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Data Analysis with Python, Pandas and NumPy

Name: Data Analysis with Python, Pandas and NumPy
Rating: 4.2 (78 reviews)

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

Created byPrashant Shekhar

Last updated 10/2020

English

What you'll 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.

Course content

13 sections • 139 lectures • 17h 40m total length

Introduction to NumPy5:35
Technical Details of NumPy3:41
Is NumPy Faster ?3:20
Basic terms of NumPy3:15
Summary of NumPy Operation6:35
NumPy Array Creation4:28
NumPy Array Creation with datatype details5:18
Hands-ON ( NumPy Installation & Array creation )5:36
Hands-ON (NumPy array creation in one dimension )7:31
Hands-ON ( NumPy Array Creation with multiple dimension )10:06
Arithmetic operation in NumPy5:00
Hands-ON ( Arithematic operation in NumPy )7:55
Indexing & Slicing Operation in NumPy Array12:38
Hands-ON ( Indexing & Slicing operation in NumPy Array in 1 dimension )16:16
Row and Column Slicing using boolean info5:49
Hands-ON ( Row & Column slicing using boolean info )6:41
Fancy Indexing7:15
Hands-ON ( Fancy Indexing )4:32
Transpose Array ( Theory & Hands-ON )7:41
Universal Function in NumPy Array6:00
Hands-ON ( Universal function in NumPy Array )8:05
Vectorization, Meshgrid & np.where8:52
Hands-ON ( Vectorization, MeshGrid & np.where )6:14
Statistical Function ( Theory & Hands-ON)7:59
Boolean Array ( Theory & Hands-ON )9:13
Sort, Unique & Set operation in NumPy Array ( Theory & Hands-ON )13:24
File Operation, Linear Algebra & Random Number Generation ( Theory & Hands-ON )13:22
MCQ Assignment-1

Pandas Dataframe Creation ( Theory & Hands-ON )9:27
Pandas Dataframe Column Access ( Theory & Hands-ON )8:38
Theory & Hands-ON - Column update, delete, transpose, rename index & columns etc12:20
Pandas Row and Column Index - immutable, repeat property ( Theory & Hands-ON )7:50
Reindexing, dropping, in-place changes & reordering row and column in dataframe7:25
Hands-ON ( Reindexing, dropping, re-oredering & in-place changes )9:27
Introduction to Hierarchical indexing, selection, filtering, LOC, iLOC, AT, iAT15:54
Hands-ON ( Indexing, Selection, Filtering )15:27
Hands-ON ( Hierarchical Indexing )2:23
Introduction to negative indexing1:35
Introduction to Arithmetic operation & fill_value function9:11
Hands-ON to arithmatic operation & fill_value function9:40
Introduction to Function mapping ( apply, applymap and map function)6:49
Hands-ON to function mapping8:09
Introduction to sorting, ranking and relationship with duplicate labels13:50
Hands-ON ( Sorting, Ranking )5:45
Introduction to summarizing data9:06
Hands-ON ( Summarizing data )8:35
MCQ Assignment-3

Introduction to data storage with pandas3:24
Introduction to data storage with CSV file5:40
Hands-ON to CSV file storage6:22
Handling Missing Values9:06
Handling large files - Theory & Hands-on8:32
Writing to CSV file ( Theory & HandsON )8:15
Introduction to data storage with JSON, HTML & Pickle file.5:43
HandsON ( Storage with JSON, HTML & Pickle file )11:39
Introduction to data storage with HDF5 and Excel file9:57
Hands-ON ( Data storage with HDF5 )11:53
HandsON ( Data Storage with Excel file )4:26
Data Storage with SQLite ( Theory & HandsON )9:28
MCQ Assignment-4

Introduction to data preparation5:54
Introduction to identifying missing data ( Theory & HandsOn )6:59
Delete missing data ( Theory & HandsOn )10:48
Introduction to impute missing data10:44
HandsOn ( Impute Missing data )8:51
Introduction to handling Duplicate Values4:59
HandsOn ( Handling Duplicate Values )9:29
Introduction to data transformation6:06
HandsOn ( Data Transformation )6:24
Update Row & Columns Axis ( Theory & HandsOn )9:05
Binning & bucketing data ( Theory & HandsOn )12:13
Introduction to Outlier Detection, Sampling & Dummy Variable8:06
HandsOn to outlier detection, sampling & dummy variable9:13
MCQ Assignment-5

Introduction to multilevel indexing in Series object10:50
HandsOn of multilevel indexing in Series object9:47
Introduction of multilevel indexing in dataframe object4:44
HandsOn to multilevel indexing in dataframe object6:42
Reordering and sorting on row index in dataframe ( Theory & HandsOn )6:14
Introduction to Aggregation of data in multilevel indexing4:31
HandsOn to aggregation of data in multilevel indexing7:40
Explore data aggregation with multilevel indexing, aggregating at root and column levels, naming levels, and computing sums, means, and standard deviations using axis-based access.
HandsOn to Changing index to column and column to index in dataframe5:12
Merging datasource (Inner Join)5:59
Merging Dataframes ( Outer Join )4:31
Merging dataframe ( Left & Right Join )5:20
Merging dataframe with multiple keys ( columns )2:18
HandsOn on Merging (Inner,Outer,Left&Right join with one/multiple keys(columns))14:41
Introduction to Merging dataframe (Column + Index , Index + Index )6:20
HandsOn to merging dataframe ( Column + Index , Index + Index )9:56
Introduction & HandsOn to Merge with "JOIN" command8:28
Introduction to merging by CONCATENATION7:18
HandsOn to merging by CONCATENATION9:14
Introduction & HandsOn to merging with COMBINE_FIRST5:50
Introduction to PIVOT and MELT and their interaction with each other11:22
HandsOn to PIVOT and MELT function and interaction9:40
MCQ Assignment-6

Introduction to data aggregation5:03
Introduction to SPLIT, APPLY and COMBINE4:40
SPLIT, APPLY & COMBINE with all columns8:35
GROUP BY clause with FOR loop8:02
HandsOn of GROUP BY clause with FOR loop4:00
Grouping Columns with GROUP BY clause6:07
HandsOn to grouping columns with GROUP BY clause7:29
GROUP BY with aggregate ( AGG ) function6:23
HandsOn of GROUP BY with aggregate ( AGG ) function5:56
GROUP BY with APPLY function ( Theory & HandsOn )8:46
Binning & Bucket Analysis ( Theory & HandsOn )5:46
Introduction to PIVOT-TABLE and CROSS-TABULATION5:54
HandsOn to PIVOT-TABLE and CROSS-TABULATION7:01
MCQ Assignment-7

Introduction to Data Visualization & Matplotlib5:08
Introduction to figure & subplot in matplotlib5:11
HandsOn ( Figure & Subplot )10:42
Introduction to subplot properties11:54
HandsOn Subplot properties -1 ( sharex, sharey, wspace, hspace )4:53
HandsOn Subplot properties -2 ( linestyle, color, marker, label )9:15
HandsOn Subplot properties-3(xlabel, yLabel, title, set_xticks, set_xticklabels)6:03
Introduction to Annotation and Saving plots4:13
HandsOn ( Annotation & Saving figure and plots )4:30
Introduction to bar graph ( Horizantal, Vertical & Multiple bar graph )7:04
HandsOn to bar graph ( Horizontal, Vertical and Multiple bar graph )8:22
Introduction & HandsOn to 3-dimension plot6:17
Introduction to Scatter, Pie, Box, histogram & violin plot in matplotlib6:33
HandsOn ( Scatter, Pie & Histogram Plot )12:05
HandsOn ( Box & Violin Plot )4:26

Requirements

Basic and intermediate understanding of Python programming.
Enthusiasm to learn the data analysis with python and willingness to devote time for learning.

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.

Who this course is for:

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, Pandas and NumPy

What you'll learn

Explore related topics

Course content

Introduction to NumPy27 lectures • 3hr 22min

Pandas Basic, Installation & Pandas Series object7 lectures • 50min

Pandas Dataframe object18 lectures • 2hr 42min

Data Ingestion and Storage with Pandas12 lectures • 1hr 34min

Data Preparation13 lectures • 1hr 49min

Data Wrangling21 lectures • 2hr 37min

Data Aggregation & Grouping13 lectures • 1hr 24min

Visualization with Matplotlib15 lectures • 1hr 47min

Visualization with Pandas Object3 lectures • 29min

Visualization with Seaborn4 lectures • 35min

Requirements

Description

Who this course is for: