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Category: R Programming

Stay Home and Upskill to Beat the Impact of a Global Recession

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Stay Home and Upskill to Beat the Impact of a Global Recession

The US economy, as it was officially announced by the United States National Bureau of Economic Research on June 8, entered a recession in February after hitting a peak of economic activity and growth. This is the first time the US economy has undergone a recession since the global financial crisis of 2008-09, says a report.

In the US alone, 19.6 lakh cases of covid-19 positive patients have been reported till date with 1.1 lakh cases of deaths recorded, the highest for any country in the world. In such a dire situation, the silver lining seems to be the fact that this recession, intensified by the lockdown that the country has imposed on itself to abate the spread of the disease, might be deep but short lived, The New York Times reported.

Irrespective of when the recession will end, poverty levels have already begun spiking the world over. The World Bank has said that, “the highest share of countries in 150 years would enter recessions at the same time. As many as 90% of the 183 economies () examined are expected to suffer from falling levels of gross domestic product (GDP) in 2020, even more than the 85% of nations suffering from recession during the Great Depression of the 1930s”, The Guardian reported.

This will lead to dramatic rise in levels of poverty the world over. However, India might fare better on the global front for more reasons than one. Some economists feel “the (Indian) economy may do better than some other developing economies, which are heavily dependent on world trade” because of “lower dependence on exports (that) means less exposure to the decline in world trade. This and the low price of crude oil, our biggest import, may mean that we don’t suffer an external shock”.

In such circumstances, it is advisable that you stay home and not despair. Doing nothing but fretting will only add to your woes and not help the situation. Neither will binge-watching web series help. Instead, what you can do is ready yourself for a post COVID-19 world. You can do this by primarily upskilling yourself i.e.upgrading your skill set.

The only way to do this is remotely, though online classes available by the dozen. In fact, celebrities like Shakira have begun taking online classes (she in ancient philosophy) this lockdown while others like director Kevin Smith have finished old pending projects. The best skills to upgrade would, however, be those pertaining to computer science courses like big data, machine learning, deep learning or even credit risk modelling. These high-in-demand courses will look good on your résumé and instantly add to your employability wherever you plan to move to next.

Data Science Machine Learning Certification

In India, DexLab Analytics, a premier institute offering some of the best credit risk modelling training courses and R programming courses in Gurgaon, suggests you try and learn a new programming language or enrol in a new business analytics course so your résumé stands stronger than it was before the lockdown. This will help you beat competition when you will be searching for work opportunities post the lockdown.

 


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Bayes’ Theorem – Application in R and Python

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Bayes’ Theorem – Application in R and Python

Bayes’ theorem, named after 18th century (1763) British mathematician Thomas Bayes, is a mathematical formula for determining conditional probability.  In the discussion of conditional probability we indicated that revising probability when new information is obtained is an important phase of probability analysis. Often, we begin our analysis with initial or prior probability estimates for specific events of interest. Then, from sources such as a sample, a special report, a product test, etc we obtain some additional information about the events. Given this new information, we update the prior probability values by calculating revised probabilities, referred to as posterior probabilities.

The steps involved in this probability revision process are depicted in the digram below:

  • Theorem:

An event A can occur only if one of the mutually exclusive and exhaustive set of events B1, B2,… ,Bn occurs. Suppose that the unconditional probabilities

And the conditional probabilities

are known. Then the conditional probability of a specified event Bi, when A is stated to have actually occurred, is given by

This is known as Bayes’ Theorem.

  • Proof:

An event A can happen in mutually exclusive ways, B1 A, B2A,… Bn A, i.e. either when has occurred, or. So by the theorem of total probability

 

Again,

Since the events ABi and BiA are equivalent, their probabilities are also equal.

Hence

So that

Substituting for P(A) from above, the theorem is proved.

Equation (1) is also known as “Bayes” formula for calculating probabilities of hypothesis. Because B1, B2,…Bn may be considered as hypothesis which account for the occurrence of A. The probabilities P(B1),P(B2 ),…P(Bn) are called ‘a prior’ probabilities of the hypothesis.

While are known as a‘a posteriori’ probabilities of the same hypothesis.

Data Science Machine Learning Certification

For more on this, do peruse the Dexlab Analytics website today. Dexlab Analytics is a premiere institute for R programming courses in Gurgaon.

 


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Machine Learning Jobs in 2019: Freezing your own Job

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Machine Learning Jobs in 2019: Freezing your own Job

Machine Learning surely needs no introduction. Joining forces with Data Science and Big Data, Machine Learning is one of the principal technologies, which is carving the future for us. From self-propelled cars to voice assistants, to surgical robots, Artificial Intelligence is already amongst us.

Besides, with this cutting-edge technology, marketing is also witnessing a fresh bloom, irrespective of the field you are working on. Thus, it is obvious that the career opportunities have quickly and radically shifted in the way of the candidates who are well-versed with Machine Learning platforms and languages. If you are also looking forward to shooting your career up, the premium Machine Learning course in India is the place you should reach now!

2

Learning Machine Learning is No More a Pain Now!

Whether you are a professional or a fresher planning your way to be successful as a Machine Learning professional, you must ensure that you are updated. Besides, you should also be careful that you have certain skills in your grip that you can work on!

However, if you are not aware of them still, here are the skills that you need to focus on to rest assured:

Programming Languages

As you speak English and/or your regional languages accepted to your society in order to communicate comprehensibly, you also need to be well-versed with the languages specific to Machine Learning.

In a nutshell, R programming certification and Machine Learning Using Python are undoubtedly the most significant ones when it comes to Machine Learning.

Data Modeling

If you believe that you can already boast of considerable knowledge of R & Python, then you shall extend your knowledge a bit more towards the advanced methods of analysis. Brief know how of the coding structures, Data Modeling and Data Visualization will help you steer your career forwards.

Deep Learning and AI using Python

Statistics and Probability

If you are seeking to make a career out of Machine Learning, it is important to note that you should have a good grip of statistics and probability. Now, with the thorough courses of Python for Data Analysis along with extensive knowledge of statistics and probability from Dexlab Analytics, it will be easier than ever.

Besides all these, you also need to grasp significant insights into the improved algorithms and clustering methods. 

 

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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: 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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Statistical Application in R and Python: Calculating Binomial Distribution

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Statistical Application in R and Python: Calculating Binomial Distribution

In this blog, we will take a look at the Binomial distribution. This blog is among the series of blogs through which you’ll have a vivid idea of the Statistical Application using R and Python. Statistical Application In R & Python: Chapter 1 – Measure Of Central Tendency is the first of such blogs.

The binomial distribution is an extension of the Bernoulli distribution. In Bernoulli, we have only one parameter, i.e. the probability of success.

Now, consider a case where we have “n” number of trials and we want to predict the probability of success from it. This is the Binomial case.

Binomial distribution has two parameters, i.e.: number of trails (n) AND probability of success (p). The mean of the binomial is a product of its two parameters, i.e. n multiplied by p. It is a discrete probability distribution. Here, each trial is assumed to have only two outcomes, either success or failure.

If X be a discrete random variable (taking only non-negative values), it is said to be following binomial distributions with a probability mass function as:-


Application:

A food shop starts a offer for a festive season, They have 12 different baskets, each basket has 5 combos and only 1 of them is non-veg. Find the probability of having 4 or less non-veg combos, if a consumer tries every combos at random.

Since, only 1 out of 5 combos is non-veg, the probability of choose a non-veg combos by random is 1/5 = 0.2

Calculate Binomial Distribution in R:

In R the probability of one non-veg combos choose by random in 5 is 13.28%, whereas the probability of four or less combos choose by random in a twelve baskets is 92.44%

Calculate Binomial Distribution in Python:

In Python the probability of one non-veg combos choose by random in 5 is 16.66%.

Conclusion:-

Binomial Distribution is the process by which we can calculate the probability of success from “n” number of trails. In Binomial Distribution we can find only two outcomes like “Yes” or “No”.

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Application of Median Using R And Python: Calculating Median On the Go

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Application of Median Using R And Python: Calculating Median On the Go

This blog is in continuation of STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY and takes you through a comprehensive way to calculate the Median in R and Python.

The term ‘Median’ is derived from the Latin word – ‘Medius’ means the center of something. In mathematics, Median is treated is that unique observation which would divide your data set into two equal halves.

If you are still unclear about Mean and/or seeking easier ways to calculate Mean using R & Python, then check APPLICATION OF HARMONIC MEAN USING R AND PYTHON and CALCULATING GEOMETRIC MEAN USING R AND PYTHON.

Median is special because unlike its rival, the Mean, Median is not ridiculed by the curse of extreme values. To illustrate the curse of extreme values, we bring you the following example:

Imagine I had the following data about the average annual salaries:

In Lacs

8.5
9
11
7
8
8.5
36

The mean of the above data set is: 88/7 = 12.57 lacs.

Whereas, to get the median we would have to first arrange the data into ascending order and look for the midpoint of my data i.e.,(1/2 + n/2)th observation. Where “n” is the number of observations.

The median would then be:

7
8
8.5
8.5
9
11
36

Median is the 4th observation, which is 8.5 lacs.

Looking at the mean and median, it would be fair to conclude that median is the better choice to accurate summarizing the data set whenever extreme values are present. However, this may be a crude generalization which should be taken with a pinch of salt. Despite its flaws, the mean still has statistical properties used in predictive analytics which the median lacks.

Application:

A construction company gave wages to their 10 labor (Let name A to J)  as a weekly basis, the wages are 2000, 2100, 1900, 2150, 2500, 2450, 1800, 2600, 2200, 2300. Compute the Median wages of the construction company.

Sr.NoLaborsWages (Weekly)
1A2000
2B2100
3C1900
4D2150
5E2500
6F2450
7G1800
8H2600
9I2200
10J2300

Calculation Median in R:

Python Certification

The Median wage is 2175, calculate in R.

Calculate Median in Python:

Create a data frame of the data in Python.

R Programming Certification

Now, calculate Median in Python.

R Programming Certification

The Median wage is 2175, calculated in R.

This concludes the post. If you have any queries with regards to this post, you can reach us at Dexlab Analytics. Furthermore, you can also look up for interesting and quality courses of R Programming Certification, Python Certification. Also, you can enroll with us for our combined courses of Data Science with Python Certification, Deep Learning and AI using Python, among others. So, hurry up and grab the best course!

 

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6 Essential Skills Data Scientists Need to Add to Their Resumes

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6 Essential Skills Data Scientists Need to Add to Their Resumes

Like all other career paths, cracking the hottest job of 21st century is mainly about gaining knowledge and developing important skills relevant to the job. And your resume should reflect all these skills. So what must the resume of a professional data scientist look like? Here are 6 key skills that must be in the fingertips of a good data scientist.

Stats and Math:

Not only blue-chip tech companies, even medium and small scale enterprises are operated by data science these days. And statistical knowledge is vital for that. You should be thorough with general statistical concepts, like distributions, tests, range, likelihood estimators, etc.

In mathematics, one must know the basics of linear algebra and multivariable calculus. This will definitely make a difference in your work outcomes as it enables you to improve predictive presentations.

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Excellent Programming and Computing Skills:

Simply put, being good at coding is a must. So, if you are a budding data scientist you must actively work towards developing a computing mind; you should be able to understand, write and even analyze code whenever necessary. This level of dexterity only comes through meticulous study and practice of not one, but a number of programming languages.

If you want to develop a programming skill which is especially designed for data scientists, then get enrolled for R programming certification. Over 40 percent data scientists prefer R for solving stat problems. But it must be noted that R isn’t easy to learn, especially for those who aren’t comfortable with codes.

Python is another language which is highly preferred by data scientists because it is very adaptable and hence, can be employed in all the different steps part of a data science project. Moreover, data sets can be created with ease and SQL tables can be imported into working codes when required. Considering these benefits and the fact that over 50% data scientists favor Python, an excellent Python Certification in Delhi should be first in your list of courses to undertake.

Live Projects

Learning isn’t effective unless you implement it practically. Moreover, your skills get duly appreciated when it’s demonstrated. Hence, always look for live projects you can join and try to understand the data architecture behind the screen. It may be up there in your head, but it needs to be implemented. Large companies actually prefer candidates who have more practical experience rather than just bookish knowledge.

Managing Unstructured Data

Unstructured data is any type of content that doesn’t fit into traditional database tables. These data types aren’t well organized and hence, sorting them becomes very difficult. Blogs, videos and customer reviews are some examples of unstructured data. Being able to manage unstructured data is an important skill for data scientists. Apache Hadoop, NoSQL and Microsoft HDI insight are some good software for tackling unstructured data. If you are interested to learn the techniques, you can look up the course details for Hadoop certification in Delhi at DexLab Analytics.

Storytelling with Data

Data scientists might have to work with complicated models and datasets, but they must know how to express their deductions in lucid language that’s simple and engaging. Hence their raw data must be expressed in the form of tables, charts and graphs, which are visually appealing and can capture the attention of stakeholders.

Academics and Degrees

A strong educational background is the door to the world of data science. Big companies prefer applicants who are master degree holders in either stats or math or computer science or physical science.

Data science is definitely the trendiest job and you might be eager to land one, but it’s not easy to acquire the above mentioned skills. If you are looking for guidance from experts who have previously worked in this field, then you should get enrolled for Data Science Courses in Delhi right away. The industry experts at DexLab Analytics tailor the courses to the unique needs of students and incorporate ample practical cases to help them get ready for the challenges ahead.

 

Reference: www.analyticsindiamag.com/7-things-data-scientists-must-have-in-their-resumes

 

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R Programming: The Language Marketers Use to Tame Data

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R Programming: The Language Marketers Use to Tame Data

How to manage data? This is a question that’s baffles us each and every time, whenever we look at data.

The real challenge is not about managing data, but how to synchronize processes to expose the issues with data. Today’s marketers may have a tough time tackling these challenges. Even more for non-tech-savvy marketers, they may be feel a bit overwhelmed, but we’ve a solution – R programming language is capable of performing specific tasks while preparing data for machine learning models or advanced analytics.

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Basics of R

R programming is a popular open source language ideal for smart data visualization and statistical modeling. Generally, it functions through a terminal on a laptop, but you can also enjoy development environment software that makes R quite user-friendly.

One of the most sought after Integrated Development Environment (IDE) is RStudio – it’s very popular amongst practitioners mostly owing to its quad-window view, which let users view their results in the terminal beside the whiteboard platform.

Exploring Data with R

Data importing is the starting point of analyzing data. Fortunately, a more than sufficient number of R programming libraries exist today that are up to interface with a database or an API. Some of these libraries are: twitteR, RMongo and Jsonlite. A quick search across Comprehensive R Archive Network will help you find them.

Next, you have to turn your attention to data wrangling. It’s the method of mapping one row format to another, while amalgamating, dividing and rearranging rows and columns. Map out the metrics after ascertaining whether a task falls under one of the following mathematical categories:

  • Discrete Metrics
  • Continuous Metrics

Another significant step is corroborating the columns decided: are headers from the data source given? R Programming helps add headers on data as soon as data is imported. Furthermore, another question that pops up here is that are the headers from the same labels of parties who have access to data? Now, this question is instrumental in answering whether there is any more efficient way to have access to data consecutively without manually rectifying columns before placing the data in a model.

For R programming, some of the basic libraries to consider are as follows:

Readr – It helps estimate functions and read data in rectangular tabular formats

Tidyr – It helps in organizing missing field values and arranging tabular data in an effective and compatible structure

Dplyr – Ideal for transforming data after it’s added in R

Marketing Knowledge Is Still an Add-On Factor

Lastly, marketers should never ignore their domain knowledge, while modeling data. At times, your experience will help you tackle an outlier for a model in the best way possible. Or else, you might ask your technical team to adjust and manage data in cloud in a situation where other teams try to downstream assess data.

Thus, a relevant marketing knowledge is essential. It will help decide which data to be queried or how to parse it well.

If you are thinking of learning a popular yet effective programming language to tame your data, R Programming certification in Delhi NCR is the best solution for you. A good R programming training will help you understand and evaluate data like a pro.

 

The blog first appeared on ― www.cmswire.com/digital-marketing/how-marketers-can-plan-data-mining-with-r-programming

 

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