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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.

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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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Application of Mode using R and Python

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Application of Mode using R and Python

Mode, for a given set of observations, is that value of the variable, where the variable occurs with the maximum or the highest frequency.

This blog is in continuation with STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY. However, here we will elucidate the Mode and its application using Python and R.

Mode is the most typical or prevalent value, and at times, represents the true characteristics of the distribution as a measure of central tendency.

Application:

The numbers of the telephone calls received in 245 successive one minute intervals at an exchange are shown in the following frequency distribution table:

 

No of Calls
Frequency
0
14
1
21
2
25
3
43
4
51
5
40
6
51
7
51
8
39
9
12
Total
245

 

 [Note: Here we assume total=245 when we calculate Mean from the same data]

Evaluate the Mode from the data.

Evaluate the Mode from the data

Calculate Mode in R:

Calculate mode in R from the data, i.e. the most frequent number in the data is 51.

The number 51 repeats itself in 5, 7 and 8 phone calls respectively.

Calculate Median in Python:

First, make a data frame for the data.

Now, calculate the mode from the data frame.

Calculate mode in Python from the data, i.e. the most frequent number in the data is 51.

The number 51 repeats itself in 5, 7 and 8 phone calls respectively.

Mode is used in business, because it is most likely to occur. Meteorological forecasts are, in fact, based on mode calculations.

The modal wage of a group of the workers is the wages which the largest numbers of workers receive, and as such, this wage may be considered as the representative wage of the group.

In this particular data set we use the mode function to know the occurrence of the highest number of phone calls.

It will thus, help the Telephone Exchange to analyze their data flawlessly.

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Note – As you have already gone through this post, now, if you are interested to know about the Harmonic Mean, you can check our post on the APPLICATION OF HARMONIC MEAN USING R AND PYTHON.

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Application of Harmonic Mean using R and Python

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Application of Harmonic Mean using R and Python

Harmonic mean, for a set of observations is the number of observations divided by the sum of the reciprocals of the values and it cannot be defined if some of the values are zero.

This blog is in continuation with STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY. However, here we will discover Harmonic mean and its application using Python and R.

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Application:

A milk company sold milk at the rates of 10,16.5,5,13.07,15.23,14.56,12.5,12,30,32, 15.5, 16 rupees per liter in twelve different months (January-December), If an equal amount of money is spent on milk by a family in the ten months. Calculate the average price in rupees per month.

Table for the problem:

Month

Rates (Rupees/Liter)

January

10

February

16.5

March

5

April

13.07

May

15.23

June

14.56

July

12.5

August

12

September

30

October

32

November

15.5

December

16

Calculate Harmonic Mean in R:-

So, the average rate of the milk in rupees/liter is 12.95349 = 13 Rs/liter (Approx)

We get this answer from the Harmonic Mean, calculated in R.

Calculate Harmonic Mean in Python:-

First, make a data frame of the available data in Python.

Now, calculate the Harmonic mean from the following data frame.

So, the average rate of the milk in rupees/liter is 12.953491609077956 = 13 Rs/Liter (Approx)

We get this answer from Harmonic mean, calculated in Python.

Summing it Up:

In this data, we have a few large values which are putting an effect on the average value, if we calculate the average in Arithmetic mean, but in Harmonic mean, we get a perfect average from the data, and also for calculating the average rate.

Use of Harmonic mean is very limited. Harmonic mean gives the largest value to the smallest item and smallest value to the largest item.

Where there are a few extremely large or small values, Harmonic mean is preferable to Arithmetic mean as an average.

The Harmonic mean is mainly useful in averages involving time, rate & price.

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Note – If you want to learn the calculation of Geometric Mean, you can check our post on CALCULATING GEOMETRIC MEAN USING R AND PYTHON.

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Python is the Leader in Data Science: Know Why

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Python is the Leader in Data Science: Know Why

From being simple and effective to being updated and thereby, solving almost everything that the booming industry of Data Science of today can look up to, Python boasts of it all.

It’s not a shock that Python is finding its uses in an array of industries. It is, in fact, the language that the Data Scientists rely on. Thus, our tailored courses of Python Certification Training in Delhi would be helpful for all in this digital age.

Let’s see some more of the advantages for which Python stands distinguished among the other programming languages:

Handling Data without a Hassle

The field of Data Science is entrusted with the handling of incredibly large amounts of data which is found to be intricate to compute. However, with Python, it is now simpler than ever. Any of the other high-level programming languages would make it rather difficult and messy compared to the peerless Python, if we talk about analytical and quantitative computing.

Open Source Programming Language

Python is an open-source programming language. Wonder why this programming language is the most preferred still?

It truly opens a whole lot of opportunities that the language can build upon, being open-source in nature. Furthermore, there is not a single restriction regarding Python. Thus, you can be as creative as you wish on this programming language.

It is Powerful and Easy to Use

Python is an easy language right from the start for which it has become so popular. Any of the beginners with just the rudimentary knowledge can start fine with Python. Besides, once you are on with this programming language, you can start progressing with it day by day at your own pace.

The implementation of the code has a slower approach in the languages: Java, C and C#, but if you try Python, you would discover that it is fast to debug and effective to perform. The prompt results in coding would aid with an added boost in your work.

In the Library of Python

Python is an all-absorbing language that even supports the cutting edge technologies of Machine Learning and Artificial Intelligence. And on top of it, Python also offers its users a colossal database of libraries. Therefore, you can simply check in the libraries, import them and then implement all of them in your day to day coding.

It is Highly Scalable

In the parameter of scalability, Python superbly stands out. The programming languages: R and Java certainly falls short in this factor. Thus, with the ease of scalability and quicker turnaround times, data scientists and nearly all of the organisations exploring Data Science, are choosing Python over any other existing languages.

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It is Peerless in Visualisation and Graphics

As the smooth rendering of quality graphics and visualisation is the demand of the age, Python fits in quite comfortably here. With an exhaustive range of options for visualisation, which are simple and efficient, the world of Data Science is rooting for Python.

With all the benefits that you can reap, Python for data analysis is a must, if you want to be absorbed in the industry of Data Science.


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Calculating Geometric Mean Using R and Python

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Calculating Geometric Mean Using R and Python

In this blog, we are going to discuss the Geometric Mean and its application using Python and R.

Geometric Mean of group of ‘n’ observations is the nth root of their product. It is defined only when all observations have the same sign and none of them is zero.

Application:

Calculate the Geometric Mean of the salary increment of 12 employees. From the following table, calculate the average salary increment of the year (2019-2020):-

 

Name

Salary Increment in

Percentage (%)

Ritesh

10.09%

Heena

15.45%

Kritika

9%

Anuradha

13.06%

Gaurav

20%

Prakash

14%

Aarti

16%

Meena

6.25%

Utkarsh

12.85%

Chirag

10%

Neha

18%

Smrita

21.36%

 

Calculate the Geometric Mean in R:

So, from the data of the employee’s in R we calculate the G.M. and get that the average salary increment in the year (2019-2020) = 13.17618 or 13.18% (approx).

Calculate the Geometric Mean in Python:

First, make a data frame in Python from the following table.

Now, calculate the Geometric Mean from the data-frame.

So, from the data of the employee’s in Python we calculate the G.M. and get that the average salary increment in the year (2019-2020) = 13.176183416401196 or 13.18% (approx).

We use Geometric Mean for calculating ratios, rates and percentages. And it is not affected by the extreme value or outlier. In this particular problem, we use Geometric Mean because an average of the salary increment of the employee’s not affected by the extreme highest or extreme lowest value, that’s why the salary increment rates of Meena and Smrita do not have any effect on the total average rate.

Geometric Mean gives small value than Arithmetic Mean.

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Note: This is a continuation of the blog: Statistical Application in R & Python: Chapter 1 – Measure of Central Tendency. It would be better to go through the first installment and then read this one. More blogs are to be followed, so stay tuned.

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Statistical Application In R & Python: Chapter 1 – Measure Of Central Tendency

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Statistical Applcation In R & Python: Chapter 1 – Measure Of Central Tendency

Statistical analysis helps explore data relationship and develop high-end models to frame better decisions. It’s an intricate process of collecting and evaluating data to define the nature of data that has to be analyzed.

Below, we dig into the basics of statistical application in R and Python using the measure of central tendency.

  • Introduction:-

As body methods for the study of numerical data, if some rows or columns are too long, in such cases, it becomes necessary to summarize data in an easily manageable form. The purpose is to serve by classifying the data in the form of frequency distribution and various graphs. When data relate to a variable, the process of summarization can be taken a step further by using certain descriptive measures. The dim is to focus on certain features that are central frequency and description.

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  • Central Tendency :

In a set of data, they have a tendency, notwithstanding their variability, to cluster-around a central value and the tendency of the quantitative statistical observations is called central tendency.

The three measures of the central tendency are commonly used is:-

  • Mean
  • Median
  • Mode

The description of these 3 estimators start below:-

  • Mean:-

Mean is the average of central tendency and is the most commonly used measures.

The concept of mean is divided into three parts:-

  • Arithmetic mean.
  • Geometric mean.
  • Harmonic mean.

Mainly the mean refers to an arithmetic mean.

  • Arithmetic Mean (A.M.):-

The arithmetic mean of a set of observations is defined to be their sum, divided by the number of observations.

For n numbers of observation (x1,x2,… ,xn )

  • Weighted A.M.

For frequency distribution where  have  frequencies. (i=1,2,3…)

  • Application of A.M.:-

Let’s, calculate the mean of Age, Height & Weight from the given data.

NameSexAgeHeightWeight
RiteshM246.9112.5
HeenaF235.6584
KritikaF236.5398
AnuradhaF246.28102.5
GauravM246.35102.5
PrakashM225.7383
AartiF225.9884.5
MeenaF256.25112.5
UtkarshM236.2584
ChiragM225.999.5
NehaF215.1350.5
SmritaF246.4390

Calculating Mean in Python:

Therefore,

Age (Mean) = 23.08333333, Height (Mean) = 6.12, weight(Mean) = 85.625

Calculating Mean in R:

  • Application of Weighted A.M.:-

The weighted mean is denoted that the mean with frequency.

Data to solve:

Calculate the average price per ton of coal purchased by the industry for the half-year.

Month

Price Per TonTons Purchased

January

Rs. 52.4926

February

Rs. 62.2334
MarchRs. 87.26

40

AprilRs. 45.25

54

MayRs. 78.56

13

June

Rs. 69.25

45

Data to solve:

Month

Price (Rs)

Per Ton

(x)

Tons

Purchased

(f)

fx=y

(Main Data)

January

 52.49261364.74

February

 62.2334

2115.82

March

 87.2640

3490.4

April

45.2554

2443.5

May

 78.5613

1021.28

June

69.2545

3116.25

Total395.04N=212

13551.99

 

The price is denoted as x (52.49, 62.23, 87.26, 45.25, 78.56, 69.25 [in Rs.])=395.04

The amount of purchased (frequency) is denoted by f (26, 34, 40, 54, 13, 45) = 212 (N)

Then multiply the x and f and we get the total amount which is denoted by y, fx(y) = 13551.99

Calculate Weighted Mean in R:

Calculate Weighted Mean in Python:

To calculate the weighted mean from R & Python we get the same result = 63.9244811.

Want to know more about the nature of data? Keen to perform high-end statistical analysis using Python and R? Follow DexLab Analytics, an excellent Python training center in Gurgaon, India. Our team of consultants will help you learn the basics of R and Python in the easiest manner possible.

 

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Demand for Data Analysts is Skyrocketing – Explained

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Demand for Data Analysts is Skyrocketing - Explained

The salary of analytics professionals outnumbers that of software engineers by more than 26%. The wave of big data analytics is taking the world by storm. If you follow the latest studies, you will discover that there has been a prominent growth in median salary over several experience levels in the past three years (2016 to 2018). In 2019, the average analytics salary has been capped at 12.6 lakh per annum.

The key takeaway is that the salary structure of analytics professionals continues to beat other tech-related job roles. In fact, data analysts are found out-earning their Java correspondents by nearly 50% in India alone. A latest survey provides an encompassing view of base and compensation salaries in data science along with median salaries followed across diverse job categories, regions, education profiles, experience, tools and skills.

In this regard, a spokesperson of a prominent data analytics learning institute was found saying, “The demand for AI skills is expected to increase rapidly, which is also reflected by the fact that AI engineers command a higher salary than peers.” She further added, “Many of our clients have realized that investing in data-driven skills at the leadership level is a determining factor for the success of digital and AI initiatives in the organization. With the increasing adoption of digital technologies, we expect an enduring growth of Data Science and AI initiatives to offer exciting and lucrative career options to new age professionals,”

Over time, we are witnessing how markets are evolving while the demand for skilled data scientists is following an upward trend. It is not only the technology firms that are posting job offers, but the change is also evident across industries, like retail, medical, retail and CPG amongst others. These sectors are enhancing their analytical capabilities implying an automatic increase in the number of data-centric jobs and recruitment of data scientists.

Points to Consider:

  • In the beginning, nearly 76% of data analysts earn 6-lakh figure per annum.
  • The average analytics salary observed in 2018-19 is 12.6 lakh.
  • In terms of analytics career, Mumbai offers the highest compensation of 13.7 lakh yearly, followed by Bangalore at 13 lakh.
  • Mid-level professionals proficient in data analytics are more in demand.
  • Knowing Python is an added advantage; Python Programming training will help you earn more. Expect a package of 15.1 lakh.
  • Nevertheless, we often see a pay disparity for female data scientists against their male counterparts. While women’s take-home salary is 9.2 lakh, male from the same designation and profession earns 13.7 lakh per annum.

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As endnotes, the demand for data science skills is skyrocketing. If you want to enter into this flourishing job market, this is the best time! Enroll in a good data analyst course in Delhi and mould your career in the shape of success! DexLab Analytics is a top-notch data analyst training institute that offers a plethora of in-demand skill training courses. Reach us for more.

 

This article has been sourced from — www.tribuneindia.com/news/jobs-careers/data-analytics-professionals-ride-the-big-data-wave/759602.html

 

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Top 4 Python Industrial Use-Cases: Explained

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Top 4 Python Industrial Use-Cases: Explained

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Python is one of the fastest-growing and most popular coding languages in the world; a large number of developers use it on daily basis and why not, it works brilliantly for a plethora of developer job roles and data science positions – starting from scripting solution for sysadmins to supporting machine learning algorithms to fueling web development, Python can work wonders across myriad platforms!

Below, we’ve rounded up 4 amazing Python industrial use-cases; scroll ahead:

Insurance

Widely used in generating business insights; courtesy machine learning.

Case Study:

Smaller firms driven by machine learning gave stiff competition to a US multinational finance and insurance corporation. In return, the insurer formed teams and devised a new set of services and applications based on ML algorithms to enjoy a competitive edge. However, the challenge was that with so many data science tools, numerous versions of Python came into the picture and gave rise to compatibility issues. As a result, the company finalized only one version of Python, which was then used in line with machine learning algorithms and tools to derive specific results.

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Finance

Data mining helps determine cross-sell opportunities.

Case Study:

Another US MNC dealing in financial services showed interest in mining complex customer behavioral data. Using Python, the company launched a series of ML and data science initiatives to dig into its structured data that it has been gathering for years and correlated it with an army of unstructured data, gathered from social media and web to enhance cross-selling and retrieve resources.

Aerospace

Python helps in meeting system deadlines and ensured utmost confidentiality.

Case Study:

Recently, the International Space Station struck a deal with an American MNC dealing in military, defense and aerospace technology; the latter has been asked to provide a series of systems to the ISS. The critical safety systems were mostly written in languages, like Ada; they didn’t fare well in terms of scripting tasks, data science analysis or GUI creation. That’s why Python was chosen; it offered bigger contract value and minimum exposure.

Retail Banking

Enjoy flexible data manipulation and transformation – all with Python!

Case Study:

A top-notch US department store chain equipped with an in-store banking division gathered data and stored it in a warehouse. The main aim of the company was to share the information with multiple platforms to fulfill its supply chain, analytics, retail banking and reporting needs. Though the company chose Python for on-point data manipulation, each division came up with their own versions of Python, resulting in a new array of issues. In the end, the company decided to keep a standard Python; this initiative not only resulted in amplifying engineering speed but also reduced support costs.

As end notes, Python is the next go-to language and is growing each day. If you have dreams of becoming an aspiring programmer, you need to book the best Python Certification Training in Delhi. DexLab Analytics is a premier Python training institute in Delhi; besides Python, it offers in-demand skill development courses for interested candidates.

 

The blog has been sourced from— www.techrepublic.com/article/python-5-use-cases-for-programmers

 

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Now Machine Learning Can Predict Premature Death, Says Research

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Now Machine Learning Can Predict Premature Death, Says Research

Machine Learning yet again added another feather in its cap; a team of researchers tried and tested a suave machine learning system that can now predict early death. Yes, premature death can now be estimated, courtesy a robust technology and an outstanding panel of researchers from the University of Nottingham! At first, it may sound weird and something straight out of a science fiction novel, but fret not – machine learning has proved itself in improving the status of preventive healthcare and now it’s ready to venture into new unexplored medical territories.

Prediction at Its Best

Published in PLOS ONE in one of their special editions of Machine Learning in Health and Biomedicine, the study delves into how myriad AI and ML tools can be leveraged across diverse healthcare fields. The technology of ML is already reaping benefits in cancer detection, thanks to its sophisticated quantitative power. These new age algorithms are well-equipped to predict death risks of chronic diseases way ahead of time from a widely distributed middle-aged population.

To draw clear conclusions, the team collected data of more than half a million people falling within the age group of 40 and 69 from the UK Biobank. The data collection is from the period 2006-2010, followed up till 2016. With this data in tow, the experts analyze biometric, demographic, lifestyle and clinical factors in each individual subject. Robust machine learning models are used in the process.

Adding in, the team observed dietary consumption of vegetables, fruits and meat per day of each subject. Later, the team from Nottingham University proceeded to predict the mortality of these individuals.

“We mapped the resulting predictions to mortality data from the cohort, using Office of National Statistics death records, the UK cancer registry and ‘hospital episodes’ statistics,” says Dr. Stephen Weng, assistant professor of Epidemiology and Data Science.  “We found machine-learned algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert.”

Accuracy and Outcome

The researchers involved in this ambitious project are excited to the bones. They are eager about the outcomes. They are in fact looking forward to a time where medical professionals would be able to distinguish potential health hazards in patients with on-point accuracy and evaluate the following steps that would lead the way towards prevention. “We believe that by clearly reporting these methods in a transparent way, this could help with scientific verification and future development of this exciting field for health care”, shares Dr. Stephen Weng.

As closing thoughts, the research is expected to build the foundation of enhanced medicine capabilities and deliver customized healthcare facilities tailoring risk management for each individual patient. The Nottingham research draws inspiration from a similar study where machine learning techniques were used to predict cardiovascular diseases.

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In case, you are interested in Machine Learning Using Python training course, DexLab Analytics is the place to be. With a volley of in-demand skill training courses, including Python certification training and AI training, we are one of the best in town. For details, check out our official website RN.

 
The blog has been sourced from —
interestingengineering.com/machine-learning-algorithms-are-now-able-to-predict-premature-death
 


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