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Data Science Online Training



Online Training by Industry Expert

Data Science Introduction


 What is Data Science
 Data Scientists
 Examples of Data Science
 Python for Data Science

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

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--
Course Id:
DS001 
Course Fees:
301 USD