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DAT-325 - Data Validation: Quality and Cleaning

(SNHU-DAT325.AJC1)
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1

Fundamentals of Data Analysis

  • The Importance of Quality Source Data
  • Data Structure Types
  • Centralized Data Benefits
  • Structured vs. Unstructured Data
  • Types of Data
  • Typical Sources of Business Data
  • Data Protection Policies
  • Search Engine Optimization
  • Data Life Cycle Management (DLM)
  • Data Analysis Process
  • Lesson Summary
2

Introduction to Data Wrangling with Python

  • Introduction
  • Python for Data Wrangling
  • Lists, Sets, Strings, Tuples, and Dictionaries
  • Summary
3

Introduction to Data Quality

  • Impact of Data Errors
  • Importance of Organizational Strategy and Data Quality in Data Analytics
4

Introduction to NumPy, Pandas, and Matplotlib

  • Introduction
  • NumPy Arrays
  • Pandas DataFrames
  • Statistics and Visualization with NumPy and Pandas
  • Summary
5

Introduction to Databases and Cloud

  • Cloud-Based Data
  • Typical Databases Used for Data Analysis
  • Data Modeling 
6

RDBMS and SQL

  • Introduction
  • Refresher of RDBMS and SQL
  • Using an RDBMS (MySQL/PostgreSQL/SQLite)
  • Reading Data from a Database in SQLite
  • Summary
7

Tools for Capturing and Analyzing Data

  • Data Analytics Tools
  • Capturing Data: Tableau Public
  • Capturing Data: Google BigQuery
  • Capturing Data: OpenRefine
  • Overview: Hadoop-Based Environments
  • Capturing and Analyzing Data in Hadoop
  • The R Project
  • Additional Software for Data Capture
  • Lesson Summary
8

Getting Comfortable with Different Kinds of Data Sources

  • Introduction
  • Reading Data from Different Text-Based (and Non-Text-Based) Sources
  • Introduction to Beautiful Soup 4 and Web Page Parsing
  • Summary
9

Working with Data Sources

  • Data E-Harmony: Working with Different Departments to Bring Data Together
  • The Purpose of Customer Relationship Management (CRM)
  • CRM Integration: A Banking Scenario
  • Obtaining Data from Email and User Forums
  • Obtaining Data from Other Knowledge Bases
  • Obtaining Data from CRM and Business-to-Business Frameworks
  • Transaction, Payment and Inventory Data
  • Using Multiple Data Sources
  • Lesson Summary
10

A Deep Dive into Data Wrangling with Python

  • Introduction
  • Subsetting, Filtering, and Grouping
  • Detecting Outliers and Handling Missing Values
  • Concatenating, Merging, and Joining
  • Useful Methods of Pandas
  • Summary
11

Learning the Hidden Secrets of Data Wrangling

  • Introduction
  • Advanced List Comprehension and the zip Function
  • Data Formatting
  • Identify and Clean Outliers
  • Summary
12

Application of Data Wrangling in Real Life

  • Introduction
  • Applying Your Knowledge to a Real-life Data Wrangling Task
  • An Extension to Data Wrangling
  • Summary

1

Introduction to Data Wrangling with Python

  • Sorting a List
  • Generating a List
  • Deleting a Value from a Dictionary
  • Accessing and Setting Values in a Dictionary
  • Slicing a String
2

Introduction to NumPy, Pandas, and Matplotlib

  • Generating Arrays Using arange and linspace
  • Multiplying Two Arrays
  • Adding Two NumPy Arrays
  • Creating a NumPy Array
3

RDBMS and SQL

  • Deleting the Data
  • Using Joins
  • Using the Foreign Key
  • Using the ORDER BY Clause
  • Using the SELECT Statement
4

Getting Comfortable with Different Kinds of Data Sources

  • Bypassing the Headers of a CSV File
  • Reading Data from a CSV File
  • Stacking URLs from a Document Using bs4
  • Counting Tags
5

Working with Data Sources

  • DAT-325 Project 2
6

A Deep Dive into Data Wrangling with Python

  • Subsetting a DataFrame
  • Grouping a DataFrame
  • Dropping the Missing Values
  • Replacing Missing Values in a DataFrame
  • Joining DataFrames
  • Concatenating Data Frames
  • Counting Values
7

Learning the Hidden Secrets of Data Wrangling

  • Using the zip Function
  • Using a One-Liner Generator Expression
  • Using a Generator Expression
  • Using the format Function
  • Using a Box Plot
8

Application of Data Wrangling in Real Life

  • Skipping the First Row of the Data Set
  • DAT-325 Project 3

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