A common misconception among beginners is that data science is hard. Yes, it does have some technical needs that might make it relatively more challenging than other fields of technology. But the good news is that it can be seamlessly learnt with active guidance. You need to develop a good understanding of maths, statistics, visualisation, reporting, problem-solving and computer programming to master data science since it is a mix of multiple disciplines.
GeekLurn offers a comprehensive Data Science Architect Program in partnership with Nasscom and IBM. The program includes a 100% placement guarantee through a network of over 500 hiring partners. EMI options are also available with 0% interest for ease of payment. The duration of the program is 24 months, divided into:
- 6 months of live interactive classes for senior data scientists or industry experts.
- 18 months of sponsored project work at authorised centres, funded by IIM and ISB.
You also gain 1.5 years of real-life experience with certification from reputed research centres, along with the opportunity to work on more than 50 research projects and 320+ hours of live training. To prepare you for interviews, there is 1-on-1 career mentorship. Candidates can also learn growth hacks and valuable insights for a successful career. The entire program is based on simple language in a conducive environment, so that data science is easy to learn for you.
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What Could Make Data Science Tough?
Data science is no less than a long-term investment. You cannot master it within a few months, despite what some institutions might promise. One needs real data science knowledge with plenty of practice for a successful career. This helps avoid ruining previous data pipelines and losing many hours of hard work. Further, learning how to code is not tough but starting might seem slightly difficult. This is because languages like Python, Ruby, Java, R, JavaScript, C#, Scala and TensorFlow require patience, time and discipline.
If you are wondering is data science hard, well, it gets better with time once you grasp the fundamentals and elements like text strings and variables.
| Skills Required for a Data Scientist | Skill List |
| – Programming | |
Technical Skills | – SAS and Other Analytical Tools (Hadoop, Hive, Pig and R) – Working with Unstructured Data – Statistical Analysis/Computing |
Non-Technical Skills | – Strong Communication Skills – Business Acumen – Critical Thinking – Intellectual Curiosity – Proactive Problem Solving |
The debate of whether data science is easy or hard depends on how much you are willing to work towards it. One must have a strong desire to learn new technologies or take up new and interesting challenges. You can make it to the end with a high-end course like those offered by GeekLurn, clearing doubts, asking questions, and getting the basics like presentation, handling large datasets, machine learning algorithms and analytical skills in place.
Data Science as a Career
Making your career in data science is not the toughest task in the world. The opportunities are abundant in India for anyone with a bachelor’s degree. You do not even need a PhD or even a master’s degree.
The next step is to consider enrolling in a program offered by industry experts with a well-structured curriculum to help students master the essentials. Gain exposure to tech talks, webinars, data science meetups, AI and Big Data conferences, and forums led by proficient speakers.
Take up case studies and problem statements and try to understand the actions to be taken. Do analysis, data wrangling and modeling. These will help you achieve learning goals and show you that data science is not hard to learn.
How to Make Data Science Easier for Yourself?
There are a few tips and tricks that can help you derive meaningful information from structured, unstructured and semi-structured data. You may also become seasoned at solving problems of great complexity without any hiccups. Here’s a look:
- Develop a high mathematical outlook to tackle difficult problems.
- Specialise in Big Data tools like Spark to handle large-scale data.
- Master the sub-constituents of key disciplines like mathematics and programming.
- Avoid learning everything at once. Grasp the theoretical concepts and implement each one gradually for best results.
- Gain domain knowledge to build better products through careful analysis.
- Practice developing models in the context of business problems.
- Know the ins and outs of data collection, formatting, cleaning, transforming, automating and running analytics projects.
Data science might have a steep learning curve. But boot camps and courses impart highly informative and useful knowledge, along with practical experience to help you start focusing on the necessary skills and become job-ready for this industry.
The two principles of data science are: knowing “what” to do and knowing “how” to do it. It can future-proof your career and help you enjoy a high salary and great perks. Job security is also high with advanced skills. In conclusion, it’s time to stop wondering is data science hard and start finding the best course to master the discipline.
Neel Neeraj
Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.
