Data is a crucial element of the modern world and the most crucial pillar that industries run on. Data has different forms, and it can be stored and used easily when needed. Business organizations analyze it, which allows them to make crucial decisions for enhancing profit.

 

The term ‘data science’ comes into existence as professionals rightly extract the values from every data. These professionals are known as data scientists. The data scientists use several tools to analyze data and reach a specific conclusion. However, they must know the usage of the tools.

 

If you dream of being a data scientist, the best you can do is take an online course in data science. However, you must have an idea about the most used data science tools. Here is a comprehensive comparative study of the leading data science tools like Python, R, and Excel.

 

Moreover, you can study data science applications and the right way to become a professional data scientist.

What is Data Science?

 

Data science is the study and application of different methods to deal with data. It mainly revolves around preparing the data for thorough analysis. To enhance data analysis, cleansing and manipulation are necessary, and it comes under data science too.

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As far as the methods of data analysis are concerned, you can either take the scientific approach or AI support. A data analytics course from XLRI can provide you with the right approach to know everything about data science.

 

Data Science: A Resource for Machine Learning

 

At present, most companies deal with a huge amount of data. Data generation also takes place at a rapid pace. According to an article published on the Oracle website, people upload 10 million photos per hour on Facebook.

 

In most cases, huge data remains untouched. However, top organizations can analyze this data for business benefits. Data science comes into play in such a scenario. Machine Learning prominently associates with data science, for it enables professionals to make different models.

 

The data models help professionals reach the underlying value of the data. It has a clear difference between ML and AI. In the same way, data science tools are different from both mentioned earlier. You should not confuse it with the difference between Python and R, as they are Data science tools.

 

Distinguishing ML, AI, and Data Science

 

Here are the core differences between data science, AI, and ML. This might help you get a better understanding of the two concepts.

 

Artificial intelligence: Artificial intelligence results from technological advancement where a computer can work as a human being. AI experts generally design this computer according to the type of work it is appointed to do.

 

When it comes to data science, artificial intelligence can be used to analyze data. An AI computer is programmed with languages like R, Python, and Java, the leading data science tools.

 

Artificial intelligence has many advantages, and the most significant ones are given below:

 

  • AI can be used to manage tasks related to heavy data
  • All data-related outcomes are consistent if the work is done on AI
  • A lot of virtual agents are present for AI

 

Machine Learning: Machine learning is nothing but a subpart of AI. The field of ML deals basically with the techniques by which computers with AI can deal with the data the right way.

 

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There are several machine learning courses that you can take and learn about the subject in detail. While studying with an ML course, you can understand the difference between it and data science. In the course, you can learn about deep learning too. It is another subpart of machine learning that deals with complex problems.

 

Data Science: Data science is also considered to be a subpart of AI. It comprehensively deals with data analysis and its methodologies. It is applied when the professionals need to get insight into data.

 

On the online courses of data science, you can know the various ways to reach the insight. If you have learned Excel, R or Python earlier, you can easily grasp the course content.

 

Need of Data Science in the Contemporary World

 

As per content published on the site of The Scientific World, data science is used to get insights from data (Big Data). The companies can design their marketing strategies based on the insights. All they need to do is analyze the data properly.

 

You can go through the following note to understand why data science is necessary in the modern world.

 

Data Science Supports Marketing

 

Every company needs to put effort into marketing to ensure profit. It also secures the future of the company. Marketing can be improved with the correct application of data science. In most cases, data-driven decisions made by an organization lead it to the way of success.

 

Every organization can put on better advertisements if it has access to data insights. However, a set of skilled data science professionals is the primary necessity for every company. If you want to be a skilled data science professional and join a high-paid job, choose the Business analytics course from XLRI.

 

Relevance of Data Science in Multiple Industries

 

At present, data analysis is shaping the future of multiple industries. There are also multiple trends that are shaping the field of data analytics too. So, data science professionals can expect to get jobs in different sectors. Since social media, e-commerce websites, surveys, call lists can be the best data sources, businesses are always in need of a team.

 

Establishments in every industrial sector are always looking forward to making data-related tools for better operation. So, the relevance of data science always remains intact. Furthermore, many experts think that data will rule the future world. In such a condition, the relevance of data science is expected to increase more over time.

 

Here is a short note on how data science can help in different industries:

 

  • Data Science in the Legal Field

 

All types of legal research become easier with the application of data science. Lawyers can hire data scientists who can choose the necessary data from genuine legal sources and analyze it. This way, legal research gains perfection, and the lawyer can expect to win a trial easily.

 

  • Data Science in the Healthcare Sector

 

Modern healthcare machines run on data. At present, most X-ray and Mammographic machines run on computer algorithms. They generate data, and it can be analyzed to get the result. Moreover, modern spectrum analysis technology can be used on patients to diagnose cancer early. Similarly, it can also detect other infections like organ anomalies, tumours, stenosis, etc.

 

Algorithms from data science applications can also improve the track of genetic studies and engineering. So, in the healthcare sector, doctors might always need the support of data scientists.

 

  • Data Science in the Travel Sector

 

With a proper application of data science, travel companies can easily predict certain scenarios, like flight delays and improve the travelling experience. Moreover, the dynamic hotel tariffs and deals can be predicted early. This can help the tourists to choose the best deal for themselves.

 

Application of Data Science Tools to Reduce Time

 

Dealing with a humongous amount of data can be a tough job. This is where data science comes into play. Data scientists can find several ways to easily choose the necessary data and analyze them. Every data science tool reduces time when it comes to data handling.

 

So, it is clear that joining an online course in data science can lead to multiple job options in different sectors. Most of these job posts are high-paid, and you can expect a comfortable, professional life being a data scientist.

 

Benefits of Data Science Tools Based on Their Type

 

The data science tools are the main elements with which scientists have to work. These tools have multiple benefits. However, it differs from the specific tool used by a data scientist.

 

Here are some basic facts about the three top data science tools used by professionals at present. If you are already in a course where you learn Excel, you might grasp every fact mentioned about it. Have a look.

 

Top Data Science Tools

 

Python

 

Python is a very popular data science tool to support all four stages of the data lifecycle. Firstly, you can easily execute a collection of huge data. Even in an unstructured format, Python can help you to bring it in the right shape.

 

Secondly, data modelling is easy with Python. Proper modelling can always help you to observe patterns in the data. This helps business organizations to make proper decisions for the future.

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Finally, Python gives way to clear data visualization. As a result, any business entity can make proper reports for the outcome. Furthermore, Python has numerous sub tools for each of the stages discussed above. Some of them are given below:

 

  • Data Collection: In data collection, you can use the Python tools, such as Data APIs, Beautiful Soup, and Wget.
  • Data Modelling: Spicy Ecosystem- NumPy, Imbalanced- learn, and Pandas, some data modelling tools that you can access while using Python.
  • Data Visualization: With Python, you can use the tools like Matplotlib, MoviePy, and Seaborn. All these tools can provide you with the support for data visualization.
  • NLP Tool: Python provides you with a bonus tool for string matching. It is known as FuzzyWuzzy, and it helps you to execute token ratios and comparison ratios. So, if you are looking to become a data scientist and an expert in Python, find a genuine online course in data science. Many of these courses are quite affordable and provide you with proper education.

 

R

 

Like Python, R is also a data science tool. It has been launched in the market by the R Foundation. Continuous development of R is active as the R Project is still ongoing. Some crucial facts about R are given here:-

 

R is mainly used for data analysis in the field of data science. As a result, you can handle the data or store it. Finally, you can analyze it too. As open-source software, it is quite adaptive. If you are new to data science as an expert, you can easily work with R. However, it depends on which industry you are working for and what type of data you are dealing with. The standard quality of R is also high as it is open-source software.

 

R is a convenient tool to use in statistical analysis. It can provide the analyzed report regarding the data graphically. Although other tools also allow this, the representation of R is simpler to understand.

 

The community of R is quite responsive. Many experts have in-depth knowledge about the computer language. In the community, 20 contributors work continuously fixing bugs and improving the tool according to the suggestions by the users.

 

Excel

 

Excel is a basic software most people learn while they are in school. However, it allows dealing with huge amounts of data. If you are a data scientist who deals with 2D data, Excel is the best tool for you.

 

As you learn Excel, you can edit and format the data easily. Moreover, documents in Excel can be easily shared. The Analysis ToolPak in Excel can be activated for accessing the advanced powers.

 

The Analysis ToolPak enables machine learning, and any data professional can easily carry out data analysis. A few data professionals might find Analysis ToolPak a bit outdated, but it can still be used in the present time.

 

Another fact why people find data analysis to be tricky is due to the type of coded functions it supports, especially with Excel. While working with data on Excel, you can only use functions like SAS and SPSS, which are quite tough. One good thing about Excel as a data science tool is that it enables users to use Python. However, you can only use it while accessing Excel on a Windows system.

 

Detailed Comparisons

 

This section will take you through a comparative study of the most popular data science tools like Python, R, and Excel. This study can help you choose the right tool to expertise on if you are willing to be a data scientist.

 

Excel

When it comes to Excel as a data science tool, you should keep some usage scenarios, advantages, and disadvantages in mind.

 

Usage Scenarios

 

Excel can only support medium and small-scale business organizations as a data science tool. It is not suitable for large-scale business organizations as the volume of data remains quite high.

 

Simple data analysis can be executed on Excel. It can be the best data science tool for a school or bank. It allows the data scientist to execute regression analysis, variance analysis, etc. So, it is clear that Excel can be used the best for dealing with the data generated from a general office.

 

Excel does not provide the user with a platform to create data analysis reports as a data science tool. While using Excel for data analysis, you have to use Word and PowerPoint to create reports. It is convenient for data visualization. You can easily make charts on it. So, concluding can be an easy task.

 

Advantages of Excel

 

Excel is a well-known software, and it has many advantages. The most significant ones are:-

 

  • Easy Learning: All operations on Excel are quite easy to learn. A beginner who wants to be a successful data scientist can start learning Excel at first. In the data analytics course from XLRI, you can learn the ways to handle data on Excel.
  • Enables Multiple Operations: Excel allows the users to do a lot of things with data. As mentioned earlier, data visualization is easy in Excel. Moreover, you can make dynamic charts on it too. Simple reports can be made on Excel, while the complicated ones need support.
  • Learning the Basic Operations: You can learn all basic operations related to data science on Excel. This can ease your Python and R learning process.

 

Disadvantages of Excel

 

Despite the advantages of Excel mentioned above, there are some disadvantages too. Have a look:-

 

  • Chances of Data Stuttering: You know that Excel can only fit medium and small organizations. This fact is quite right, as data stuttering is normal in its case. Excel might not be the right option when it comes to dealing with huge amounts of data.
  • VBA is Necessary: To apply data science on a tool like Excel, VBA (Visual Basic Applications) is necessary. It is a tough programming language that might take a lot of time to learn. So, most data science professionals avoid using Excel as a tool.

 

In an accredited online course in data science, you might get some knowledge of basic Excel. However, you will surely get exposure to modern tools and learn about them.\

 

R

 

The usage scenarios, advantages, and disadvantages of R as a tool of data science are discussed below. Take a look if you strive to have a basic idea about R as compared to Excel.

 

Usage Scenarios

 

R has complete coverage in any area where there is a necessity for data. The functions of R can help the user cover the areas of both general and academic data analysis.

 

While you use R, you can work on multiple aspects of data science. R mainly helps a user with the cleansing of data. Moreover, it allows web crawling and data visualization too. Report output can also be done with R. The ‘R markdown’ enables the user to get the data analysis report output.

 

Both statistical modelling and statistical hypothesis testing can be done easily with R. There are different types of algorithms that come under both of these. They are given below:

 

  • t-test.
  • Variance analysis.
  • Chi-square test.
  • Logistic regression.
  • Linear regression.
  • Neural network.
  • Tree model.

 

Advantages of R

 

Here are the key advantages you can get while learning the usage of R. In a data analytics course from XLRI, you can study R in depth and enjoy the following advantages:

 

  • Easy to Learn: The primary advantage of R is the ease it provides the user to learn the usage.
  • Centralized Learning: Centralized learning can make a student know all the basics of R in a mere 10 to 12 classes. You can learn about structuring, importing, exporting, and visualizing data after completing the basic course.
  • Quicker Approach to Solving Problems: R has several help files on the network. These files can help the user solve particular problems in no time.

 

Disadvantages of R

 

As a data science tool, R has only a few disadvantages. They are:

 

  • Less Speed: In data analytics, R as a data science tool is quite slower than other options.
  • Complicated language: To some data science professionals, R is quite a complicated language to deal with. However, it is simpler than VBA, which is necessary for working with Excel.

 

Python

 

Like R, Python has more or less identical usage scenarios. As you use Python, you can work on similar aspects of data as R. What outshines  Python than R is that you can execute data mining. In this aspect, it has already taken the lead in comparison to R. Similar to R, Python also demands proper programming from the user. The advantage of Python is that it allows a professional to take the scientific computing approach. It is just a branch of this language.

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Apart from data science, Python is largely used in web designing. Game developers also use this language to design modern game interfaces. Lastly, all work related to operations and maintenance can be done with Python.

 

Learning Python and R

 

Data science tools, like Python and R, are programming languages. Learning these languages for the first time can be challenging. If you have no idea about programming, things might seem a bit confusing at first.

 

The best way you can learn Python and R is to choose one at a time. You can pick Python at first and master it before starting on with R. As an aspiring data scientist, you must have a clear idea regarding the type of work you would do.

 

In a credible online course in data science, you can choose either Python or R. There are many features common in R and Python. Excel stands nowhere in comparison to both. It has limited features and is quite tough to learn.

 

Trends of Data Science

 

The trends of data science keep on changing from time to time. It is not the same as it was five years ago. Here are some recent trends of data science that you must know. By observing these trends, you can understand the condition of the data science market.

 

  • Most Data Applications are Made with Python: Python’s popularity as a programming language is increasing day by day. Data science professionals can access multiple data libraries while working on Python. Some of them are Scikit-learn and Pandas.

 

The lessons of Python are simple and easily graspable for beginners. So, most data science courses offer Python learning modules. Moreover, an esteemed analyst firm Redmonk shows that Python has become the 3rd most used computer language.

 

  • End-to-end AI Solutions Are in Trend: The presence of Dataiku has been a game-changer in the data science field. It allows the professionals to automate all management-related tasks regarding data.

 

Dataiku is an AI company that makes AI models for data management. So, this has already become a trend. Google has also purchased stakes of Dataiku as late as December 2019.

 

In the present era, all business organizations want automated solutions for data analytics. So, the AI companies are going well with the trend enriching the field of data science.

 

  • Huge Demand for Data Analysts: The ongoing digital transformation has made this world data-centric. As a result, data analytics has become the need of the hour. So, as data science is getting enriched, the market demand for data analysts is increasing.

 

Professional data scientists who have done a course in data science online can structure big messy data. So, companies of all sizes are always looking forward to hiring them.

 

  • The Kaggle Community Enriches with Time: Kaggle, a data science community, is becoming trendy as more data scientists are joining it. As per the data published on Exploding Topics, it has over 5 million members. These members are from 194 countries.

 

Data scientists join Kaggle to keep proper track regarding the progression of ML projects and post about it.

 

  • A Buzz of Data Protection: As data science is enriching, data privacy and protection are becoming alarming issues. The leading social platforms, which used to harvest data freely before, face legal trials.

 

This data protection buzz can prove to be a big limitation for data science in the future. However, professionals can make new algorithms to access the necessary consumer data in the future.

 

  • Deepface Audio and Video Explosion: Deepfake videos were already present in the field of data science. Nowadays, Deepfake audio is also becoming trendy. Any deep-faked data keeps the potential to get viral in the market easily. So, if you want to be a data scientist in the future, you need to be careful about such things.

 

  • IoT Is a New Trend: In the case of smart computing, everything depends on data. So, considering the Internet of Things as a source of data would not be wrong. Apart from Google Assistant, most smart appliances like AC, TV, etc., are connected through IoT.

 

  • Predictive Analysis Is Crucial for the Future: At present, the need for predictive analysis is crucial in the field of data science. A tool that allows a professional to create an algorithm for predictive analysis is always preferred.

 

In such a scenario, many tools of data science have become obsolete. On the other hand, some new tools are coming into the market and getting the deserved exposure.

 

Predictive analysis is vital for a business. It helps organizations check the pattern of the data and understand the behaviour of the customers. In the long run, this helps an organization to both retain and attract customers.

 

Ways to Become a Data Scientist

 

As the youngsters are mostly tech-savvy these days, they are aspiring to become data scientists. Given the relevance of data science in the modern era, it is now a hot career option. Moreover, the term ‘Big Data’ has become synonymous with the industry.

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Nowadays, many people are aware that the field of data science can provide them with high-paid jobs. The best you can do is take an online course that provides you with a genuine degree. Here are the basic steps to become a data scientist.

 

  • Opt for an IT Degree: If you want to become a data scientist, opting for an IT degree is necessary. Choose a bachelor’s degree in IT after you complete your school education. However, you must have subjects like physics, maths and computer science in your X+2.
  • Complete Your Master’s Degree: There are multiple data-related courses on which you can take the master’s degree. The universities will only grant you a masters’ degree course if they find your bachelor’s degree genuine. So, you should be precise while choosing the right course.
  • Get Experience: Once you complete your master’s degree in data, you can choose a specific industry and join as a data professional. For starters, you can choose the fields like business, healthcare, and physics.

 

Special Requirements to Become a Data Scientist

 

In the case of becoming a successful data science researcher, you might need a doctorate. If you choose this field, you can either be a data researcher or hold an advanced position in your company.

 

Generally, very few students take the doctorate in the case of data science. Nowadays, opting for a certificate course in data science is more trendy.

 

A Good Candidate Is a Need for the Hour

 

When it comes to the field of data science, a good candidate is always preferred by most organizations. You need to have some traits to be a promising candidate in the field of data science. Have a look if you are that gifted one:

 

  • An Urge to Learn: A student of data science should have the urge to learn new things. Curiosity in a student is always expected as it can lead to finding new and smarter ways to solve data-related problems.
  • A Data Science Learner Must be Well-organized: The organizing ability of a data science student is a crucial necessity. For aspiring candidates and degree students, proper organizing ability always keeps them in the lead.

 

At times, data organization can become too complicated. In such scenarios, a proper organization can only lead to correct conclusions. Proper concentration is always expected from a person who deals with data science.

 

  • A Cool Brain: Data science might lead a student or a professional to frustrating scenarios. So, everyone attached to this field should always have a cool brain. Mostly, staying cool can help get the solution for a problem quickly.

 

So, as you join the data analytics course from XLRI, you should remember to properly make up your mind. To make sure you go through the deal with the subject as a student, try meditating regularly. You can keep this habit as you reach your professional life. Always remember that advancement as a data scientist can lead you to more subject-related complications.

 

  • Being Attentive: All data scientists should have the quality to be attentive. Being inattentive towards work can lead any data science project to nuisance. None can reach the right conclusion if a data science tool is misused.

 

You should always keep in mind that all the tools of data science are programming languages. So, everything would depend on the way a program is written and run.

 

About Online Courses and Degrees of Data Science

Several universities and registered organizations provide online courses in data science. You have to choose one that fits your aim and affordability. There are some common facts that you should check in each course.

 

They are:

 

  • Special Educational Requirements to Join a Course: There are some special requirements in many online data science courses. You should check if the one you choose has anything as such. Mostly, these courses ask for your GMAT/GRE course.

 

  • Tenure of the Course: Data science online courses are not lengthy. You should always check if it completes within 14 to 24 months. Avoid choosing the ones which have a course structure exceeding two years. Consider these as substandard as the coursework might not be well-knit.

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  • Convenient Timings: All online courses are primarily flexible. It is so as professionals choose them to increase their skills and knowledge. In such a condition, these courses should have flexible timings.

 

  • The Ratio of Students and Instructors: This is an essential fact that you must check in a data science online course. The lesser ratio of teacher and student in a data science class ensures proper training. In most courses, the ratio of students and teachers remains around 15:1 to 20:1. It will be better if you avoid methods that have a high student-teacher ratio.

 

  • Check if the Course Provides Your Specialization: You should always check if the online data science course provides your specialization. Be precise about the tool you want to learn. If you are confused about it, refer to the comparative study section. It would help you decide which tool to learn from.

 

  • Connect with the Course Provider: There are certain reasons for which you should connect with your course provider. While talking in detail, you must mention the level of knowledge you have regarding data science.

 

Always be honest with the course provider and convey the limit of your knowledge of programming language. You should also clearly tell about your goal and know which tool would be the best for you. Consider giving a second thought on finding a mismatch in what you think about your data science specialization and the information given by the course provider.

 

  • Check if the Course Is Affordable: After you have known everything about an online course in data science, check if it suits your budget. You can compare multiple courses and choose one that is suitable.

 

There are many genuine courses, like the data analytics course from XLRI. These courses are well affordable and provide a comprehensive education.

 

What Does a Data Analytics Course Include?

 

A data analytics course includes the following:-

 

  • Chapters of data analytics, technique, and tools
  • Concepts on customer analytics and retail analytics
  • Chapters on business fundamentals
  • Concepts on social media analytics
  • Details on statistics and probability
  • Relational database management and its concepts

 

Conclusion

Data science, without a doubt, is a field that has come into existence due to technological advancement. In the present day, no business can run digitally without the generation and analysis of data. This is where data science gets its relevance.

 

Data science professionals work for choosing and structuring data and reaching conclusions after analysis. Reports are made from these conclusions that help the organizations to make business-related decisions. There are different job roles data professionals get placed into. Some of them are data engineers, data analysts, marketing analysts, data scientists, etc.

 

Data scientists who work with a huge amount of data at a time mainly need to work with tools (programming languages like Python, R, Excel). To be an efficient data scientist, proper expertise on at least one of the tools is necessary.

 

If you know the difference between Python and R, you can easily pick the one you should learn. Remember, it is just a programming language, and you need to execute proper programming to make the data analytics process easier.

 

As far as job markets are concerned, you might have already known by now about how data science is necessary for several industries. So, it will always be a good idea to think about a career in data science, especially considering that the future of e-learning is bright.

 

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