Data science is an interdisciplinary field that uses scientific methods , processes, algorithms and systems to extract knowledge and insights from huge volume of data, and apply these knowledge into different domains of applications. Data Scientists are responsible for data mining and machine learning.
Data Science uses different techniques and theories within the context of mathematics, statistics, computer science and information science.
Big data is becoming a major role in everywhere. Data driven business is expanded a lot recently from million dollar to trillion dollar industry. Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations.As big data continues to have a major impact on the world, data science does as well due to the close relationship between the two.
Role of Python in Data science
Python is one of the world’s most popular programming languages, and there are a few reasons why Python is so popular:
- Python’s syntax is very simple to learn and to follow for even non programmers
- Python supports both conceptual programming and Object-oriented programming. In an object-oriented programming language, everything you create is an object, different objects have different properties, and you can operate on different objects in different ways.
- Python can be extensible with other software components, making it a general purpose language that can be used to build a full application – starting with data, cleaning a model, and building that straight into production components.
Finance Sectors– Used for reporting, predictive models, and academic research for both students and professionals.
- Web Development – Developers, engineers, and data scientists use Python for web scraping applications
- Report Generation– Analysts or product managers who need to make the same excel report every single week can use Python to help create reports and save time.
- Simulations in systems– Can be used in simulations to study various different behaviors with a computer before making models.
Why do you think Python has recently overtaken R in popularity
There are a couple of reasons I think Python has to take up the role. Python is a general purpose language, used by data scientists and developers, which makes it easy to collaborate across your organization through its simple syntax. People choose to use Python so that they can communicate with other people. The other reason is rooted in academic research and statistical models. I would say that R has better statistical packages than Python, but Python has deep learning, structured ways to do machine learning, and can deal with larger amounts of data.
Our Python for Beginners (Python Bootcamp)
Python is an excellent programming language for beginners because its simple syntax allows you to quickly hit the ground running. Python is flexible in that you can use it to do just about anything. Python will try to interpret what you mean.
Within any field, you have to get the fundamentals of Python down first before you can move on to more interesting things. Here’s a list of fundamentals you can started with in order:
Contents of Python Fundamentals
- Understand what data types are (integers, strings, floating point numbers) and how all of those data types are different in declaration and usage.
- Learn loops and conditionals – Loops execute a block of code several times and conditionals tell the program when to stop executing that block of code.
- Learn how to manipulate data – Practice this by reading data into your Python program and then doing some kind of computations on it, cleaning it up, and maybe even writing it out to a CSV file. Data manipulation is done by Data Scientists.
- Algorithms – use algorithms to build models and maybe even create your own models or projects.
- Data Visualizations – This is my favourite part of data science! Creating reports and to comparative study using those reports are really wonderful. There are multiple Python libraries or packages to help you do this.
Communication – Begin communicating these things that you’ve learned in a way that other people can explain to solidify that learning.
What level of Python would someone need to know before they apply to Data Science Bootcamp
There are a couple of fundamentals that you need to get down before you move on to something more complicated. Those basic parts of Python, definitely data types and data structures, lists, the dictionary, those kinds of types of constructs you should know before going for basic courses.
You’ll also want to know at least these three basics:
- Conditional algebra – true and false tests. You’ll basically have some kind of input, you will test it against a condition, and if that test happens to be true, you’ll execute one block of code. If it’s false, you might execute a totally different block of codes. It’s kind of a gatekeeper.
- Loops – repeatable pieces of code. Anytime you need to repeat the same actions on many different items in a group, you might write a loop for that. This would execute over all the different elements in your group of inputs to produce some kind of standard outputs.
- Functions – reusable code, not to be confused with repeatable code. If you want to perform the same type of calculation at various points in your code, you’ll write a function. You can reuse that bit of code any time you want the same outputs.
To apply to Python bootcamp program, you’ll at least need to be able to solve a conditional statement and be able to check inputs against some true or false statement and then perform various actions depending on if that was true or false.
It’s hard to talk about Python without talking about libraries. A library is a collection of saved code that someone else has written for you. You can import various bits of code so that you don’t have to do everything on your own!
A few libraries that are perfect for beginners:
- Random – This is used to generate random numbers, which can be interesting. You could build your own game using this.
- Math – This one gives you access to all kinds of math functions like square root, cosine, sine, and more.
- Collections – This will help you interface with your computer or collections, which gives you actual access to additional data structure types within Python.
Once you have a handle on the fundamentals, our students can learn:
- Pandas – For data manipulation because it allows a user to read data in, change it, look for missing values, read data out.
- NumPy – For fast computation because it speeds up all of the different calculations that you’re doing. Pandas actually uses NumPy for some of its manipulations.
- Scikit-Learn – For machine learning because it has all of the algorithms you’ll want to use for regression, classification, and unsupervised learning. When you get expertise in Bootcamp, you’ll be leveraging Scikit-Learn pretty well.
- Matplotlib and Seaborn – For data visualizations. The most common ones will both be able to help you produce some nice visuals like charts and diagrams.
Jupyter Notebook is an Integrated Development Environment (IDE) coming with anaconda, and it is very useful for two reasons:
- It helps you understand what your code is doing instantaneously. You’ll be writing small blocks of code in cells and then executing that code and you can get more idea about how it works.
- You can also write in Jupyter Notebooks with text. As an instructor, I can include an image of a code block for my students thus you can get an instant idea about how it is done and output obtained.
Jupyter Notebook is great for building projects, structuring homework, and collaborative projects. The annotation feature is amazing because students can record their thought process and you can use this in a real-world work environment too!
Python Resources for Complete Beginners
We offers a Python for Beginners course and it’s written for people who have never seen Python before. If you are a person who are a Graduate with Mathematics background, even if you are not having coding knowledge or any language, Python is suitable for you.
Once you’ve got the Python fundamentals well, try our Data Science course. This is a great course for people who are serious about a data science career but not quite ready to take the bootcamp. This course will help you brush up on both Python and math as it pertains to data science. From there, you can enroll for our Immersive Data Science Bootcamp, which covers machine learning and the visualizations.
If you complete Python Bootcamp for Beginners course would you be ready to apply to Data Science Bootcamp?
The Python for Beginners course will really launch your journey because it gets you comfortable with programming in general. Then, we cover data types, which you have to have down before you can move on. Next, we go through each of those three core foundations: the conditionals, the loops, and the functions.
What is your advice for a complete beginner who is beginning to learn Python?
Sometimes beginners can get frustrated because they want to automatically be excellent at Python. It’s going to take practice. It’s about every single day getting a little bit better. Maybe you’re not solving everything from the beginning, but know that you are getting incrementally better. As long as you’re willing to put in the work to get a little bit better every day, you’re going to be off to a great start with Python.
If you are Graduate with mathematics awareness and if looking for job opportunities, here is a way to success
We are offering Python Bootcamp for free to those who are eligible for.
On success you are entering into Data Science bootcamp where your career starts.
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