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Data Analysis with Python, Pandas and NumPy

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Data Analysis with Python, Pandas and NumPy, available at $59.99, has an average rating of 4.15, with 139 lectures, 7 quizzes, based on 67 reviews, and has 393 subscribers.

You will learn about 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. This course is ideal for individuals who are Python developers who aim to learn data ingestion, data analysis and data visualization or Data analyst who would like to derive business insight out the data. It is particularly useful for Python developers who aim to learn data ingestion, data analysis and data visualization or Data analyst who would like to derive business insight out the data.

Enroll now: Data Analysis with Python, Pandas and NumPy

Summary

Title: Data Analysis with Python, Pandas and NumPy

Price: $59.99

Average Rating: 4.15

Number of Lectures: 139

Number of Quizzes: 7

Number of Published Lectures: 139

Number of Published Quizzes: 7

Number of Curriculum Items: 146

Number of Published Curriculum Objects: 146

Original Price: ₹7,900

Quality Status: approved

Status: Live

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.

Who Should Attend

  • Python developers who aim to learn data ingestion, data analysis and data visualization
  • Data analyst who would like to derive business insight out the data.

Target Audiences

  • Python developers who aim to learn data ingestion, data analysis and data visualization
  • Data analyst who would like to derive business insight out the data.

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 goalof 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 ( matplotliband 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.

Course Curriculum

Chapter 1: Introduction to NumPy

Lecture 1: Introduction to NumPy

Lecture 2: Technical Details of NumPy

Lecture 3: Is NumPy Faster ?

Lecture 4: Basic terms of NumPy

Lecture 5: Summary of NumPy Operation

Lecture 6: NumPy Array Creation

Lecture 7: NumPy Array Creation with datatype details

Lecture 8: Hands-ON ( NumPy Installation & Array creation )

Lecture 9: Hands-ON (NumPy array creation in one dimension )

Lecture 10: Hands-ON ( NumPy Array Creation with multiple dimension )

Lecture 11: Arithmetic operation in NumPy

Lecture 12: Hands-ON ( Arithematic operation in NumPy )

Lecture 13: Indexing & Slicing Operation in NumPy Array

Lecture 14: Hands-ON ( Indexing & Slicing operation in NumPy Array in 1 dimension )

Lecture 15: Row and Column Slicing using boolean info

Lecture 16: Hands-ON ( Row & Column slicing using boolean info )

Lecture 17: Fancy Indexing

Lecture 18: Hands-ON ( Fancy Indexing )

Lecture 19: Transpose Array ( Theory & Hands-ON )

Lecture 20: Universal Function in NumPy Array

Lecture 21: Hands-ON ( Universal function in NumPy Array )

Lecture 22: Vectorization, Meshgrid & np.where

Lecture 23: Hands-ON ( Vectorization, MeshGrid & np.where )

Lecture 24: Statistical Function ( Theory & Hands-ON)

Lecture 25: Boolean Array ( Theory & Hands-ON )

Lecture 26: Sort, Unique & Set operation in NumPy Array ( Theory & Hands-ON )

Lecture 27: File Operation, Linear Algebra & Random Number Generation ( Theory & Hands-ON )

Chapter 2: Pandas Basic, Installation & Pandas Series object

Lecture 1: Pandas Basic & Installation

Lecture 2: Pandas Series ( Introduction & Hands-ON )

Lecture 3: Introduction to Pandas Series Creation & Element Access

Lecture 4: Hands-ON to Pandas Series Creation and Element Access

Lecture 5: Filter Operation ( Theory & Hands-ON )

Lecture 6: Mathematical Operation on Pandas ( Theory & Hands-ON )

Lecture 7: Series Creation with dictionary, Check NULLs & Misc Function (Theory & Hands-ON)

Chapter 3: Pandas Dataframe object

Lecture 1: Pandas Dataframe Creation ( Theory & Hands-ON )

Lecture 2: Pandas Dataframe Column Access ( Theory & Hands-ON )

Lecture 3: Theory & Hands-ON – Column update, delete, transpose, rename index & columns etc

Lecture 4: Pandas Row and Column Index – immutable, repeat property ( Theory & Hands-ON )

Lecture 5: Reindexing, dropping, in-place changes & reordering row and column in dataframe

Lecture 6: Hands-ON ( Reindexing, dropping, re-oredering & in-place changes )

Lecture 7: Introduction to Hierarchical indexing, selection, filtering, LOC, iLOC, AT, iAT

Lecture 8: Hands-ON ( Indexing, Selection, Filtering )

Lecture 9: Hands-ON ( Hierarchical Indexing )

Lecture 10: Introduction to negative indexing

Lecture 11: Introduction to Arithmetic operation & fill_value function

Lecture 12: Hands-ON to arithmatic operation & fill_value function

Lecture 13: Introduction to Function mapping ( apply, applymap and map function)

Lecture 14: Hands-ON to function mapping

Lecture 15: Introduction to sorting, ranking and relationship with duplicate labels

Lecture 16: Hands-ON ( Sorting, Ranking )

Lecture 17: Introduction to summarizing data

Lecture 18: Hands-ON ( Summarizing data )

Chapter 4: Data Ingestion and Storage with Pandas

Lecture 1: Introduction to data storage with pandas

Lecture 2: Introduction to data storage with CSV file

Lecture 3: Hands-ON to CSV file storage

Lecture 4: Handling Missing Values

Lecture 5: Handling large files – Theory & Hands-on

Lecture 6: Writing to CSV file ( Theory & HandsON )

Lecture 7: Introduction to data storage with JSON, HTML & Pickle file.

Lecture 8: HandsON ( Storage with JSON, HTML & Pickle file )

Lecture 9: Introduction to data storage with HDF5 and Excel file

Lecture 10: Hands-ON ( Data storage with HDF5 )

Lecture 11: HandsON ( Data Storage with Excel file )

Lecture 12: Data Storage with SQLite ( Theory & HandsON )

Chapter 5: Data Preparation

Lecture 1: Introduction to data preparation

Lecture 2: Introduction to identifying missing data ( Theory & HandsOn )

Lecture 3: Delete missing data ( Theory & HandsOn )

Lecture 4: Introduction to impute missing data

Lecture 5: HandsOn ( Impute Missing data )

Lecture 6: Introduction to handling Duplicate Values

Lecture 7: HandsOn ( Handling Duplicate Values )

Lecture 8: Introduction to data transformation

Lecture 9: HandsOn ( Data Transformation )

Lecture 10: Update Row & Columns Axis ( Theory & HandsOn )

Lecture 11: Binning & bucketing data ( Theory & HandsOn )

Lecture 12: Introduction to Outlier Detection, Sampling & Dummy Variable

Lecture 13: HandsOn to outlier detection, sampling & dummy variable

Chapter 6: Data Wrangling

Lecture 1: Introduction to multilevel indexing in Series object

Lecture 2: HandsOn of multilevel indexing in Series object

Lecture 3: Introduction of multilevel indexing in dataframe object

Lecture 4: HandsOn to multilevel indexing in dataframe object

Lecture 5: Reordering and sorting on row index in dataframe ( Theory & HandsOn )

Lecture 6: Introduction to Aggregation of data in multilevel indexing

Lecture 7: HandsOn to aggregation of data in multilevel indexing

Lecture 8: HandsOn to Changing index to column and column to index in dataframe

Lecture 9: Merging datasource (Inner Join)

Lecture 10: Merging Dataframes ( Outer Join )

Lecture 11: Merging dataframe ( Left & Right Join )

Lecture 12: Merging dataframe with multiple keys ( columns )

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 0 votes
  • 3 stars: 7 votes
  • 4 stars: 28 votes
  • 5 stars: 31 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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