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Fortinet Network Security Expert (NSE) -programmet er et otte-niveau uddannelses- og certificeringsprogram for at undervise ingeniører i deres netværkssikkerhed for Fortinet FW-færdigheder og erfaring.

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    • Data Wrangling with Python

    Enroll Now
    Duration
    3 Dage
    Delivery
    (Online Og På stedet)
    Price
    Pris på forespørgsel
    Data is the new oil, but it comes crude. To do anything meaningful - modelling, visualization, machine learning, for predictive analysis “ you first need to wrestle and wrangle with data. This Data Wrangling with Python course teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The course starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.

    Lesson 1: Introduction to Data Structure using Python

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

    Lesson 2: Advanced Operations on Built-In Data Structure

    • Advanced-Data Structures
    • Basic File Operations in Python

    Lesson 3: Introduction to NumPy, Pandas, and Matplotlib

    • NumPy Arrays
    • Pandas DataFrames
    • Statistics and Visualization with NumPy and Pandas
    • Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame

    Lesson 4: Deep Dive into Data Wrangling with Python

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

    Lesson 5: Get Comfortable with a Different Kind of Data Sources

    • Reading Data from Different Text-Based (and Non-Text-Based) Sources
    • Introduction to BeautifulSoup4 and Web Page Parsing

    Lesson 6: Learning the Hidden Secrets of Data Wrangling

    • Advanced List Comprehension and the zip Function
    • Data Formatting

    Lesson 7: Advanced Web Scraping and Data Gathering

    • Basics of Web Scraping and BeautifulSoup libraries
    • Reading Data from XML

    Lesson 8: RDBMS and SQL

    • Refresher of RDBMS and SQL
    • Using an RDBMS (MySQL/PostgreSQL/SQLite)

    Lesson 9: Application in real life and Conclusion of course

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

    Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.

    Hardware:

    For an optimal student experience, we recommend the following hardware configuration:

    • Processor: Intel Core i5 or equivalent
    • Memory: 8GB RAM or higher
    • Internet Connection

    Software:

    You will also need the following software installed in advance:

    • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X
    • Browser: Google Chrome/Mozilla Firefox Latest Version
    • Notepad++/Sublime Text as IDE (Optional, as you can practice everything using Jupyter note course on your browser)
    • Python 3.4+ (latest is Python 3.7) installed (from https://python.org)
    • Python libraries as needed (Jupyter, NumPy, Pandas, Matplotlib, BeautifulSoup4, and so)
    Overview
    Data is the new oil, but it comes crude. To do anything meaningful - modelling, visualization, machine learning, for predictive analysis “ you first need to wrestle and wrangle with data. This Data Wrangling with Python course teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The course starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.

    Lesson 1: Introduction to Data Structure using Python

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

    Lesson 2: Advanced Operations on Built-In Data Structure

    • Advanced-Data Structures
    • Basic File Operations in Python

    Lesson 3: Introduction to NumPy, Pandas, and Matplotlib

    • NumPy Arrays
    • Pandas DataFrames
    • Statistics and Visualization with NumPy and Pandas
    • Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame

    Lesson 4: Deep Dive into Data Wrangling with Python

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

    Lesson 5: Get Comfortable with a Different Kind of Data Sources

    • Reading Data from Different Text-Based (and Non-Text-Based) Sources
    • Introduction to BeautifulSoup4 and Web Page Parsing

    Lesson 6: Learning the Hidden Secrets of Data Wrangling

    • Advanced List Comprehension and the zip Function
    • Data Formatting

    Lesson 7: Advanced Web Scraping and Data Gathering

    • Basics of Web Scraping and BeautifulSoup libraries
    • Reading Data from XML

    Lesson 8: RDBMS and SQL

    • Refresher of RDBMS and SQL
    • Using an RDBMS (MySQL/PostgreSQL/SQLite)

    Lesson 9: Application in real life and Conclusion of course

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

    Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context.

    Hardware:

    For an optimal student experience, we recommend the following hardware configuration:

    • Processor: Intel Core i5 or equivalent
    • Memory: 8GB RAM or higher
    • Internet Connection

    Software:

    You will also need the following software installed in advance:

    • OS: Windows 7 SP1 64-bit, Windows 8.1 64-bit or Windows 10 64-bit, Ubuntu Linux, or the latest version of OS X
    • Browser: Google Chrome/Mozilla Firefox Latest Version
    • Notepad++/Sublime Text as IDE (Optional, as you can practice everything using Jupyter note course on your browser)
    • Python 3.4+ (latest is Python 3.7) installed (from https://python.org)
    • Python libraries as needed (Jupyter, NumPy, Pandas, Matplotlib, BeautifulSoup4, and so)
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