Python Training & Certification
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Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.
Python course helps you gain expertise in Quantitative Analysis, data mining, and the presentation of data to see beyond the numbers by transforming your career into Data Scientist role. You will use libraries like Pandas, Numpy, Matplotlib, Scikit and master the concepts like Python Machine Learning Algorithms such as Regression, Clustering, Decision Trees, Random Forest, Naïve Bayes and Q-Learning and Time Series. Throughout the Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR and so on
Learning Objectives: You will get a brief idea of what Python is and touch on the basics.
- Overview of Python
- The Companies using Python
- Different Applications where Python is used
- Discuss Python Scripts on UNIX/Windows
- Values, Types, Variables
- Operands and Expressions
- Conditional Statements
- Command Line Arguments
- Writing to the screen
- Creating “Hello World” code
- Demonstrating Conditional Statements
- Demonstrating Loops
- Fundamentals of Python programming
Python supports a variety of operations applicable to all sequences, including strings, lists, and tuples. Some sequence operations apply to all containers (including, for example, sets and dictionaries, which are not sequences), and some apply to all iterables (meaning “any object on which you can loop,” as covered in Iterables; all containers, be they sequences or otherwise, are iterable, and so are many objects that are not containers, such as files, covered in File Objects, and generators, covered in Generators). In the following, I use the terms sequence, container, and iterable, quite precisely and specifically, to indicate exactly which operations apply to each category. Sequences are containers with items that are accessible by indexing or slicing. The built-in len function takes any container as an argument and returns the number of items in the container.
Python Object-Oriented Programming (OOP): Tutorial. Tackle the basics of Object-Oriented Programming (OOP) in Python: explore classes, objects, instance methods, attributes and much more! Object-Oriented programming is a widely used concept to write powerful applications.
Python is increasingly being used as a scientific language. Matrix and vector manipulations are extremely important for scientific computations. Both NumPy and Pandas have emerged to be essential libraries for any scientific computation, including machine learning, in python due to their intuitive syntax and high-performance matrix computation capabilities. In this post, we will provide an overview of the common functionalities of NumPy and Pandas. We will realize the similarity of these libraries with existing toolboxes in R and MATLAB. This similarity and added flexibility have resulted in wide acceptance of python in the scientific community lately.
The first step of data science is mastering the computational foundations on which data science is built. We cover the fundamental topics of programming relevant for data science – including pandas, NumPy, SciPy, matplotlib, regular expressions, SQL, JSON, XML, checkpointing, and web scraping – that form the core libraries around handling structured and unstructured data in Python. Learners gain practical experience manipulating messy, real-world data using these libraries. They also walk away with a firm understanding of tools like pip, git, IPython, Jupyter notebooks, PDB, and unit testing that leverage existing open source packages to accelerate data exploration, development, debugging, and collaboration.