Data Science with Python Training

Data Science with Python Training Overview

Python Programming language is powerful open source language. It is developed with data science tool and which is used to simplify and easily access the data and store the data easily. By R Programming language we can easily manipulate the data, also it can help in the analysis of Data, we can create the wonderful visualization and helps to access the high-quality content. This Data Science with Python Training provides you to learn data manipulation and cleaning of data using python.

Objectives of the Course

• Complete basics of Data Science
• Understand the concepts of BigData and able to work in Data mining.
• Understand the usage and how to use the tools like a tableau, map-reduce…

Pre-requisites of the Course

• Any IT experienced Professional who are interested to build their career in development/ data scientist.
• Any B.E/ B.Tech/ BSC/ MCA/ M.Sc Computers/ M.Tech/ BCA/ BCom College Students in any stream.
• Fresh Graduates.

Who can attend this course

The course can learn by any IT professional having basic knowledge of:

• Mathematics
• Statistics
• Any Programming Language

Data Science with Python Course Content

Data Science with Python Course Introduction

• Course Overview with Data Science

Environment Set-Up

• Environment Set-up and Installation –
• Set up Anaconda, Jupyter, Ipython and install Python.
• Set up an IDE – Option to choose from installing – PyCharm CE or Sublime or
• VIM or Emacs or VI

Jupyter Overview

• Jupyter Notebooks
• Optional: Virtual Environments

Python Crash Course

• Introduction to Python Crash Course
• Python Crash Course – Part 1 – Basics
• Python Crash Course – Part 2 – OOPS concepts
• Python Crash Course – Part 3 – Modules
• Python Crash Course – Part 4 – Final
• Python Crash Course Exercises – Overview
• Python Crash Course Exercises – Solutions

Python for Data Analysis-NumPy

• Introduction to Numpy
• Numpy Arrays
• Quick Note on Array Indexing
• Numpy Array Indexing and Operations
• Numpy Exercises Overview and Solutions

Python for Data Analysis-Pandas

• Introduction to Pandas
• Series
• Data Frames – Part 1 Introduction
• Data Frames – Part 2 Organizing
• Data Frames – Part 3 Set up
• Missing Data
• Group by
• Merging Joining and Concatenating
• Operations
• Data Input and Output

Python for Data Analysis-Pandas Exercises

• Salaries Exercise Overview
• Note on SF Salary Exercise
• SF Salaries Solutions
• E-commerce Purchases Exercise Overview
• E-commerce Purchases Exercise Solutions

Python for Data Visualization-Matplotlib

• Introduction to Matplotlib
• Matplotlib Part 1 Set up
• Matplotlib Part 2 Plot
• Matplotlib Part 3 Next steps
• Matplotlib Exercises Overview
• Matplotlib Exercises – Solutions

Python for Data Visualization-Seaborn

• Introduction to Seaborn
• Distribution Plots
• Categorical Plots
• Matrix Plots
• Regression Plots
• Grids
• Style and Color
• Seaborn Exercise Overview
• Seaborn Exercise Solutions

Python for Data Visualization-Pandas Built-in Data Visualization

• Pandas Built-in Data Visualization
• Pandas Data Visualization Exercise
• Pandas Data Visualization Exercise- Solutions

Python for Data Visualization-Plotly and Cufflinks

• Introduction to Plotly and Cufflinks
• Plotly and Cufflinks

Python for Data Visualization-Geographical Plotting

• Introduction to Geographical Plotting
• Choropleth Maps – Part 1 – USA
• Choropleth Maps – Part 2 – World
• Choropleth Exercises
• Choropleth Exercises – Solutions

Introduction to Machine Learning

• Link for ISLR
• Introduction to Machine Learning
• Machine Learning with Python

Linear Regression

• Linear Regression Theory
• Model selection Updates for SciKit Learn
• Linear Regression with Python – Part 1 Introduction
• Linear Regression with Python – Part 2 Deep Dive
• Linear Regression Project Overview and Project Solution

Logistic Regression

• Logistic Regression Theory – Introduction
• Logistic Regression with Python – Part 1 – Logistics
• Logistic Regression with Python – Part 2 – Regression
• Logistic Regression with Python – Part 3 – Conclusion
• Logistic Regression Project Overview and Project Solutions

K Nearest Neighbours

• KNN Theory
• KNN with Python
• KNN Project Overview and  Project Solutions

Decision Trees and Random Forests

• Introduction to Tree Methods
• Decision Trees and Random Forest with Python
• Decision Trees and Random Forest Project Overview
• Decision Trees and Random Forest Solutions Part 1
• Decision Trees and Random Forest Solutions Part 2

Support Vector Machines

• SVM Theory
• Support Vector Machines with Python
• SVM Project Overview
• SVM Project Solutions

K Means Clustering

• K Means Algorithm Theory
• K Means with Python
• K Means Project Overview
• K Means Project Solutions

Principal Component Analysis

• Principal Component Analysis
• PCA with Python

Recommender Systems

• Recommender Systems
• Recommender Systems with Python – Part 1 The Foundation
• Recommender Systems with Python – Part 2 Deep Dive

Natural Language Processing

• Natural Language Processing Theory
• NLP with Python
• NLP Project Overview
• NLP Project Solutions

Big Data and Spark with Python

• Big Data Overview
• Spark Overview
• Local Spark Set-Up
• AWS Account Set-Up
• Quick Note on AWS Security
• EC2 Instance Set-Up
• SSH with Mac or Linux
• PySpark Setup
• Lambda Expressions Review
• Introduction to Spark and Python
• RDD Transformations and Actions

Neural Nests and Deep Learning

• Neural Network Theory
• Welcome to the Deep Learning Section!
• What is TensorFlow?
• Changes with TensorFlow
• TensorFlow Installation
• TensorFlow Basics
• MNIST with Multi-Layer Perceptron
• TensorFlow with ContribLearn
• Tensorflow Project Exercise Overview
• Tensorflow Project Exercise – Solutions