Data science is one of the hottest fields today, with demand for data scientists far exceeding supply. However, many aspiring data scientists are intimidated by the heavy coding requirements of the field. So, is data science really too much coding?
What is data science?
Data science involves extracting insights from data using scientific methods and algorithms. The end goal is to uncover patterns, make predictions, and optimize processes. Data scientists employ techniques and theories drawn from various fields, including statistics, computer science, mathematics, information visualization, and domain expertise.
Some of the key responsibilities of a data scientist include:
- Collecting and cleaning data
- Processing and transforming data
- Applying machine learning algorithms and statistical models
- Developing data products
- Data visualization
- Communicating insights from data analysis
The coding requirements of data science
There’s no denying that coding plays a significant role in data science. Here are some of the key coding needs:
- Data cleaning and preprocessing – This involves tasks like handling missing values and anomalies, transforming data formats, integrating diverse datasets, etc. Python and R are commonly used languages.
- Exploratory data analysis (EDA) – Graphing relationships in data to glean insights. Again, Python visualization libraries like Matplotlib, Seaborn are used.
- Building machine learning models – Data scientists train ML algorithms to uncover patterns and make predictions from data. Python’s Sklearn and Tensorflow are popular libraries for this.
- Model deployment – The trained models need to be deployed in production applications. This requires web development skills.
- Data pipelines – For automating repeatable ETL and ML workflows. Languages like Python, Scala and tools like Apache Spark are used.
In a nutshell, data science practitioners need to be adept in:
- Python or R for data tasks
- SQL skills for database access
- Math and stats fundamentals
- Data visualization abilities
- Machine learning coding skills
- Software engineering for deployment
Is the coding overhyped?
Given the coding requirements listed above, it’s understandable if some feel data science is all about coding. However, that’s not entirely accurate. Here are a few counter perspectives:
- Strong coding skills alone won’t make you an effective data scientist. You need a multi-disciplinary skillset combining soft skills, math & stats knowledge, and business acumen.
- The choice of coding languages and libraries changes rapidly. It’s impossible to be fluent in everything. Core competencies and fundamentals are more important.
- Coding is a means to an end, not the end goal itself. The emphasis should be on deriving insights from data.
- Many tasks like data collection, study design and communication of results don’t require coding.
- Automation and low-code tools are increasingly enabling non-programmers to do data science.
- Data science teams have a variety of roles like data engineers and analysts that are less coding-heavy.
The bottom line is coding skills are certainly very useful, if not mandatory, in data science. But being a good coder alone will not make you succeed in data science. One needs a fusion of diverse skills.
How much coding is required in data science?
There is no single definitive answer here – the coding needs vary based on the role and project. But as a rule of thumb:
- Data analysts may code around 30% of their time for tasks like data cleaning, visualization and exploring datasets.
- Data scientists may code 60% to 70% for machine learning tasks and production deployments.
- Data engineers could spend 90%+ time building and maintaining data pipelines and infrastructure.
Also, coding needs vary by industry. Analysts in finance may mostly use Excel and SQL while e-commerce analysts could use Python and R heavily.
Here is a rough breakdown of coding needs across data science job roles:
| Role | Coding time |
|---|---|
| Data analyst | 30% |
| Business analyst | 20% |
| Data scientist | 60-70% |
| Machine learning engineer | 80% |
| Data engineer | 90%+ |
Which coding languages are most useful?
For aspiring data scientists, it is wise to start off building strong skills in Python. Here’s why Python is the most useful and versatile coding language for data science:
- Python has numerous libraries tailored specifically for data tasks – NumPy, Pandas, Scikit-learn, TensorFlow, Keras, etc.
- It is the most popular language for data science and AI. Large community support is available.
- Python code is simple to write, read and debug.
- It is a general purpose multi-paradigm language suitable for a variety of tasks.
- Interfaces well with languages like R, SQL, Scala, Julia. Integrating Python with other languages is easy.
- Has good graphics and visualization capabilities with Matplotlib and Seaborn.
Besides Python, knowledge of other languages like SQL, R, Scala, Java proves handy. But Python is undoubtedly the best language to start with for aspiring data scientists.
How to efficiently learn coding for data science?
Here are some tips to learn coding efficiently for data science:
- Focus on core Python fundamentals before specialized libraries tailored for data tasks.
- Learn coding concepts by working on data-oriented projects instead of generic ones.
- Take data-focused courses that teach coding skills in the data science context.
- Work through curated project-based learning programs like Dataquest, Datacamp, Springboard, etc.
- Experiment actively with new data libraries after reading the docs and guides.
- Don’t try to master everything. Stick to the popular data libraries like Pandas, Numpy and Scikit-learn.
- Learn just enough software engineering principles to write production-grade code.
- Practice on Kaggle competitions to apply coding skills on real-world problems.
The key is to take a structured learning path tailored for data science, not a generic one. Learn coding concepts by immediately applying them on data tasks. This builds data intuition while reinforcing coding skills.
Coding mistakes data science beginners make
Here are some common coding mistakes beginners make when getting started with data science:
- Learning disparate libraries without focus, instead of mastering fundamental packages like Numpy, Pandas first.
- Underestimating the importance of algorithms and math. Coding without strong foundations.
- Getting distracted by new libraries and syntax instead of honing problem-solving skills.
- Not writing modular, well-documented code. Lack of software engineering skills.
- Copying code from internet without understanding how it works.
- Not testing code thoroughly. Lack of debugging skills.
- Ignoring the infrastructure and deployment challenges. Only coding models.
The basic coding process and constructs remain the same across languages. The hard part is not syntax, but problem-solving and abstraction skills. Hence it is critical to strengthen the core CS fundamentals.
Should non-programmers pursue data science?
Here are a few perspectives on whether non-programmers can thrive in data science.
Possible, with effort: Those new to coding can pick up programming alongside data science with commitment and consistent practice. The key is taking a structured learning path. Coding may not come naturally, but can be learned through practice like any skill.
Better suited for business analyst roles: Data analyst and business analyst roles involve relatively less coding compared to data scientists. Those averse to coding may be better suited for such roles that leverage domain expertise.
Steep learning curve: While possible to learn coding from scratch, the initial learning curve may be very steep especially for those lacking programming experience. Needs strong grit and persistence.
Leverage drag-and-drop and low-code tools: Emerging no-code and low-code data science platforms significantly lower barriers for non-programmers. Useful supplement but not full replacement for coding skills.
Overall, developing basic coding literacy greatly expands opportunities in data. While coding can be learned with effort, playing to individual strengths is also important. Those inclined towards business should target analyst roles over heavy programming ones.
Conclusion
Data science indeed involves a substantial amount of coding. Core languages like Python and R, supported by specific libraries like Pandas, NumPy and Scikit-learn are mandatory for practising data scientists. However, coding alone does not guarantee success in data science. One needs a diverse set of skills combining software, analytics, business expertise and communication abilities. For beginners, the key is taking a structured learning path focused on building coding skills in the data science context, not generic programming. While the initial learning curve may be steep, coding fundamentals can be grasped with commitment and consistent hands-on practice on data problems.