top of page

Data Science with Python

Duration:

3 Days (24 Hours)

Class Size:

Maximum 25 participants

Class Type:

Physical Class

Data Science with Python

Course Overview

This course includes the fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course also introduces data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and Data Frame as the central data structures for data analysis, along with tutorials on how to use functions such as group by, merge, and pivot tables effectively. By the end of this course, participants will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Who Should Attend?

This course "Data Science with Python" is intended for learners who have basic python knowledge and wants to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data.

Course Objectives

After completing this course, you should be able to:

  • Explore Python fundamentals, including basic syntax, variables, and types

  • Create and manipulate regular Python lists

  • Use functions and import packages

  • Build Numpy arrays, and perform interesting calculations

  • Create and customize plots on real data

  • Supercharge with control flow, and get to know the Pandas DataFrame

  • Use Python to read and write files

  • Illustrate Supervised Learning Algorithms

  • Identify and recognize machine learning algorithms around us

Prerequisites

There are no prerequisites for this course but python knowledge with a little programming background is preferred.

Course Modules

Module 1: Python Crash Course


Module 2: Python Object Oriented


Module 3: Error Handling and Testing


Module 4: Working with Files and Directories


Module 5: Accessing Databases


Module 6: Python for Data Analysis - NumPy


Module 7: Python for Data Analysis – SciPy


Module 8: Python for Data Analysis - Pandas


Module 9: Python for Data Visualization


Module 10: Machine Learning


Module 11: Natural Language Processing


Module 12: Neural Nets and Deep Learning

bottom of page