Data Science on Python 2021-22

A clear understanding about the data science theory, techniques and its application in Jupyter Notebook platform

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.

What you’ll learn

  • 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 performing 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 Time 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. Time Series theory and its application in Python.
  • 12. Web Scraping using BeautifulSoup in Python.

Course Content

  • Introduction to Python –> 8 lectures • 54min.
  • Data Structures and Conditional Executions in Python –> 6 lectures • 1hr.
  • Conditions and Loops in Python –> 5 lectures • 35min.
  • Working with Pandas in Python –> 5 lectures • 32min.
  • Plotting in Python –> 9 lectures • 1hr 16min.
  • Statistical Analysis and Application in Python (part I) –> 12 lectures • 1hr 34min.
  • Statistical Analysis and Application in Python (part II) –> 5 lectures • 32min.
  • Theory of Factor and Cluster Analysis in Python –> 12 lectures • 1hr 6min.
  • Building a Predictive Model (Linear Regression) in Python –> 6 lectures • 46min.
  • Building a Predictive Model (Logistic Regression) in Python –> 8 lectures • 46min.
  • Time Series theory and its application in Python –> 9 lectures • 1hr 7min.
  • Web Scraping using BeautifulSoup in Python –> 1 lecture • 11min.

Data Science on Python 2021-22

Requirements

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.

  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. Time Series theory and its application in Python
  12. 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.

  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. Time Series theory and its application in Python
  12. Web Scraping using BeautifulSoup in Python