Python Data Analytics With Pandas and NumPy
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Python Data Analytics: With Pandas and NumPy
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 932 MB
Duration: 2 hours | Genre: eLearning Video | Language: English
Learn Get complete to handle complex data-sets and analyze your data in a principled way with Pandas, Python and NumPy.
What you'll learn
Learn to work with pandas to analyze data.
Learn to use NumPy to work with arrays and matrices of numbers.
Learn to work with Jupyter Notebook.
Learn to work with matDescriptionlib from within pandas.
Requirements
Basic Python programming experience
Description
Welcome to " Python Data Analytics: With Pandas and NumPy "
Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!
You will learn how to:
Import data sets
Clean and prepare data for analysis
Manipulate pandas DataFrame
Summarize data
Build machine learning models using scikit-learn
Build data pipelines
Posing a question
Wrangling your data into a format you can use and fixing any problems with it
Exploring the data, finding patterns in it, and building your intuition about it
Drawing conclusions and/or making predictions
Communicating your findings
Data Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts:
Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions
COURSE SYLLABUS
Module 1 - Installation
Lecture 1: Installing the Anaconda Python distribution
Lecture 2:Writing and running Python in the iPython notebook
Module 2 - Refresher Data Containers in Python
Lecture 3:Python containers overview
Lecture 4:Using Python lists and the slicing syntax
Lecture 5:Using Python dictionaries
Lecture 6:Comprehensive
Module - 3 Word Anagrams in Python
Lecture 7:Word anagram overview
Lecture 8:Loading the dictionary
Lecture 9:Finding anagrams
Lecture 10:Challenge
Lecture 11:Solution
Module - 4 Introduction to NumPy
Lecture 12:NumPy overview
Lecture 13:Creating Numpy Arrays
Lecture 14:Doing math with arrays
Lecture 15:Indexing and slicing
Lecture 16:Records and dates
Module - 5 Weather Data with NumPy
Lecture 17:Weather data overview
Lecture 18:Downloading and parsing data files
Lecture 19:Temperature analysis
Lecture 20:Integrating missing data
Lecture 21:Smoothing data
Lecture 22:Computing daily records
Lecture 23:Challenge
Lecture 24:weather data Solution
Module - 6 Introduction to Pandas
Lecture 25:Pandas overview
Lecture 26:Series in Pandas
Lecture 27:DataFrames in Pandas
Lecture 28:Using multilevel indices
Lecture 29:Aggregation
You'll also learn how to use the Python libraries NumPy, Pandas, and MatDescriptionlib to write code that's cleaner, more concise, and runs faster.
Take this course today and start your journey now!
Regards,
EliteHakcer Team
Who this course is for:
You should be familiar with if statements, loops, functions, lists, sets, and dictionaries. To learn about any of these topics, take the course Intro to Computer Science.
You should also be familiar with classes, objects, and modules. To learn about these topics, take the course Programming Foundations with Python.
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