Price 25,000.00 GST

Course Features
language
Language: English
access_time
60 hours
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Study Level: Intermediate
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Certificate of Completion

Data Science with Python Certification Training Course Agenda

Lesson 1: Data Science Overview

  • Data Science
  • Data Scientists
  • Examples of Data Science
  • Python for Data Science

Lesson 2: Data Analytics Overview

  • Introduction to 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

Lesson 3: 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

Lesson 4: 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

Lesson 5: 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

Lesson 6: 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

Lesson 7: 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

Lesson 8: 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

Lesson 9: 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

 

Lesson 10: 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

Lesson 11: 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

Lesson 12: 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

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