What do developers actually use Python for? According to a developer survey by JetBrains (which also introduced Kotlin, the up-and-coming language for Android development), some 49 percent say they use Python for data analytics, ahead of web development (46 percent), machine learning (42 percent), and system administration (37 percent). Significant numbers of developers also use the language for software testing (25 percent), software prototyping (22 percent), and "educational purposes" (20 percent). Far fewer chose it for graphics, embedded development, or games/mobile development. This data just reinforces the general idea that Python is swallowing the data-analytics space whole.
A Python Developer with strong Python, Machine Learning and DevOps experience is required by a FinTech Start-up in Central London that has just secured a substantial amount of funding. You will be one of the first 40 people to join this business which is set to double in size by this time next year. You will be a key contributor to the Back End of their product and the ecosystem as a whole using modern technologies and practices. You will be responsible for engineering web apps that integrate RESTful web services with distributed cloud computing. This is an excellent opportunity for a Python Developer (Python, Machine Learning, DevOps) who has the desire to grown with a business and become a Senior member of the team within it.
This Python tutorial for Data Science and Machine Learning will kick-start your learning of Python concepts needed for data science, as well as programming in general. Understand how to use the Jupyter Notebook, Understanding of Python from the beginning, Learn to use Object Oriented Programming with classes, Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! This course will teach you from Python basics to advanced concepts in a practical manner, with Hands on exercises covered as well. This Python tutorial for data science will kick-start your learning of Python concepts needed for data science, as well as programming in general. Python is required for data science because, Python programming is a versatile language commonly preferred by data scientists and big tech giant companies around the world, from startups to behemoths.
You can learn in 2 months. Once you have a list, now ask yourself how much time you can invest every day based on your current situation. If you are super serious (kind of crazy), you can learn Python in 2 months. If you can maintain this routine for 2 months, no one can stop you. If you have a full-time job or you are a student, you can finish it in 5 months.