Data Science with Python

Data Science with Python Training In Bangalore

Data Science with Python
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* Course Id : DSCI-PYTH
* Duration : 40 Hours

Overview
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* Python is a programming language which is more popular for data science
* Python is used to simplify and easily access the data and store the data easily
* This training provides you to learn data manipulation and cleaning of data using python

Pre-Requisites
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* The participants should have the basic knowledge of statistics and any Programming Language

Objectives
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* Understand the concepts of Data Science
* Able to work in Data mining and understand data analysis in python
* Understand the concepts of BigData
* Understand how to use the tools like a tableau, map-reduce..

Course Structure
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* We provide more focus on hands-on in our technical courses (typically 80% hands-on/20% theory)
* Students get the capability to apply the material they learn to real-world problems

Materials Provided
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* PDF of slides and hands-on exercises
* Access to instance with lab environment

Software Requirements
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* Any current internet browser

Hardware Requirements
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* Processor: 1.2 GHz
* RAM: 512 MB
* Disk space: 1 GB
* Network Connection with low latency (<250ms) to Internet

Course Outline
Daywise 
Data Science with Python course outline
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Day 1

* Introduction
* Environment Set-Up
* Jupyter Overview
* Python Crash Course
* Data Analysis-NumPy

Day 2
* Data Analysis-Pandas
* Data Analysis-Pandas Exercises
* Data Visualization-Matplotlib
* Data Visualization-Seaborn
* Data Visualization-Pandas Built-in Data Visualization

Day 3
* Data Visualization-Plotly and Cufflinks
* Data Visualization-Geographical Plotting
* Introduction to Machine Learning
* Linear Regression
* Logistic Regression

Day 4
* K Nearest Neighbours
* Decision Trees and Random Forests
* Support Vector Machines
* K Means Clustering
* Principal Component Analysis

Day 5
* Recommender Systems
* Natural Language Processing
* Big Data and Spark with Python
* Neural Nests and Deep Learning

Detailed Data Science with Python course outline
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* Introduction
* Overview with Data Science

* Environment Set-Up
* Environment Set-up and Installation
* Setup Tools – Anaconda, Jupyter and Ipython
* Install Python
* Set up PyCharm CE/Sublime IDE
* Install VIM or Emacs or VI

* Jupyter Overview
* Jupyter Notebooks
* Optional: Virtual Environments

* Python Crash Course
* Introduction to Python Crash Course
* Basics
* OOPS concepts
* Modules
* Final
* Python Crash Course Exercises – Overview
* Solutions

* Data Analysis-NumPy
* Introduction to Numpy
* Numpy Arrays
* Quick Note on Array Indexing
* Numpy Array Indexing and Operations
* Numpy Exercises Overview and Solutions

* Data Analysis-Pandas
* Introduction to Pandas
* Series
* Data Frames – Introduction
* Data Frames – Organizing
* Data Frames – Set up
* Missing Data
* Group by
* Merging Joining and Concatenating
* Operations
* Data Input and Output

* 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

* Data Visualization-Matplotlib
* Introduction to Matplotlib
* Set up
* Plot
* Next steps
* Matplotlib Exercises
* Overview
* Solutions

* Data Visualization-Seaborn
* Introduction to Seaborn
* Distribution Plots
* Categorical Plots
* Matrix Plots
* Regression Plots
* Grids
* Style and Color
* Seaborn Exercise Overview
* Seaborn Exercise Solutions

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

* Data Visualization-Plotly and Cufflinks
* Introduction to Plotly and Cufflinks
* Plotly and Cufflinks

* Data Visualization-Geographical Plotting
* Introduction to Geographical Plotting
* Choropleth Maps – USA
* Choropleth Maps – 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 – Introduction
* Linear Regression with Python – Deep Dive
* Linear Regression Project Overview and Project Solution

* Logistic Regression
* Logistic Regression Theory – Introduction
* Logistics
* Regression
* 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
* Overview
* Decision Trees and Random Forest Exercise and Solutions

* 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
* The Foundation
* 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

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