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A Quick Guide To Natural Language Processing (NLP) And Its Applications

 A Quick Guide To Natural Language Processing (NLP) And Its Applications

When you interact with Alexa, or, conduct a voice search on Google, do you wonder about the technology behind it? What is it that makes it possible to communicate with machines as you would with a human being?

Natural Language Processing (NLP) AKA computational linguistics is a subset of Artificial Intelligence, makes all of it possible by combining artificial intelligence, machine learning, and language to facilitate interaction between computers and humans.

So how does NLP work?

When you put a voice command, it gets recorded and then the audio gets converted into text, and the NLP system starts working on the context and the intention of the command. Basically, the words that we speak are labeled and get converted into a set of numbers. However, Human language is complex and has many nuances and underlying subtexts. The same word under a different context can have different connotations. So, when a simple command is put is gets easier for the machine to follow through as it contains simpler words and clear context, but, the system needs to evolve more to fully process the complex language patterns that evolved through ages. There are courses available such as natural language processing course in gurgaon that can help one acquire specialized knowledge in this field.

NLP and its applications

NLP despite being in a nascent stage is getting recognized for its potential and being applied for executing various tasks.

Sentiment Analysis to assess consumer behavior

  This functionality of NLP is an important part of social media analytics that parses the comments and reactions of users regarding products, brand messages spread over social media platforms to detect the mood of the person. This helps businesses gauge customers’ behavior and to make necessary modifications accordingly. 

Email filtering and weeding out spam

If you are a user of Gmail then you must have noticed the way your emails get segmented once they arrive. Primary, Social, and Promotions are the three broad categories followed by others, there is a segment for spams as well. This smart segmentation is a stark example of NLP at work.

Basically text classification technique is used here to assign a mail to a certain category that is pre-defined. You must have also noticed how well your spam is sorted, this is another result of the application of NLP where certain words trigger the spam alert and the mail gets sent straight to the spam folder. However, this sorting is yet to be perfected via further research.

Automatic summarization to find relevant information

Now thanks to digitization we have to deal with huge amounts of data which has led to information overload. This massive amount of data needs to be processed to find actionable information. Automatic Summarization makes it possible by processing a big document and presenting an accurate and short summary of it.


No discussion on NLP can ever be complete without mentioning the chatbots. The customer service segment is gaining huge benefits from these smart chatbots that can offer virtual assistance to the customers 24×7.  Chatbots can not only enhance customer experience but, are also great for reducing costs for any business. However, modern-day chatbots can handle simple, mundane queries that do not require any special knowledge and skill, in the future we could hope to see the bots handling specialized queries in real-time.

Spell and grammar checker

If you have ever used Grammarly and felt impressed with the result then you must have wondered at some point how does it do it? When you put in a text, it not only looks for punctuations but also points out spelling errors and also shows grammatical errors in places where there is no subject-verb agreement. In fact, you also get alternative suggestions to improve your writing. All of this is possible thanks to transformers used by NLP.

Machine Translation

If you are familiar with Google and its myriad apps then you must be familiar with Google Translate. How quickly it translates your sentences in a preferred language format, machine translation is one application of NLP that is transforming the world. We always talk about big data but making it accessible to people scattered across the globe divided by language barriers could be a big problem. So, the NLP enabled us with machine translation that uses the power of smart algorithms to translate without the need of any human supervision or intervention. However, there is still huge room for improvement as languages are full of nuanced meanings that only a human is capable of understanding.

 What are some examples of NLP at work?

We are not including Siri, or, Alexa  here as you are already familiar with them

  • SignAll is an excellent NLP powered tool that is used for converting sign language into text.
  • Nina is a virtual assistant that deals with banking queries of customers.
  • Translation gets easier with another tool called Lilt that can integrate with other platforms as well.
  • HubSpot integrated the autocorrect feature into its site search function to make searching hassle-free for users.
  • MarketMuse helps writers create content that is high-quality and most importantly relevant.

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Just like AI and its various subsets, NLP is also a field that is still evolving and has a long journey ahead. Language processing is a function that needs more research because simulating human interaction is one thing and processing languages that are so nuanced is not a cakewalk. However, there are plenty of good career opportunities available and undergoing an artificial intelligence course in delhi would be a sound career move.



How Can AI Help In Streamlining Small Business Operations?

How Can AI Help In Streamlining Small Business Operations?

Running a business is a challenging job, especially when business operations take place on a small-scale platform. Small business owners need constant motivation and brainstorming to keep their business in a profitable position.

However, as the world is busy deriving benefits from AI technology, and professionals opting for artificial intelligence course in delhi for better career prospects, small business owners too should seize this opportunity to power up their businesses. 

Why the small business owners are shying away from AI?

With biggies like Google, Amazon,  Apple, Microsoft empowering themselves with AI tools, small businesses are somewhat showing reluctance towards the new technology. Only a small percentage of businesses ranging from small to medium have so far been influenced by it. A 2018 survey showed the number to be around 13.6%.

This indicates there is some inhibition in the small business community, but, it might not just be that, when questioned most small business owners often cite reasons like lack of expertise, financial concern to be the causes.

They are mostly under the misconception that such advanced technology is best suited for giant platforms and their small scale businesses are not going to rake in any profit, even if they invest. They don’t even have tons of data like most businesses, to begin with. So, AI being a data-driven technology, might not work for them.

Their perception is gradually changing because of the way AI has started seeping through the very fiber of civilization and impacting so many aspects of life. It is not possible for small businesses to indulge in AI research or, develop a platform specifically for their business needs for feasible reasons but, they can get ideas regarding how best to conduct business the AI way.

Let’s find out how AI can be incorporated into small business infrastructure to improve five core areas.

Smarter sales and marketing with AI-powered CRMs

A CRM is an indispensable tool for any business, let alone a small one. Basically, a CRM works to garner customer data from various platforms to enable the sales and marketing team to keep track of their valuable customers while pursuing new leads.

The fusion of AI and CRM could do wonders as it is evident from the way Einstein AI, introduced by SalesForce is working. 

You stand to gain insight into the customer mindset as this fusion will work to analyze customer mindset by analyzing the conversations that happened across different channels. This insight can help shape your sales and marketing efforts accordingly especially if you can upskill your team with customer market analysis courses.

Keep an eye on your rivals

Staying one step ahead of your biggest competition in the market is a crucial need no business owner big or small can afford to ignore. However, it is not easy to monitor every move they make, but, AI can be your biggest ally in helping you track your rival’s every digital move.

AI-powered Crayon, is a smart tool that monitors what your competition is doing on social media, across websites and applications, you can gauge their performance and activities and keep a tab on their marketing strategies, pricing, and other such issues to make suitable modifications to your own.

Automate customer service

 Handling your customers is an important but, tedious task and as your business starts to grow so does your customer base and their queries. Investing in a big support team might not always be a feasible option for you, so why don’t you take advantage of chatbots to automate the whole process and make it more efficient?

Not all queries are important, some of these are generic which the chatbots can handle while your sales team can focus on more personalized or, technical queries to keep your customers happy. Answering support tickets can be easy with a tool like Digital Genius. It is a great option even for those businesses which can’t afford a support team.

Smoother HR operations

No matter how small scale your business might be, you still have to manage your employees and hire new ones, which means having an HR team ready round the clock. Now you can manage this segment bypassing all hassles thanks to AI-powered platforms that automate your HR functions, be it screening applications, scheduling interviews or, onboarding the new employees, every segment will be well taken care of. Not just that but, the administrative jobs that the HR have to do repetitively could be automated too.

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Customize your customer journey

Customers like to be pampered, when they shop from big brand names they hardly get to experience that personalized approach. The trendy products aimed at mass-market leave them wishing for something that would suit their style. Retail Analytics Courses can help you develop a better understanding of the whole issue.

 Being a small business owner you already have the advantage to take care of this issue and you can be near perfect in your approach if you get support from smart AI algorithms that can browse through customer data and detect patterns to help you understand the personal preferences of customers and thereby allow you to modify your products accordingly to suit their needs.

Coupled with AI power you could also improve your logistics to ensure your supply chain does not experience any glitch.

The development of AI platforms programmed to perform specialized tasks need to be recognized by the small business community, only then they would find the motivation to channelize the power in the right direction. They can also consider upskilling themselves with deep learning for computer vision course, to be able to harness the power of AI.



Retail Therapy Powered By AI

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.



Studies Show Indian Employers Prefer Experienced Workers Over Freshers

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 ( ) 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,, 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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A Handbook of the Basic Data Types in Python 3: Strings

A Handbook of the Basic Data Types in Python 3: Strings

In general, a data type defines the format, sets the upper & lower bounds of the data so that a program could use it appropriately. Data types are the classification or categorization of data items which describes the character of a variable. The most used data types are numeric, non-numeric and Boolean (true/false).

Python has the following standard Data Types:

  • Booleans
  • Numbers
  • String
  • List
  • Tuple
  • Set
  • Dictionary

Mutable and Immutable Objects

Data objects of the above types are stored in a computer’s memory for processing. Some of these values can be modified during processing, but the contents of the others can’t be altered once they are created in the memory.

Number values, strings, and tuple are immutable, which means their contents can’t be altered after creation.

On the other hand, the collection of items in a List or Dictionary object can be modified. It is possible to add, delete, insert, and rearrange items in a list or dictionary. Hence, they are mutable objects.


A Boolean is such a data type that almost every programming language has, and so does Python. Boolean in Python can have two values – True or False. These values can be used for assigning and comparison.


Numbers are one of the most prominent Python data types. In Numbers, there are mainly 3 types which include Integer, Float, and Complex.


A sequence of one or more characters enclosed within either single quotes ‘or double quotes” is considered as String in Python. Any letter, a number or a symbol could be a part of the string. Multi-line strings can be represented using triple quotes,”’ or “””.

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Python list is an array-like construct which stores a heterogeneous collection of items of varied data typed objects in an ordered sequence. It is very flexible and does not have a fixed size. The Index in a list begins with a zero in Python.


A tuple is a sequence of Python objects separated by commas. Tuples are immutable, which means tuples once created cannot be modified. Tuples are defined using parentheses ().


A set is an unordered collection of items. Set is defined by values separated by a comma inside braces { }. Amongst all the Python data types, the set is one which supports mathematical operations like union, intersection, symmetric difference etc. Since the set derives its implementation from the “Set” in mathematics, so it can’t have multiple occurrences of the same element.


A dictionary in Python is an unordered collection of key-value pairs. It’s a built-in mapping type in Python where keys map to values. These key-value pairs provide an intuitive way to store data. To retrieve the value we must know the key. In Python, dictionaries are defined within braces {}.

This article is about one specific data type, which is a string. The String is a sequence of characters enclosed in single (”) or double quotation (“”) marks.

Here are examples of creating strings in Python.

Counting Number of Characters Using LEN () Function

The LEN () built-in function counts the number of characters in the string.

Creating Empty Strings

Although variables S3 and S4 do not contain any characters they are still valid strings. S3 and S4 both represent empty strings here.

We can verify this fact by using the type () function.

String Concatenation

String concatenation means joining one or more strings together. To concatenate strings in Python we use + operator.

String Repetition Operator (*)

Just like in numbers, * operator can also be used with strings. When used with strings * operator repeats the string n number of times. Its general format is: 1 string * n,

where n is a number of type int.

Membership Operators – in and not in

The in or not in operators are used to check the existence of a string inside another string. For example:

Indexing in a String

In Python, characters in a string are stored in a sequence. We can access individual characters inside a string by using an index.

An index refers to the position of a character inside a string. In Python, strings are 0 indexed. This means that the first character is at index 0; the second character is at index 1 and so on. The index position of the last character is one less than the length of the string.

To access the individual characters inside a string we type the name of the variable, followed by the index number of the character inside the square brackets [].

Instead of manually counting the index position of the last character in the string, we can use the LEN () function to calculate the string and then subtract 1 from it to get the index position of the last character.

We can also use negative indexes. A negative index allows us to access characters from the end of the string. Negative index starts from -1, so the index position of the last character is -1, for the second last character it is -2 and so on.

Slicing Strings

String slicing allows us to get a slice of characters from the string. To get a slice of string we use the slicing operator. Its syntax is:


str_name[start_index:end_index] returns a slice of string starting from index start_index to the end_index. The character at the end_index will not be included in the slice. If end_index is greater than the length of the string then the slice operator returns a slice of string starting from start_index to the end of the string. The start_index and end_index are optional. If start_index is not specified then slicing begins at the beginning of the string and if end_index is not specified then it goes on to the end of the string. For example:

Apart from these functionalities, there are so many built-in methods for strings which make the string as the useful data type of Python. Some of the common built-in methods are as follows: –

capitalize ()

Capitalizes the first letter of the string

join (seq)

Merges (concatenates) the string representations of elements in sequence seq into a string, with separator string.

lower ()

Converts all the letters in a string that are in uppercase to lowercase.

max (str)

Returns the max alphabetical character from the string str.

min (str)

Returns the min alphabetical character from the string str.

replace (old, new [, max])

Replaces all the occurrences of old in a string with new or at most max occurrences if max gave.

 split (str=””, num=string.count(str))

Splits string according to delimiter str (space if not provided) and returns list of substrings; split into at most num substrings if given.


Converts lowercase letters in a string to uppercase.


So in this article, firstly, we have seen a brief introduction of all the data types of python. Later in this article, we focused on the strings. We have seen several Python operations on strings as well as the most common useful built-in methods of strings.

Python is the language of the present age, wherein almost every field there is a need for Python. For example, Python for data analysisMachine Learning Using Python has been easy and comprehensible than they were ever before. Thus, if you are also interested in Python and looking for promising courses Computer Vision Course PythonRetail Analytics using PythonNeural Network Machine Learning Python, then get in touch with Dexlab Analytics now and step into the world of opportunities!


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Python Statistics Fundamentals: How to Describe Your Data? (Part II)

Python Statistics Fundamentals: How to Describe Your Data? (Part II)

In the first part of this article, we have seen how to describe and summarize datasets and how to calculate types of measures in descriptive statistics in Python. It’s possible to get descriptive statistics with pure Python code, but that’s rarely necessary.

Python is an advanced programming language extensively used in all of the latest technologies of Data Science, Deep Learning and Machine learning. Furthermore, it is particularly responsible for the growth of the Machine Learning course in IndiaMoreover, numerous courses like Deep Learning for Computer vision with Python, Text Mining with Python course and Retail Analytics using Python are pacing up with the call of the age. You must also be in line with the cutting-edge technologies by enrolling with the best Python training institute in Delhi now, not to regret it later.

In this part, we will see the Python statistics libraries which are comprehensive, popular, and widely used especially for this purpose. These libraries give users the necessary functionality when crunching data. Below are the major Python libraries that are used for working with data.

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NumPy and SciPy – Fundamental Scientific Computing

NumPy stands for Numerical Python. The most powerful feature of NumPy is the n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities. NumPy is much faster than the native Python code due to the vectorized implementation of its methods and the fact that many of its core routines are written in C (based on the CPython framework).

For example, let’s create a NumPy array and compute basic descriptive statistics like mean, median, standard deviation, quantiles, etc.

SciPy stands for Scientific Python, which is built on NumPy. NumPy arrays are used as the basic data structure by SciPy.

Scipy is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transforms, Linear Algebra, Optimization and Sparse matrices. Specifically in statistical modelling, SciPy boasts of a large collection of fast, powerful, and flexible methods and classes. It can run popular statistical tests such as t-test, chi-square, Kolmogorov-Smirnov, Mann-Whitney rank test, Wilcoxon rank-sum, etc. It can also perform correlation computations, such as Pearson’s coefficient, ANOVA, Theil-Sen estimation, etc.

Pandas – Data Manipulation and Analysis

Pandas library is used for structured data operations and manipulations. It is extensively used for data preparation. The DataFrame() function in Pandas takes a list of values and outputs them in a table. Seeing data enumerated in a table gives a visual description of a data set and allows for the formulation of research questions on the data.

The describe() function outputs various descriptive statistics values, except for the variance. The variance is calculated using the var() function in Pandas.

The mean() function, returns the mean of the values for the requested axis.

Matplotlib – Plotting and Visualization

Matplotlib is a Python library for creating 2D plots. It is used for plotting a wide variety of graphs, starting from histograms to line plots to heat plots. One can use Pylab feature in IPython notebook (IPython notebook –pylab = inline) to use these plotting features inline. If the inline option is ignored, then pylab converts IPython environment to an environment, very similar to Matlab.

matplotlib.pylot is a collection of command style functions.

If a single list array is provided to the plot() command, matplotlib assumes it is a sequence of Y values and internally generates the X value for you.

Each function makes some change to a figure, like creating a figure, creating a plotting area in a figure, decorating the plot with labels, etc. Now, let us create a very simple plot for some given data, as shown below:

Scikit-learn – Machine Learning and Data Mining

Scikit-learn built on NumPy, SciPy and matplotlib. Scikit-learn is the most widely used Python library for classical machine learning. But, it is necessary to include it in the discussion of statistical modeling as many classical machine learning (i.e. non-deep learning) algorithms can be classified as statistical learning techniques. This library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensional reduction.


In this article, we covered a set of Python open-source libraries that form the foundation of statistical modelling, analysis, and visualization. On the data side, these libraries work seamlessly with the other data analytics and data engineering platforms, such as Pandas and Spark (through PySpark). For advanced machine learning tasks (e.g. deep learning), NumPy knowledge is directly transferable and applicable in popular packages such as TensorFlow and PyTorch. On the visual side, libraries like Matplotlib, integrate nicely with advanced dashboarding libraries like Bokeh and Plotly.


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

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.


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

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:


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 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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Watch Out: Top Retail Trends 2018 That Might Redefine Industry Goals

They must change – retailers finally understood this basic but true fact. For years, the retail honchos were averse to change – they preferred everything to be smooth and consistent like they were in previous years.

Watch Out: Top Retail Trends 2018 That Might Redefine Industry Goals

Now, the retail game-play is changing altogether. Today, it’s the customer who defines the entire shopping experience. No longer, storing data in traditional silos is termed as a viable option – the integration of omni-channel trade and tech-inspired merchandizing is the go-to option. Already, several well-funded retailers and global store giants are on their way to exploit the data power – they are adjusting their working mechanisms and resorting to assortment and innovations because that’s the only way to survive and sail away!

Looking ahead, here are some of the biggest retail trends to watch in 2018:

A dramatic evolution in technology

Technological transformation holds a fresh can of possibilities for retailers, but its implementation demands a lot of attention. While 2017 was reckoned to be the year of digital discovery, 2018 is going to be the year when retailers will adapt with the changing market and experience evolution in their customer’s needs. Hence, evolution will be the key to success.

Opportunities in AI are also on the rise. Chatbots, robotics and facial recognition and image recognition technologies are unleashing robust opportunities this year. Retailers are hoarding large chunks of data to curate personalized experiences for customers, and win their hearts away. More data means improved algorithm performance, and the best thing is that retailers are going on generating significant amounts of data, through both offline and online mediums. Artificial intelligence in retail can be utilized in many ways, right from improving product specifications and enhancing customer service experience.

Artificial intelligence coupled with machine learning and Internet of Things supports customer experience – there exists amazing opportunity for retailers to gain by using these new age concepts. For better data utilization, get yourself an excellent data analyst training from DexLab Analytics.

Mobile payments will usher us into a cashless economy

China has already gone cashless; thanks to AliPay and WeChat Pay. Following that, the rest of the world is looking up to the likes of Amazon Pay, Walmart Pay, Apple Pay and other types of cryptocurrencies. It’s only a matter of time before global consumers replace their plastic debit cards with more efficient and faster mobile payment options.

Work on improving offline experiences too

Not only online, but retailers should consider looking into offline experiences – how they can keep shopping as human, real and visual as possible. The mode of shopping might be transforming, but humans and their preferences are still the same. Customer experience is still important and offline experience will just focus on that.

Robotic retail is scaling up

In the E-commerce industry, the robot to human ratio is fast changing. While Walmart is testing retail robots, drone delivery is increasingly becoming popular and a viable solution. By 2020, its predicted consumer facing robots will show up in retail stores, all over.


Improvements in technology mean a lot of retail growth. And when its technology, we can’t leave behind DATA. It’s like the new currency in the retail scenario – for a comprehensive Retail Analytics Courses, visit DexLab Analytics.

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