started.

1. Use AI Chatbots to Provide Exceptional Customer Service

The most popular use of AI in business is in the form of customer service chatbots. 

As a small business, you might still be building your brand and customer trust. Therefore, it is especially important for you to deliver good customer service and build your credibility.

AI can help you solve one of the biggest customer service problems: long wait times and delayed problem resolution. 

Use AI-based chatbots to provide prompt service to your customers round the clock. This way, you can serve customers from all over the world without any delay.

This will improve your customers’ experience with your business and earn you customers’ trust and loyalty.

2. Leverage AI to Automate Repetitive Tasks

One key benefit of artificial intelligence is that it can be used to automate routine tasks and processes in almost any business or industry. 

So, make a list of repetitive tasks that your team spends a lot of time on, but can be easily automated. Then, look for specific AI tools and software that can help you automate those tasks.

There are many marketing automation tools available in the market to automate your sales and marketing tasks. You can also find AI tools and platforms that can automate your accounting and bookkeeping tasks. 

Similarly, there are AI-based automation tools for other business applications as well. Do your research and find the most relevant tools for your business.

3. Create Automated and Personalized Email Campaigns

Another major application of AI for small businesses is to create and run automated and personalized drip email campaigns. 

Using AI-powered email marketing platforms, you can design highly-targeted email campaigns and achieve many marketing goals. As a small business, you might not have a team of employees to manually run email campaigns. You can invest in a good AI tool to do that for you and save a lot of money in the long run.

Email marketing is a cost-effective marketing strategy that every small business should use. And, AI can help you optimize your campaigns and run automated email campaigns to get better results.

4. Understand Customer Journey and Online Behavior Using AI

AI can track each of your website visitor’s online behavior and activities to gather valuable customer insights. 

You can, for example, understand how each user moves through your sales funnel to finally make a purchase. This helps you understand your customers’ buyer journeys and design better sales funnels.

AI-powered analytics tools also provide a lot of other insights that are helpful for optimizing your marketing and SEO strategy. It can identify specific areas for improvement on your website and help you get more traffic and conversions through your website.

If you want to take this to the next level, you can even opt for third-party CRO services to get the best results.

5. Improve Sales with Personalized Recommendations

AI chatbots are not only good for answering customer queries but can also help with sales and marketing. 

They can ask questions and direct your site visitors to relevant resources and product pages. This, in turn, improves customer experience by helping them find exactly what they are looking for.

AI can also make personalized product recommendations to your website visitors based on their past purchases and search histories. This can help you increase your sales and get more conversions from your website.

You can even take this a notch higher by investing in a tool that can personalize your ad campaigns. These will show different ads to different users based on their past online behavior and the products they are most interested in. 

Additionally, AI can help in designing engaging graphics for your small business to get better conversions.

Overall, AI can help you drive more sales by making your marketing campaigns more targeted and personalized.

Conclusion

AI offers a lot of benefits and untapped potential for small businesses. In fact, AI will change the future of marketing and the way we do business. You simply have to invest in good AI tools to realize its numerous benefits and grow your small business.

These are some of the best ways in which you can utilize AI for your business. So, start with one or more of these and then expand your use of AI to other areas of your business.

All the best!

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How can data science put wings to your career development?

Data science is a highly sought-after job in a variety of industries due to the rising data landscape and the need to process large data sets. As a result of the COVID-19 crisis, businesses are shifting their activities to remote jobs, and communications through digital interfaces are increasingly rising, as is their emphasis on data and computers. This is why companies are aggressively recruiting data engineers and data scientists to store, churn, and evaluate data in order to provide expertise.

Through analyzing complex databases to extricate insights, data scientists have the ability to work between the enterprise and IT ecosystems and push industries. Data science professionals are in high demand, according to industry insiders, making it a widely sought-after career choice for those who choose to play in the digital data world.

If you want to get a head start on your career and want to work in the data science field, you have a lot of options. Best online data science courses are available to get started or one could easily opt for the good old data science certification course. The market is flourishing with opportunities.

What is Data Science?

Data science is the analysis of data. It is the method of processing, analyzing, visualizing, managing, and saving data in order to derive insights. These insights assist businesses in making informed data-driven decisions. Both unstructured and organized data must be used in data science. 

It is a multidisciplinary discipline with origins in mathematics, statistics, and computer science. Thanks to the proliferation of data scientist positions and a lucrative pay scale, it is one of the most sought-after careers. So, that was a quick introduction to data science; now let’s look at the benefits and drawbacks of data science. 

Roles in Data Science

The below are a couple of the more popular career titles for data scientists:

Business Intelligence Analyst

An ABI analyst will assist in determining market and business trends by analysing data to get a clearer picture of where the market is at.

Data Mining Engineer

The data mining engineer examines not just their own data but also that of others. In addition to analysing data, a data mining engineer may create sophisticated algorithms to help better understand it.

Data Architect

Users, device builders, and engineers collaborate together with data architects to build blueprints that data management systems use to centralize, incorporate, preserve, and protect data sources.

Data Scientist

Data scientists begin by translating a business case into an analytics agenda, developing ideas, and comprehending data—as well as spotting trends and determining how they impact businesses. They also find and choose algorithms to help in data processing. They’ll use business research to figure out not only what effect data will have on an organisation in the future, but also how to come up with new strategies to help the company move forward.

Why explore Data Science as a Career Option?

The big data revolution is gaining traction, and to ride it, you’ll need a deeper understanding and experience of how to dive deep into the data to derive and deliver insights. This is where data science, which includes data processing, data mining, predictive analytics, business intelligence, deep learning, and other techniques, comes into play. Data analytics certification is in high demand and could be your answer to the question. 

Anyway, here’s the list of advantages of learning Data Science if you are still having second thoughts

It’s in High Demand

Data science is in high demand. Job seekers have a plethora of options available to them. It is the fastest rising work on Linkedin, with 11.5 million jobs expected to be created by 2026. As a result, Data Science is a highly employable field.

Positions in Abundance

Only a few people possess all of the necessary skills to become a full-fledged Data Scientist. As a result, Data Science is less saturated than other IT markets. As a result, Data Science is a vastly diverse area with many resources. The area of data science is in high demand, but there are few Data Scientists available.

A Lucrative Career

One of the highest-paying occupations is data science. Data Scientists earn an average of $116,100 a year, according to Glassdoor. As a result, Data Science is a very lucrative career choice.

Data Science is Versatile

Data Science has a wide range of uses. In the healthcare, finance, consulting, and e-commerce markets, it is commonly used. Data science is a discipline with a wide range of applications. As a result, you will be able to serve in a variety of areas.

Data Science Improves Data

Data scientists are needed by businesses to process and interpret their data. They not only interpret but also increase the accuracy of the results. As a result, Data Science is concerned with enriching data and making it more useful to their company.

Data Scientist is a prestigious position

Companies can make more strategic decisions with the help of data scientists. Companies focus on Data Scientists to make use of their knowledge to deliver better value to their customers. This elevates Data Scientists to a key role within the organization.

Tedious tasks? Bye Bye

Various companies have used data science to simplify redundant operations. Companies are training robots to do routine activities using historical records. This has made formerly difficult work easier for humans.

Smarter products with Data Science

Machine Learning has allowed businesses to develop better solutions designed specifically for consumer needs thanks to Data Science. E-commerce portals, for example, employ Recommendation Systems to provide consumers with customized recommendations based on their previous orders. As a result, machines are now capable of comprehending human actions and making data-driven decisions.

Data Science Has the Potential to Save Lives

Because of Data Science, the healthcare system has significantly changed. It is now easier to diagnose early-stage tumors thanks to advances in machine learning. In addition, Data Science is being used by many other healthcare industries to assist their customers.

Wrapping up

A career in data science has been the hottest work of the twenty-first century, with millions of job vacancies around the world. Obtaining data analytics certification is an excellent way to achieve a competitive advantage in this rapidly changing sector. They can allow applicants to improve their talents while also assisting recruiters and recruiting managers in finding the right candidates.

As a data scientist, an aspirant would have a promising career outlook if they obtain the required qualifications. Best online data science courses are available to help you out.

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Top Data and Analytics Trends for 2021

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ERP Systems: How It Benefits From Artificial Intelligence

Compared to other new technologies, artificial intelligence has been around for a while now. However, that has had no impact on its efficacy or potential; in fact, it has only been rendered increasingly important in the world around us, especially in the context of companies and their endeavors to help people. Don’t believe it? Well, recent studies have shown that over 40 percent of digitally sound companies already use AI as an integral part of their business strategy. Not only that — researchers have also found that as many as 83 percent of companies believe AI is critical to the success of their endeavors to ensure their business growth. There is no doubt that AI has proven to be a champion for all industries across the globe.

But one is bound to wonder in which particular context it stands to help. Of course, in countless aspects; but, its application in the context of enterprise resource planning has been particularly interesting, especially now that they are deemed central to the seamless operations of any company in the modern age. AI can help further ameliorate this aspect of the business in more than one impact way; for example, it can assist companies with data optimization, i.e. ensuring all their data is not only updated but also optimized and complete. ERP solutions fortified with AI are also able to help companies close any gaps between various departments within the organization, empower executives to make sound, data-driven decisions, and so much more.

Now, let us take an in-depth look at some of the other benefits of this duo of ERP systems and AI.

1. Improved decision making: Of course, making informed decisions is crucial to the success of any business. The union of AI and ERP solutions can help in this regard by, first, helping you better handle and process your data. It then uses this data to make accurate analysis and forecasts — such information can then be used to drive informed decisions in the interest of the company. So, be it audience segmentation, marketing strategies, logistics, or storage — you can rest assured that the quality of decisions will be decidedly better.
2. Cut down costs: A key goal for any company, at any given point in time, is to ensure a viable reduction in its costs. While that is easier said than done, one does end up saving considerably when you integrate the ERP with AI, meaning you no longer need an individual team to deal with the ERP any longer. Plus, it also offers detailed reports about investments, possible opportunities for savings, etc.
3. Better customer experiences: Yet another vital concern for any company is to improve its customers’ experiences. Unfortunately, this has been quite a challenge as customers’ demands and expectations evolve continually. AI-driven ERP solutions, then, offer solutions such as chatbots that can quickly learn from the company’s data and then use it to improve customers’ journeys and experiences with the brand.

Integrating AI may seem like a mammoth task, but remember all that you stand to gain from it. Automated workflows, advanced predictive analytics, better employee productivity, etc. — you get the drift. Plus, when you find a trusted provider for enterprise software development services, you will also have the requisite expertise that will further serve to ensure the success of your endeavor to fortify your ERP solution with AI.

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Water Leakage Detection System: How IoT Technology Can Help?

Water leakage is one of the chief reasons for water dearth in the world. Numerous countries are fronting massive water loss due to water leakage. The main reason behind water loss is that people are unable to find the points in the area from where the water is getting leaked. According to research done by the World Bank, up to 25-30% of water gets leaked due to the leakage problem. Apart from water scarcity, water leakage can cause some other problems such as infrastructural succulence and accidental slip-up.  Water leakage can be more hazardous in factories subsequently causing vast damage to the apparatuses in the industries.

The best treatment of this problem is implementing a water leakage detection system in the area where the probability of water leakage is extreme. IoT-based water leakage detection system has made this process effortless by making it smart enough to detect water leakage automatically and sending alerts to the manager. Smart leakage detection system uses sensors to identify the leakage in the tanks or pipes. Buildings and industries must implement this automated system to augment the safety from any kind of water-related impairment. The IoT-based water detection system has the power to detect leaks with an accuracy level of 75%.

Industries in which IoT grounded Leakage Detection System can be employed:

• Engine rooms

This system can be used in engine rooms to detect water leakage to avert the computer system from any kind of damage. Any fault in the engine’s computer system can influence the working of the machinery which can initiate severe damage. Smart leakage detection system has the latent to improve it significantly by categorizing the point from where water is getting leaked and can land to the computer system.

• Cold chain logistics

Water leakage detection system can be used here to prevent frozen food from losing its excellence in the long run. Maintaining the temperature of the food is extremely imperative in cold chain logistics to ensure the quality of the product. Therefore, the installation of a water leakage detection system is very vital to manage water leakage.

• Warehouses

A leakage detection system helps to detect the leaks from floorboards to maintain the quality of products. Implementing a smart water leakage detection solution in a warehouse can help manger variously in making important decisions regarding the management of inventory and setting the product quality accordingly.

• Industrial workshops

Ensure the safety of equipment’s by identifying the potential water leakage is validating that the machines are not getting jammed by coming in contact with leaking water. Damage of hefty industrial machinery can lead to substantial production and financial loss.

• Base stations

Prevent short circuits and ensure employee safety in base stations by detecting leakages. Large number of wires are present in base station which controls the local area network. Even a small leak in the base station can lead to vast damage, therefore it is very critical to identify all the leakages promptly.

Features of IoT based Water Leakage Detection System

• IoT-based water leakage detection system have the potential to manage data smartly.
• Predictive analytics is one of the most chief features of IoT water leakage detecting solution in which smart algorithms are used to analyze the condition of leaking zones.
• Multiple sensor integration in IoT water leakage detector is the feature that helps to identify both spot and zone leaks in the area.
• IoT grounded leakage detection system is very flexible and scalable in nature.
• Applications and dashboards are used along with IoT devices for data visualization and to get alerts about leakage detection.

Benefits of using IoT based Water Leakage Detection System

• Real-time alerts

Sensor’s ability of IoT technology is exploited for real-time persistence and it is very beneficial for preventing the industries from water damage. Real-time alerts and notifications to the user concerning water leakage guarantee the complete safety of the area. Users can get alerts on the directly connected phone or other devices through the dashboard or app ubiquitously. The real-time capability of the sensor devices is so quick that as soon as the water comes in contact with the floor, a user is updated with a notification which helps to take the opportune action to avert the damage.

• Building security

Neighboring zones to the collected water get destructively exaggerated and becomes the cause of various disease. Additionally, collected water also impacts the quality of infrastructure. Smart water detection system helps to detect the barely discoverable water leakage points in the area and help to regulate them consequently. IoT sensor with gateway gives high-quality services and helps to monitor the leakage effortlessly in the less time conceivable. This helps to maintain security from water-born diseases and other problems.

• Advance analytics

Various variable parameters allied with water give crucial evidence about the water and helps to analyze the degree to which the water is likely to cause manage. Some of these parameters are water flow, temperature, humidity, and pressure. Advance analytical capacity of IoT devices plays a noteworthy role in analyzing these parameters.  Technologies like Big data unified with IoT helps to perform this analysis and generate decidedly valuable insights.

• Predictive maintenance

The truthful outcomes through IoT sensors are engendered by the advanced algorithms entrenched in the IoT solutions.  These solutions contribute to making industrious decisions by predicting the future possibility of water leakage in the factories. Predictive maintenance can lead to highly precautionary measures in the industries resulting in saving a huge amount of water loss and preventing the machines and goods from getting damaged.

The inference of advanced technologies sideways with IoT is turning the water leakage detection process entirely effortless. The benefits of using smart leakage detection system are huge and help industries to prevent themselves from considerable loss. Industries are showing inordinate interest in implementing smart water leakage detection solution by concentrating on the useful and easily handy features of the system. The solution is accessible for numerous industries which shows the flexibility and scalability level of the system evidently.

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Using Artificial Intelligence and ML in data quality management.

In recent years the technology has become prominent. AI and Machine learning are evolving quickly today. Almost today, everyone will have some interaction with a form of AI daily, some examples like Siri, Google Maps, etc. Artificial Intelligence is an app in which a machine can perform human-like tasks. Same time, ML is a system that can automatically learn and improve from experience without being directly programmed.

As data volumes have grown companies are under pressure to manage and control their data assets systematically. Also, traditional data processing practices are insufficiently scalable and cannot manage ever-increasing data volumes.
AI/ML augmented data quality management platform, can support you in your data management activities

How has AI and ML transformed quality management?

• Automatic Data Collection:
  Asides from data predictions, AI helps in data quality improvement by automating data entry through executing intelligent capture. This ensures that all valuable information is captured, and there are no gaps in the system.
• Recognize duplicates:
  Twofold entries of data can lead to outdated records, resulting in poor data quality. AI helps organizations to eliminate duplicate records in their database and keep precise records in the database.
• Anomalies are detected:
  A small human error can drastically affect the accuracy and the quality of data in a CRM. An AI-enabled system can detect and eliminate flaws in a system. The implementation of machine learning-based anomalies can also improve data quality.
• Fill data gaps:
  While many automation can cleanse data based on programming rules, it’s almost impossible for them to fill in missing data gaps without human involvement or additional data source feeds. Machine learning can make calculated assessments on missing data based on how it perceives the situation.
• Match and validate data:
  It may take a long time to come up with rules to match data collected from various sources. Machine learning models can be programmed to learn the rules and predict matches for new data.

Most companies look for fast analytics with high-quality insights to deliver real-time benefits based on quick decisions. Many leading data quality tools and solution providers have dabbled in machine learning territory in expectation of increasing the effectiveness of their solutions. As a result, it has the ability to be a game-changer for businesses seeking to improve data quality.

Try an AI and ML-based data quality tool to automate all your Data Quality management.

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