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


DOWNLOAD
uploadgig


rapidgator


nitroflare