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Python for Data ScienceData Science Online Training

Learn Data Science by Industry Expert

Data Science Introduction

 What is Data Science
 Applications of Data Science
 Fields of Data Science

Preliminaries

Why Python for Data Analysis?
Essential Python Libraries
Installation and Setup
Installing Python
Installing or Updating Python Packages
Python 2 and Python 3
Integrated Development Environments (IDEs) and Text Editors
Installing libraries like Numpy, Pandas, Scikit-learn, NLTK, Spacy
Python Language Basics
Invoking the Interpreter
An Informal Introduction to Python
Control Flow Tools
Functions
Data Structures
Errors and Exceptions
Classes
Creating Logging module
Reading data from Config file


Data Analytics Overview


 Data Visualization
 Processes in Data Science
 Data Wrangling, Data Exploration, and Model Selection
 Exploratory Data Analysis or EDA
 Data Visualization
 Plotting
 Hypothesis Building and Testing

Statistical Analysis and Business Applications


 Introduction to Statistics
 Statistical and Non-Statistical Analysis
 Some Common Terms Used in Statistics
 Data Distribution: Central Tendency, Percentiles, Dispersion
 Histogram
 Bell Curve
 Hypothesis Testing
 Chi-Square Test
 Correlation Matrix
 Inferential Statistics

Python: Environment Setup and Essentials

 Introduction to Anaconda
 Installation of Anaconda Python Distribution - For Windows, Mac OS, and Linux
 Jupyter Notebook Installation
 Jupyter Notebook Introduction
 Variable Assignment
 Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
 Creating, accessing, and slicing tuples
 Creating, accessing, and slicing lists
 Creating, viewing, accessing, and modifying dicts
 Creating and using operations on sets
 Basic Operators: 'in', '+', '*'
 Functions
 Control Flow

Mathematical Computing with Python (NumPy)

 NumPy Overview
 Properties, Purpose, and Types of ndarray
 Class and Attributes of ndarray Object
 Basic Operations: Concept and Examples
 Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
 Copy and Views
 Universal Functions (ufunc)
 Shape Manipulation
 Broadcasting
 Linear Algebra

Scientific computing with Python (Scipy)

 SciPy and its Characteristics
 SciPy sub-packages
 SciPy sub-packages –Integration
 SciPy sub-packages – Optimize
 Linear Algebra
 SciPy sub-packages – Statistics
 SciPy sub-packages – Weave
 SciPy sub-packages - I O

Data Manipulation with Python (Pandas)

 Introduction to Pandas
 Data Structures
 Series
 DataFrame
 Missing Values
 Data Operations
 Data Standardization
 Pandas File Read and Write Support
 SQL Operation

Natural Language Processing with NLTK, Spacy

 NLP Overview
 NLP Applications
 Major NLP Libraries
 NLP Environment Setup
Installing NLTK, Spacy, Gensim, WordNet, Word2Vec, ...etc
Saving and Loading NLTK and Spacy models
 Stemming and Lemmatization
 Part of Speech Tagging
 Chunking
 Chinking
 Named Entity Recognition
 Wordnet
 Text Classification
- Feature Extraction Processes
- Model Building using Naive Bayes classifier

 Converting words to Features
 Naive Bayes Classifier
 Sentiment Analysis
 Phrase Handling
 Model Training

Machine Learning with Python (Scikit–Learn)

 Introduction to Machine Learning
 Machine Learning Approach
 How Supervised and Unsupervised Learning Models Work
 Scikit-Learn
 Supervised Learning Models - Linear Regression
 Supervised Learning Models: Logistic Regression
 K Nearest Neighbors (K-NN) Model
 Unsupervised Learning Models: Clustering
 Unsupervised Learning Models: Dimensionality Reduction
 Pipeline
 Model Persistence
 Model Evaluation - Metric Functions

Natural Language Processing with Scikit-Learn

 NLP Overview
 NLP Approach for Text Data
 NLP Environment Setup
 NLP Sentence analysis
 NLP Applications
 Major NLP Libraries
 Scikit-Learn Approach
 Scikit - Learn Approach Built - in Modules
 Scikit - Learn Approach Feature Extraction
 Bag of Words
 Extraction Considerations
 Scikit - Learn Approach Model Training
 Scikit - Learn Grid Search and Multiple Parameters
 Pipeline

Data Visualization in Python using Matplotlib

 Introduction to Data Visualization
 Python Libraries
 Plots
 Matplotlib Features:
 Line Properties Plot with (x, y)
 Controlling Line Patterns and Colors
 Set Axis, Labels, and Legend Properties
 Alpha and Annotation
 Multiple Plots
 Subplots
 Types of Plots and Seaborn

Data Science with Python Web Scraping

 Web Scraping
 Common Data/Page Formats on The Web
 The Parser
 Importance of Objects
 Understanding the Tree
 Searching the Tree
 Navigating options
 Modifying the Tree
 Parsing Only Part of the Document
 Printing and Formatting
 Encoding

Python integration with Hadoop, MapReduce and Spark

 Need for Integrating Python with Hadoop
 Big Data Hadoop Architecture
 MapReduce
 Cloudera QuickStart VM Set Up
 Apache Spark
 Resilient Distributed Systems (RDD)
 PySpark
 Spark Tools
 PySpark Integration with Jupyter Notebook--

Deep Learning

 Deep learning basics
 Neural Network
 Convolution Neural Network(CNN)
 Recurrent Neural Network(RNN)
 LSTM
Course Id:
PYT007 
Course Fees:
301 USD