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Tag: courses on data science

Do Data Scientist Use Object Oriented Programming?

Posted on by Imarticus Learning

It is estimated that there are 2.5 quintillion bytes of data produced every day in our world. In this data-driven world, the career opportunities for a skilled data scientist are endless. With the data production rate predicted to go higher than of now, the career opportunities for those who can manage data are not going anywhere. This article discusses whether data scientists are using Object-Oriented Programming. Let’s find out.

What is Object-Oriented Programming
Object-Oriented Programming or OOP is a model of the programming language organized around objects rather than the actions. It also emphasizes data rather than the logic. Traditionally, a program is considered to be a logical procedure that converts input data into output.

In such cases, the challenge was to come up with a logic that works. The OOP model redefined that concept. It takes the view that we should care more about the objects we are trying to manipulate rather than the logic we use. These objects could be anything from humans defined by names and addresses to little widgets such as buttons on the desktop.

The main advantages of OOP are:
• Programs with a clearer modular structure.
• Codes are reusable through inheritance.
• Flexibility through polymorphism.
• Very effective problem-solving.

Object-Oriented Programming in Data Science
Using Object-Oriented Programming for data science may not always be the best choice. As we said, the OOP model cares more about the objects than the logic. This type of approach is most suited for GUI, interactive application, and APIs exposing mutable situations. When it comes to data science, functional programming is preferred more due to superior performance than compared to the OOP model. The advantage of better maintainability offered by OOP is sacrificed in the data science for the sake of performance.

Polymorphism is an important feature of OOP. It allows a loosely coupled architecture, where the same interface can be easily substituted for different implementations. This feature is very helpful when dealing with applications of large size. However, data scientists seldom use large codebase. They always use small scripts and prototypes. So, OOP would be far too much overhead with no significant benefits.

Although, machine learning libraries are a must needed thing for data scientists. Most of these libraries make use of object-oriented programming, at least the ones in Python. Machine learning libraries such as Scikit-learn heavily make use of OPP. Data scientists who work with R and SQL will never have to use OOP.

Conclusion
It is clear that even though Object-Oriented Programming Offers a lot of benefits, it is not exactly what data science need. So in general, object-oriented programming is seldom used by the data scientists.

If the data science career seems to suit you, wait no more. Imarticus is offering courses on data science prodegree, which will provide you with all the skills and knowledge to excel in your career. This Genpact data science course allows you to start your journey on the right foot with placement assistance at so much more.

How do Data Scientists Use Programming?

Posted on by Imarticus Learning

 

There are few fields in the world which have grown in stature as data science has in recent years. As a discipline, the growth of data science has been unparalleled – machine learning and AI are now implemented in a host of large organizations around the world. 

With every passing second, new data is created by millions of users around the world. Companies try to leverage this data by analyzing it, and data science is used to get business value from the findings. This means that using data science as both a business tool and a product, a number of companies have made business decisions which have made them grow quicker than ever. 

With the rise of data science, the demand for capable data scientists has also improved. It is now one of the most lucrative career options and one which is extremely accessible too. However, how do you become a data scientist, and is programming truly required for being successful in the discipline?

The Importance of Programming for a Data Scientist

Programming remains to be one of the main technical skills which any data scientist requires to succeed. In the initial stages of learning, one does not need to know much programming. This is because the focus is one using the tools at hand to implement algorithms, in order to analyse the data at hand. 

However, when you grow in your career, you will find that you need to modify algorithms or even create new ones for analysis. This means that you will have to modify the codes behind them, or even write a new algorithm from scratch. This requires you to have a command over at least one programming language – be it Python, R or Java. 

Knowledge in programming and statistics can go a long way in helping you implement your own algorithms and analyse the data in a more efficient manner. It will help you manipulate the data set, and bring any unconventional ideas you have to life. If programming is something you can’t do, you will be forced to use the tools at hand without the option of trying to change those tools themselves. While you may be able to manage with this, it will have an impact on your career in the long run. 

Can Data Scientists Be Good Without Programming?

Of course, if you do not know how to program, the obvious thing would be to start learning it since it can improve your career over the long term. However, there are some other ways in which you can excel at your job too. You should try and master a GUI tool, like Tableau so that you are able to visualize your data in a much more efficient manner. You should become a storyteller, and must be able to convey the story the data tells you in a concise manner to the client. Excelling at sales would also help, since you may have to interact with the client a lot. 

If you find yourself interested in machine learning and data science, you should definitely check out the data science course at Imarticus Learning. It is one of the best data science training in Banglore. 

How Can You Start Learning Data Science and Become a Master in it?

Posted on by Imarticus Learning

 

Being a new and fast-growing field, Data Science is in desperate need of skilled individuals. With lucrative opportunities and pay scales, enterprises around the globe have been in search of skilful professionals to work for them.

You too can make use of this possibility and have a career of your dreams. But becoming a data scientist isn’t an overnight thing. It takes time and effort. So, How do we start to learn data science at right foot? We will find out.

Following are the few steps you could follow to learn data science.

  1. Find If Its Right for You
    Before fixing on to this career choice, you have to make sure you are totally interested in this. You can ask following questions yourself to find if its right for you.
    • Do you really enjoy programming and statistics?
    • Are you willing to work in a field where you have to learn about the new techniques and technologies constantly?
    • Are you okay with job titles like Data Analyst, business analyst etc. ?
    If you have yes for an answer, then you can start learning Data Science right away.
  2. Mathematics
    You have to get familiar with a few topics in Maths in order to conquer data science. The main topic you need to study are the following
    • Probability – A lot of data science works are based attempting to measure the probability of events. Textbooks are a good source of information for this subject.
    • Statistics – This branch of mathematics deals with interpreting and analyzing the data. Fortunately, great textbooks are available online for you to refer.
    • Linear Algebra – This branch of maths covers the study of vector spaces and linear mapping among this space. Linear algebra is a must to understand how machine learning algorithms work.
    Once you are familiar with programming and various libraries, you may not have to dive deep into these mathematical details. But to understand them properly, you will need a sound base in these mathematical topics.
  3. The Programming
    Data Science community has chosen Python and R as their primary languages for programming due to various advantages. You have to learn and practice programming in these two languages at least for the following topics.
    • Data Analysis – NumPy and Pandas, are the two common libraries used for data analysis in Python. Tidyverse is a popular compilation of packages in R for data analysis.
    • Data Visualization – Matplotlib is the most used data visualization tool in Python. The most popular plotting library in R is ggplot2.
    • Machine Learning – Python mostly make use of SciKit-learn library to do the machine learning works. When it comes to R, it offers a huge variety of packages including CARET, PARTY, random forest and many other.
    When you complete these steps, you have a solid base required for a Data Scientist. Even if you find it hard to learn all this stuff on your own, the courses on data science prodegree by Imarticus is available to help you master the Data Science. The course provides comprehensive coverage of statistics and data science along with hands-on training on the leading analytical tools. so, stop wasting your time start preparing for your data science career right away.