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Data Analysis - How to Extract the Data
Rating: 3.6 out of 5(3 ratings)
38 students

Data Analysis - How to Extract the Data

Data Extraction from different formats like csv, excel, url, databases, etc using Python and R
Created byDATAhill Solutions Srinivas Reddy
Last updated 1/2020
English

What you'll learn

  • Data Extraction from CSV File
  • Data Extraction from Excel File
  • Data Extraction from CLIPBOARD, URL, XML & JSON Files
  • Data Extraction from Databases
  • Loading data into Python
  • Loading data from CSV, Excel & URL into Python
  • Data Analysis on the loaded data

Course content

1 section • 11 lectures • 6h 8m total length

  • Introduction1:38

    Learn to load data from diverse sources, handle structured and unstructured data, and connect to databases, comma separated values, and Excel to enable analysis.

  • 1. Data Extraction from CSV File42:02

    Explore extracting data from CSV files in R by loading into a data frame, setting the working directory, using read.csv with header and separator options, and subsetting data.

  • 2. Data Extraction from Excel File50:40

    Learn to extract data from csv and excel files in R, set the working directory, read and write csv and excel data, and subset or select columns.

  • 3. Data Extraction from CLIPBOARD, URL, XML & JSON Files50:04

    Explore practical data extraction techniques from clipboard, URLs, XML and JSON sources using R, including reading Excel, CSV, and XML/JSON into data frames for analysis.

  • 4. Data Extraction from Databases - Part 152:31

    Learn to extract data from relational databases using R by connecting with DBI, selecting databases and tables, and fetching results into data frames for analysis.

  • 5. Data Extraction from Databases - Part 252:39

    Learn to connect R to databases like MySQL, read and manipulate data with DBI and dplyr, and perform queries, inserts, updates for analysis.

  • 6. Loading data into Python40:33

    Load data into python using the read function, manage headers and skip rows, set delimiters, and tune data types and missing values for large datasets like wine quality data.

  • 7. Loading data from CSV, Excel & URL into Python21:24

    Learn how to load data from csv, excel, and urls into Python using read_csv and read_excel, handle columns, indices, and separators, and work with sample olympics medals data.

  • 8. What is Data Science30:42
  • 9. What is Machine Learning25:06

    Explore what machine learning is, how it differs from traditional programming and statistics, and how data science uses data collection, cleaning, and modeling to build predictive systems.

  • Summary1:31

Requirements

  • Basic experience with the Python and R programming language
  • No Programming Experience needed
  • Basic statistic skills is enough

Description

Why learn pandas?

If you've spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you!


Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language.

Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets -- analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more!

I call it "Excel on steroids"!

Over the course of more than 19 hours, I'll take you step-by-step through Pandas, from installation to visualization! We'll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We'll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package.

Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas!

Whether you're a new data analyst or have spent years (*cough* too long *cough*) in Excel, Data Analysis with pandas and Python offers you an incredible introduction to one of the most powerful data toolkits available today!


1. Data Extraction from CSV File

2. Data Extraction from Excel File

3. Data Extraction from CLIPBOARD, URL, XML & JSON Files

4. Data Extraction from Databases - Part 1

5. Data Extraction from Databases - Part 2

6. Loading data into Python

7. Loading data from CSV, Excel & URL into Python

8. What is Data Science

9. What is Machine Learning

Who this course is for:

  • Data analysts and business analysts
  • Excel users looking to learn a more powerful software for data analysis
  • Data Scientists

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