What Should Be The Next Step After Learning Python For Data Science?

Developed by Guido Van Rossum in 1991, Python is a general-purpose, high-level language used by developers all over the world. Its simple-to-understand and intuitive syntax make it one of the easiest languages to learn if you are a beginner.

It supports different programming styles when creating programs, such as object-oriented, functional, reflective, etc. YouTube, Pinterest, Quora, Instagram, and many other popular digital platforms and tools are created using Python.

This OOP (object-oriented programming) driven language supports multiple platforms and systems and is complemented by extensive library support. Its packages like NumPy and SciPy are applicable in scientific computing, mathematics, and engineering.

In addition, Python libraries such as PyTorch, TensorFlow, OpenCV, and sci-kit-learn are helpful for building programs in data science, machine learning, computer vision, and image processing.

Features That Make Python Popular

Python’s simplicity, quick adaptability, and flexible integration make it one of the most widely used programming. Some other reasons for Python’s popularity are mentioned below.

Easy to Learn

Python’s easy and clean syntax makes it a beginner-friendly language. Beginners can learn its basics very easily by signing up a 4- or 6-months Python trainingcourse in California. The course will cover everything from fundamental concepts to file handling, data types, Django, Lambda, and other skills to make you proficient in Python.

Performance

With Python, you can increase your productivity. A program that takes 20 lines of code in Java can be written in just 2-3 lines of code. This quality makes it ideal for large and complex programs as you can develop programs in less time using fewer lines of code. Fewer codes and simple syntax make it perfect for complex data science and machine learning programs.

Supportive Community

Being over 31 years old, Python enjoys a stable and supportive community of developers who work on making the language better and more accessible. In SlashData 2018 survey, there were 8.2 million Python developers, and it was expected that 6 million new developers would be added in the next three years. The support of the community is essential when you are beginning your career.

Libraries and Frameworks

Python is blessed with plenty of libraries and frameworks like Django, Tensorflow, Pandas, Keras, Flask, Seaborn, Matplotlib, etc., which help develop, visualize, and data munging, and several other tasks. With the help of these libraries and frameworks, you don’t have to do everything from scratch. When you enroll in a Python training course in California, make sure they teach you all about these important tools.

What Jobs Can You Take After Learning Python?

Learning Python offers plenty of job opportunities in the field of manufacturing, finance, banking, retail, healthcare, etc. The best data science course in California will help you learn the most in-demand Python skills so that you can make a career in data science.

Data Analyst

Python has powerful libraries and frameworks for analyzing, manipulating, and visualizing data. Numpy, Pandas, Scikit learn, Keras, TensorFlow, PyTorch, and Matplotlib are some of the popular Python libraries that support the process of data analysis.

Besides Python, you should also have knowledge of SQL, data visualization tools, excel, and mathematical and statistical operations to become a data analyst. A data analyst collects, organizes, processes, and performs data analysis on large datasets. The analysis is then deduced for solving business problems.

You can earn an average of $65,226 as an entry-level data analyst. The salary grows with experience and industry.

Data Scientist

Data science is a trending technology, and Python is a key language for it. Data science is another career option for Python professionals. Data scientists are experts in data and are demanded in all industries. The best data science course in California will prepare you for the job responsibilities of a data scientist and equip you with the right tools.

Data scientists are multi-talented professionals and have knowledge of several technologies and subjects such as mathematics, statistics, computer science, programming, etc. With this knowledge, they analyze data and help the stakeholders make critical decisions regarding the businesses.

If you love solving problems and have a knack for data, you can go for a data scientist job. An entry-level data scientist can make about $100k per year on average.

Machine Learning Engineer

Just like data science, Python is also used extensively in machine learning. So, you must learn Python by enrolling in a Python training course in California to begin a career in machine learning.

ML is a subfield of artificial intelligence where machines are trained to perform autonomously. Machine learning involves dealing with a lot of categorical and numerical data, time-series data, and text.

Python helps perform complex machine learning tasks and build prototypes for testing the product for machine learning. A machine learning engineer researches, builds, and designs independent artificial intelligence (AI) systems to automate predictive models.

Your task will be to create ML models and retrain them when needed. As an ML engineer, you will be supposed to work with data analysts, data scientists, data administrators, data engineers, and data architects and pick appropriate data sets and data, representation models.

According to Glassdoor, the average base salary you can get as a machine learning engineer is $1L /year in the U.S.

Test Automation Engineer

Automation is the future of most jobs. According to the weforum report, automation will affect 85 million jobs worldwide in medium and large enterprises. So, choosing the profession of test automation engineer will be a great decision.

Test automation engineers are involved in designing, programming, simulation, and testing automated equipment and processes. By choosing this profession, you can find employment in industries including energy, manufacturing, food processing, and robotics.

You can make an average of $87,621 per year as a test automation engineer.

Conclusion

You have plenty of career options after pursuing the best data science course in California. While in this article, a few career paths have been mentioned, there are many more in the field of data science. However, when you pick a career option for you, make sure you choose a profession that you are passionate about.

SynergisticIT offers training in leading technologies like Java, Python, data science, machine learning, and artificial intelligence.