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Retail Therapy Powered By AI

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Retail Therapy Powered By AI

Retail has been one of those smart sectors that embraced the power of AI technology to offer a personalized shopping experience to the customers. Let’s have a look at how AI has been a great tool in helping the stores rake in money.

 Be it smart product recommendations by analyzing shopping patterns, or, offering better inventory management solutions today’s retailers are doing it just right. The sector needs to focus on training their employees, as undergoing customer market analysis courses is of paramount importance.

Offer personalized product recommendations

This has been one of the most revolutionary changes in the e-commerce industry. With AI-powered technology  retailers can offer personalized product recommendations to the customers. Using smart tools they can analyze the shopping preferences, shopping patterns of a customer as well as their browsing history.

The data provides them with valuable insight which they apply to recommend products following that specific customer need. By doing so, they can retain customers and experience a better conversion rate. Tools such as  Stitch Fix,  Boomtrain are carrying out this task successfully to recommend products that suit customer whimsies.

Smarter inventory management

Inventory management is one of the key areas the retailers have to focus on. Previously they would just stock up on items without having access to any valuable customer data. Now that they can sift through big data, they can analyze past trends and could predict what upcoming trends are to look out for. Being armed with data they now make decisions accordingly.

 In fact, to ensure that there is no gap in the supply chain robots are being put to use. Self-scanning robots used by Walmart could be a case in point here. These robots look for items that need restocking. Some stores are going one step further to use algorithms to analyze receipts to find out which products are in most demand and they restock accordingly.

Virtual assistants taking care of customers

Customers have no access to virtual assistants, chatbots who not only offers constant support but, also interacts with them offering personalized recommendations, as these bots are powered with NLP technology, they are more intuitive and capable of engaging with customers. In fact, with automation being available, sending a faster response to customer queries has also become more efficient. Navvi is a robot that handles customers along with handling other responsibilities.

Enabling shoppers to take immediate action

Any average person these days spends a good amount of time browsing through social media platforms, different sites which more often than not are used as advertising platforms. So, when a prospective customer finds something interesting, they check it out and then they go on to something else and later might forget about it.

But with AI-powered tools like Lens feature, they can capture the image of the product they like and search for it, thereby ensuring that they can embark on their shopping quest. This feature was initially introduced by Pinterest. With further application of deep learning for computer vision with python, there could be more developments in the field.

Taking chaos out of shopping with smart solutions

When buyers visit a store physically or, virtually they usually browse through scores of products to find what they need. Oftentimes they have difficulty locating the product they had selected online in the physical store. But, with a unique tool like Amazon Go, they can completely be at ease.

They can select the items and put in a virtual basket and when they enter the physical store they can easily track the items they had previously selected and that’s it. No complications involved and they enjoy seamless shopping experience. Zara takes a step further and deploys robots who fetch the product ordered and delivers it. 

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Identifying prospective leads

AI has introduced some exclusive features such as face recognition, which is being now utilized by retailers to target potential leads. This is leading to a seamless merger of experience one might get in a physical and virtual store. Face recognition feature is being used to find out which products customers are spending time on in the store and based on that, recommendations are being sent online.

The shoppers are no doubt having the time of their lives enjoying this digital shopping experience, they are now able to find and buy products they need instead of wasting money on something random. The retail sector is all set to take the next big leap with AI. Retail Analytics Courses are going to be in demand as the sector needs personnel who are proficient in data handling.

 


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Studies Show Indian Employers Prefer Experienced Workers Over Freshers

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Studies Show Indian Employers Prefer Experienced Workers Over Freshers

Employability and the scramble for top Jobs in India 

Looking to hire new talent or searching for a job? Well, some insights several studies and surveys provide about the job scenario in India might interest you.

The Millenial

Indian millennials, aged between 18 and 35 years, according to studies ( wheebox.com/assets/pdf/ISR_Report_2020.pdf ) makes nearly half the Indian workforce and looks likely to remain so for the next decade. This generation of workers are not only working hands but likely consumers as well, strong in their opinions, with access to the internet and social media across urban and rural areas. What they are most ardently looking for are jobs that respect their talent, pay them adequately and improve their employability in the market. 

Employability in India

Employability has remained stagnant for several years now with around 46 per cent candidates job-ready. Of those employed, trends revealed

  • MBA’s in India are now projecting a rate of 54 per cent employability, acquiring the highest paying jobs
  • Employers prefer candidates with work experience, especially 1-5 years. Freshers are least preferred at 15 per cent.
  • The AI industry is showing promise wherein some reports pegged the number of job openings in AI and Machine learning sector at almost 1million in India last year. 
  • Employability for pass-outs of B.Pharma, B.com, BA and Polytechnics showed an increase of around 15% since 2019.
  • Prospective workers from Maharashtra, Tamil Nadu and Uttar Pradesh were found to be most employable
  • While women are as employable as men, women’s participation in the workforce remains at a low 25 per cent vis a vis that of men.

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What employers seek

  • Domain knowledge
  • Adaptability to the work environment
  • Learning ability and agility
  • Positive attitude

What employees seek

  • Majority of Students, around 88 per cent of those surveyed, sought internship opportunities though the supply did not meet demand in most cases
  • Maharashtra, Tamil Nadu and Andhra Pradesh were preferred and most sought after in terms of work opportunity
  • Over 55% students expect the annual salary to be above Rs. 2.6 lacs, a figure which has remained constant for the past few years

Ways to improve employability

Most students or potential candidates, surveys show, seek proper guidance and training and internship opportunities as varied as customer market analysis courses to customer marketing analysis training and courses teaching retail analytics using Python. While most universities lack the wherewithal to skill their outgoing students, students prefer to sign up for short courses online to equip themselves with the requisite knowledge specific to their industry. All this done with a view to increase their employability in a market deeply customer driven.

 

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An All-Inclusive Guide on Python and its Changing Trends

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An All-Inclusive Guide on Python and its Changing Trends

Python is an extremely readable and versatile high-level programming language. Many companies such as Google, YouTube, Dropbox use the language for developing applications. It also finds its use extensively in diverse fields as in Python for data analysis, Machine Learning Using Python, Natural Language Processing, Web Development, Scientific Computing, Image processing, Robotics, Computer Vision and many more.

It supports both Object-oriented programming and Functional programming. Python is generally referred to as an interpreted language which implies that each line of code is executed one by one and if the interpreter finds an error, it stops immediately with an error message on the screen.

Another important feature of Python is its interactive prompt. A Python statement can be typed and immediately executed, which is in sharp contradiction to any other compiled language.

What are Python 2.x and Python 3.x?

There are two main versions of Python: Python 2.x and Python 3.x. If someone is new to Python, then he/she might be in confusion about which version to use. However, in the current scenario, we can easily migrate from Python 2 to Python 3, as the Python Software Foundation has finally taken the step to formally announce that Python 2 will reach the end of life (EOL) on January 1st, 2020.

Key differences between Python 2.x and Python 3.x

This article discusses the differences between these two versions of Python, making Python 3 less confusing for a new programmer.

  1. Print Function

In Python 2, print is a statement. There is no need of parenthesis.

In Python 3, print is a function. It needs parenthesis.

  1. Integer Division

In Python 2, if the division operator is performed on two integers, then the output will be an integer for example: – 7/3 = 2.

In Python 3, if the division operator is performed on two integers, then the output will be accurate. It can also be in float for example: – 7/3 = 2.33.

To get the result in an integer only a different division operator is used that is (//) it returns an integer result for example, – 7//3 = 2.

 3. Unicode Support

Both the versions of Python can handle strings (sequences of characters) differently.

Python 2 uses the ASCII encoding standard by default. ASCII is limited to representing 256 characters. This limits the flexibility of Python to encode the characters, particularly non-standard ones. Using Unicode in Python 2 requires extra syntax—for example when using print, the input text is to be wrapped in the Unicode() function to handle special characters.

In Python 3, Unicode is the default. The Unicode standard is much more versatile—it supports over 128,000 characters. There is no need for an extra syntax to define the Unicode values—they get printed automatically as utf-8 strings.

  1. Range Function

In Python 2, the range function returns a list of numbers.

In Python 2, the xrange class represents an iterable that provides the same object.

 In Python 3, original range function is removed and xrange is renamed to range:

In Python 3, it is needed to convert the range object to a list if someone desires the same result as the range function provides in Python 2.

  1. ­­­­Input() Method

Mainly what is expected from the input() method is that it reads input as string, then it can be converted into any datatype as per the requirement.

In Python 2, it has both the input() and raw_input() methods for taking input. The difference between the raw_input() and input()is that the raw_input() reads input as a string while the input() reads input as string only if it is inside quotes else reads as an integer.

In Python 3, there is no raw_input() method. The raw_input() method is replaced by input() in python 3. 

If someone still wants to use the input() method like in python 2, then it can be availed by using eval() method.

There are many other differences between Python 2 and Python 3 like: –

  1. Next() Method

In Python 2, .next() method is used and in Python 3 next() function is used to iterate the next element of an iterator.

  1. Raising Exception

To raise an exception in Python 3, the argument should be in parenthesis, while in Python 2, it is not necessary.

  1. Handling Exception

Handling exception is also changed in Python 3, “as” keyword is used in Python 3, while it is not necessary in Python 2.

So, if someone is a beginner, then it is strongly recommended to use Python 3 because it is the future of Python and also January 1, 2020, will be the last day of Python 2. It means that no improvement will be done anymore after that day, even if someone finds a security problem in it.

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It is highly recommended to upgrade the version of the programming language to Python 3. Some ways can help the Python 2 users in porting their code from Python 2 to Python 3 and get the feel of Python 3 and figure out how it is different from Python 2. The code can be imported by using tools like “Futurize” and “Modernize”. Also, if someone wants to check the availability of Python 3 as part of his tests, then “caniusepython3.check()” can be used.

As a final note, everyone must look for upgrading their Python version to Python 3 to understand the subtleties of the new version and usher in the future. However, if you are interested in Deep learning for computer vision with Python and similar courses, then opt for the premium Python training institute in Delhi now!


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R Vs Python: A Debate Forever

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R Vs Python: A Debate Forever

In this blog, we will bring forth the age old question and check which one is better, R programming and Python programming, when it comes to data science?

To be very honest, this question does not have a strict answer to it. However, in this blog we will lay down the key components of both the languages to give you a clearer picture. In the end, please decide for yourself and leave your comments in the section below.

The aim of this blog is to objectively put forward the pros and cons of both languages strictly from the perspective of data science.

We will discuss only about three main components, which are as follows:

  • Syntax
  • Performance
  • Applicability

There are other metrics, such as, trends in Industries and adaptation in the recent years which are beyond the scope of this blog. However, you can safely declare Python as the clear winner if those perspectives were concerned.

So let’s get started:

Syntax

Both R and Python are object-oriented languages. This is to say that everything is created as an object in which the information is mapped with the idea of using that object later in the analysis. However, when it comes to the syntax, i.e., the grammar of programming, R and Python are indeed very different.

R Programming

R programing is more suited to more seasoned coders who have prior experience of coding. The syntax is actually very similar to that of the previous languages, such as C, or C++ or Java and so on. The fundamental rules are that of C programming language. Also, use of semicolons is deemed optional in R. However, semicolons are necessary for multiple lines in a code inside a code block.

Deep Learning and AI using Python

Python

Python on the other hand, is the language more adaptable to the new generation of programmers. You can come from a non-programming background and still learn Python with relative ease.

Python is one of the most user friendly languages for the beginners. The syntax is designed to prioritize readability over preciseness of the code. In layman’s terms – coding in Python is very close to reading and writing with hand. In this regard, it is really popular amongst beginners in Data Science.

Performance

The performance is essentially measured by speed essentially when it comes to programming.

R Programming

As far as the general consensus goes R programming is much slower in terms of speed. The reason behind this is that R programming was initially designed to be used by statisticians for data analysis. Thus, R programming stresses more on precision than the speed.

Python

Python on the other hand, is relatively faster than R. Python offers the same level of precision whilst acting on a faster speed.

Note – The speed is taken into account independent of packages and libraries.

Applicability

Lastly, we will discuss the popular domains in which these languages are used.

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R Programming

As mentioned above, R was developed specifically for statisticians. For this reason, R is mainly used in various research organizations and academia in general. However, R is now quickly being absorbed in the enterprises as well, mainly because of its popularity and the availability of a large number of packages for statistical computation.

Python

Python is a gene

As Python is a general-purpose programming language we can use to build different kinds of applications. We can use Python to build web applications using popular frameworks like Django or Flask.

Lately, Python is becoming popular amongst data scientists as the language of choice given the simplicity of syntax, high speed and performance it has to offer. There has been a trend which has seen a sharp rise in the adaptability of Python over R in the last few years in Data Science.

So, there you have it folks. Decide for yourself now! We will meet you soon in the next blog.

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Statistical Application in R & Python: Poisson Distribution

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Statistical Application in R & Python: Poisson Distribution

Continuing with the series of blogs, the first of which was Statistical Application In R & Python: Normal Probability Distribution, here we bring you a post on how you can calculate Poisson distribution effortless using R & Python. So, stay tuned!

Poisson distribution is a counting process which is a discrete probabilistic model. It has only one parameter, (lambda or “m”) which is essentially the average rate of change. Poisson distribution is used to model “number of anything”. The probability distribution function of a Poisson distribution is given by the below expression.

If m is the mean occurrence per interval, then the probability of having x occurrence with in a given interval is:

Application:

A business firm receives on an average 6.5 telephone calls per day during the time period 11:00 – 11:15 A.M., Find the probability that on a certain day, the firm receives exactly9 calls during the same period.

The random variable x is the ‘number of telephone calls received during the period 11:00 – 11:15 A.M, since x is assumed to Poisson distribution. The parameter m is equal to the mean of the distribution; i.e.  m = 6.5 and x = 9, then the equation is:

Calculate Poisson Distribution in R:

So, while calculating Poisson distribution in R, we notice that the probability of occurring exactly 9 calls instead of average 6.5 calls in a given particular time (11:00 A.M – 11:15 A.M ) = 85.81%

Calculate Poisson Distribution in Python:

So, while we calculate Poisson distribution in Python, we notice that the probability of occurring exactly 9 calls instead of average 6.5 calls in a given particular time (11:00 A.M – 11:15 A.M) = 85.81%

Conclusion:

Companies can use the Poisson distribution to contrive effective steps to improve their operational efficiency. For instance, an analysis done with the Poisson distribution might reveal how a company can arrange staffing in order to be able to handle the peak periods efficiently, when the customer service calls keep on pouring.

In this problem we see that the business firm receives on an average 6.5 telephone calls per day during the time period 11:00A.M – 11:15A.M, then the probability of the firm receives exactly 9 calls in a same is 85.81%.

Dexlab Analytics is the best Python training institute in Delhi, bringing you the all-inclusive courses of Python for Data Analysis and R Predictive Modelling Certification, among others to start your career in Data Science and Analytics.

 

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Statistical Application in R & Python: Normal Probability Distribution

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Statistical Application in R & Python: Normal Probability Distribution

Gauss, the famous French Mathematician is responsible for developing one of the most significant distributions in all of statistics, i.e. – The Normal Distribution. Please refer to the blog on Central Limit Theorem: www.dexlabanalytics.com/blog/the-almighty-central-limit-theorem. It will help you fully grasp the significance of the Normal Distribution. However, if you want to revisit our series of blogs by following it from the start, you can reach STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY right now!

Essentially, the Normal Distribution provides “approximations” to most other distributions such as the Binomial, Poisson, Gamma, Exponential, etc. This is to say as sample sizes get statistically large enough, most distributions approximate into a normal shaped curve.

Every distribution has important features known as its “parameters”. Normal distribution has two parameters. These are Mean ( ) and Variance (σ²). The normal distribution has a bell-shaped curve, where the probability of likelihood peaks at its mean in the middle.

The Normal Distribution has vast practical applications in the field of Business, Finance, Medicine, and Physics and so on. Things like weights, heights, IQ scores follow the Normal Distribution.

Normal Distribution, Gaussian distribution, is a continuous probability distribution and is defined by the Probability Density Function (PDF).

Where,

Application:

Assume that the credit score fits a Normal Distribution.

Suppose Mr. Arjun’s last 10 month’s credit score are:

789, 635, 739, 687, 724, 810, 817, 735, 819, 820

What is the probability that the percentage of credit score will 825 or more in the 11th month?

Months

Credit Score

January

789

February

635

March

739

April

687

May

724

June

810

July

817

August

735

September

819

October

820

 

Calculating Normal Distribution in R:

If we go to calculate Normal Probability Distribution in R, we can predict that the probability of the 11th month credit score will be 825 or greater than that is 14.60%, whereas in another case, the probability of the 11th month credit score will be 825 or less than that is 85.40%.

Calculate Normal Distribution in Python:

Make a data frame of the data and calculate Mean and Standard Deviation for calculate Normal Distribution.

Now, we can easily calculate Normal Distribution in Python

So, in calculating the Normal Probability Distribution in Python, we can predict that the probability of the 11th month credit score will be 825 or greater than that is 14.60%, whereas in another case, the probability of the 11th month credit score will be 825 or less than that is 85.40%.

Conclusion:

Normal Distribution is used for calculating parameters. It is represented by the bell curve, where the total area of the curve is 1. Normal Distribution has its use in Finance, Business, Salaries, Blood Pressures, Measurement etc and many other fields.

Here, we have used Normal Distribution to predict Mr. Arjun’s 11th month credit score, and set the target (825). By Normal Distribution we can predict the percentage of possibility to achieve the target.

Calculating Binomial Distribution might be tricky for many but with Dexlab Analytics it won’t be hassle anymore. So, get hold of our STATISTICAL APPLICATION IN R AND PYTHON: CALCULATING BINOMIAL DISTRIBUTION blog, to get around all your problems.

 

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Retail 4.0: How Trending Technologies Are Influencing the Retail Industry?

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Retail 4.0: How Trending Technologies Are Influencing the Retail Industry?

The retail industry is undergoing unprecedented changes: courtesy Retail 4.0! It is the term used to denote the transformation that’s taking place at a rapid pace. Technological advancements and customer expectation are key driving factors behind the evolution.

Customers are the bedrock of the retail industry. They are fickle and demanding. With higher spending power and low brand loyalty, they are redefining the consumer trends and forcing retailers to harness the power of big data to ensure a seamless, positive customer experience coupled more secure payment methods and easier online store formats.

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Data is Power

For years, retailers have been working on consumer’s behavior and how to serve them well. Today, amidst increasing competition, data explosion and advanced technological implementations, they seem to lose their erstwhile charm. Data is the answer. In a digital-enabled landscape, retail industry players need to leverage several emerging technologies, such as augmented reality, virtual reality, mixed reality, AI and Internet of Things and draw clear actionable insights.

Gone are the days when retailers relied on their instincts and formulated marketing strategies. Today, predictive analytics is used to boost informed decision-making and conclude the future success of an enterprise. Put simply, retail analytics using Python is the tool to drive optimization, follow corrective measures and reduce revenue leakage. With data at the forefront, retail analytics and its diverse platforms are providing customers with relevant products, superior service and the facility to experience the products even before purchase.

How Does It Work?

Retail analytics targets customer acquisition and focuses on customer study. Through data analysis, the retailers ascertain buying patterns and curated customer engagement strategies. For that, deep insights are generated based on their search criteria, purchase records and frequency of shopping.

Also, retailers can now predict demand precisely. Based on a customer’s historical data, they anticipate when he/she is likely to make a purchase decision and within what duration of time. They can also predict the products the customers are going to re-purchase with the help of AI. Robust machine learning algorithms deliver insights that specify accurate customer recommendations, which help increase retailers’ profit margin.

Deep Learning and AI using Python

Understanding the nuances of consumer behavior is of utmost importance. This is why IoT and AI are combined and used in monitoring customer-store interactions – resulting in better service engagements and higher revenue. Social media has added to the effect. Extracting user information from social media platforms has become a piece of cake. Retail market players can now leverage the social media data, influence customer purchase decisions and enjoy a certain edge against the tailing rivals.

As endnotes, retailers need to embrace the digital transformation and create fresh, enhanced experiences to entice the consumers. After all, the future belongs to the data-inspired companies. So, just stay ahead of the curve using data as the power tool.

 

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Straight Out of College? Grasp These Killer Data Science Skills

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Straight Out of College? Grasp These Killer Data Science Skills

Data Science is one of the most demanding fields in the present world. Going hand in hand with the Artificial Intelligence, Data Science is showing a colossal growth in the coming years. So, honestly speaking, you should be prepared with all of the cutting-edge tools and up skill yourself accordingly to pace up with the modern world.

According to Derek Steer, CEO of Mode, the world will generate 50 times more data than what we were present in 2011. Moreover, with the data processing power becoming easy and inexpensive for most of the firms, candidates with real skill and a hunger for knowledge would only see their way through till the end, added Steer.

Among various other skills like retail analytics using Python, neural network machine learning Python, which are dominating and/or expected to rule the world of technology in the upcoming years, here we list you some of them:

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Data Visualization

This is one of the top notch skills that you can find now. It is process of maintaining data with the help of graphical representations. This further makes the interpretation and thereby, the comprehension of data, much easier.

This is an extremely relevant skill which is not to be found among the high schoolers. This makes the undergraduates or post graduates with the knowhow of data visualisation all the more important everywhere.

Data Modelling

Data Modelling is the second most wanted skill that the entire world is seeking for. In a nutshell, Data Modelling is the process of understanding and using data to seek relationships across varied sets of information.

It is, in fact, a skill which is gaining an immense popularity among the fresh graduates. You can also reach Dexlab Analytics to gain an insight of all the industry relevant courses and enrol yourself asap to speed up your career!

Deep Learning and AI using Python

Python

Python is undoubtedly the most demanding language ever in the history of computer science; hence, it enjoys all the attention that it gets.

With its welcoming nature to every other architecture, which is in sharp contradiction to Java and C++, Python is preferred all the way. Secondly, Python is quite a powerful language and effective too, when it comes to bulk data and a need to process them faster.

It is basically an open source program which is easy accessible and largely customised. This is really a gift for upcoming world of Data Science. Thus, Python for data analysis is an invaluable skill that you can develop to make yourself marketable like never before.

We hope you liked our post! You can Take A Deep Look On How Machine Learning Boosts Business Growth! and more such topics on our website.

 

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Hacking is Wide and Dangerous in India, CBI Reports

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Hacking is Wide and Dangerous in India, CBI Reports

The recent conference organized by the Central Bureau of Investigation on Cyber forensic notes that over 22,000 websites were hacked in India between April 2017 – Jan 2018. Not the best of the news for the nation which is largely counting on their citizens to be tech-savvy.

In the conference, CBI disclosed of its plans to build a cutting edge Centralised Technology Vertical (CTV) to fight crimes, voiced by Minister of State for Personnel, Jitendra Singh. The CTV is a huge project involving around Rs 99 crore, which will not only share the real-time information about the cyber attacks but also of the perpetrators.

From young superintendents of police to top brass of security agencies, police forces, law enforcement officers and the Intelligence attended this conference and discussed about the alarming rise of cybercrimes throughout the country.

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The Major Issue

Jurisdictional issues were a main problem and hit greatly on the investigation in these cases because most of the incidents of cybercrimes are triggered from foreign lands. Though the total loss of money from the recent cybercrimes weren’t disclosed, some debilitating cases in cybercrimes were dicussed once again, which included the loss of USD 171 million from union Bank of India’s Swift.

To End it

To lessen the magnitude of the cybercrimes, the CBI is on their way towards reinforcing them with the state of the art technology. Besides, you can also take up courses in PHP, HTML, Python Certification Training in Delhi, to be informed of the trending languages and be future proof.

 

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