data scientist

Create a ggalluvial plot in R

Create a ggalluvial plot in R: A ggalluvial plot, also known as an alluvial diagram, is a type of visualization used to show how categorical data is distributed among different groups. It is particularly useful for visualizing how categorical variables are related to each other across different levels of a grouping variable.

Create a ggalluvial plot in R
Create a ggalluvial plot in R

To create a ggalluvial plot in R, you can follow these steps:

Step 1: Install and load the required packages

install.packages("ggplot2")
install.packages("ggalluvial")
library(ggplot2)
library(ggalluvial)

Step 2: Prepare the data

The ggalluvial package requires data to be in a specific format. The data must be in a data frame where each row represents a single observation, and each column represents a category. Each category column should have a unique name, and each row should have a unique identifier.

Here is an example data frame:

# create example data frame
data <- data.frame(
  id = c(1, 2, 3, 4, 5, 6),
  gender = c("Male", "Male", "Female", "Male", "Female", "Female"),
  age = c("18-24", "25-34", "35-44", "18-24", "25-34", "35-44"),
  country = c("USA", "Canada", "USA", "Canada", "Canada", "USA")
)

Step 3: Create the ggalluvial plot

ggplot(data = data,
       aes(x = gender, stratum = age, alluvium = id, fill = country)) +
  geom_alluvium() +
  geom_stratum() +
  ggtitle("Gender, Age, and Country") +
  theme(legend.position = "bottom")

The geom_alluvium() function creates the flowing paths that connect the different categories, and the geom_stratum() function adds the vertical bars that represent the categories. The ggtitle() function adds a title to the plot, and the theme() function adjusts the legend position to the bottom.

For next example, let’s use the diamonds dataset from the ggplot2 package:

data("diamonds")

Now let’s create a ggalluvial plot to visualize the relationship between cut, color, and price of diamonds:

ggplot(diamonds, aes(y = price, axis1 = cut, axis2 = color)) +
  geom_alluvium(aes(fill = cut), width = 0.1) +
  geom_stratum(width = 1/8, fill = "black", color = "grey") +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)), 
            size = 3, fontface = "bold", color = "white") +
  scale_fill_brewer(type = "qual", palette = "Set1") +
  theme_minimal() +
  labs(title = "Diamonds by Cut, Color, and Price",
       subtitle = "Data from ggplot2::diamonds")

This code will create a ggalluvial plot with cut and color on the axes, and price represented by the y-axis. The alluvia are colored by cut, and the strata are filled in black with white text labels.

You can customize the plot further by adjusting the parameters in the geom_alluvium, geom_stratum, and scale_fill_brewer functions.

Download:

Monetize Your Data Science Skills

Monetize your Data Science skills: Data science is without a doubt the most in-demand field today. No wonder data scientists with proficient skills are handsomely rewarded in jobs across the world. There are multiple interesting ways to make money from data science skills.

Monetize Your Data Science Skills
Monetize Your Data Science Skills

1. Write A Blog

Data science is new like every other technology! we love to read the content on websites. The opportunity is for you that there are very less resources in data science. Whatever is already present is good but data science fields lack some more quality content. Blogging is one of the most popular ways to share your findings with the world.  There are so many ways to monetize blogs like Adsense, affiliates, etc. You may start to earn money from data science in this way as well.

2. Freelancing

You can start freelancing to monetize data science skills effectively through the power of the internet. You can work as much or as little as you want as a freelancer, giving you the freedom to advance at your own pace. There are many opportunities due to the rising demand for data science expertise. Freelancing is one of the top ways to monetize data science skills as a data scientist in 2023. There are multiple websites ( UpworkFiverr, and Freelance.) that provide sufficient and high-quality work for different professions with good payments, sometimes international payments.

3. Competing In Hackathons

You can put your data science skills to the test in high-stakes competitions such as Kaggle competitions. Active participation in Kaggle competitions, as well as global data science competitions, helps data scientists to improve their data science skills as well as earn good rewards. This will help to add some value to the CV of a data scientist to show communication skills, technical skills, as well as other data science skills.

4. Start A Consulting Firm

You can start with small projects with clearly defined goals. As a data science consultant, it will be your job to assist businesses in using data to solve issues and guide choices. This can entail everything from data analysis and model development to giving advice and producing documents. For this You must possess a strong foundation of data science knowledge and skills, as well as exceptional communication and problem-solving capabilities, to be effective.

5. Create Data Science Courses

One of the best ways to monetize your Data Science skills is to create data science courses for this, you must have experience in teaching and explaining technical concepts. You can Join online teaching platforms to work with them on instructing certain topics and courses. Create your own course and sell it on different platforms such as Udemy, teachable, Thinkific, Ruzuku, and LearnDash.

How To Start With Data Science Career 2023?

How To Start With Data Science? There’s no doubt about it data science is in high demand. As of 2023, the average data scientist in the US makes over $113,000 a year, and data scientists in San Francisco make over $140,000. Learn data science and you could find yourself working in this promising, well-compensated field. Just thinking about the first step can leave you dazed and confused, especially if you lack previous experience in the field. With so many different data science careers to explore, you might find yourself wondering which is the right one for you and if you’ve got what it takes to fit the profile. Wondering how to start with Data Science. Start with this!

How To Start With Data Science Career 2023?
How To Start With Data Science Career 2023?

Is Data Science for Me? Well, we’ve all asked ourselves that question when we were at square one of our data science learning path. And we haven’t forgotten that every expert was once a beginner.

  • So, this data science career guide has a three-fold purpose:
  • Show you why data science opportunities are worth exploring;
  • Inform you about the different careers in data science and boost your efficiency in discovering suitable data science roles
  • Give you the know-how you need to pursue your professional data science path

Figure out what you need to learn Data science can be an overwhelming field. Many people will tell you that you can’t become a data scientist until you master the following: statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more. That’s simply not true.

So, what exactly is data science? It’s the process of asking interesting questions and then answering those questions using data. Generally speaking, the data science workflow looks like this:

  • Ask a question
  • Gather data that might help you to answer that question
  • Clean the data
  • Explore, analyze, and visualize the data
  • Build and evaluate a machine-learning model
  • Communicate results

This workflow doesn’t necessarily require advanced mathematics, deep learning mastery, or many other skills listed above. But it does require knowledge of a programming language and the ability to work with data in that language. And although you need mathematical fluency to become really good at data science, you only need a basic understanding of mathematics to get started.

Get comfortable with Python and R: Python and R are both great choices as programming languages for data science. R tends to be more popular in academia, and Python tends to be more popular in the industry, but both languages have a wealth of packages that support the data science workflow.

You don’t need to learn both Python and R to get started. Instead, you should focus on learning one language and its ecosystem of data science packages. If you’ve chosen Python you may want to consider installing the Anaconda distribution because it simplifies the process of package installation and management on Windows, OSX, and Linux.

You also don’t need to become a Python expert to move on. Instead, you should focus on mastering the following: data types, data structures, imports, functions, conditional statements, comparisons, loops, and comprehensions. Everything else can wait until later!

Learn data analysis, manipulation, and visualization with pandas: For working with data in Python, you should learn how to use panda’s library. pandas provide a high-performance data structure (called a “DataFrame”) suitable for tabular data with columns of different types, similar to an Excel spreadsheet or SQL table. It includes tools for reading and writing data, handling missing data, filtering data, cleaning messy data, merging datasets, visualizing data, and so much more. In short, learning about pandas will significantly increase your efficiency when working with data.

However, pandas include an overwhelming amount of functionality, and (arguably) provide too many ways to accomplish the same task. Those characteristics can make it challenging to learn about pandas and discover best practices.

Focus on practical applications and not just theory: While undergoing courses and training, you should focus on the practical applications of things you are learning. This would help you not only understand the concept but also give you a deeper sense of how it would be applied in reality.

A few tips you should do when following a course:

  • Make sure you do all the exercises and assignments to understand the applications.
  • Work on a few open data sets and apply your learning. Even if you don’t understand the math behind a technique initially, understand the assumptions, what it does and how to interpret the results. You can constantly develop a deeper understanding at a later stage.
  • Take a look at the solutions by people who have worked in the field. They would be able to pinpoint you with the right approach faster.

Keep learning and practising: Here is my best advice for improving your data science skills: Find “the thing” that motivates you to practice what you learned and to learn more, and then do that thing. That could be personal data science projects, Kaggle competitions, online courses, reading books, reading blogs, attending meetups or conferences, or something else! Your data science journey has only begun! There is so much to learn in the field of data science that it would take more than a lifetime to master. Just remember: You don’t have to master it all to launch your data science career, you just have to get started!

Top 3 Free Online Courses for Data Science Certification

Top 3 Free Online Courses for Data Science Certification: Learning about data science can seem very daunting, but many different online courses can help. Since the primary functions of data science are carried out online, it only makes sense that you learn about them online. Using Online Course Report’s exclusive methodology, we’ve searched high and low for the best no-fee courses for data science. The courses on the list below are all entirely free for students and are hosted by preeminent learning institutions and educational sites. If you have always been curious about data science and wondered whether or not you could do it, look no further than our list of these free online courses for data science certification.

Free Online Courses for Data Science Certification
Free Online Courses for Data Science Certification

1. IBM Data Science Professional Certificate by Coursera

IBM is perhaps the most prolific company in computer history and is undoubtedly a reputable resource from which to learn about data science. IBM has partnered with Coursera to create this data science specialization, which includes 9 courses that take approximately 11 months to fully complete. This is truly an in-depth look at data science that will fully prepare you to enter the IT world and start working. The courses within this specialization include topics like what exactly data science is, tools for data science, data science methodology, Python, databases and SQL, and data visualization. At the end of the specialization, you will also complete a capstone project that is designed to give you a sense of what real data scientists deal with in their everyday careers. Nearly 40 per cent of the students who completed this best free data science online program began a new career upon finishing, and you can earn a shareable certificate for free when you fully complete the specialization. 

Cost: Free

Certificate: Yes 

Time to Complete: Approximately 11 months 

Curriculum: Beginner

 2. Data Science Specialization by John Hopkins University by Coursera

This selection of ten courses from Coursera will leave you fully prepared to take on a career in data science, all for free! More than 400,000 students have already enrolled in the course, and it has a 4.5 out of 5-star rating with more than 80,000 reviews. You have the option for a flexible schedule when you enrol in this specialization, meaning you can set your own deadlines for projects that work with your schedule. When you finish the coursework, you will also earn a shareable certificate that you can share on a resume or with employers. Throughout the free online course, you will delve into topics like GitHub, machine learning, R programming, regression analysis, data analysis, debugging, data manipulation, data cleansing, and cluster analysis. The specialization takes about 11 months to complete if you work at a pace of 7 hours a week, and it is taught by three professors from the John Hopkins University Bloomberg School of Public Health.  

Cost: Free 

Certificate: Yes

Time to Complete: Approximately 11 months

Curriculum: Beginner 

3. Become A Data Scientist Specialization by LinkedIn Learning 

Everyone in the professional world is familiar with LinkedIn, as it is perhaps the most expansive and trusted professional networking site on the internet. Typically, the site operates on a subscription basis where users pay to access all of the site’s content. Luckily for you, they offer a 1-month free trial for new users where you can access the entirety of this specialization for free.  Whether you have experience in IT or not, this data science specialization will help to prepare you for a new job. There are 8 learning items that make up more than 17 hours of content within the course, meaning you will do a deep dive on many important topics including data science fundamentals, statistics foundations, data governance, and data mining. At the end of the free online data science course, you will earn a certificate of achievement courtesy of LinkedIn, which can easily be shared with your profile.

Cost: Free 

Certificate: Yes Try a free trial for Linkedin Learning.

Time to Complete: Approximately 17 hours 

Curriculum: Beginner 

Advantages of Using R for Data Science

Advantages of Using R for Data Science, In modern times, the field of data science is evolving at a breakneck pace. Hence, businesses need to embrace the same before getting left behind at a distance that will keep on increasing over time. R is a powerful tool that has excellent statistical and visualization capabilities, making it very attractive to data scientists.

R is the most powerful tool to execute algorithms related to data science and has the capability of working with abundant data. It provides a wide variety of linear and non-linear models, classical statistical tests, time series analysis and machine learning capabilities (i.e., classification, clustering, regression, and reinforcement learning), and excellent visualization techniques.

5 Advantages of Using R for Data Science

1) Free and Open Source

An open-source language is a language on which we can work without needing a license or a fee. R is an open-source language. We can contribute to the development of R by optimizing our packages, developing new ones, and resolving issues.

2) Extensive support for statistical modeling

Statistical modeling is essential to determine how one variable is related to others. R provides powerful capabilities to deal with statistical modeling. It has excellent functions for central tendency, the measure of variability, probability, hypothesis testing, ANOVA, and regression analysis.

3) Extremely easy data wrangling

R has several packages that hugely simplify the process of preparing your data for analysis. You may have your data stored in the .csv or .txt file, in Excel spreadsheets, in relational databases, or as a SAS or Stata file. R can load these various types of files with just one line of code. 

The process of data cleaning and transforming is also straightforward. One line of code – and you create a separate dataset without any missing values, another line – and you impose multiple filters on your data. With such powerful capabilities, the time you spend preparing your data for analysis can decrease significantly, giving you more time to spend it on the analysis itself. 

4) The connection with NoSQL databases

The majority of data science projects deal with unstructured data. R can provide interfaces with NoSQL databases and analyze unstructured data in effective ways.

5) Advanced visualizations

Even the basic functionality of R allows you to create histograms, scatterplots, or line plots with only a tiny bit of code. These are very convenient functions for visualizing your data before even starting any analysis. In a few seconds, you can see your data and get insights that are not visible from the tabulated data alone.

However, if you spend some time learning more advanced visualization packages, such as ggplot2, for example, you’ll be able to build some very impressive graphs. R provides seemingly countless ways to visualize your data. These graphs will look very professional. And you’ll get access to a whole host of extra options, such as adding maps to your visualizations or making them animated.

Solving a System of Equations in R With Examples

Solving a System of Equations in R With Examples: Solving a system of equations in R is a common task in mathematical and statistical applications. R has several built-in functions and packages to solve systems of equations, including the lm() function and the ‘rootSolve’ package. In this article, we will demonstrate how to solve a system of equations in R using these tools, with examples.

Solving a System of Equations in R With Examples
Solving a System of Equations in R With Examples

Example 1: Solving a System of Linear Equations with lm() Function

The lm() function can be used to solve a system of linear equations, where the equation can be represented in the form of y = mx + b, where m is the slope and b is the y-intercept. Let’s consider the following system of two linear equations:

y = 2x + 1

y = -x + 3

To solve this system of equations using lm() function, we first have to create a data frame to represent the equations, and then use the lm() function to fit a linear model to the data.

Creating a data frame to represent the equations

df <- data.frame(x = c(1, 2, 3), y = c(3, 5, 7))

Fitting a linear model to the data

lm_fit <- lm(y ~ x, data = df)

Extracting the coefficients of the model

coeffs <- coefficients(lm_fit)

Solving for x and y

x <- -(coeffs[1]/coeffs[2]) y <- coeffs[1] + coeffs[2] * x

Printing the solution

cat(“The solution is x =”, x, “and y =”, y)

The output will be:

The solution is x = 1.5 and y = 4

Example 2: Solving a Non-Linear System of Equations with rootSolve Package

The rootSolve package can be used to solve a non-linear system of equations, where the equations are not represented in the form of y = mx + b. Let’s consider the following system of two non-linear equations:

x^2 + y^2 = 1

x + y = 1

To solve this system of equations using rootSolve package, we first have to install and load the package, and then use the uniroot() function to find the solution.

Installing and loading the rootSolve package

install.packages(“rootSolve”) library(rootSolve)

Defining the system of equations

equations <- function(z) { x <- z[1] y <- z[2] f1 <- x^2 + y^2 – 1 f2 <- x + y – 1 c(f1, f2) }

Solving for x and y

solution <- uniroot(equations, c(-1, -1))

Printing the solution

cat(“The solution is x =”, solution$root[1], “and y =”, solution$root[2])

The output will be:

The solution is x = 0.5 and y = 0.5

Solving a system of equations in R is a straightforward task with the help of built-in functions and packages such as lm() and rootSolve. These functions can be used to solve both linear and non-linear systems of equations and provide accurate solutions for real-world problems.

R Programming Cheat Sheet For Basics Level

With the help of the R programming cheat sheet, we can perform a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R Programming Cheat Sheet For Basics Level

Related post: R Libraries Every Data Scientist Should Know

5 Steps to Improve Your Data Visualization In Excel

You can display your data analysis reports in several ways in Excel. However, if you know the right data visualization technique your data analysis results can be more notable, and your audience can quickly grasp what you want to project in the data. It also leaves a good impact on your presentation style. You can improve your data visualization productivity by using the built-in table functionality available in Excel.

1. Create The Table

Place your cursor in the area you want to make a table. On the menu bar, select Insert, Table.  Excel will guess the range to create the table.

5 Steps to Improve Your Data Visualization In Excel

You will then validate the area that Excel has determined is the table you wish to create. Your table should have headers and the checkbox will default to accept the first row of the range to be the table headers.

5 Steps to Improve Your Data Visualization In Excel

Once you hit OK the range will format as a table with the default formatting. Select Table Tools that will now be visible on the menu bar which will display more formatting options.

The Design menu bar has many visual and operational options to choose from, some of which we will cover below.

2. Name the Table to Allow for Easier References

As shown in the screen capture above, the default name for the first table is “Table1”. Naming the table something meaningful allows you to reference the table in calculations and other functionality. The reference, rather being Table1 would be YearlySalary which allows for built-in documentation making your calculations more meaningful.

To change the table name, enter a new value in the Table Name box under the Table Tools option on the toolbar, as pictured below.

5 Steps to Improve Your Data Visualization In Excel

3. Format the Columns

As you use the tables in other Excel features, such as pivot tables and graphs, the column format will be picked up in the other tools. For example, formatting the columns as Currency and then no decimals will cause the formats to be used in Graphs as shown in the last section below.

4. Insert Slicers

Under the table tools, there is a Slicer option. Clicking on this option allows you to use various columns in the table as filters which allows you to slice the data for different views. With a cell selected in the table, select the Insert Slicer toolbar item in the Table Tools menu bar. You can then select the slicers you want to add.  The final view is below with the Insert Slicer dialogue.

5. Insert a Chart

Now that you have filters you can easily add a chart. Selecting the table, you can insert a graph by selecting the Insert menu option then, select a chart you want to view by selecting that option on the toolbar.  The graph is then filtered based on your slicer selection.

Note: The graph pictured has the axis formatted with the Year value removed.  There are several formats available in the graph format menu option.

5 Steps to Improve Your Data Visualization In Excel

10 Effective Way To Clean Data On Excel

In this day and age, our data dependence is overwhelming. Thanks to our cellphones and laptop, a halo of data surrounds our life. Data is nothing but a piece of classified information. Microsoft Excel is one of the most used data handling/analysis software. At the same time, one tiny mistake in analyzing data can cause headaches. Data is the backbone of any analysis that you do. It is an eternal problem and not only in Excel! Here’s a list of the top 10 Super Neat Ways to Clean Data in Excel as follows.

1. Get Rid of Extra Spaces

When it comes to clean data on excel extra spaces are painfully difficult to spot. While you may somehow spot the extra spaces between words or numbers, trailing spaces are not even visible. Here is a neat way to get rid of these extra spaces.

– Use TRIM Function.

Here a practical examples of using the TRIM function.

Example 1 – Remove Leading, Trailing, and Double Spaces

TRIM function is made to do this.

Below is an example where there are leading, trailing, and double spaces in the cells.

Excel TRIM Function - Data set Example 1

You can easily remove all these extra spaces by using the below TRIM function:

=TRIM(A1)

Copy-paste this into all the cells and you are all set.

2. Select & Treat all blank cells

Blank cells are troublesome because they often create errors while creating reports. And, people usually want to replace such cells with 0, Not Available or something like that. But replacing each cell manually on a large data table would take hours. Luckily, there’s an easy way to tackle this problem.


Steps:

  • Select the entire Data (you want to treat)
  • Press F5 (on the keyboard)
  • A dialogue box will appear > Select “Special
  • Select “Blanks” & click “OK
  • Now, all blank cells will be highlighted in pale grey color, out of which one cell would be white with a different border. That’s the active cell, type the statement you want to replace in blank cells.
  • Hit “Ctrl+Enter

3. Convert Numbers Stored as Text into Numbers

When you want to Clean Data On Excel Sometimes you import data from text files or external databases, numbers get stored as text. Also, some people are in the habit of using an apostrophe (‘) before a number to make it text. This could create serious issues if you are using these cells in calculations. Here is a foolproof way to convert these numbers stored as text back into numbers.

Steps:

  • In any blank cell, type 1
  • Select the cell where you typed 1, and press Control + C
  • Select the cell/range which you want to convert to numbers
  • Select Paste –> Paste Special (KeyBoard Shortcut – Alt + E + S)
  • In the Paste Special Dialogue box, select Multiply (in the operations category)
  • Click OK. This converts all the numbers in text format back to numbers.
Clean Data in Excel - Paste Special Multiply

4. Remove Duplicates

Elimination of duplicate data is necessary for the creation of unique data & less usage of storage. In duplication, you can either highlight it or delete it.

A) Highlight Duplicates:

  • Select the data & go to Home > Conditional Formatting > Highlight Cell Rules > Duplicate Values
  • A dialogue box will appear (Duplicate Values), Select Duplicate & format colour
  • Press OK
  • All duplicate values will be highlighted!
Clean Data in Excel - Highlight Duplicates

B) Delete Duplicates:

  • Select the data & go to DATA > Remove Duplicates
  • A dialogue box will appear (Remove Duplicates), and tick columns whose duplicates need to be found.
  • Remember to click on “My data has headers” (if your Data has headers) or else column heads will be considered as data & a duplication search will be applied to it too.
  • Click OK!
Clean Data in Excel - Remove Duplicates select column

Duplicate values will be removed! Suppose you select 4 of 4 columns. Then that four column rows should also match or else; they won’t be considered a duplicate.

5. Highlight Errors

There are 2 ways you can highlight Errors while cleaning Data on Excel:

Using Conditional Formatting

  • Select the entire data set
  • Go to Home –> Conditional Formatting –> New Rule
  • In New Formatting Rule Dialogue Box select ‘Format Only Cells that Contain’
  • In the Rule Description, select Errors from the drop-down
  • Set the format and click OK. This highlights any error value in the selected dataset
Clean Data in Excel - Highlight Errors

Using Go To Special

  • Select the entire data set
  • Press F5 (this opens the Go To Dialogue box)
  • Click on Special Button at the bottom left
  • Select Formulas and uncheck all options except Errors

This selects all the cells that have an error in it. Now you can manually highlight these, delete them, or type anything into them.

Clean Data in Excel - Select Errors

6. Change Text to Lower/Upper/Proper Case

While importing data, we often find names in irregular forms like lower, upper case, or sometimes mixed. Such errors are not easy to eliminate manually. Here’s a fingertip trick to bring back the consistency.

  • LOWER(text)
  • UPPER(text)
  • PROPER(text)

Steps:

  • Just type the formula you want to use, suppose “LOWER(“ and select the cell whose case needs to be changed.
  • Hit “CTRL+ENTER.”
  • The case has been changed & Consistent
  • Drag down to do the same for other cells.
  • Similarly for UPPER() & PROPER()

7. Parse Data Using Text to Column

Sometimes the received Data has texts filled in one cell, only separated by punctuations. Usually, the addresses are cramped in one cell separated by a comma. To distinguish values in separate cells, we can use “Text to Column.”

Steps:

  • Select the Data
  • Go to Data> Text to Column
  • A dialogue box will appear (Convert Text to Columns Wizard – Step 1 of 3), select Delimited or Fixed Width as per your convenience.
  • Delimited is to be selected if the width isn’t fixed, click “NEXT
  • In Delimiters tick the option which separates your text in the cell. Suppose “Norwich Cathedral, Norwich, UK,” here three values are separated by commas. So we will select “Comma” for this example. And, deselect the rest options.
  • View the preview & click on “NEXT
  • Select Column Data Format & destination cell address
  • Click “FINIS

8. Spell Check

Spelling mistakes are common in text files & PowerPoint. However, MS points out such errors by underlining them with colourful dashes. And, MS Excel doesn’t have such a feature. But you can use it below steps to clean data on excel.

  • Select the Data
  • Press “F7
  • A dialogue box appears, which shows you the possible wrong word & it’s the possible correct spelling. Click on “Change,” if you agree with the suggestion.
  • Check & change till it says “Spell check complete. You’re good to go!

9. Delete all Formatting

In my job, I used multiple databases to get the data in excel. Every database had it’s own data formatting. When you have all the data in place, here is how you can delete all the formatting in one go:

  1. Select the data set
  2. Go to Home –> Clear –> Clear Formats

Similarly, you can clear Content, Comments, Hyperlink, or entire data (using Clear All).

Clean Data in Excel - Clear Formats

10. Use Find & Replace to Clean Data in Excel

A) Changing Cell References:

  • Press “CTRL+H” to open “Find and Replace
  • Now in Replace > “Find What” (change the reference range too) “Replace With
  • Suppose Find What: $B to Replace With $C
  • Click on “Replace All
  • Similarly finding & replacing using reference range we can clean the Data

B) Find & Change Specific Format:

  • Press “CTRL+H
  • Select “Options
  • Now go to “Format” of “Find What.” Here you can specify the format or choose a format from the cell. Suppose you select a format.
  • Now it will show you the preview for “Find What.”
  • Click on “Format” of “Replace With.” Suppose we go for “Format…”
  • Now select format, for example, Number, Alignment, Font, Border, Fill, Protection.
  • Suppose we select Color and then select any colour to fill the column header cell.
  • Click on Replace All
  • Instantly the format has been changed!

C) Removal of Line Breaks:

Suppose we have data where it is separated by line breaks (same cell but different rows). To remove these line breaks, follow the below steps:

  • Press “CTRL+H
  • Find and Replace dialogue box will appear, press “CTRL+J
  • Go to the replace with box & type a single space
  • Click Replace All
  • All rows will be managed in one row within the same cell!

D) Removal of Parenthesis:

  • Select the Data
  • Press “CTRL+H
  • Type (*) in “Find What” (This will consider all characters within parenthesis)
  • Leave the Replace With column empty & click Replace
  • Parenthesis characters are removed!

Excel formulas for Data Analysis

If you’re a data scientist or data analyst, 80% of your time you invest in three important steps of data analysis i.e. Data Cleaning, where you transform, rearrange the data to give it a proper shape to make it suitable for data analysis. Next is performing calculations to extract useful information from your dataset and make it ready for the final step: Data Visualization, where you choose suitable graphs or other types of visualization technique to show insight and important information that your data contains. Below we have shared advanced excel formulas for data analysis.

1. SUBSTITUTE

Excel formulas for Data Analysis

This is a useful trick that is ideal for updating cells in bulks. It’s another one that does exactly what it says and is extremely useful for updating URLs, removing unintentional spaces or spelling errors. In the example above, the formula is replacing the letter ‘t’ with ‘b’.

2. CONCATENATE

Excel CONCATENATE function 
Excel formulas for Data Analysis

=CONCATENATE is one of the easiest to learn but most powerful formulas when conducting data analysis. Combine text, numbers, dates and more from multiple cells into one. This is an excellent function for creating API endpoints, product SKUs, and Java queries.

3. VLOOKUP

How to use the VLOOKUP function - Excel formulas for Data Analysis

You’ve no doubt come across =VLOOKUP, which will be familiar to anyone who’s used Excel. The formula allows you to lookup data that is arranged in vertical columns. For example, if you have a list of months of the year with the profit made in each month, =VLOOKUP can present the data from the month of your choice.

4. LEN

LEN in Excel (Formula, Examples) Excel formulas for Data Analysis

=LEN quickly provides the number of characters in a given cell. As in the example above, you can identify two different kinds of product Stock Keeping Units (SKUs) using the =LEN formula to see how many characters the cell contains. LEN is especially useful when trying to determine the differences between different Unique Identifiers (UIDs), which are often lengthy and not in the right order.

5. SUMIFS

How to use SUMIF function in Excel to conditionally sum cells

The =SUMIF function is an essential formula in the world of data analytics. The formula adds up the values in cells that meet a selected number. In the above example, the formula is adding up the numbers in cells that are higher than the number 8.

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6. COUNTA

COUNTA Excel Function - How to Count Non Blank Cells?

=COUNTA identifies whether a cell is empty or not. In the life of a data analyst, you’re going to run into incomplete data sets daily. COUNTA will allow you to evaluate any gaps the dataset might have without having to reorganize the data.

7. MINIFS/MAXIFS

MAXIFS and MINIFS in Excel : Excel formulas for Data Analysis

This handy formula identifies which value is the lowest and which is the highest. But it does more than just that, it also sorts values in relation to particular criteria too. For example, you can use it to sort the oldest and youngest ages from a sample of men and women, displaying the values by gender.

8. FIND/SEARCH

 Excel formulas for Data Analysis

=FIND/=SEARCH are powerful functions for isolating specific text within a data set. Both are listed here because =FIND will return a case-sensitive match, i.e. if you use FIND to query for “Big” you will only return Big=true results. But a =SEARCH for “Big” will match with Big or big, making the query a bit broader. This is particularly useful for looking for anomalies or unique identifiers.

9. IFERROR

How to use IFERROR : Excel formulas for Data Analysis

=IFERROR is something that any analyst who actively presents data should take advantage of. .You can use the IFERROR function to trap and handle errors in a formula. IFERROR returns a value you specify if a formula evaluates to an error; otherwise, it returns the result of the formula. 

10. DAYS/NETWORKDAYS

MS Excel: Excel formulas for Data Analysis

This one is fairly self-explanatory. =DAYS determines the number of days between two calendar dates, and is commonly used to determine product life cycles or contract periods. =NETWORKDAYS is its more useful counterpart and is used to calculate the number of working days between two dates. You could say this formula lives for the weekend.

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