Udemy

Platform

English

Language

Programming Languages

Category

Data Analysis with Python, Pandas and NumPy

Data Analysis with Python libraries - NumPy, Pandas, MatplotLib and Seaborn | 150+ MCQ Question | 2 Projects

4.60 (23 reviews)

Students

17.5 hours

Content

Oct 2020

Last Update
Regular Price


What you will learn

Student will learn data analysis techniques using numpy, pandas, matplotlib and seaborn.

This course provides theoretical and practical understanding of the key concept of data analysis and data visualization

The course provides excellent learning tool for creating strategies and correct business decision from the data at hand.

Student will learn NumPy and Pandas introduction, Data ingestion, Data Preparation, Data Wrangling and Data Aggregation.

Student will learn Data Visualization techniques using matplotlib, seaborn & pandas object.


Description

Data Analysis with Python is for everyone who would like to create meaningful insight out of the data with the power of Numpy, Pandas, Matplotlib & Seaborn. The course has the right recipe to equip student with the right set of skill to ingest, clean, merge, manipulate, transform and finally visualize the data to create the meaning out of the data at hand.

The goal of this course is many fold :

- To provide theoretical and practical understanding of data analysis with Python package like NumPy and Pandas.

- To provide the knowledge of visualization tool ( matplotlib and seaborn ) so that one will be able to visualize and make correct decision based on the data.

- And finally practice with real life data to feel confident of the topic and be able to ready to work on data analysis project or interview.


The whole project is divided into following module :

- NumPy introduction

- Pandas introduction (Series and dataframe objects )

- Data ingestion & Storage ( CSV, Excel, SQLite, JSON, HTML, Pickle and HDF5 storage etc. )

- Data Preparation ( Identify missing data, Handle missing data, handling duplicate data, Data transformation, Manipulating Row & Columns, Bucket Analysis, Outlier detection, Sampling, Creating dummy variable etc. )

- Data Wrangling ( Data Aggregation, Merging, Joins - Inner, Outer, Left & Right join, Join, Concatenate, Pivot, Melt etc. )

- Data Aggregation (Split, Apply & Combine, GroupBy clause, Binning data, Pivot table and Cross tabulations etc. )

- Visualization ( MatplotLib, Pandas Object visualization, Seaborn )

- Project - Practice data analysis with real life datasets.


Screenshots

Data Analysis with Python, Pandas and NumPy
Data Analysis with Python, Pandas and NumPy
Data Analysis with Python, Pandas and NumPy
Data Analysis with Python, Pandas and NumPy

Content

Introduction to NumPy

Introduction to NumPy

Technical Details of NumPy

Is NumPy Faster ?

Basic terms of NumPy

Summary of NumPy Operation

NumPy Array Creation

NumPy Array Creation with datatype details

Hands-ON ( NumPy Installation & Array creation )

Hands-ON (NumPy array creation in one dimension )

Hands-ON ( NumPy Array Creation with multiple dimension )

Arithmetic operation in NumPy

Hands-ON ( Arithematic operation in NumPy )

Indexing & Slicing Operation in NumPy Array

Hands-ON ( Indexing & Slicing operation in NumPy Array in 1 dimension )

Row and Column Slicing using boolean info

Hands-ON ( Row & Column slicing using boolean info )

Fancy Indexing

Hands-ON ( Fancy Indexing )

Transpose Array ( Theory & Hands-ON )

Universal Function in NumPy Array

Hands-ON ( Universal function in NumPy Array )

Vectorization, Meshgrid & np.where

Hands-ON ( Vectorization, MeshGrid & np.where )

Statistical Function ( Theory & Hands-ON)

Boolean Array ( Theory & Hands-ON )

Sort, Unique & Set operation in NumPy Array ( Theory & Hands-ON )

File Operation, Linear Algebra & Random Number Generation ( Theory & Hands-ON )

Pandas Basic, Installation & Pandas Series object

Pandas Basic & Installation

Pandas Series ( Introduction & Hands-ON )

Introduction to Pandas Series Creation & Element Access

Hands-ON to Pandas Series Creation and Element Access

Filter Operation ( Theory & Hands-ON )

Mathematical Operation on Pandas ( Theory & Hands-ON )

Series Creation with dictionary, Check NULLs & Misc Function (Theory & Hands-ON)

Pandas Dataframe object

Pandas Dataframe Creation ( Theory & Hands-ON )

Pandas Dataframe Column Access ( Theory & Hands-ON )

Theory & Hands-ON - Column update, delete, transpose, rename index & columns etc

Pandas Row and Column Index - immutable, repeat property ( Theory & Hands-ON )

Reindexing, dropping, in-place changes & reordering row and column in dataframe

Hands-ON ( Reindexing, dropping, re-oredering & in-place changes )

Introduction to Hierarchical indexing, selection, filtering, LOC, iLOC, AT, iAT

Hands-ON ( Indexing, Selection, Filtering )

Hands-ON ( Hierarchical Indexing )

Introduction to negative indexing

Introduction to Arithmetic operation & fill_value function

Hands-ON to arithmatic operation & fill_value function

Introduction to Function mapping

Hands-ON to function mapping

Introduction to sorting, ranking and relationship with duplicate labels

Hands-ON ( Sorting, Ranking )

Introduction to summarizing data

Hands-ON ( Summarizing data )

Data Storage with Pandas

Introduction to data storage with pandas

Introduction to data storage with CSV file

Hands-ON to CSV file storage

Handling Missing Values

Handling large files - Theory & Hands-on

Writing to CSV file ( Theory & HandsON )

Introduction to data storage with JSON, HTML & Pickle file.

HandsON ( Storage with JSON, HTML & Pickle file )

Introduction to data storage with HDF5 and Excel file

Hands-ON ( Data storage with HDF5 )

HandsON ( Data Storage with Excel file )

Data Storage with SQLite ( Theory & HandsON )

Data Preparation

Introduction to data preparation

Introduction to identifying missing data ( Theory & HandsOn )

Delete missing data ( Theory & HandsOn )

Introduction to impute missing data

HandsOn ( Impute Missing data )

Introduction to handling Duplicate Values

HandsOn ( Handling Duplicate Values )

Introduction to data transformation

HandsOn ( Data Transformation )

Update Row & Columns Axis ( Theory & HandsOn )

Binning & bucketing data ( Theory & HandsOn )

Introduction to Outlier Detection, Sampling & Dummy Variable

HandsOn to outlier detection, sampling & dummy variable

Data Wrangling

Introduction to multilevel indexing in Series object

HandsOn of multilevel indexing in Series object

Introduction of multilevel indexing in dataframe object

HandsOn to multilevel indexing in dataframe object

Reordering and sorting on row index in dataframe ( Theory & HandsOn )

Introduction to Aggregation of data in multilevel indexing

HandsOn to aggregation of data in multilevel indexing

HandsOn to Changing index to column and column to index in dataframe

Merging datasource (Inner Join)

Merging Dataframes ( Outer Join )

Merging dataframe ( Left & Right Join )

Merging dataframe with multiple keys ( columns )

HandsOn on Merging (Inner,Outer,Left&Right join with one/multiple keys(columns))

Introduction to Merging dataframe (Column + Index , Index + Index )

HandsOn to merging dataframe ( Column + Index , Index + Index )

Introduction & HandsOn to Merge with "JOIN" command

Introduction to merging by CONCATENATION

HandsOn to merging by CONCATENATION

Introduction & HandsOn to merging with COMBINE_FIRST

Introduction to PIVOT and MELT and their interaction with each other

HandsOn to PIVOT and MELT function and interaction

Data Aggregation & Grouping

Introduction to data aggregation

Introduction to SPLIT, APPLY and COMBINE

SPLIT, APPLY & COMBINE with all columns

GROUP BY clause with FOR loop

HandsOn of GROUP BY clause with FOR loop

Grouping Columns with GROUP BY clause

HandsOn to grouping columns with GROUP BY clause

GROUP BY with aggregate ( AGG ) function

HandsOn of GROUP BY with aggregate ( AGG ) function

GROUP BY with APPLY function ( Theory & HandsOn )

Binning & Bucket Analysis ( Theory & HandsOn )

Introduction to PIVOT-TABLE and CROSS-TABULATION

HandsOn to PIVOT-TABLE and CROSS-TABULATION

Visualization with Matplotlib

Introduction to Data Visualization & Matplotlib

Introduction to figure & subplot in matplotlib

HandsOn ( Figure & Subplot )

Introduction to subplot properties

HandsOn Subplot properties -1 ( sharex, sharey, wspace, hspace )

HandsOn Subplot properties -2 ( linestyle, color, marker, label )

HandsOn Subplot properties-3(xlabel, yLabel, title, set_xticks, set_xticklabels)

Introduction to Annotation and Saving plots

HandsOn ( Annotation & Saving figure and plots )

Introduction to bar graph ( Horizantal, Vertical & Multiple bar graph )

HandsOn to bar graph ( Horizontal, Vertical and Multiple bar graph )

Introduction & HandsOn to 3-dimension plot

Introduction to Scatter, Pie, Box, histogram & violin plot in matplotlib

HandsOn ( Scatter, Pie & Histogram Plot )

HandsOn ( Box & Violin Plot )

Visualization with Pandas Object

Introduction to Visualization with Pandas Object

HandsOn -1 (Pandas Series Object )

HandsOn -2 ( Pandas Dataframe object )

Visualization with Seaborn

Introduction (Lineplot, Barplot, Histogram, Scatter Plot )

Introduction to Category Plot & Joint Plot

HandsOn -1 ( Line, Bar, Histogram , Scatter, Regression plot )

HandsOn-2 ( jointplot, Boxplot, Barplot)

Python Refresher (Video from my Python Programming Bible course )

Introduction to Python

Python Installation ( Anaconda & Jupyter editor )

Introduction to Python data types

Projects

Project-1 : Analysis of movie dataset

Project-2 : Analysis of Titanic Dataset

Materials used during lecture

Jupyter Assignment Files


Reviews

S
Sarvjeet8 January 2021

The trainer seems to know his stuff. First theory , and then hands on , really a good approach. Covering all aspects of panda & numpy


3369448

Udemy ID

7/27/2020

Course created date

10/11/2020

Course Indexed date
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