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Is Data Science Hard? Taming the Data Science Dragon

Is Data Science Hard? Taming the Data Science Dragon

It was only twelve years ago that the title ‘data scientist’ was coined. The practice of data science has not existed for much longer either. As a young field, though growing rapidly around the globe, the role of a data scientist is often misunderstood. What exactly do data scientists do, how do they contribute to the business, what skills do they need are all commonly asked questions among aspirants — be it fresh graduates, entry-level professionals or mid-level managers seeking a career transition.

 

Is Data Science Hard?

Our mentors and counsellors believe that this question comes more from a place of misunderstanding. Given that this field is still maturing, there are only a few mentors/ seniors in the field, and most traditional colleges still don’t offer data science as a structured course, it leaves aspirants with many unanswered questions. Let us answer them one by one.

1. Will a data science job be hard?

A generalist data scientist is a jack of many trades. Their job includes:

  • Identifying business problems
  • Writing hypothesis
  • Performing data mining and data wrangling
  • Cleaning and classifying data
  • Conducting statistical analysis
  • Building algorithms and testing them
  • Writing code and deploying in production
  • Making presentations and visualisations
  • Collaborating with business teams etc.

 

2. Is it hard to gain data science skills?

People ask this question because data science is not a singular skill. As far as technical skills go, data scientist jobs combine mathematics, statistics, programming and domain expertise. Being able to meaningfully bring together the relevant skills across these fields can be a hard task for young professionals. However, you must understand that these are all related skills, building on top of one another, making them a coherent whole.

For instance, let’s say you’re a data scientist at a bank. You need not know everything there is to know about banking. But you need to understand financial concepts, within the context of your project. If you’re using DS for fraud detection in credit card purchases, you need to have in-depth knowledge of the credit landscape and fraud patterns.

Data science skills are a lot more than that. Some other skills you need are:

Critical thinking and problem-solving: Being able to critically analyse the situation at hand and device innovative ways to solve business problems.

Business acumen: Understanding the real-world context in which the business works to make data-driven decisions to business problems.

Communication: Making presentations, building consensus among stakeholders, persuading team members, etc.

Self-learning: Being able to adapt to changing circumstances and keeping oneself updated on technological developments.

 

3. Is it necessary to have experience to start a data science career?

While experience is non-negotiable, it doesn’t have to be ‘job experience’. Hands-on practice with concepts of DS is good enough. You can gain this kind of experience in myriad ways:

  • Data science projects, which you can use to showcase your skills/interests.
  • Competitions and hackathons, which offer opportunities to solve real-world problems with data.
  • Data science internships, which give you a professional environment to learn the practice of this field.
  • Freelance projects, which allow you to take on small assignments and work independently.
  • Online programs that include portfolio projects solving real-world problems.

 

4. Is getting a data science job really hard?

With the right skills and data science portfolio, you will be able to land your dream job. Here are a few pointers to keep in mind while applying for data science jobs:

  • Make a CV that presents your strengths confidently
  • Customise your CV to suit the job you’re applying for
  • Present a portfolio of projects you’ve worked on, based on the job you’re applying to, present the relevant ones prominently
  • Prepare for commonly asked data science interview questions and practice your answers
Python Training

Python Programming Language Advantages and Features

Python is a programming language that you’ve probably heard of if you’ve worked in the IT industry for a time. Its popularity has grown as a result of the numerous features and benefits it offers, including:

 

It’s Open-Sourced

The term “open-source” refers to the fact that anyone can contribute to the project. That’s because its high level of accessibility not only makes it popular among programmers, but it also makes adding new features a breeze. This is reflected in the number of libraries that have been identified thus far.

This means that problem solving has never been easier for your company than it is now. With the open-source language’s high level of accessibility, your development team will have no trouble solving any problems that arise throughout the course of their job, whether it’s web development or data analytics. This benefit has no disadvantages.

 

Beginner Friendly

Coding, without a doubt, necessitates a great deal of thought and time. The sophisticated syntax and semantics of coding languages make this much more difficult. Python is a simple-to-learn method to coding that reduces the learning curve and makes it accessible to even the most inexperienced programmer.

What does that imply for your company? Python coders are generally simple to come by, so the hiring process won’t take long, and you’ll be able to increase your staff rapidly after your product is released and users provide feedback.

 

Applications Across Various Industries

Python is being utilized by an increasing number of programmers to execute a variety of jobs since its rise to prominence. Web development, desktop GUI, web application, and software development, as well as machine learning and artificial intelligence, are all examples of real-world uses for this programming language. This demonstrates that your applications will be created using a programming language that has been properly tested.

 

The Availability of Libraries and Support

One of the most significant advantages of Python code is the large online community that builds problem-solving libraries. Machine learning methods, for example, are already available online and may be used to construct solutions without paying any costs.

Furthermore, for quantitative computing, standard Python is not competitive with languages like C and C++. Python may still be enhanced with data structures and algorithms using specialist libraries. Python data visualization is also made possible by a variety of graphing libraries and their numerous functionalities.

 

It’s Portable

Python’s portability is another noteworthy characteristic. This implies that anyone on your team may write clear, intelligible code that works just as well as it did when you ran it. Python is unique among web frameworks in that it is supported by any modern computer OS due to its high-level nature.

 

It’s Embeddable

Other programming languages may be able to perform some jobs better than Python, but that doesn’t stop you from combining the two, making Python a formidable ally for your complicated commercial projects.

 

It’s Interpreted

Python code is interpreted line by line, making CPU management and code debugging considerably easier.

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