Metodologías ágiles de desarrollo de software

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Metodologías ágiles de desarrollo de software
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In practical terms, agile software development methodologies are all about delivering small pieces of working software quickly to improve customer satisfaction. These methodologies use adaptive approaches and teamwork to focus on continuous improvement.
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Advantage of this course

  • Data Exploration
  • Time series Analysis
  • Career Opportunities
  • Real world Applications
  • Vast Ecosystem
  • Versatility

About The Course

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! Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include:

  • installing
  • sorting
  • filtering
  • grouping
  • aggregating
  • de-duplicating
  • pivoting
  • munging
  • deleting
  • merging
  • visualizing
  • and more!

What Will You Learn

  • Perform a multitude of data operations in Python's popular pandas library including grouping, pivoting, joining and more.
  • Possess a strong understanding of manipulating 1D, 2D, and 3D data sets
  • Learn hundreds of methods and attributes across numerous pandas objects
  • Resolve common issues in broken or incomplete data sets

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Course Contents

DataFrames I: Introduction

Intro to DataFrames I Module, Methods and Attributes between Series and DataFrames, Differences between Shared Methods, Sort a DataFrame with the sort_values Method, A Review of the DataFrames I Module

DataFrames II: Filtering Data

Module's Dataset + Memory Optimization, Filter DataFrame with More than One Condition (AND - &), Filter DataFrame with More than One Condition (OR - |), Check for Inclusion with the isin Method, Check for Null and Present DataFrame Values with the isnull and notnull Methods, Check For Inclusion Within a Range of Values with the between Method, Check for Duplicate DataFrame Rows with the duplicated Method, Delete Duplicate DataFrame Rows with the drop_duplicates Method, Identify and Count Unique Values with the unique and nunique Methods

Working with Text Data

Intro to the Working with Text Data Section, Use the str.replace method to replace all occurrences of character with another, Filter a DataFrame's Rows with String Methods, More DataFrame String Methods - strip, lstrip, and rstrip, Invoke String Methods on DataFrame Index and Columns, Split Strings by Characters with the str.split Method, More Practice with the str.split method on a Series, Exploring the expand and n Parameters of the str.split Method

MultiIndex

ntro to the MultiIndex Module, Create a MultiIndex on a DataFrame with the set_index Method, Extract Index Level Values with the get_level_values Method, Change Index Level Name with the set_names Method, The sort_index Method on a MultiIndex DataFrame, Extract Rows from a MultiIndex DataFrame, The transpose Method on a MultiIndex DataFrame, The swaplevel Method, The stack and unstack Method, The pivot Method, Use the pivot_table method to create an aggregate summary of a DataFrame

The GroupBy Object

Intro to the GroupBy Module, First Operations with groupby Object, Retrieve a group from a GroupBy object with the get_group Method, Methods on the Groupby Object and DataFrame Columns, Grouping by Multiple Columns, The ag method, Iterating through Groups

Merging, Joining and Concatenating Datasets

Intro to the Merging, Joining, and Concatenating Section, The pd.concat Method, Inner Joins, Outer Joins, Left Joins, The left_on and right_on Parameters, Merging by Indexes with the left_index and right_index Parameters, The .join() Method, The pd.merge() Method

FAQ

Yes, many data analytics courses provide a certificate of completion, which can be valuable for showcasing your skills to potential employers.

Data analytics is the process of examining, interpreting, and extracting meaningful insights from data to support decision-making. It is crucial for businesses to make data-driven decisions, identify trends, and gain a competitive advantage.

While prior knowledge of data analytics is not always required, having basic mathematical skills and familiarity with computer applications can be beneficial. Some courses are designed for beginners and assume no prior experience.

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