Trending December 2023 # Top 10 Tips To Overcome Data Science Imposter Syndrome # Suggested January 2024 # Top 21 Popular

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It is quite common to find this syndrome in almost every field that one can think of. Data science tends to be no different

Imposter syndrome is that feeling of inadequacy and incompetence which further paves the way for countless issues and problems. Thus, dealing with it and implementing ways to overcome it has become the need of the hour. These days, it is quite common to find this syndrome in almost every field that one can think of. Data science tends to be no different. Considering how important it is to overcome it, we have come up with a list of top 10 tips to overcome data science imposter syndrome. Have a look!

It is absolutely okay to not know everything

As a matter of fact, data science is an ever-changing field where there is a need for new technologies constantly. However, the very realization that your learning curve will get steeper with time plays a vital role. One needs to accept the fact that it is absolutely okay to not know everything about data science. End of the day, you should be in a position to do what’s assigned.

Learn to ask questions

People refrain from asking questions presuming that doing so would make them look silly. Yes, it might be a little scary to ask questions upfront to your teammates and superiors but what is important to note is that it is way sillier if you don’t ask questions. Asking questions is the best way to learn and overcome data science imposter syndrome.

Stop comparing

During our entire lifetime, we have, for once at least, comparing ourselves to others. It’s a natural human tendency. Every person is different and has his/her own strengths and weaknesses. Comparing those to that of yours is something you should stop henceforth.

Be kind to yourself

Yes, competition is at its peak these days. But being harsh on yourself doesn’t work anyway. Just like comparing yourself to others doesn’t make it any easier, so does self-criticism. All this will just add up to the already existing data science imposter syndrome.

Break the silence

Breaking silence is considered to be yet another powerful tool when it comes to overcoming data science imposter syndrome. As you start to feel that you are having a strong emotional reaction, try to describe, or name it – whether to yourself or to others.

Facts and beyond

As far as data science as an industry is concerned, it goes way beyond facts and numbers. Just in case you happen to experience a certain emotion like self-doubt, put it out there and analyze whether what you are feeling is based on facts or based on something else.

Get out of a toxic environment

Get out of a toxic environment

It has been a common observation that someone working in a toxic environment tends to develop every possible trait of imposter syndrome. Toxic environments can have devastating effects which are why getting out of such an environment is critical.

Learning from the failures

There is probably no better way to learn and grow from the failures and mistakes you make. It is through failure that we learn the greatest lessons that life could probably teach us.

Create your own path

Rather than following the path laid by someone else, creating your own path is always appreciated. By doing so, one not only gets to learn various prospects of getting better but also to overcome data science imposter syndrome.

No self-criticism

Self-criticism doesn’t work. This will not lead to any improvement in areas that you think might work. Data science is a field that has a lot to do with data and numbers. The field is such that it requires good analytical skills as well. Here, self-criticism doesn’t seem to be of any use. Rather it adds up to result in a data science imposter syndrome.

Appreciating yourself for all the little achievements

Irrespective of how small or big an achievement is, always celebrate it. This way, you are not only bringing in positivity but also getting closer to getting away from imposter syndrome.

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Top 6 Data Science Jobs In The Data

This data science career is doing very well on the market. Data science is making remarkable progress in many areas of technology, economy and commerce. It’s not an exaggeration. It is no surprise that data scientists will have many job opportunities.

It is true. Multiple projections show that the demand for data scientists will rise significantly in the next five-years. It is clear that demand is far greater than supply. Data science is a highly specialized field that requires a passion for math and analytical skills. This gap is perpetuated by the insufficient supply of these skills.

Every organization in the world is now data-driven. Data-driven organizations are the First Five: Google, Amazon, Facebook, Meta, Apple, Microsoft, and Facebook. They aren’t the only ones. Nearly every company in the market uses data-driven decision-making. The data sets can be customized quickly.

Amazon keeps meticulous records of all our choices and preferences in the world of shopping. It customizes the data to only send information that is relevant to the search terms of specific customers. Both the client and the company benefit from this process. This increases the company’s profit and helps the customer by acquiring goods at lower prices than they expected.

Data sets have a wider impact than just their positive effects. Data sets have positive effects on the health sphere by making people aware about critical health issues and other health-related items. It can also have an impact on agriculture, providing valuable information to farmers about efficient production and delivery of food.

It is evident that data scientists are needed around the globe, which makes their job prospects bright. Let’s take a look at some of the most exciting data science jobs available to data scientists who want to be effective in data management within organizations.

Top 6 Data Science Jobs in the Data-driven Industry 1. Data scientists

Average Salary: US$100,000.

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2. Data architects

Average Salary: US$95,000/annum

Roles and Responsibilities This employee is responsible for developing organizational data strategies that convert business requirements into technical requirements.

3. Data engineers

Average Salary: US$110,000 an Year

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4. Data analysts

Average Salary: US$70,000 an Year

Roles and Responsibilities. A data analyst must analyze real-time data using statistical techniques and tools in order to present reports to management. It is crucial to create and maintain a database and analyze and interpret current trends and patterns within those databases.

5. Data storyteller

Average Salary: US$60,000 an Year

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6. Database administrators

Average Salary: US$80,000 an Year

Roles and Responsibilities of a database administrator: The database administrator must be proficient in database software to manage data effectively and keep it up-to date for data design and development. This employee will manage the database access and prevent loss and corruption.

These are only a few of the many data science jobs available to the world. In recent years, data science has been a thriving field in many industries around the globe. In this fast-paced world, data is increasingly valuable and there are many opportunities to fill data-centric roles within reputable organizations.

Top 10 Data Science Prerequisites You Should Know In 2023

Data science paves an enticing career path for students and existing professionals. Be it product development, improving customer retention, or mining through data to find new business opportunities, organizations are extensively relying on data scientists to sustain, grow, and stay one step ahead of the competition. This throws light on the growing demand for data scientists. If you, too, are aspiring to become a successful data scientist, you have landed at the right place for we will talk about the top 10 data science prerequisites you should know in 2023. Have a look!


As a matter of fact, data science has a lot to do with data. In such a case, statistics turn out to be a blessing. This is for the sole reason that statistics help to dig deeper into data and gain valuable insights from them. The reality is – the more statistics you know, the more you will be able to analyze and quantify the uncertainty in a dataset.

Understanding analytical tools

Yet another important prerequisite for data science is to have a fair understanding of analytical tools. This is because a data scientist can extract valuable information from an organized data set via analytical tools. Some popular data analytical tools that you can get your hands on are – SAS, Hadoop, Spark, Hive, Pig, and R.


Data scientists are involved in procuring, cleaning, munging, and organizing data. For all of these tasks, programming comes in handy.  Statistical programming languages such as R and Python serve the purpose here. If you want to excel as a data scientist, make sure that you are well-versed in Python and R.

Machine learning (ML)

Data scientists are entrusted with yet another important business task – identifying business problems and turning them into Machine Learning tasks. When you receive datasets, you are required to use your Machine Learning skills to feed the algorithms with data. ML will process these data in real time via data-driven models and efficient algorithms.

Apache Spark

Apache Spark is just the right computation framework you need when it comes to running complicated algorithms faster. With this framework, you can save time a lot of time while processing a big sea of data. In addition to that, it also helps Data Scientists handle large, unstructured, and complex data sets in the best possible manner.

Data Visualization

Yet another important prerequisite for data science that cannot go unnoticed is data visualization, a representation of data visually, through graphs and charts. As a data scientist, you should be able to represent data graphically, using charts, graphs, maps, etc. The extensive amount of data generated each day is the very reason why we require data visualization.

Communication skills

The fact that communication skill is one of the most important non-technical skill that one should possess, no matter what the job role is, goes without saying. Even in the case of data science, communication turns out to be an important prerequisite. This is because data scientists are required to clearly translate technical findings to the other non-technical teams like Sales, Operations or Marketing Departments. They should also be able to provide meaningful insights, hence enabling the business to make wiser decisions.


Excel is one tool that is extremely important to understand, manipulate, analyze and visualize data, hence a prerequisite for data science. With Excel, it is quite easy to proceed with manipulations and computations that have to be done on the data. Having sound Excel knowledge will definitely help you become a successful data scientist.


How To Learn Data Science From Scratch

Data science is the branch of science that deals with the collection and analysis of data to extract useful information from it. The data can be in any form, be it text, numbers, images, videos, etc. The results from this data can be used to train a machine to perform tasks on its own, or it can be used to forecast future outcomes. We are living in a world of data. More and more companies are turning towards data science, artificial intelligence and machine learning to get their job done. Learning data science can equip you for the future. This article will discuss how to learn data science from scratch.  

Why is data science important?

You are always surrounded by zettabytes and yottabytes of data. Data can be structured or unstructured. It is important for businesses to use this data. This data can be used to:

visualize trends

reduce costs

launch new products and services

extend business to different demographics

Your Learning Plan 1. Technical Skills

We will start with technical skills. Understanding technical skills will help you understand the algorithms with mathematics better. Python is the most widely used language in data science. There is a whole bunch of developers working hard to develop libraries in Python to make your data science experience smooth and easy. However, you should also polish your skills in R programming. 1.1. Python Fundamentals Before using Python to solve data science problems, you must be able to understand its fundamentals. There are lots of free courses available online to learn Python. You can also use YouTube to learn Python for free. You can refer to the book Python for Dummies for more help. 1.2. Data Analysis using Python Now we can move towards using Python in data analysis. I would suggest chúng tôi as the starting point. It is free, crisp and easy to understand. If you want a more in-depth knowledge of the topic, you can always buy the premium subscription. The price is somewhere between $24 and $49 depending on the type of package you opt for. It is always useful to spend some money for your future. 1.3. Machine Learning using Python The premium package for chúng tôi already equips you with the fundamentals of ML. However, there are a plethora of free resources online to acquire skills in ML. Make sure whichever course you follow, it deals with scikit-learn. Scikit-learn is the most widely used Python library for data science and machine learning. At this stage, you can also start attending workshops and seminars. They will help you gain practical knowledge on this subject. 1.4. SQL In data science, you always deal with data. This is where SQL comes into the picture. SQL helps you organize and access data. You can use an online learning platform like Codeacademy or YouTube to learn SQL for free. 1.5. R Programming It is always a good idea to diversify your skills. You don’t need to depend on Python alone. You can use Codeacademy or YouTube to learn the basics of R. It is a free course. If you can spend extra money, then I would say opt for the pro package for Codeacademy. It may cost you somewhere around $31 to $15  

2. Theory

While you are learning about the technical aspects, you will encounter theory too. Don’t make the mistake of ignoring the theory. Learn the theory alongside technicalities. Suppose you have learned an algorithm. It’s fine. Now is the time to learn more about it by diving deep into its theory. The Khan Academy has all the theory you will need throughout this course.  

 3. Math

Maths is an important part of data science. 3.1. Calculus Calculus is an integral part of this curriculum.  Every machine learning algorithm makes use of calculus. So, it becomes inevitable to have a good grip on this topic. The topics you need to study under calculus are: 3.1.1. Derivatives

Derivative of a function

Geometric definition

Nonlinear function

3.1.2. Chain Rule

Composite functions

Multiple functions

Derivatives of composite functions

3.1.3. Gradients

Directional derivatives


Partial derivatives

  3.2. Linear Algebra Linear algebra is another important topic you need to master to understand data science. Linear algebra is used across all three domains – machine learning, artificial intelligence as well as data science. The topics you need to study under linear algebra are: 3.2.1. Vectors and spaces


Linear dependence and independence

Linear combinations

The vector dot and cross product

3.2.2. Matrix transformations

Multiplication of a matrix

Transpose of a matrix

Linear transformations

Inverse function

3.3. Statistics Statistics are needed to sort and use the data. Proper organization and maintenance of data need the use of statistics. Here are the important topics under this umbrella: 3.3.1. Descriptive Statistics

Types of distribution

Central tendency

Summarization of data

Dependence measure

3.3.2. Experiment Design




Hypothesis testing

Significance Testing

3.2.3. Machine Learning



Inference about slope

4. Practical experience

Now you are ready to try your hands in some real-world data science problem. Enroll in an internship or contribute in some open-source project. This step will help you enrich your skills.  

Data Science Lifecycle

Every data science project goes through a lifecycle. Here we describe each of the phases of the cycle in detail.

Discovery: In this phase, you define the problem to be solved. You also make a report regarding the manpower, skills and technology available to you. This is the step where you can approve or reject a project.

Data Preparation: Here you will need to prepare an analytical sandbox that will be used in the remaining part of the project. You also need to condition the data before modeling. First, you prepare the analytical sandbox, then prepare ETLT, then data conditioning and finally visualization.

Model Planning: Here you will need to draw a relationship among the variables. You need to understand the data. These relationships will be the basis of the algorithm used in your project. You can use any of the following model planning tools: SAS/ACCESS, SQL or R.

Model Building: Here you need to develop data sets to train your system. You have to make a choice between your existing tools or a new more robust environment. Various model-building tools available in the market are SAS Enterprise Manager, MATLAB, WEKA, Statistica, Alpine Miner, etc.

Operationalize: In this step, you deliver a final report, code of the system and technical briefings. You also try to test the system in pilot mode to ascertain how it functions before deploying it in the real world.

Communicate Results: Now your work is done. In this step, you communicate with the stakeholders, whether or not your system complies with all their requirements ascertained in step 1. If they accept the system, your project is a success, or else it is a failure.

Data Science Components

Data: Data is the basic building block of data science. Data is of two types: structured data (is basically in tabular form) and unstructured data (images, emails, videos, PDF files, etc.)

Programming: R and Python are the most widely used programming language in data science. Programming is the way to maintain, organize and analyze data.

Mathematics: In the field of mathematics, you don’t need to know everything. Statistics and probability are mostly used in data science. Without the proper knowledge of mathematics and probability, you will most probably make incorrect decisions and misinterpret data.

Machine Learning: As a data scientist, you will be working with machine learning algorithms on a daily basis. Regression, classification, etc. are some of the well-known machine learning algorithms.

Big Data: In this era, raw data is compared with crude oil. Like we refine crude oil and use it to drive automobiles, similarly, the raw data must be refined and used to drive technology. Remember, raw data is of no use. It is the refined data that is used in all machine learning algorithms.

Now you know everything about data science. Now you have a clear road map on how to master data science. Remember this will not be an easy career. Data science is a very young market. Breakthrough developments are taking place almost every day. It is your job to keep yourself acquainted with all the happenings in the market. A little effort and a bright future await you.    

About Author:

Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkata) with over 25 years of professional experience Specialized in Data Science, Artificial Intelligence, and Machine Learning. PMP Certified ITIL Expert certified APMG, PEOPLECERT and EXIN Accredited Trainer for all modules of ITIL till Expert Trained over 3000+ professionals across the globe Currently authoring a book on ITIL “ITIL MADE EASY” Conducted myriad Project management and ITIL Process consulting engagements in various organizations. Performed maturity assessment, gap analysis and Project management process definition and end to end implementation of Project management best practices   Name: Ram Tavva Designation: Director of ExcelR Solutions Location: Bangalore

8 Data Visualization Tips To Improve Data Stories


Get to know the essential data visualization tips and techniques to improve your data stories

Understand the effects of these data visualization tips

Choosing the right data visualization techniques for the task

Avoiding unnecessary information in visualizations


I love data visualization. The sheer amount of information it conveys to the viewer in such a limited space and without much explanation is amazing. It is so easy to convey a message using data visualization because it makes the trends and patterns in the data come alive! Not only that, but given how the human brain works, it also allows the audience to grasp an insight into the fastest and the simplest way possible. Therefore, it goes without saying that the importance of data visualization for analysis in any domain is immense!

But hundreds of visualizations are created every day. Some are taken well by the audience while others are outright rejected. Why so? Well, the answer lies in the creation.

Data visualizations are not as easy to create as they look. There is a lot of work and effort that goes into it. There needs to be the right balance between all the visual elements. If you do too little or too much, your visualization will never create an impact. All the correct elements need to be present in the right proportion and, at the same time, certain mistakes need to be avoided to create a meaningful visualization.

In this article, you will find important data visualization tips to improve your visualizations and certain mistakes you need to avoid. So rest assured as it is bound to take your visualization game up a notch!

Table of Contents

Data visualizations should be audience-specific with a clear requirement

Choose the right visualization for your data

Keep your visualizations simple

Label your data visualizations

Understand the importance of text in charts

Use colors effectively in data visualizations

Avoid deceiving with your visualizations

Make interpretable data visualizations

1. Data visualization should be audience-specific with a clear requirement

Amongst the data visualization tips, this is the first one. While creating data visualizations, it is important to know the requirement of the chart and the audience it is for. These two things alone can take your visualization from zero to hero. This makes sure that you not only create a visualization with a strategic purpose that answers a specific question but also one that can be easily understood by the audience.

For example, if your audience doesn’t have a background in science, then don’t create a visualization that is filled with scientific information. Similarly, bombarding your chart with multiple trends will most likely divide the attention of the viewer and defeats the purpose of the visualization.

Know the requirement of the visualization. This allows you to create a chart that conveys a message in a clear and crystal manner. Also, it makes sure that you aren’t overloading your chart with unnecessary information that might confuse the audience. Therefore, know what is required of the visualization and keep it simple by highlighting a specific point. This will have a lasting impact on the viewer.

Know your audience. Before making the visualization, it is best to ask yourself what the audience will be looking for in the chart. Understand the requirements and preferences of your viewer. Know their background. Do they have enough time for a detailed visualization? How aware are they about the context of the visualization? What additional information are they looking for? Are they aware of the graphs being used? And so on. Your viewer’s information needs should be your guide in creating effective and compelling data visualizations.

The visualization below has so much information making it very hard to understand its purpose. This makes it so difficult to convey the message to the viewer.

Stacked bar graphs are difficult to comprehend for beginners and require patience to interpret. The visualization below isn’t easy to comprehend if you are someone who has no knowledge of stacked bar graphs or someone who is looking to quickly go through visualizations.

2. Choose the right data visualization for your data

Amongst the data visualization tips, this one is of utmost importance. There is a myriad of visualization graphs out there. But choosing the right one is important so as to effectively highlight the key trend in the data. Also, choosing the right graph for your visualization will make sure the message is easy to grasp and the viewers are attracted to your work. Each graph has a specific purpose and one should know where to use which graph.

Bar graphs are one of the most popular types of data visualizations. They offer a great amount of information in a quick glance. They are best to compare a few values within the same category. For example, comparing the sales of two different products over the years.

Line plots are useful for visualizing the trend in a numerical value over a continuous time interval. They effectively capture the trends and patterns in data and can be used to compare multiple values. An example of such a data visualization would be to show the trend in the monthly income of a company over the last few months.

Scatter plots are useful for showing the relationship between two variables. Any correlation between variables or outliers in the data can be easily spotted using scatter plots. For example, it can be used to compare how the price of a house varies with the size of the living room.

Pie charts are suitable to show the proportional distribution of items within the same category. But they should be used prudently otherwise they do more harm than good. For example, the percentage of android users to iOS users in a country.

Histograms show the distribution of numeric data through a continuous interval by segmenting data into different bins. They are great for showing the distribution of data. For example, visualizing the number of orders for a product over the years.

And so on. Also, don’t be afraid to combine more than one type of graph in your visualizations. Sometimes it offers a chance for the viewer to explore the data in detail.

You can have a look at the following cheat sheet from the Harvard CS-109 extension program (online resource) to understand when to use a particular visualization graph.

You can also follow this cheatsheet and this comprehensive article on the usage of graphs in visualization.

The visualization below uses a simple horizontal bar graph, but it is so effective in communicating the message. The viewer can easily determine how well the states are doing in producing wind energy. The rankings are clear and the user experience is simple yet spot on!

3. Keep your visualizations simple

It is very easy to put up too much information in a visualization. But harder to get rid of the unnecessary information. A minimalist visualization that is devoid of distractions and unnecessary patterns is likely to convey the message to the viewer more effectively.

All the visual elements in a graph that are not important in helping the viewer comprehend the information in the graph are termed as Chartjunk by Edward Tufte. These can be anything from unnecessary gridlines, distracting visual patterns, redundant axes, shadows, and so on. Chartjunks are an eyesore to the viewers. Have a look for yourself.

The visualization above is a bad example where there is an unnecessary use of gridlines, bold illegible text, and 3D graphs. A simple 2D bar graph would suffice here.

Another example would be the chart below. The numbers are doing all the talking here because the logos seem so unnecessary and confusing. Always avoid such chartjunk. Just have a look at the GDP of Afghanistan. It’s so hard to decipher just from the picture. A simple bar chart would have been sufficient to convey the message to the viewer.

Therefore, it is best to pay close attention to this data visualization technique and only add those elements to your visualization that provide a value-for-money and simplify the chart for the viewer.

4. Label your data visualizations

An important data visualization technique is to label your visualization. This better conveys what the visuals are trying to say. They are an easy miss when creating the visualization, so make sure you double-check for labeling before rolling out your visualization.

Labels should be legible. If it is not clear, it is of no use. Therefore, make sure the labels are easy to read and comprehensible.

Give a title to the graph. Viewers can easily get instant gist of what the graph is about when you give a suitable title to your graph.

Use a legend wisely. A legend makes it easier to spot the difference between the various lines in the graph. But when using line charts, try to label directions.  This makes it easier to identify lines.

Label your axes. Sometimes it might not be clear from the title what the axes represent. Therefore, you might want to label your axes at times.

Pay attention to the labeling on the axes. Sometimes, you don’t need to label all the ticks on the axes. You can instead label them at intervals if they still convey the right message.

In the visualization below, the title is present which indicates what the visualization is trying to convey. We label the axes properly. An appropriate range for the ticks is used on the axes so that the axes labels don’t become too crowded. The lines have been labeled directly which makes it very easy to focus on what’s happening in the visualization. Although it isn’t a very fancy visualization, the labeling makes sure that the message is delivered to the viewer clearly.

In the data visualization example below, a legend is being used instead of labeling the lines directly. Although the visualization is technically correct, the use of legend to indirectly label the lines makes it difficult to compare the lines to their labels.

5. Understand the importance of text in charts

Data visualization is not just about numbers. The text provides an important context that conveys the right message to the viewer. Headings, sub-headings, and annotations that you put alongside the graphs, explain what is being presented in the visualization. But reiterating the same message in every text and unnecessarily putting too much text can backfire. It can end up doing more harm than good. Therefore, it is best to use text in moderation.

Try using simple phrases wherever possible. The aim is to allow the visualization to speak for itself.

Keep only those annotations that provide relevant information. Putting up annotations for every data point will distract the viewer and unnecessarily clutter the visual.

You might need to use bold or italic text to highlight important parts of the graph, but try not to use them excessively otherwise there will be no difference between regular and emphasized text.

Avoid text reiterating the same message. For example, the heading and subheading repeating the same message might not be the prudent thing to do.

Avoid using distracting fonts that are hard to read. The viewer should be able to grasp the message in the graph instantly without much work.

In the data visualization example below, the sub-heading text seems unnecessary. The Life satisfaction scale on the y-axis seems self-explanatory. Also, using “Real GDP per capita (PPP)” on the x-axis would have done the job instead of providing an explanation in the sub-heading.

6. Use colors effectively in data visualizations

Everyone knows the power of colors and the amount of impact it can have on the viewer. It is one of the most important data visualization tricks that you can employ for your visualization. It can provide the right amount of zest that your visualization needs to entice the viewers. But improper use of colors can end up misleading the viewer. Therefore, the data visualization technique requires close attention.

Use the same color for the same kind of data. For example, a bar graph indicating sales for cars over the year can be indicated in one color while sales of bikes can be depicted in a separate color.

The color of text annotation should be the same as the bar or line it is representing. This will make sure the viewer is easily able to identify which data is being represented by the text.

You can use shades of the same color to depict the waning intensity of data. Choropleth maps use this to indicate patterns.

Limit the use of different colors. The use of too many colors can create a cacophony in your visualization.

Use colors that the viewer can associate with. For example, using red for hot temperature and blue for cool temperature can be easily understood by the viewer even if no explanation is provided.

The visualization below depicts the population density in different countries using shades of the same color where a darker color indicates high density and lighter color indicates low density. It is a classic case of using the same shade of color to drive the argument home.

The visualization below uses colors effectively. Different colors highlight the Deficit and Surplus in the chart. Also, the text is the same color as the region of the line it represents. All of this makes it super easy to comprehend the chart without having to look for an explanation!

7. Avoid deceiving with your visualizations

While we are trying so much to create a stunning visualization, it can very easily deceive the viewer. And sometimes we won’t even know it while deceiving the viewer. Small things like cherry-picking data, omitting baseline, information overload, etc. can all lead to deception. Therefore, one should avoid such naive mistakes while creating visualizations.

Include baselines in your graphs. Avoiding baselines is the most basic form of deception. This creates a faux image that artificially augments the difference between two data groups.

Not including complete data in the graph can give an incomplete picture to the viewer resulting in incorrect decision-making. For example, visualizing only a small part of sales data that indicates an upward trend, while when taken as a whole, there is actually a downward trend!

Hiding important data. This makes sure that the viewer is kept distracted from the actual part of the graph that matters the most.

One very smart way of deceiving the viewer is to put an excessive amount of information in the graph. This creates confusion for the viewer who can’t focus on any particular trend.

Going against convention. For example, using a green color to indicate something wrong and red to indicate something right.

Stock market visualizations are a classic example where deception is very common. If you don’t display the complete picture, you can get a false idea of how a company is doing. The visualization on the left gives the performance of the company in the past month. It looks like the company isn’t doing so well. But, if we look at the stock prices for the past 6 months, we can see that it actually went up and the company is doing relatively well!

The data visualization example below highlights another deception. The viewer instantly focuses on the line depicting the usage of Facebook. But if you see carefully, you can notice a constant increase in the usage of Instagram, while that of Facebook stagnates!

8. Make interpretable data visualizations

The last of the data visualization tips is that the interpretability of the visualization matters more than its visual appeal. All the points we have covered so far should make the visualization more interpretable. Visuals images, patterns, colors, etc. are only good if they don’t distort the message to the viewer. In the end, even if a simple line graph is able to deliver the message across to the viewer, then you don’t really need to put fancy logos or images in your visualizations!

The visualization below is a perfect example of beauty preceding over interpretability. The creator tried to represent the usage of different Indian languages on India’s map. They have not only used arbitrary shapes to represent the numbers but also the positioning of the languages is wrong with respect to the region they belong to. For example, the North has Telegu, and the South has Gujrati.

A better way to represent such data would be to simply use a bar chart which would have accentuated the rankings of the different languages.

Have a look at another data visualization example below. The visualization displays the contributed by each country as a percentage of world GDP. By giving more weightage to beauty the visualization has become unnecessarily difficult to interpret. Not only are the countries difficult to find, but their positioning isn’t intuitive at first glance. Again, a data visualization along the lines of a bar chart would have done a great job here.

Therefore, whenever it comes to data visualization always give precedence to interpretability over beauty.


Data visualization is an art that needs to be mastered over time. These data visualization tips and techniques, though aren’t exhaustive, but will surely help you move in the right direction. Understanding the perspective of the end viewer is the key to creating a successful and effective data visualization. You should always try to ascertain what the end viewer wants to know.

But before employing these data visualization tips, it is important to understand the tool you are using. Therefore, if you want to learn the popular data visualizations tools and how to use them, you can check out the following resources on our platform:


Data Science Roles In Telecom Industry


Big Data and Cloud Platform

In the early years, telecommunications data storage was hampered by a variety of problems such as unwieldy numbers, a lack of computing power, prohibitive costs. But with the new technologies, the dimension of problems has changed.

The areas of use of Technology are:

· Cloud Platform enabling Data storage expenses to drop every day. (Azure, AWS)

· Computer processing power is increasing exponentially (Quantum Computing)

· Analytics software and tools are cheap and sometimes free (Knime, Python)

In earlier days, the data stores were expensive, and data was stored in siloed – separated and often incompatible – data stores. This was creating barriers to make use of an enormous volume and variety of information. Business Intelligence (BI) vendors like IBM, Oracle, SAS, Tibco, and QlikTech are breaking down these walls between data storage and this provides a lot of jobs for telecom data scientists.

Data Scientist roles in Telecom Sector 1. Network Optimization

When a network is down, underutilized, overtaxed, or nearing maximum capacity, the costs add up

In the past, telecom companies have handled this problem by putting caption data and developing tiered pricing models.

But now, using real-time and predictive analytics, companies analyze subscriber behavior and create individual network usage policies.

When the network goes down, every department (sales, marketing, customer service) can observe the effects, locate the customers affected, andimmediately implement efforts to address the issue.

When a customer suddenly abandons a shopping cart, customer service representatives can soothe concerns in a subsequent call, text, oremail.

Building360-degree profile of Network using CDRs, Alarms, Network Manuals, TemIP, etc. gives a better overview of the network health.

Not only does this make happy customers, but it also improves efficiencies and maximizes revenue streams.

Telecoms also have the option to combine their knowledge of network performance with internal data (e.g., customer usage or marketing initiatives) and external data (e.g., seasonal trends) to redirect resources (e.g., offers or capital investments) towards network hotspots.

2. Customer Personalization

Like all the industries, Telecom has much more scope to personalize the services such as value-added services, data packs, apps to recommend based on following the behavioral patterns of customers. Sophisticated 360-degree profiles of customers assembled from all below help to build personalized recommendations for customers.

Customer Behaviour

voice, SMS, and data usage patterns

video choices

customer care history

social media activity

past purchase patterns

website visits, duration, browsing, and search patterns.

Customer Demographics

age, address, and gender

type and number of devices used.

service usage

geographic location

This allows telecom companies to offer personalized services or products at every step of the purchasing process. Businesses can tailor messages to appear on the right channels (e.g., mobile, web, call center, in-store), in the right areas, and in the right words and images.

Customer Segmentation, Sentiment analysis, Recommendation engines for more apt products for the customers are the illustrative areas where Data scientists can help for improvements.

3. Customer Retention

Due to customer dissatisfaction in any of the areas such as poor connection/network quality, poor services, high cost of services, call drops, competitors, less personalization, customer churn. This means they jump from network to network in search of bargains. This is one of the biggest challenges confronting a telecom company. It is far more costly to acquire new customers than to cater to existing ones.

To prevent churn, data scientists are employing both real-time and predictive analytics to:

Combinevariables (e.g., calls made, minutes used, number of texts sent, average bill amount, the average return per user i.e.ARPU) to predict the likelihoodof change.

Know when a customer visits a competitor’s website changes his/her SIM or swaps devices.

Use sentiment analysis of social media to detect changes in opinion.

Target specific customer segments with personalized promotions based on historical behavior.

React to retains customers as soon as the change is noted.

Predictive models, clustering would be the ways to predict the prospective churners.

Implemented Solution Approach

Using big data and python, I have developed the solution to find the upcoming network failure before it takes place. The critical success factor defined were:

· Identify and prioritize the cells with call drop issues based on rules provided by the operator.

· Based on rules specified, provide relevant indicative information to network engineers that might have caused the issue in the particular cell.

· Provide a 360-degree view of network KPIs to the network engineer.

· Build a knowledge management database that can capture the actions taken to resolve the problem and

· Update the CRs as good and bad, based on effectiveness in resolving the network issue

As a huge data was getting created, the database used was Hadoop -Big Insights.

Data transformation scripts were in spark.

And the neural network was the ML technique used to find out the system parameters when historically alarms (the indication of network failure) in the system got generated.

This information was fed as a threshold and once in the real scenario the parameters start approaching the threshold, the internal alert for those cell sites get generated for the Network engineer to focus on as preventive analytics.

Once the network engineer identifies the problem and solves it, it gets documented in the knowledge repository for future reference.

And when exactly a similar situation occurs, network the engineer will not get notification of internal alert but also steps to solve which is build using knowledge repository.


The reduction in process time, dropped call rate, the volume of (transient) issues handled by engineer, mean time to solve the problem, cost, people and increase in Revenue, customers, customer satisfaction, efficiency, and productivity of network engineers are the main area of any industry which Data scientists would be of help.

Various data generation sources under Telecom sectors are booming areas for Data Scientists to innovate, explore, value add, and help the provider to provide data-driven AI/ML solutions by preventive analytics, process improvements, optimizations, predictive analytics.


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