Have you ever wondered why data science is taking such a long time to master despite being one of the most promising career options? The reason is simple: most people think data science is hard to learn. To some extent, this perception is correct, but that’s not true in all the cases. Stereotyping data science without knowing the reason isn’t fair.
Let us find out if data science is actually hard to learn or is it just a perception.
Is data science harder than software programming?
The comparison between software programming and data science may sound absurd, as there is no common link between these two fields. But most of the students struggle to find which one is the best option for beginners or which career option is hard.
Both software programming and data science are different. Software programming is for people who are good in coding and programming languages, while data science is favorable for statistics and analytical skills.
Data science may seem difficult if you are a beginner, but this doesn’t imply software programming is easier. Both require the same amount of effort and time. But the plus point of software programming is that later you can switch to data science.
Now, the fundamental question is: Is data science hard to learn?
Is data science hard to learn?
Is data science hard to learn? The short answer is yes. Data science can be challenging for beginners. Prospective candidates must master multiple aspects of statistics, computer science, mathematics, and machine learning. Additionally, the prospective candidate should have a firm grasp of data analysis and solving complex problems.
Data science is interdisciplinary. And that’s what makes data science hard.
Anyone who desires to start a career in data science should start learning an array of skills –
Statistics (data analysis and data interpretation)
Computer science (programming and algorithms)
Let’s take an example to understand how these concepts are used!
Data science is used to personalize the customer experience in e-commerce stores and on social media. Using all these concepts, data scientists create customer profiles from the previous data to gain insights into customers’ behavior. These insights are then used for retargeting ads or improving the user experience.
By this point, you know data science is hard to begin, but the next question is how hard it is. Let’s find out!
How hard is it to learn data sciences?
Typically, when we think about the difficulty level, we first have to find out what makes it difficult. And in the case of data science, the main reason is that you have to master multiple aspects of mathematics, statistics, and computer science.
To understand the difficulty level, we’ll find out what you need to learn in data science.
1. Programming Language
Data analysis is performed using programming languages such as Python and R.
Suppose you opt for Python, learn the basics, and become competent in Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn. These are Python libraries used at various stages of data analysis.
Some use cases of python libraries are:
Pandas help in data manipulation and wrangling.
NumPy performs operations on large arrays and matrices.
Seaborn performs data visualizations (making histograms, bar charts, heatmaps, density plots, etc. )
Scikit-learn is used for splitting the training and test data sets etc.
Data scientists/data analysts can only excel in their careers if they are proficient in statistics. It’s important to know the tools and the basic theories of statistics.
When it comes to statistics you must acquire the skill to do analysis using tools and software. Next, you need to learn key statistics concepts, including probability distributions, statistical significance, hypothesis testing, and regression. Once you have mastered these move on to the more advanced concepts like:
Core statistics: This includes descriptive statistics, distributions, hypothesis testing, and regression.
Bayesian Thinking: This includes conditional probability, posteriors, priors, and maximum likelihood.
Statistical machine learning: Learn how statistical concepts are used in machine learning and data science.
When we talk about mathematics for data science, the three topics that consistently come up are :
Linear algebra helps to understand the algorithms. Learn about vectors, matrices, tensors, and operations.
Calculus is used to formulate the functions to train algorithms to reach their objectives.
Statistics and probability: These concepts are used to analyze and understand data and to discover and infer valuable insights and hidden patterns.
How do I start a career in data science?
Data science is demanding, but anyone willing to learn all the concepts can have a wonderful career. One advantage of data science is that there are ample opportunities. Jobs as a data scientist or data analyst are a good option for anyone who excels in the above-mentioned skill set.
If you want to start a career in data science, read on to find out how to enter the market.
Anyone with a bachelor’s degree in data analytics, computer science, engineering, mathematics, statistics, or a related field can opt for data science. According to a Data365 survey, around 21% of data scientists worldwide have a bachelor’s degree in data analysis, while other percentages include computer science, statistics, or mathematics.
Learn essential skills such as programming, data analysis, statistics, and handling big data.
Once you have enough knowledge, search for entry-level jobs. Data analysis, business intelligence, statisticians, and data engineers are common jobs.
The answer is yes and no. Yes, for anyone willing to work hard. No, for those whose primary skill is not data analysis.
If you haven’t gained any insights from the preceding analysis, here’s another question that might help – Do you have the skills and knowledge required to become a data scientist/analyst?
This question will help you know where you stand and how much effort you need to put into polishing your skills. Then only you can get a fair idea if learning data science is hard or not. Therefore, analyze yourself, your skills, knowledge, and expertise, and then decide.
To learn about data science and machine learning, read our complete list of helpful guides and blogs on data science.