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Not every 16-year-old gets to attend the United Nations Climate Action Summit, and even fewer get there by sailing from Sweden to New York in an eco-friendly, 60-foot racing yacht. Greta Thunberg is pretty much the only one.

Thunberg is superlative in a lot of ways—she’s already a Nobel Peace Prize nominee and would be the youngest laureate ever if she wins—and this boat trip is no exception. She also doesn’t fly. As part of her relentless campaign against climate change, she’s led school strikes and spoken publicly in many countries, but she’s always traveled using some other method to do her part in reducing carbon emissions. Trains, for instance, can get you far in Europe.

Crossing the Atlantic is a bit harder sans aircraft, but the skipper of the Malizia II stepped up to offer his services. When it’s not ferrying teenage Nobel nominees, the Malizia II is an elite racing yacht, so while it’s not equipped with luxuries like showers or refrigerators, it is designed to safely cross large distances over open ocean. The trip will take about two weeks and during that time Thunberg and her compatriots will produce zero emissions. The craft makes use of solar panels and underwater turbines to get where it’s going, unlike other boats which burn fuel to run generators and power motors.

But we can’t all be Greta Thunberg. Here’s a guide to traveling greener if you don’t have a racing yacht available.

Take the bus

You probably don’t have great impressions of long-haul Greyhound-type buses, but the reality is they’re the best public transit option in terms of carbon cost. If you’re traveling solo, they emit roughly half the carbon dioxide as driving an electric car (assuming you get your electricity from an oil-based power station).

If you must drive, go electric

For a one-person trip, driving is generally not the way to go. But if you have the funds to invest in a completely electric vehicle, you’re probably still coming out of this trip a winner. The absolute least carbon-intensive way would be to also get your power from wind or solar energy, though not everyone lives in a place where that’s possible. The good news is that even if you’re getting the electricity in your house by burning oil, using an electric car still puts you ahead of flying.


Plenty of people can’t yet afford completely electric vehicles (or simply can’t afford to replace their current car), so if you’ve got a gas-guzzler, at least share your ride with others. The more people you can pack in, the more you can divide that carbon footprint amongst you. Per passenger-mile on a solo trip, both regular cars and SUVs cost far more in carbon dioxide than a first class plane ticket. But the more folks you can pack into your vehicle, the more you can divide that carbon footprint.

Try a train

Though they’re more carbon-intensive than buses, trains are still a better way to travel than planes. Plus they’re great for getting between cities. If you live in Europe, especially, rail is often more convenient than driving wherever you’re going. As a bonus, there’s no security line at the train station so you also don’t have to waste two hours of your life sitting in an airport.

Fly economy

You may all be on the same plane, but the folks in first class are taking up valuable space that could be used to squeeze more people in. We all hate how small airplane seats are getting, and we’re not saying airlines are doing that out of concern for the environment, but the truth is that packing more people per plane drives down each person’s carbon footprint. Since first class seats take up about twice as much room as a coach spot, researchers estimate that the fancier ticket ends up costing twice the carbon dioxide as well.

We’re going to need a lot more changes like this to curb global warming—and really, we need it to be more on the scale of folks giving up cars entirely—but that doesn’t mean even your small contributions don’t help. Driving a hybrid car or even just choosing a more efficient one can add up. We just have to actually start.

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Master The Basics Of R Programming

Requirements to Learn R Programming

If you want to start programming in R, you need to install the last versions of R and R studio. You are surely asking yourself why you need to install both. If you prefer, you can install only R and you will have a basic tool to write the code. In addition, R studio provides an intuitive and efficient graphical interface to write code in R. It allows to divide the interface into subwindows to visualize separately the code, the output of the variables, the plots, the environment, and many other features.


When we program in R, the entities we work with are called objects [1]. They can be numbers, strings, vectors, matrices, arrays, functions. So, any generic data structure is an object. The assignment operator is <-, which combines the characters < and -. We can visualize the output of the object by calling it:

x <- 23 x #[1] 23

A more complex example can be:

x <- 1/1+1*1 y <- x^4 z <- sqrt(y) x [1] 2 y [1] 16 z [1] 4

As you can notice, the mathematical operators are the ones you use for the calculator on the computer, so you don’t need the effort to remember them. There are also mathematical functions available, like sqrt, abs, sin, cos, tan, exp, and log.

Vectors in R Programming

In R, the vectors constitute the simplest data structure. The elements within the vector are all of the same types. To create a vector, we only need the function c() :

v1 <- c(2,4,6,8) v1 # [1] 2 4 6 8

This function simply concatenates different entities into a vector. There are other ways to create a vector, depending on the purpose. For example, we can be interested in creating a list of consecutive numbers and we don’t want to specify them manually. In this case, the syntax is a:b , where a and b correspond to the lower and upper extremes of this succession. The same result can be obtained using the function seq()

v2 <- 1:7 v2 #[1] 1 2 3 4 5 6 7 v3 <- seq(from=1,to=7) v3 #[1] 1 2 3 4 5 6 7


[1] 1 2 3 4 5 6 7

The function seq() can also be applied to create more complex sequences. For example, we can add the argument by the step size and the length of the sequence:

v4 <- seq(0,1,by=0.1) v4 #[1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 v5 <- seq(0,2,len=11) v5 #[1] 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

To repeat the same number more times into a vector, the function rep() can be used:

v6 <- rep(2,3) v6 v7 <-c(1,rep(2,3),3) v7 #[1] 2 2 2 #[1] 1 2 2 2 3

There are not only numerical vectors. There are also logical vectors and character vectors:

x <- 1:10 y <- 1:5 l <- x=y l c <- c('a','b','c') c #[1] TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE #[1] "a" "b" "c" factors in R Programming

factors are specialized vectors used to group elements into categories. There are two types of factors: ordered and unordered. For example, we have the countries of five friends. We can create a factor using the function factor()

states <- c('italy','france','germany','germany','germany') statesf<-factor(states) statesf #[1] italy france germany germany germany #Levels: france germany italy

To check the levels of the factor, the function levels() can be applied.

levels(statesf) #[1] "france" "germany" "italy" Matrices in R Programming

As you probably know, the matrix is a 2-dimensional array of numbers. It can be built using the function matrix()

m1 <- matrix(1:6,nrow=3) m1 # [,1] [,2] #[1,] 1 4 #[2,] 2 5 #[3,] 3 6 m2 <- matrix(1:6,ncol=3) m2 # [,1] [,2] [,3] #[1,] 1 3 5 #[2,] 2 4 6

It can also be interesting combine different vectors into a matrix row-wise or column-wise. This is possible with rbind() and cbind() :

countries <- c('italy','france','germany') age <- 25:27 rbind(countries,age) # [,1] [,2] [,3] #countries "italy" "france" "germany" #age "25" "26" "27"


countries <- c('italy','france','germany') age <- 25:27 cbind(countries,age) # countries age #[1,] "italy" "25" #[2,] "france" "26" #[3,] "germany" "27" Arrays in R Programming

Arrays are objects that can have one, two, or more dimensions. When the array is one-dimensional, it coincides with the vector. In the case it’s 2D, it’s like to use the matrix function. In other words, arrays are useful to build a data structure with more than 2 dimensions.

a <- array(1:16,dim=c(6,3,2)) a lists

The list is a ordered collection of objects. For example, it can a collection of vectors, matrices. Differently from vectors, the lists can contain values of different type. They can be build using the function list() :

x <- 1:3 y <- c('a','b','c') l <- list(x,y) l #[[1]] #[1] 1 2 3 # #[[2]] #[1] "a" "b" "c" Data frames in R Programming

A data frame is very similar to a matrix. It’s composed of rows and columns, where the columns are considered vectors. The most relevant difference is that it’s easier to filter and select elements. We can build manually the dataframe using the function data.frame() :

countries <- c('italy','france','germany') age <- 25:27 df <- data.frame(countries,age) # countries age #1 italy 25 #2 france 26 #3 germany 27

An alternative is to read the content of a file and assign it to a data frame with the function read.table() :

df <- read.table('titanic.dat')

Like in Pandas, there are other functions to read files with different formats. For example, let’s read a csv file:

df <- read.csv('titanic.csv')

Like in Python, R provides pre-loaded data using the function data() :

data(mtcars) head(mtcars)

The function head() allows visualizing the first 6 rows of the mtcars dataset, which provides the data regarding fuel consumption and ten characteristics of 32 automobiles. The features are

To check all the information about the dataset, you write this line of code:


In this way, a window with all the useful information will open. To have an overview of the dataset’s structure, the function str() can allow having additional insights into the data:


From the output, it’s clear that there are 32 observations and 11 variables/columns. From the second line, there is a row for each variable that shows the type and the content. We show separately the same information using:

the function dim() to look at the dimensions of the data frame

the function names() to see the names of the variables

dim(mtcars) #[1] 32 11 names(mtcars) [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" "carb"

The summary statistics of the variables can be obtained through the function summary()


We can access specific columns using the expression namedataset$namevariable. If we want to avoid specifying every time the name of the dataset, we need the function attach().

mtcars$mpg # [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 #17.3 15.2 10.4 10.4 14.7 32.4 30.4 #[20] 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 #21.4 attach(mtcars) mpg # [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 #17.3 15.2 10.4 10.4 14.7 32.4 30.4 #[20] 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 #21.4

In this way, we attach the data frame to the search path, allowing to refer to the columns with only their names. Once we attached the data frame and we aren’t interested anymore to use it, we can do the inverse operation using the function detach().

We can also try to select the first row in the data frame using this syntax:


Note that the index starts from 1, not from 0! If we want to extract the first columns, it can be done in this way:

mtcars[,1] #[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 #17.3 15.2 10.4 10.4 14.7 32.4 30.4 #[20] 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 #21.4

We can also try to filter the rows using a logical expression:

we can also specify the column while we filter:

#[1] 21.0 21.0 22.8 21.4 24.4 22.8 32.4 30.4 33.9 21.5 27.3 26.0 30.4 21.4

for and while in R Programming

The for loop is used to iterate elements over the sequence like in Pandas. The difference is the addition of the parenthesis and curly brackets. It has slightly different syntax:

for (var in seq) statement

for (i in 1:4) {print(i)} #[1] 1 #[1] 2 #[1] 3 #[1] 4

while executes a statement or more statements as long as the condition is true

while (cond) statement

i<-1 while (i<6) {print(i) i<-i+1} #[1] 1 #[1] 2 #[1] 3 #[1] 4 #[1] 5 if statement in R Programming

The syntax of the if statement is similar to the one in Python. As before, the difference is the addition of the parenthesis and curly brackets.

if (cond1) {statement1} else {statement2}


if (cond1) {statement1} else if {statement2} else {statement3}

for (i in 1:4) {if (i%%2==0) print('even') else print('odd') } #[1] "odd" #[1] "even" #[1] "odd" #[1] "even"

If we want to compare two numbers and see which number is greater of the other, we can do it in this way:

a <- 10 b <- 2 print('b is greater than a') }else if (a == b){ print('a and b are equal') }else { print('a is greater than b') } # [1] "a is greater than b"

There is also a vectorized version of the if statement, the function ifelse(condition,a,b) . It’s the equivalent of writing:

if condition {a} else {b}

For example, let’s check if a number is positive:

x<-3 # [1] "positive" Function in R Programming

The function is a block of code used to perform an action. It runs only when the function is called. It usually needs parameters, that need to be passed, and returns an output as result. It’s defined with this syntax in R:

namefunction <- function(par_1,par_2,…)


Let’s create a function to calculate the average of a vector:

average <- function(x) { val = 0 for (i in x){val=val+i} av = val/length(x) av } average(1:3) #[1] 2 Probability distributions in R Programming

A characteristic of R is that it provides functions to calculate the density, distribution function, quantile function and random generation for different probability distributions. For example, let’s consider the normal distribution:

dnorm(x) calculates the value of the density in x

pnorm(x) calculates the value of the cumulative distribution function in x

qnorm(p) calculates the quantile of level p

rnorm(n) generates a sample from a standard normal distribution of n dimension

Now, I show a table with the most known distributions available in R:

Plotting commands in R Programming

The graphs are very important to get insights into the data. R provides plotting commands to display a huge variety of plots:

plot(x) is the most common function used to produce scatterplots

pairs(X) is used to display multivariate data. It produces a pairwise scatterplot matrix of the variables contained in X.

hist(x) is used to display the histogram

box(x) is used to display the boxplot

qqplot(x) is used to produce the Q-Q plot, useful to check if the distribution analyzed is normal or not.

abline(h=y) and abline(v=x) are the most used function to add horizontal and vertical lines in the already built plot

curve(expr,add=FALSE) is used to display a curve, that can be added or not to an already existing graph.

par(mfrow=(r,c)) is used put multiple graphs in a single plot. The mfrow parameter specifies the number of rows and the number of columns.

legend(x,y,legend,...) is used to specify the legend in the plot at the specified position (x,y)

For example, we can generate a sample with 200 units from a normal distribution. Let’s suppose we don’t know the distribution and we want to display the histogram and the boxplot:

x <-rnorm(200) par(mfrow=c(1,2)) hist(x,ylim=c(0,0.5),prob=TRUE) curve(dnorm(x),add=TRUE) boxplot(x)

Since the sample size is high, the histogram appears similar to a normal density curve, as shown in the figure. From the boxplot, it can be seen that the distribution is symmetric and there are three outliers that have lower values than the minimum.

Linear Regression in R

The models of linear regression are the most widely known models that want to predict a real-valued output Y [2]. It has the following form:

normality of the response variable Y

a linear relationship between the mean of the response variable and the dependent variables. Indeed, the coefficients βj can be interpreted as the average effect on the response variable Y of one unit increase in the dependent variable, corresponding to the coefficient considered, while fixing the values of all the other predictors.

homoscedasticity of the response variable

Moreover, it allows to study the strength and the relationship between the variables, providing more insights into the data. In the model, βj are unknown parameters and need to be estimated through the ordinary least squares method (OLS), by minimizing the sum of squared residuals:

Another important aspect is the hypothesis test, which allows checking if there is a relationship between the response and the predictor xj. This is possible testing the null hypothesis, called in this way because it tests if the coefficient βj is equal to 0, and, then, calculating the standardized coefficient:

There is also another test used to assess the significance of many coefficients at the same time: H₀: β₁ = … = βp = 0 against the hypothesis that at least one coefficient is non-zero. In this case, the F statistic is used:

Let’s take again the mtcars dataset and let’s suppose that we want to perform the linear regression to see the estimated coefficients. As the first trial, I include only one dependent variable, the number of cylinders in the model, which is called linear regression. The syntax of the formula within the function lm is response~terms, where the response is the response variable, while terms refer to one or more dependent variables included in the model.

data(mtcars) attach(mtcars) lm1 <- lm(mpg~cyl) lm1$coefficients #(Intercept) cyl # 37.88458 -2.87579

Looking at the parameter of cyl, we can understand that there is negative relationship between the number of cylinders and mpg. To better understand, we can visualize the scatterplot between the two features:

It seems that increasing the number of cylinders lead to a decrease miles/(US) gallon. The most relevant results of the linear model are provided using the function summary() .


It’s the summary of the results obtained performing the linear regression model on the data. At the top of the output, we can see the variables included in the model. There are some statistics (minimum, first and third quartiles, median, maximum) regarding the residues of the estimated model. After, there is a table containing the estimated coefficients of the model, where each row corresponds to a coefficient. Each row has the following information:

the value of the estimated coefficient

the standard Error

the observed t-value

the observed level of significance: in case it’s smaller than 0.05, the parameter is significant and, then, there is a linear relationship between that variable and the response variable.

We can see that both coefficients are significant with p-value<0.05 and R² is high, near 1, considering that we only included a variable. cyl’s coefficient is negative and, then, indicates the decrease of value for each increase of one unit in mpg.

We can also try to include another predictor and include the interaction term between cyl and disp:

lm2 <- lm(mpg ~ cyl+disp+cyl:disp) lm2 <- lm (mpg ~ cyl*disp) summary(lm2)

In the code, I show different syntax formats, that allow reaching the same results. Putting the *, between the two features enables to write less code. As before, all the coefficients are significant. Now, the R² is higher, equal to 0.8. To evaluate how well the model explains well the behaviour of the data, an efficient way is to display the residuals versus the fitted values, where the residuals are the differences between the true values and the fitted values.


The red curve corresponds to the smooth fit to the residuals and has a U-shape, indicating that there are non-linear associations in the data.

After this step, we can finally predict mpg on new data using the fitted model:

newdata <-data.frame(mpg=20,cyl=8,disp=150,hp=100,drat=3,wt=2.4, qsec=17,vs=1,am=1,gear=4,carb=2) predict(lm2,newdata) # 1 #18.99105

The summary() needs two parameters, the fitted linear model and the new data, that should be a data frame object. The output shows that the new car is expected to have an mpg value equal to 18.9.

Final thoughts

I hope you found useful this guide in programming in R. Starting from the basics, you will be able to perform any type of analysis on the data. As the last topic, I covered linear regression to show the most simple example of data modelling in R. I didn’t split the dataset into training and test sets since the dataset was too small, but you can try it on a bigger dataset. Below, there are some books you can read with many examples in R. Thanks for reading. Have a nice day!

[1] W. N. Venables, D. M. Smith, and the R Core Team,

[1] W. N. Venables, D. M. Smith, and the R Core Team, An Introduction to R (2024) [2] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction , Second Edition (2024)

Master Data Analyst Subjects For A Lucrative Career In Data

blog / Data Science and Analytics 5 Best Ways to Build Domain Knowledge in Data Analysis

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The demand for professionals skilled in data analyst subjects is increasing in today’s data-driven world. According to the U.S. Bureau of Labor Statistics (BLS), the job outlook for data scientists will experience a 36% increase from 2023 to 2031. This emphasizes the growing importance of individuals who can analyze and interpret data to extract valuable insights. This blog will explore how to approach essential data analyst subjects and suggest practical strategies to help you improve your technical knowledge. Whether you are an experienced data analyst or just starting out in this field, this guide provides you with the knowledge and tools you need to succeed in data analysis. So, let us begin this journey of realizing your potential in the data domain.

What is Domain Knowledge in Data Analysis?

Domain knowledge in data analysis refers to analysts’ understanding and expertise in specific fields or industries. It includes knowledge of terminology and the complexities of data analyst subjects. Equally important, it also means that data analysts should be able to comprehend the context, ask pertinent questions, and make informed decisions. Furthermore, this knowledge enables them to extract meaningful patterns, trends, and insights from data. 

Simply put, data analysts should be able to provide valuable insights and recommendations that are specific to the domain. As a result, their analysis becomes more effective and relevant to the industry or field in question, increasing the overall value of their findings.

ALSO READ: How to Build a Successful Career in Data Science and Analytics?

How Does Industry Knowledge Help in Boosting One’s Career?

Without a doubt, industry knowledge is essential in data analysis. It allows data analysts to gain valuable insights and make sound decisions. Data analysts can comprehend the context and ask pertinent questions by leveraging their industry knowledge. Furthermore, they can spot meaningful patterns and trends in data. This knowledge enables them to interpret data accurately and make insightful recommendations tailored to industry needs.

In addition to the above, industry knowledge assists data analysts in comprehending the unique challenges, terminology, and nuances associated with data analyst subjects in a specific industry. As a result, they can communicate their findings to stakeholders effectively, bridging the gap between data analysis and industry-specific requirements. Lastly, industry knowledge enables data analysts to deliver more impactful and actionable results, resulting in increased business success.

Can a Data Analyst Succeed Without Relevant Industry Experience?

While relevant industry experience is beneficial, a data analyst can succeed without it. They have transferable skills—data analysis techniques, statistical knowledge, and so on—that can be applied across industries. They can use these abilities to comprehend and analyze data in various contexts. Furthermore, data analysts in a new industry can quickly adapt and learn about specific data-related subjects. They accomplish this through research, collaboration with domain experts, and lifelong learning. Furthermore, their ability to ask pertinent questions and effectively communicate allows them to bridge the knowledge gap. Thus, although industry experience can provide insights and context, a skilled data analyst can excel by utilizing core competencies and adapting to the needs of various industries.

What are the Best Ways to Build Domain Knowledge in a Specific Industry?

Here are the five best ways to build domain knowledge in a specific industry as a data analyst:

1. Conduct Thorough Research

Dive into industry-specific resources to learn about key concepts, trends, and challenges.

2. Engage With Industry Professionals

Actively network with industry experts, attend conferences, and participate in online forums.

3. Seek Mentorship or Guidance

Connect with experienced data analysts in the industry who can provide valuable insights and practical guidance on navigating data analyst subjects.

4. Immerse Yourself in Industry Publications

Stay updated by delving into relevant industry publications, case studies, and reports that showcase real-world applications.

5. Gain Hands-On Experience

Seek opportunities, such as internships, projects, or collaborations, to apply your skills and deepen your understanding of data analysis.

ALSO READ: Check Out These 15 Best Data Analytics Projects for Analysts

How Can One Improve Their Technical Expertise in Data Analysis? Enhance Technical Knowledge in Data Analysis by Taking Emeritus Online Courses

Mastering data analyst subjects requires knowledge, practical experience, and ongoing education. You can improve your expertise as a data analyst by staying up to date with industry trends. Therefore, invest in your growth in the world of data analysis. One way to begin is by honing your skills through Emeritus’ online data science courses, unleashing your potential’s power.

By Siddhesh Santosh

Msi Wind U110 Eco Gets Official: 9Hrs Runtime

MSI today announces U110 ECO in Wind Netbook. U110 ECO is the world’s power saving No.1 Netbook!

The battery life of U110 ECO is around 9 hours*. This amazing battery life can escalate the mobility and the productiveness of U110 ECO, which can also make your daily lives much more convenient.

*The Actual Capacity of the hard drive is based on the actual model.

On the other hand, U110 ECO features the Intel® Menlow Platform with Atom™ Processor, which is easy to connect to the internet for the mobile life. You may maximize your U110 ECO’s potential by staying connected whether you’re watching a movie at home, emailing from a café or chatting on a social networking site even on the vacation. Also, you’ll be able to stream video and enjoy all your favorite online entertainment. The power-efficient design provides longer battery life so you can keep on surfing, blogging, listening to music, watching video and communicating with the world as you move through your day.

To Freely Play

– Super Long Lasting Battery Life*

The battery life of U110 ECO is around 9 hours*. This amazing battery life can escalate the mobility and the productiveness of the U110 ECO, which can also make your daily lives much more convenient.

* The Actual battery life varies according to operating conditions and settings.

– The Excitement of Instant Communication

– Wireless Internet Connection

U110 ECO has the 802.11 b/g/n wireless all region internet connection and Bluetooth transmission interface, so you can enjoy the convenience of accessing the internet anywhere. There will be absolutely no obstacles getting around your daily life.

– Comprehensive Multi-Media Application Interface

U110 ECO offers a complete entertainment interface, which includes the 4-in-1 card reader, so it is compatible to most of the mainstream memory cards, making it easy to upload digital files into the notebook computer. Moreover, U110 ECO focuses on the common products in the market to make your life easier, therefore you may connect your peripherals such as PDA’s, digital cameras, digital video cameras, digital MP3 Players, Global GPS chúng tôi to U110 ECO through USB 2.0 Port. An external DVD burner may be added (optional) to play and burn important files at will.

To Freely Operate

– New Intel® Menlow Platform with Atom™ Processor

On the other hand, U110 ECO features the Intel® Menlow Platform with Atom™ Processor, which is easy to connect to the internet for the mobile life. You may maximize your U110 ECO’s potential by staying connected whether you’re watching a movie at home, emailing from a café or chatting on a social networking site even on the vacation. Also, you’ll be able to stream video and enjoy all your favorite online entertainment. The power-efficient design provides longer battery life so you can keep on surfing, blogging, listening to music, watching video and communicating with the world as you move through your day.

– Hard Drive with Massive Capacity

Unlike other competitors that have compromised the storage capacity to decrease in size, U110 ECO is equipped with the 2.5 inch standard hard drive〈80/120/160 GB〉*.It can operate just like regular notebooks without worrying limited storage capacity to save the greatest moments in life.

*The Actual Capacity of the hard drive is based on the actual model.

– Big-Size Keyboard and Touch Pad

MSI has the same persistence on proper ergonomically design even on the keyboards of MSI smaller notebook computers. The keyboard of U110 ECO not only has great texture, it also increases the space between the keys to 17.5mm, allowing you to be as comfortable as you can. Furthermore, with the ingeniously designed spacebar and touchpad, your fingers can move smoothly to avoid strain..

To Freely Watch

– 10″ Wide LCD Display

U110 ECO has selected a 10″ wide LCD display as oppose to the typical smaller sizes to provide better comfort while viewing or reading. In addition, the 1024x 600 resolution can relief concerns of the full display of WebPages while browsing, giving you the freedom when exploring the internet.

– The Latest in LED Power-Saving Backlight Technology

U110 ECO is embedded with the LED power-saving backlight technology in providing better color fullness and brightness to elevate the total quality of imagery. Furthermore, the lower usage of power can offer a longer operating time.

To Freely Go

– Lightness in Design

The frame is approximately 26 centimeters in length and 18 centimeters in width. It is only 33 mm in thickness making it extremely thin. The total weight with the battery is less than 1 kilogram, making U110 ECO very ideal for taking it on the go.

Is College A Waste Of Money For Entrepreneurs?

To help you decide whether college is worth your time and money, we interviewed entrepreneurs who do and don’t believe college is key to entrepreneurial success. Here’s what they said.

You can benefit from college if you plan wisely

Matas Jakutis, chief marketing officer at ForceField Digital and a self-described “serial e-com entrepreneur,” said college is worthwhile if you know what you’ll get out of it.

“Entrepreneurs can 100% benefit from college, but getting a great result requires one main thing: focus,” said Jakutis, an alumnus of Lancaster University Management School in the U.K. “These days, college is more expensive than ever, so it’s not an investment to make casually. To go to college to simply explore or test the waters without a real plan might now be a luxury of the past.”

Jakutis’ perspective suggests that entrepreneurs could benefit from a college education if they know exactly what they want from their careers. For example, let’s say you envision developing some sort of disruptive semiconductor technology. In that case, you could study at a nationally renowned engineering school with an emphasis on hands-on lab opportunities and industry-wide networking. Your experience and connections could help you be the disruptor you want to be.

“Today, entrepreneurs must do serious research on the school and program before making an investment,” Jakutis said. “Only when entrepreneurs are focused and approach education with a true purpose can they make the most of it.”

You can benefit from meeting people in college

Justin Carpenter, founder and CEO of the house cleaning platform Modern Maids, said he made important, one-of-a-kind business connections during his time at Baylor University.

“[One of my] college roommate[s] went on to get his master’s in accounting, receive the award for most outstanding grad student, and work at PwC, one of the most prestigious accounting firms in the world,” Carpenter said. “I currently use him as my CPA. Another very close college friend’s family owns and operates hotel chains across the country. Through my connection to him, we were able to negotiate lower prices on all our cleaning supplies and materials.”

Carpenter also said that interacting with people of different cultures on campus has benefited him tremendously. Rather than “the specific things I learned in the classroom [resulting] in my success,” he said, college “[forced] me to critically think, become well-rounded [and] open-minded, and learn from different cultures.” These lessons can empower entrepreneurs to innovate while prioritizing diversity and inclusion in their workplaces.

Did You Know?

According to a 2023 paper published in the journal Small Business Economics, several studies have found correlations between cities’ diversity and these regions’ consistent knowledge generation, innovation and entrepreneurship.

You can skip college if you already have resources and opportunities

Richie Huffman, CEO of the child learning center franchise Celebree School, chose to skip college because he already had everything he needed.

“I came from an entrepreneur family. My mom and dad were in the preschool business,” Huffman said. “I saw the transition between my mom working for a company and then opening her own business. I felt as if I could get where I wanted to get just by [having] my parents as mentors and giving me the knowledge instead of going and sitting in a classroom.”

Huffman said reading books, attending seminars, participating in webinars, and surrounding himself with people who had already found entrepreneurial success were also key steps in his path. So was an early bakery business he launched. 

“It really taught me how to sell, how to approach people,” Huffman said. “There are lessons I still have with me that [aren’t] taught sitting in a classroom.” Huffman’s story shows that some budding entrepreneurs can leverage their connections and access to alternative educational opportunities to find success without college.

You can drop out of college if a business opportunity appears

Shri Ganeshram, CEO and founder of Awning, dropped out of high school in 11th grade to attend MIT. He then took a leave of absence from MIT to launch the startup FlightCar, which raised $40 million before selling its tech platform to Mercedes-Benz in 2024. When the startup became his career, he knew going back to MIT wasn’t in the cards. The key was that, independent of college, he had developed the hard skills to power his career.

“I wouldn’t say that I started with, ‘I’m going to just drop out,’” Ganeshram said. “I more so started with, ‘There’s this really exciting opportunity in front of me. Let me explore this opportunity in a way in which I can keep the door open [to college]. That opportunity led to me starting a career, and luckily, in the software world … once you have hard skills, people care less about [your] degree and more about your ability to function on the job, which is what I was able to establish as an entrepreneur during the time I was taking leave from MIT.”

However, Ganeshram doesn’t entirely discount the notion that a college education can be valuable for entrepreneurs. At college, he “made friends with people who continue to be in my life long-term and had a huge impact on me.” But, he continued, “Do I think four years of being at college would’ve necessarily been the highest ROI, especially with the cost of college? I don’t think so. I think there’s probably some models for something in between. I don’t know if all of the curriculum they teach in college is truly necessary.”

Other considerations when you’re choosing whether to go to college

Beyond what the entrepreneurs we spoke with told us, you may want to consider the following factors as you choose whether to go to college:

Investor requirements. Angel investors or venture capitalists from whom you seek startup funding may look upon you more favorably if you hold a degree.

Your current business’s state. If you already own a business and it’s taking off, you might be able to skip college (or drop out if you’re already enrolled).

The hard skills required. College may be the easiest way to learn hands-on hard skills you’d use in a laboratory or scientific setting. Technical or vocational school also may be an appropriate path for certain entrepreneurs.

Your earnings potential. A startup idea that you’re confident could earn you lots of money could fund your education if you choose to attend college later.

Your ability to do both at once. Let’s say you have a great business idea but you also want to go to college. In that case, you should assess whether you have the capacity to do both at once. This can be quite time-consuming, but it could be an option. 


For additional considerations as you make your choice, read our guide to skipping college to start a business.

College vs. entrepreneurship: The choice is yours

Going to college and being an entrepreneur are far from mutually exclusive options – you can do both, sometimes simultaneously. Doing your research and considering the stories above can help you choose which route to follow and when to do so. And if one path proves more challenging than you expected, you can always head down the other.

What Is A Nan Property Of A Number Object In Javascript?

In JavaScript, the NaN property is a special value that represents “Not a Number”. It is a property of the Number object and can be accessed using Number.NaN.

The NaN property is usually produced as a result of an operation that cannot produce a meaningful result. For example, dividing 0 by 0 or trying to parse an invalid number will both produce NaN.

Here are a few examples of operations that will produce NaN −

Math.sqrt(-1); 0/0; parseInt("foo");

It is important to note that NaN is not equal to any value, including itself. So, if you want to check if a value is NaN, you cannot use the == or === operators. Instead, you should use the isNaN() function, which is designed specifically for this purpose.

Here’s an example of how to use isNaN() −

if (isNaN(someValue)) { console.log("someValue is Not a Number"); } Syntax

Following is the syntax to represent a not a umber −

NaN Number.NaN

We can call NaN from the Number objects, so even NaN represents Not a Number but a Property of a Number object.

Sometimes it is strictly required to pass a number for an operation, in that case, we can throw a NaN error to show the users that they can enter only the Number value.


You can try to run the following example to learn how to use NaN −

function showValue() { var dayOfMonth = 50; dayOfMonth = Number.NaN alert(“Day of Month must be between 1 and 31.”) } Document.write(“Value of dayOfMonth : ” + dayOfMonth ); }


Let’s create a function, sum which takes two parameters and converts them into Integers so that if users enter a Number in decimal or a Number in a string it will automatically change it into integers and sum those integers and print the value. Then we will call the function by passing some arguments.

function sum(a, b) { x = parseInt(a); y = parseInt(b); result = x + y; return result; } let outputDiv = document.getElementById(“output”);

Here when we either pass an Integer or a string containing Integers or Integers in the decimal value we will get the same results because all of the values can be parsed in integers and can be valid numbers but when we pass a string of alphabets we will get an error NaN, which means the value we passed is Not a Number.


Let’s modify the above function, and we want this function to not allow the integers in decimal or even in the string, if the function gets a value that is strictly not a number type then we will console log the NaN to show a message that the entered number is not a number.

function sum(a, b) { if (typeof (a) === “number” && typeof (b) === “number”) { document.write(a + b); } else { document.write(NaN); } } sum(“2”, 4);

JavaScript can convert the numbers from one form to another by itself if it falls under the category of a number, it is not able to convert them into another required form, i.e., the entered number is not a number type in JavaScript, so JavaScript returns an error, NaN, it means not a number. The cool part is that the NaN is also an object of Number which we can access using the chúng tôi property. We can also manually throw the error NaN by using the chúng tôi property or directly passing NaN.

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