MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 139 lectures (17h 40m) | Size: 9.4 GB
Data Analysis with Python libraries - NumPy, Pandas, MatplotLib and Seaborn | 150+ MCQ Question | 2 Projects
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.
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.
