Data science is valued across all mediums of an organization due to the need to make sense of historical, current, and predict future uses and trends using massive amounts of data. Data scientists need to utilize a general-purpose, object-oriented programming language for statistical modeling in order to identify and to visualize solutions to business problems. Two examples of such programming languages are Scala and Python, but which is better for data science?
Python is better for data science because it is easy to learn, has a huge support network, and has been running for 30 years. Scala and Python have similarities and differences, but Python is the preferred language and is considered industry-standard, making it valuable to be proficient in.
The field of data science is defined in this article along with the programming languages Scala and Python. Advantages and disadvantages of each are discussed, and resources are listed for learning and utilizing Scala and Python, including books, videos, Reddit, and GitHub. Read on to gather more information on both programming languages as confirmation that Python is the superior language for data scientists.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
The Field of Data Science
Data science pertains to almost everything relating to data, combining computing power with data analysis and manipulation. Specifically, the field of data science, which is growing tremendously year by year with no end in sight, operates by utilizing data for:
- Acquisition
- Storage
- Analysis
- Visualization
- Modeling
- Experimentation
- Problem resolution
Because computer science plays an important part in the processing of Big data, data science has evolved with technology. Big Data is defined in many ways, one being a large volume measured in terabytes and petabytes. Traditional databases are unable to handle a huge volume and storage becomes a problem as well.
The data scientist manipulates data queries and translates the results of Big Data to support the conclusion. In addition, a data scientist uses predictive analytics to statistically make future projections based on the data using modeling and experimentation techniques. And data translation or data visualization is used to create a picture using data to identify, explain, and predict market trends and various operational and financial shifts.
Predictive Analysis
Executives rely on data to make informed decisions about the pivotal goals and which direction to take related to the company; therefore, prediction using data is increasingly important. Data scientists develop statistical or mathematical models as tools to predict future insights. Models used to leverage predictive analysis are often in the form of regression models, clustering models, optimal estimation, linear regression, and text mining.
In a larger company, data analysts, statisticians, and programmers may also be involved, but the overarching strategist is the data scientist.
Data Visualization
The visualization of data in a digestible and applicable format can be created by Tableau, owned by Salesforce, a software program used to interpret data and identify resolutions based on those findings. Tableau functionality integrates well with programs written in Python, pulling data from complex data sets to develop dashboards, graphs, and presentations on results, and is known for being a wonderful solution for business intelligence.
Careers Built on Data Science
While the Chief Data Officer, who oversees company data systems, analyzes gaps, mitigates risk, and develops strategies to align with the organization’s goals, may not know how to program in Scala or Python, decisions are made based on data analytics utilizing these programs.
Therefore, hiring a data scientist that has the computer programming skill set is crucial. A few of the areas in which an executive may use data science are to:
- Track and assume responsibility for data privacy and security risks
- Control costs
- Communicate insights and statuses based on data science information and results
- Escalate, strategize, and resolve problems based on facts provided by company data scientists
Accordingly, a data scientist solves problems and must vacillate between strategy and operations. The ability to program in Python or Scala helps a scientist because having enough programming knowledge to realize the language’s capabilities helps analysis, and thereby strategy. And as a collaborator on a team, knowing how to code enables you to jump right in and fix issues.
Data scientist toolkit includes:
- Analysis of data patterns
- Data cleaning and transformation to prepare for analysis using tools such as:
- NumPy, an open-source software library in Python used for dealing with arrays
- Pandas software library, also in Python and used to manipulate time series and numerical tables
- Aggregate data, create reports and dashboards
- Perform testing and modeling as a basis for determining business strategy
- Experimentation of possible solutions
- Report performance metrics and risks
- Presentation of data-driven insights and discoveries
For the basics of programming in both of these languages, there are massive amounts of reading material that deliver information specific to any industry. But for an overall look, check out the book entitled, “Become a Python Developer: Wrestle and Defeat It,” and “Programming in Scala: A Step By Step Guide,” which provide an easy to understand overview and start you down the path of programming in these languages.
Additionally, to view how to break into a data science career in 2020, watch this seven-minute video. There are great infographics and the broad overview is informative.
What Is Scala?
A German computer scientist, Martin Odersky, using his roots in Java, developed Scala, which first came on the scene in 2004. He currently teaches courses on Coursera, a popular Massive Open Online Course (MOOC) provider. His intermediate course, Functional Programming in Scala, indicates that over 240,000 students have signed up.
The name was derived from a combination of words “scalable” and “language” to represent an intent to continually grow and develop as computing needs change over time. Scala is a high-level, functional, general-purpose, object-oriented language based on Java but created to improve some areas. Scala runs on the Java virtual machine (JVM). Moreover, its library is interchangeable with Java.
Advantages
While it integrates well with Java, is stable, fast, and built with a huge standard library of its own, it is not necessarily easier. However, the advantage is that the programming language is extensive, with features not included in Java, but concise. For example, simple collection vocabulary is used as opposed to writing a number of loops for an array in Java.
Also, Scala presents bidirectional conversions between collection types with Java, so a programmer can use one or the other.
Another positive is that Scala combines functional programming, which helps small scale manipulation of data, with object-oriented programming, which is commonly used to implement a large scale component. Hence, a software developer doesn’t have to make a choice when using this language.
Also, Scala processes in real-time and has pattern matching mechanisms that contribute to its reputation for conciseness. It is steadily growing and has communities on Reddit and GitHub, which has contributed to libraries filled with data analysis and visualization tools.
Disadvantages
Negatives of using Scala are that the same thing can be written in several different ways and the functional programming code can become indecipherable. Additionally, with this programming language, compile times are known to take longer.
The operator can also be overloaded by the programmer, which is criticized because it means that certain rules of mathematics can get incorrectly applied. If Scala is the program of choice at your place of business, Scala developers are sparse. Although, most likely, you will be able to hire Java developers excited to delve into Scala.
Reportedly, Scala 3 will be launching as a community-powered release, relieving many of the developers’ woes. But it has been 8 years since the last version, which is concerning and understandably considered a contribution to its slower adaptation. The rollout will take about 4 months of coordination and is happily anticipated by contributors and users.
Language Details
Although, to really understand a programming language is to dig into the coding by taking a look at this video course. Watch an hour and a half, step by step Scala tutorial, from setup to the basics of coding:
Or if that’s too much to start, start with the fundamentals. This course is only an hour in length and will enable you to get an excellent feel for the language when completed:
Furthermore, The Scala Center, a non-profit foundation initiated to support community resources and education for Scala users, reports a steady increase in Scala use. As a home base for improving the language and a repository for all things Scala, they have announced that the newest version, formally known as Dotty, has been rolled out in 2020, complete with multiple improvements detailed in the Scala 3 Migration Guide.
With improvements and a solid community home base, usage is expected to increase exponentially. And yet Python is still the clear leader in the volume of users.
What Is Python?
Even though the first design originated in the 1980s, Guido van Rossum released the first version of Python in 1991 to resolve issues he had with ABC Programming Language and did not retire from the lead developer position until 2018. His dedication and leadership are renowned in the Python community.
Guido van Rossum chose the unusual name, which was derived from Monty Python’s Flying Circus, a 1970’s television show. He was looking for a catchy name that was a little bit mysterious.
The philosophical roots run so deep that guidance for writing code has developed into a list of 19 principals, Zen of Python, with the 20th to be left open for Guido van Rossum to create. Written by Tim Peters in 1999, as succinct core words of wisdom to be utilized by the programming community as a guiding light, they have stood the test of time.
The Zen of Python list is, as quoted:
“Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one—and preferably only one—obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than right now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea—let’s do more of those!”
Python is celebrated for its efficiency, flexibility, and readability. The fact that it will continue to grow after all of these years is exciting and will provide job security for those who are experienced. Python is uniquely suited for predictive analysis and data modeling techniques, among other benefits.
Advantages
According to research by 365 Data Science, Starting a Career in Data Science: Ultimate Guide, in 2020, 74% of data scientists are experienced in Python. Because the industry prefers to hire and pay larger salaries for data scientists proficient in Python, its popularity is not going away.
SQL, a query language, plays well with Python. Advanced analytics, querying, manipulating, and modifying Big Data, can be leveraged using SQL and then, in turn, quickly and easily integrated with Python to accomplish business goals.
Together, Python and data visualization software Tableau create a powerful connection with TabPy, an analytics extension that expands Python scripts’ capabilities. Therefore, advanced analytics are enabled, as evident on the TabPy GitHub page.
A programmer can be very productive with a simple and easy language with extensive libraries. Because Python is portable, it can be run on UNIX, Mac, or any platform for that matter. Also, it has the capability to be integrated into other languages without changing code (for example, C++). The fact that it is an extensible language means that while in another language, like C++, you can write Python code and it will be compiled without error. But is it perfect?
Disadvantages
As a free, open-source scripted programming language that is stable, reliable, and has been around for decades, Python does not have any red flags. But annoyances, as with any programming language, are speed and memory consumption. While it’s not as efficient as C++, there are many optimization tips and tricks to manage your particular situation.
While Python can be used on mobile phone apps, Java would be more suitable. As always, there are workarounds that will help. But this is not the main concern of the data scientist who is using Python for statistical programming. Depending on where you sit, disadvantages may be advantages when it involves Python. For data scientists, it works well.
Language Details
Python is known to be the go-to language for beginning coders because it’s easy to learn. Although daunting, the twelve-hour course below is a wonderful way to start from scratch. There’s so much to learn and it’s easy to master. The section that revealed that Netflix had declared Python to be the reason for its success was an interesting tidbit, but even more so, is learning the actual core concepts for coding.
Another known resource is the Reddit community for Python. However, the Python website has every conceivable piece of information free for the taking, such as:
- Downloads
- Information for getting started
- Documents, tutorials, guides
- Standard Library
- Community support
- Jobs
- News
- Events
- Proposed enhancements
- Python Software Foundation (PSF)
- Success stories
- Archives
- Translations
- Books
- Wiki
- Getting involved
Why Is Python Better?
Scala is intuitive to use and performs beautifully, but the learning curve is quite steep. If you know Java already, you are definitely ahead of the game and may not feel it’s all that difficult. But Scala is not as steeply entrenched as Python in the business world.
If a computer programmer needs support, advice, a user group, documentation, or any support whatsoever, it can be found for Python. Plus, Python has been around 14 years longer than Scala. It’s extremely easy to learn and once mastered, a Python computer programmer is valued and paid a higher salary. The language intermingles smoothly and seamlessly with industry-standard applications used in the data science world. It’s absolutely a better choice.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
Since Scala has a lot of similarities to Java, many Scala converts are previous Java users. Although Scala is concise and has an excellent library with data analytics tools, it is not as widely used in the industry yet. But since a new release is coming out soon, Scala 3, purported to correct developer pains, it may be worth revisiting in the months to come.
While Python, having been around for many more years than Scala, has deep roots and a cultural following that lives up to its reputation. Plus, business organizations favor it and pay higher salaries to data scientists knowledgeable about Python computer programmers. Therefore, since Python is robust, efficient, and has vast user support, it is the smarter choice.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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