Data science using python

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