MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 ChGenre: eLearning | Language: English + srt | Duration: 86 lectures (10h 18m) | Size: 2.9 GB

A clear understanding about the data science theory, techniques and its application in Jupyter Notebook platform
This course will review common Python functionality and features along with Jupyter Notebook
The students will learn about the toolkits Python has for data cleaning and processing - pandas
The students will learn to create stunning data visualizations with matplotlib, and seaborn
The students will learn how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates
The students will be introduced to a variety of statistical techniques such a distributions, sampling and t-tests using real-world data
The students will involve into data cleaning activity and provide evidence for (or against!

) a given hypothesis
The students will learn perfog dimension reduction techniques like Factor analysis and Cluster Analysis
The students will learn how to perform predictive modelling using Python
The students will gain intensive knowledge in the spheres of Linear Regression, Logistic Regression and Series Regression using packages like Pandas, Numpy, scikit learn and others
The topics that will be covered in this course are listed below:
1. Introduction to Python
2. Data Structures and Conditional Executions in Python
3. Conditions and Loops in Python
4. Working with Pandas in Python
5. Plotting in Python
6. Statistical Analysis and Application in Python (part I)
7. Statistical Analysis and Application in Python (part II)
8. Theory of Factor and Cluster Analysis in Python
9. Building a Predictive Model (Linear Regression) in Python
10. Building a Predictive Model (Logistic Regression) in Python
11. Series theory and its application in Python
12. Web Scraping using BeautifulSoup in Python
For better understanding Learn Python from Scratch by OrangeTree Global is recommended
The following topics will be covered as part of this series. Each topic is described in detail with hands-on exercises done on Jupyter Notebook to help students learn with ease. We will cover all the nitty-gritty that you need to know to get started with Python along with the correction and handling of errors as and when they pop-up. The program builds a solid foundation by covering the most popular and widely used data science technologies and its applications.
Introduction to Python
Data Structures and Conditional Executions in Python
Conditions and Loops in Python
Working with Pandas in Python
Plotting in Python
Statistical Analysis and Application in Python (part I)
Statistical Analysis and Application in Python (part II)
Theory of Factor and Cluster Analysis in Python
Building a Predictive Model (Linear Regression) in Python
Building a Predictive Model (Logistic Regression) in Python
Series theory and its application in Python
Web Scraping using BeautifulSoup in Python
The following topics will be covered as part of this series. Each topic is described in detail with hands-on exercises done on Jupyter Notebook to help students learn with ease. We will cover all the nitty-gritty that you need to know to get started with Python along with the correction and handling of errors as and when they pop-up. The program builds a solid foundation by covering the most popular and widely used data science technologies and its applications.
Introduction to Python
Data Structures and Conditional Executions in Python
Conditions and Loops in Python
Working with Pandas in Python
Plotting in Python
Statistical Analysis and Application in Python (part I)
Statistical Analysis and Application in Python (part II)
Theory of Factor and Cluster Analysis in Python
Building a Predictive Model (Linear Regression) in Python
Building a Predictive Model (Logistic Regression) in Python
Series theory and its application in Python
Web Scraping using BeautifulSoup in Python
This course is highly recommended for Students, Working Professionals, Business Analysts, Consultants, Businessmen, Teachers, Lecturers and Professors.
The course is best for individuals who are in the field of Business Analytics or is eager to join the same
Also individuals who has prior experience in Python but eager to learn how to apply Data Analysis techniques using Python




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