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A Quick Guide To Predictive Analytics

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A Quick Guide To Predictive Analytics

Ever since the world woke up to discover the significance of data, there has been tremendous advancement in this field each taking us further towards the utilization of accumulated data to achieve a higher level of efficiency. Predictive analytics is all about extracting hidden information in data and combining technologies like machine learning, artificial intelligence, data analysis, statistical modeling to predict future trends.

Sifting through stored datasets comprising structured and unstructured data, predictive analytics identifies the patterns hidden and analyzes those patterns to make predictions about trends and thereby helps to identify opportunities as well as risk factors. Not just forecasting, but predictive analytics also helps you find associations that could lead you to a new breakthrough. Having undergone big data training in gurgaon, could actually prove to be a big boost for someone planning on working in this specialized field. Now, when you have access to data-based forecasting, it is easy for you to identify both negative and positive trends and in turn, it helps you take the right decisions.

Businesses especially rely heavily on predictive analytics for market analysis, targeting their customers, and assessing risk factors. Unlike before when these business strategies were based on mere guesswork, now the think-tank has access to data to anticipate an outcome.

Predictive analytics models: Predictive analytics models could be classified into two broad categories as follows

Classification models: In this model data is categorized on the basis of some specified criterion. 

Regression models: Regression models focus on identifying patterns that already exist, or, that has been continuing for a while.

So, what are the processes involved in Predictive analytics?

Predictive analytics process could be broken down to different stages and let’s take a look at what the steps are

Defining the Project: This is the first stage when you decide what kind of outcome you are expecting. Besides setting out clear business objectives you also need to be clear about the deliverables as these will have a bearing on your data collection.

Collecting all Data: This is the second stage where data from different sources are collected.

Analyzing Data: In this stage, the data collected is cleaned and gets structured and also gets transformed and modeled.

Statistics: A statistical model is used to test the assumptions, hypotheses, as well as findings.

Modeling: Through multi-model evaluation best option is chosen from an array of available options.  So, the idea is to create an accurate predictive model.

Deployment: This is the stage of deploying the predictive model and create an option for deploying the results for productive purposes in reality.

monitoring: the final and an important stage where the models created are monitored and tested with new data sets to check whether the models still have relevance.

The applications of predictive analytics

Predictive analytics models have found usage across industries

  • In the financial sector, predictive analytics could be used for credit risk measurement, detecting fraud as well as for minimizing the risk, and also for retaining customers.
  • In the field of healthcare predictive analytics could be used for detecting severe health complications that might develop in a patient in the future.
  • In business predictive analytics could be used for short-term or, long-term sales forecasting. In fact, the reaction of the customer could be anticipated and changes could be made accordingly.
  • When a huge investment is involved predictive analytics could help to identify the problematic areas that could pose risk. Accurate risk assessment could help a company secure a better ROI.
  • Predictive analytics could help companies with their customer service, marketing campaigns, sales goals. Companies can strategize better to retain customers and improve their relations with them.
  • With predictive analytics in place, it would be easier to predict equipment maintenance needs and it could also be used for forecasting an equipment failure.

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Predictive analytics is being adopted in a number of industries ranging from insurance to healthcare. The forecasting that one gets is highly accurate. However, building a reliable dataset and building a reliable model is essential. Having trained personnel on the team who have done data analyst course in delhi, could be helpful.


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A Quick Guide to Data Visualization

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A Quick Guide to Data Visualization

The growing significance of big data and the insight it imparts is of utmost significance. Data scientists are working round the clock to process the massive amount of data generated every day. However, unless you have been through Data Science training, it would be impossible for you to grasp even an iota of what is being communicated through data.

The patterns, outliers every single important factor that emerged through decoding must be presented in a coherent format for the untrained eyes. Data visualization enables the researchers to present data findings visually via different techniques and tools to enable people to grasp that information easily.

Why data visualization is so vital?

The complicated nuances of data analysis is not easier for anybody to understand. As we humans are programmed to gravitate towards a visual representation of any information, it makes sense to convey the findings through charts, graphs, or, some other way. This way it takes only a couple of moments for the marketing heads to process what is the trend to watch out for. 

We are used to seeing and processing the information presented through bars and pie charts in company board meetings, people use these conventional models to represent company sales data.

It only makes sense to narrate what the scientists have gathered from analyzing complex raw data sets, via visual techniques to an audience who needs that information to form data-driven decisions for the future.

So what are the different formats and tools of data visualization?

Data visualization can take myriad forms which may vary in the format but, these all have one purpose to serve representing data in an easy to grasp manner. The data scientist must be able to choose the right technique to relate his data discovery which should not only enlighten the audience but, also entertain them.

The popular data visualization formats are as follows

Area Chart
Bubble Cloud/Chart
 Scatter Plot
Funnel Chart
Heat Map
The formats should be adopted in accordance with the information to be communicated

Data scientists also have access to smart visualization tools which are

  • Qlikview
  • Datawrapper
  • Sisense
  • FusionCharts
  • Plotly
  • Looker
  • Tableau

A data scientist must be familiar with the tools available and be able to decide on which suits his line of work better.

What are the advantages of data visualization?

Data visualization is a tricky process while ensuring that the audience does not fall asleep during a presentation, data scientists also need to identify the best visualization techniques, which they can learn during big data training in gurgaon to represent the relationship, comparison or, some other data dynamic.
If and when done right data visualization  has several benefits to offer

Enables efficient analysis of data

In business, efficient data interpretation can help companies understand trends. Data visualization allows them quickly identify and grasp the information regarding company performance hidden in the data and enables them to make necessary changes to the strategy.

Identify connections faster

While representing information regarding the operational issues of an organization,  data visualization technique can be of immense help as it allows to show connections among different data sets with more clarity. Thereby enabling the management to quickly identify the connecting factors. 

Better performance analysis

Using certain visualizing techniques it is easier to present a product or, customer-related data in a multi-dimensional manner. This could provide the marketing team with the insight to understand the obstacles they are facing. Such as the reaction of a certain demographic to a particular product, or, it could also be the demand for certain products in different areas.  They are able to act faster to solve the niggling issues this way.

Adopt the latest trends

 Data processing can quickly identify the emerging trends, and with the help of data visualization techniques, the findings could be quickly represented in an appealing manner to the team. The visual element can immediately communicate which trends are to watch out for and which might no longer work.

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 Encourages interaction

Visual representation of data allows the strategists to not just look at numbers but, actually understand the story being told through the patterns. It encourages interaction and allows them to delve deeper into the patterns, instead of just merely looking at some numbers and making assumptions.

Data visualization is certainly aiding the businesses to gain an insight that was lost to them earlier. A data scientist needs to be familiar with the sophisticated data visualization tools and must strike a balance between the data and its representation. Identifying what is unimportant and which needs to be communicated as well as finding an engaging visual technique to quickly narrate the story is what makes him an asset for the company.  A premier Data analyst training institute can help hone the skills of an aspiring data scientist through carefully designed courses.

 


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How Legal Analytics Can Benefit Law Firms?

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How Legal Analytics Can Benefit Law Firms?

As different sectors are waking up to realize the significance of big data, the law firms are also catching up. After all it is one of the sectors that have to deal with literally massive amounts of data.

The popularity of legal analytics software like Premonition is a pointer to the fact that even though the industry was initially slow on the uptake, it is now ready to harness the power of big data to derive profit.

 So what exactly is legal analytics?

Legal analytics involves application of data analysis to mine legal documents and dockets to derive valuable insight. Now there is no need to confuse it with legal research or, to think that it is an alternative to the popular practice. Legal analytics is all about detecting patterns in past case records to enable firms strategize better in future. It basically aims to offer aid in legal research. Training received in an analytics lab could help a professional achieve proficiency.

Legal analytics platform combines sophisticated technologies of machine learning, NLP. It goes through past unstructured data and via cleaning and organizing that data into a coherent structure it analyzes the data to detect patterns.

How law firms can benefit from legal analytics?

Law firms having to deal with exhaustive data holding key information can truly gain advantage with the application of legal analytics. Primarily because of the fact it would enable them to anticipate what the possible outcome might be in order to strategize better and increase their chances of turning a case in their favor. Data Science training could be of immense value for firms willing to adopt this technology.

Not just that but implementation of legal analytics could also help the law firms whether big or, small run their operations and market their service in a more efficient manner and thereby increasing the percentage of ROI.

The key advantages of legal analytics could be as followed

  • The chances of winning a case could be better as by analyzing the data of past litigations, useful insight could be derived regarding the key issues like duration, judge’s decision and also certain trends that might help the firm develop a smarter strategy to win a particular case.
  • Cases often continue for a long period before resulting in a loss. To save money and time spent on a particular case, legal analytics could help lawyers decide whether to continue on or, to settle.
  • Often legal firms need to hire outside expertise to help with their case, the decision being costly in nature must be backed by data. With legal analytics it would be easier to go through data regarding a particular candidate and his performance in similar cases in the past.
  • There could be a significant improvement in the field of operational efficiency. In most of the situations lawyers spend huge amount of time in sorting through case documents and other data. This way they are wasting their time in finding background information when they could be spending time in offering consultation to a potential client and securing another case thereby adding financial benefit to the firm. The task of data analysis should better be handled by the legal analytics software.  
  • At the end of the day a law firm is just another business, so, to ensure that the business operations of the firm are being managed with efficiency, legal analytics software could come in handy. Whether it’s budgeting or, recruiting or retaining old staff valuable insight could be gained, which could be channeled to rake in more profit.

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There has been an increase in the percentage of law firms which have adopted legal analytics, but, overall this industry is still showing reluctance in fully embracing the power. The professionals who have apprehension they need to set aside the bias they have and recognize the potential of this technology. May be they should consider enrolling in a Data analyst training institute to gain sharper business insight.

 


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The Data Science Life Cycle

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The Data Science Life Cycle

Data Science has undergone a tremendous change since the 1990s when the term was first coined. With data as its pivotal element, we need to ask valid questions like why we need data and what we can do with the data in hand.

The Data Scientist is supposed to ask these questions to determine how data can be useful in today’s world of change and flux. The steps taken to determine the outcome of processes applied to data is known as Data Science project lifecycle. These steps are enumerated here.

  • Business Understanding

Business Understanding is a key player in the success of any data science project. Despite the prevalence of technology in today’s scenario it can safely be said that the “success of any project depends on the quality of questions asked of the dataset.”One has to properly understand the business model he is working under to be able to effectively work on the obtained data.

  • Data Collection

Data is the raison detre of data science. It is the pivot on which data science functions. Data can be collected from numerous sources – logs from webservers, data from online repositories, data from databases, social media data, data in excel sheet format. Data is everywhere. If the right questions are asked of data in the first step of a project life cycle, then data collection will follow naturally.

  • Data Preparation

The available Data set might not be in the desired format and suitable enough to perform analysis upon readily. So the data set will have to be cleaned or scrubbed so to say before it can be analyzed. It will have to be structured in a format that can be analyzed scientifically. This process is also known as Data cleaning or data wrangling. As the case might be, data can be obtained from various sources but it will need to be combined so it can be analyzed.

For this, data structuring is required. Also, there might me some elements missing in the data set in which case model building becomes a problem. There are various methods to conduct missing value and duplicate value treatment.

“Exploratory Data Analysis (EDA) plays an important role at this stage as summarization of clean data helps in identifying the structure, outliers, anomalies and patterns in the data.

These insights could help in building the model.”

  • Data Modelling

This stage is the most, we can say, magical of all. But ensure you have thoroughly gone through the previous processes before you begin building your model. “Feature selection is one of the first things that you would like to do in this stage. Not all features might be essential for making the predictions. What needs to be done here is to reduce the dimensionality of the dataset. It should be done such that features contributing to the prediction results should be selected.”

“Based on the business problem models could be selected. It is essential to identify what is the task, is it a classification problem, regression or prediction problem, time series forecasting or a clustering problem.” Once problem type is sorted out the model can be implemented.

“After the modelling process, model performance measurement is required. For this precision, recall, F1-score for classification problem could be used. For regression problem R2, MAPE (Moving Average Percentage Error) or RMSE (Root Mean Square Error) could be used.”The model should be a robust one and not an overfitted model that will not be accurate.

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  • Interpreting Data

This is the last and most important step of any Data Science project. Execution of this step should be as good and robust as to produce what a layman can understand in terms of the outcome of the project.“The predictive power of the model lies in its ability to generalise.” 

 


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A Deep Dive Into The US Healthcare System in New York

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A Deep Dive Into The US Healthcare System in New York

Unlike India’s healthcare system wherein both public and private entities deliver healthcare facilities to citizens, in the US, the healthcare sector is completely privatised.

The aim of this notebook is to study some of the numerical data we have for the US and especially data for New York. Most of us know about New York’s situation that is one of the worst in the world.

Therefore, analysing data may clarify a few things. We will be using three sets of data – urgent care facilities, US county healthcare rankings 2020 and Covid sources for counties.

For the data and codesheet click below.

 

Now pick key column names for your study with ‘.keys’ as the function name. We are interested in a few variables from health rankings so we take only the ones we think will be useful in a new data frame.

We will study each data set one by one so that we can get an understanding of the data before combining them. For this we call the plotly library that has very interactive graphs. We use the choropleth to generate a heat map over the country in question.

Fig. 1.

It is clear form the heat map that New York has a very high incidence of infections vis a vis other states. We then begin working with data on the number of ICU beds in each state. Since each state will have different populations, we cannot compare the absolute number of ICU beds. We need the ratio of ICU beds per a given number of inhabitants.

Fig. 2.

The generated heat map (Fig. 2.) shows the ICU density per state in the US. For more on this do watch the complete video tutorial attached herewith.

This tutorial was brought to you by DexLab Analytics. DexLab Analytics is a premiere data analyst training institute in Gurgaon.

 


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Covid-19 – Key Insights through Exploration of Data (Part – II)

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Covid-19 - Key Insights through Exploration of Data (Part - II)

This video tutorial is on exploratory data analysis. The data is on COVID-19 cases and it has been taken from Kaggle. This tutorial is based on simple visualization of COVID-19 cases.

For code sheet and data click below.

 

Firstly, we must call whatever libraries we need in Python. Then we must import the data we will be working on onto our platform.

Now, we must explore PANDAS. For this it is important to know that there are three types of data structures – Series, Data Frame and Panel Data. In our tutorial we will be using data frames. 

Fig. 1.

Fig. 1

Now we will plot the data we have onto a graph. When we run the program, we get a graph that shows total hospital beds, potentially available hospital beds and available hospital beds.

Fig. 2.

Fig. 2

While visualizing data we must remember to keep the data as simple as possible and not make it complex. If there are too many data columns the interpretation will be a very complex one, something we do not want.

Fig. 3.

Fig. 3

A scatter plot (Fig. 3.) is also generated to show the reading of the data available.  We study the behaviour of the data on the plot.

For more on this, view the video attached herewith. And practise more and more with data from Kaggle. This tutorial was brought to you by DexLab Analytics. DexLab Analytics is a premiere data analyst training institute in Gurgaon.


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How Company Leaders and Data Scientists Work Together

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How Company Leaders and Data Scientists Work Together

Business leaders across platforms are hungrily eyeing data-driven decision making for its ability to transform businesses. But what needs to be taken into account is the opinion of data scientists in the core company teams for they are the experts in the field and whatever they have to say regarding data driven decisions should be the final word in these matters.

“The ideal scenario is all parties in complete alignment. This can be envisioned as a perfect rectangle, with business leaders’ expectations at the top, fully supported by a foundation of data science capabilities — for example, when data science and AI can achieve management’s goal of reducing customer retention costs by automating identification and outreach to at-risk customers,”says a report.

The much sought after rectangle, however, is rarely achieved. “A more workable shape is the rhombus, depicting the push-and-pull of expectations and deliverables.”

Using the power of your company’s data.

Business leaders must have patience with developments on the part of data scientists for what they expect is usually not in sync with the deliverables on the ground.

“Over the last few years, an automaker, for example, dove into data science on leadership’s blind faith that analytics could revolutionize the driver experience. After much trial and error, the results fell far short of adding anything meaningful to what drivers found valuable behind the wheel of a car.”

Appreciate Small Improvements

Also, what must be appreciated are small improvements made impactful. For instance, “slight increases in profitability per customer or conversion rates” are things that should be taken into account despite the fact that they might be modest gains in comparison to what business leaders had invested in analytics. “Applied over a large population of customers, however, those small improvements can yield big results. Moreover, these improvements can lead to gains elsewhere, such as eliminating ineffective business initiatives.”

Healthy Competition

However, it is advisable for business leaders to constantly push their data scientists to strive for more deliverables and improve their tally with a framework of healthy competition in place. In fact, big companies form data science centers of excellence, “while also creating a healthy competitive atmosphere that encourages data scientists to push each other to find the best tools, strategies, and techniques for solving problems and implementing solutions.”

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Here are three ways to inspire data scientists

  1. Both sides must work together –Take the example of a data science team with expertise in building models to improve customers’ shopping experiences. “Business leaders might assume that a natural next step is to use AI to enhance all customer service needs.”However, AI and machine learning cannot answer the ‘why’ or ‘how’ of the data insights. Human beings have to delve into those aspects by studying the AI output. And on the other hand, data scientists also must understand why business leaders expect so much from them and how to achieve a middle path with regard to expectations and deliverables.
  2. Gain from past successes and achievements – “There is value in small data projects to build capabilities and understanding and to help foster a data-driven culture.”The best policy for firms to follow is to initially keep modest expectations. After executing and implementing the analytics projects, they should conduct a brutally honest anatomy of the successes and failures, and then build business expectations at the same time as analytics investment.
  3. Let data scientists spell out the delivery of analytics results – “Communication around what is reasonable and deliverable given current capabilities must come from the data scientists — not the frontline marketing person in an agency or the business unit leader.” Before signing any contract or deal with a client, it is advisable to allow the client to have a discussion with the data scientists so that there is no conflict of ideas between what the data science team spells out and what the marketing team has in mind. For this, data scientists will have to work on their soft skills and improve their ability to “speak business” regarding specific projects.


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Statistical Application in R & Python: EXPONENTIAL DISTRIBUTION

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Statistical Application in R & Python: EXPONENTIAL DISTRIBUTIONStatistical Application in R & Python: EXPONENTIAL DISTRIBUTION

In this blog, we will explore the Exponential distribution. We will begin by questioning the “why” behind the exponential distribution instead of just looking at its PDF formula to calculate probabilities. If we can understand the “why” behind every distribution, we will have a head start in figuring out its practical uses in our everyday business situations.

Much could be said about the Exponential distribution. It is an important distribution used quite frequently in data science and analytics. Besides, it is also a continuous distribution with one parameter “λ” (Lambda). Lambda as a parameter in the case of the exponential distribution represents the “rate of something”. Essentially, the exponential distribution is used to model the decay rate of something or “waiting times”.

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For instance, you might be interested in predicting answers to the below-mentioned situations:

  • The amount of time until the customer finishes browsing and actually purchases something in your store (success).
  • The amount of time until the hardware on AWS EC2 fails (failure).
  • The amount of time you need to wait until the bus arrives (arrival).

In all of the above cases if we can estimate a robust value for the parameter lambda, then we can make the predictions using the probability density function for the distribution given below:

Application:-

Assume that a telemarketer spends on “average” roughly 5 minutes on a call. Imagine they are on a call right now. You are asked to find out the probability that this particular call will last for 3 minutes or less.

 

 

Below we have illustrated how to calculate this probability using Python and R.

Calculate Exponential Distribution in R:

In R we calculate exponential distribution and get the probability of mean call time of the tele-caller will be less than 3 minutes instead of 5 minutes for one call is 45.11%.This is to say that there is a fairly good chance for the call to end before it hits the 3 minute mark.

Calculate Exponential Distribution in Python:

We get the same result using Python.

Conclusion:

We use exponential distribution to predict the amount of waiting time until the next event (i.e., success, failure, arrival, etc).

Here we try to predict that the probability of the mean call time of the telemarketer will be less than 3 minutes instead of 5 minutes for one call, with the help of Exponential Distribution. Similarly, the exponential distribution is of particular relevance when faced with business problems that involve the continuous rate of decay of something. For instance, when attempting to model the rate with which the batteries will run out. 

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Hopefully, this blog has enabled you to gather a better understanding of the exponential distribution. For more such interesting blogs and useful insights into the technologies of the age, check out the best Analytics Training institute Gurgaon, with extensive Data Science Courses in Gurgaon and Data analyst course in Delhi NCR.

Lastly, let us know your opinions about this blog through your comments below and we will meet you with another blog in our series on data science blogs soon.

 

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Alteryx is Inclined to Make Things Easy

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Alteryx is Inclined to Make Things Easy

Alteryx Analytics is primarily looking to ease the usability of the platform in all of the updates that are yet to come. The esteemed data analytics platform is concentrating on reducing the complexities to attract more users and thus, widen their age-old user base beyond that of the data scientists and data analytics professionals.

Alteryx is headquartered in Irvine, California. It was founded as SRC LLC in 1997 and comes with a suite of four tools to help the world of data scientists and data analysts to manage and interpret data easily. Alteryx Connect, Alteryx Designer, Alteryx Promote and Alteryx Server are the main components of the analytics platform of Alteryx. Thus, it is worth mentioning that the Alteryx Certification Course is a must if you are looking to make a career out of data science/data analytics.

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A Quick Glance at the Recent Updates 

The reputed firm launched a recent version of Alteryx 2019.3, in October, and is likely to release the Alteryx 2019.4 as a successor to it. The latter is scheduled for a December release.

What’s in the Update?

Talking about the all-new version Alteryx 2019.3, Ashley Kramer, senior vice president of product management at Alteryx, said that the latest version promises 25 new and upgraded features, all of them focussing on the user-friendliness of the platform at large.

One of the prominent features of the new version is a significant decrease in the total number of clicks that a user will take to arrive at the option of visualizing data to make analytic decisions.

Data profiling helps the users to visualize the data while they are working with it. Here, Alteryx discovered a painless way to work with data by modeling the bottom of the screen in a format similar to that of MS Excel.

All of these changes and additions are done keeping in mind the features that the “customers had been asking for,” according to Kramer.

Now, with the December update, which will come with an enhanced mapping tool, the Alteryx analytics will strive to further lower the difficulties surrounding the platform.

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If you are interested in knowing all the latest features, it is better to join one of the finest AlterYX Training institutes in Delhi NCR, with exhaustive Analytics Courses in Delhi NCR, along with other demanding courses like Python for Data Analysis, R programming courses in Gurgaon, matchless course of Big Data, Data Analytics and more.

 
The blog has been sourced from ― searchbusinessanalytics.techtarget.com/news/252474294/Alteryx-analytics-platform-focuses-on-ease-of-use
 

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