Introduction
A job in data science requires programming skills. Data science mainly uses Python programming language. Data science job seekers commonly encounter these ideas. Most of the opinions on the internet revolve around these ideas, which are only partially true. Search for “most in-demand data science skills” to find Python as one of the top skills in demand for data science. Python as a programming language has ruled the world of data science since its development. This doesn’t mean that learning Python alone is enough to land a job in data science. The reason may be on the project requirement side concerning Python features or aspirant’s programming skills – depending on python would be like putting all eggs in one basket. Python, a popular language indispensable for data scientists, is losing ground to other programming languages. A data science project goes through various phases, from data extraction to data modeling to model deployment. Although Python is a high-level language with data-oriented libraries and an easy-to-read syntax, it cannot perform tasks efficiently at all stages. Newcomers include SQL, R, Scala, Julia, etc., with benefits like better Cloud Native performance, the ability to run on modern hardware, etc.
Is Data Science Only About Building Predictive Models?
Predicting an outcome is a powerful skill. And that’s what stands out for those new to data science. Building models that predict what a customer will buy next sounds like a must-have skill.
Their first reaction is quite similar when I describe data science or Machine Learning to a non-technical person. The hype surrounding this field is unprecedented. A data scientist only builds predictive models all day at work.
Wasn’t that what DJ Patil meant when he described the role of a data scientist as “the sexiest job of the 21st century”? Well, not really.
Breaking The Myth
A data science project has multiple layers. In the broader data science life cycle, the model-building phase is really a footnote. To get a general idea, the steps involved in a typical data science lifecycle are:
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Understanding the problem statement
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Hypothesis building
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Data Collection
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Data validation
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Data cleaning
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Exploratory analysis
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Designing a model
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Model testing/validation
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Go back to the validation or cleanup phase if a mistake is discovered.
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Nothing is as straightforward as they make it seem in class or throughout a course.
Understanding how a project functions best comes from experience. Speak with someone who has experienced the entire procedure. Get a job as an intern and learn first-hand what makes a data science project successful. Get an internship and first-hand information on what makes a data science project work.
Furthermore, data science is not limited to making predictions. I am sure you have come across the market and basket analysis concept. It is a combination of clustering techniques and association rules. Or how about anomaly detection? Ability to detect outliers in data. There is so much to learn!
Our Learners Also Read: List out the libraries in Python used for Data Analysis and Scientific Computations
Develop Problem-Solving Skills
You must have problem-solving skills if you get a job in a reputed company. As a developer, one has to provide solutions for various client and business requirements. This is why organizations look for developers with excellent problem-solving skills. But if you know how to code in Python but don’t know how to use it in practical situations, you won’t get employed. To solve this problem, adhere to the methods listed below.
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Never try to learn to code, pay attention to every learning, and understand why you are doing those things. And also, think about what other ways to handle these scenarios are.
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Practice different problems daily and understand the logic behind each issue. Go to the next level after you are comfortable with the current degree of difficulty. You can find many problem sets on the internet to practice and learn.
Is Python Enough For Data Science?
Data Science jobs have indeed been mushrooming. At the same time, obtaining a decent position in this field remains notoriously tricky, especially for newcomers. This is because there is a slight difference between data science in theory and data science in real life, which is related to the problems that businesses’ daily problems.
There is a lot of emphasis on Python in academia concerning data science. Professors and instructors teach how to use Python libraries like NumPy, Pandas, and Scikit-learn to make sense of data.
While Python alone is sufficient for data science applications in some cases, unfortunately, in the corporate world, it is only a piece of the puzzle for businesses to process their large volume of data.
We all know how famous Python is, and this factor makes many aspirants jump into learning Python. But where the opportunity is vast and backed by more rewarding monetary benefits, it attracts many. Therefore, people are learning additional skills, enrolling in new certification courses, and learning Python. Here is the solution to all your questions if you’re interested in learning everything there is to know about Python, its employment possibilities, and other abilities that can increase your options. No, only understanding the basics of Python won’t get you a job; but, these talents, along with other soft skills and a solid education, will.
Learn More Skills
Learning Python alone is not enough. Companies today are looking for candidates with additional skills. Learning a python is like coffee; it won’t taste good if you don’t add sugar or milk. So, you need to know other programming languages with Python to beat other competitors and secure your job. This doesn’t mean you have to master every programming language;
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Basic knowledge of these languages will help you land your dream job.
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SQL skills at a basic level are needed. At the very least, you should understand how to create a table, run a query, combine data from two tables, etc.
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Mathematical modeling expertise is a requirement if you wish to work as an ML developer.
Python Language Limitations and Substitution
To trace the cause of Python’s inability to cover all phases of data science, from data extraction to model evaluation, it is of utmost importance to know where businesses store their data.
The majority of businesses keep their data on servers in databases. To maintain effectiveness and information accessibility, these databases must be handled concurrently.
Unfortunately, this task is beyond the capabilities of Python, and this is where SQL (Structured Query Language) comes into play. This is why SQL is understandably present in almost all data science-related jobs. For Example, roles like data analyst, business analyst, and data scientist.
Additionally, hiring managers will test candidates’ SQL knowledge before diving into the roughest of data sciences, such as
The reason is that without SQL, you can’t even get the required data to process. So from a recruiter’s point of view, SQL experience outweighs Python experience.
Conclusion
Learning Python continues to be intriguing, especially for aspiring data scientists. Its significance in data science shouldn’t be undervalued or ignored. But SQL continues to be a dark horse because of the advantage it provides over rivals when competition for the job is tight.
The learning of SQL is not simple. Working with SQL includes using more than one database program, like MySQL, SQL Server, and PostgreSQL, to mention a few. The query language also requires industrial conditions, including a server if it operates locally.
The syntax of SQL software varies just a little. Because not all organizations utilize the same software, this can be uncomfortable. Therefore, we ought to at least become familiar with the most popular ones, including MySQL and SQL Server.
