Python Programming Data Science

The Python Programming data science course is designed for the beginners and will go upto Advanced level who are looking to start their career as a Data Scientist!

Python programming training course will help you master the concepts of programming and gain in-depth experience on writing Python code.

As part of the Python programming training course, you will be working on real-world projects and case studies and get hands-on experience instructor-led in classroom Python lab.

We'll teach you how to write programs with Python, data file import export, how to generate reports, produce amazing data visualizations, and how to build Machine Learning Algorithms!

 Course Contents:

• Introduction to Installation
  • What is Python
  • Installation: Anaconda, iPython and Spyder 
• Basic Data Types
  • Int
  • Float
  • complex
  • boolean
  • Strings
  • Subsetting
  • Methods
  • Formatting
• Basic Data Containers
  • Lists
  • Tuples
  • Sets
  • Dictionaries
• Statements In Python
  • If, Else, Elif
  • For loops
  • While loops
  • Break, Pass, Continue
  • Range
  • Zip
  • Comprehensions
  • Ternary expressions
• Functions In Python
  • Intro to Functions
  • Passing Multiple Arguments and Default Values
  • Named Arguments
  • Closures
  • Lambda Functions and map
  • Functional Programming : Filter, Any, All and Reduce
  • Scoping in Python
• NumPy And Pandas
  • NumPy Basics
  • Subsetting
  • Reshape and Matrix methods
  • NumPy universal functions
  • pandas Basics
  • Series
  • DataFrames
• Matplotlib And Seaborn For Data Visualization
  • Lines, bars, and markers
  • Shapes and collections
  • Statistical plots
  • Images, contours, and fields
  • Pie and polar charts
  • Plotting univariate distributions
  • Plotting bivariate distributions
  • Visualizing pairwise relationships in a dataset
  • Categorical scatterplots
  • Distributions of observations within categories
• Machine Learning
  • Linear Regression
  • Logistic Regression
  • K Nearest Neighbors (KNN)
  • K Means Clustering
  • Decision Trees
  • Random Forests
  • Natural Language Processing (NLP)
  • Support Vector Machines (SVM)