Python With DataScience

20,000.00 15,000.00

This course aims to teach everyone the basics of programming computers using Python.

SKU: CFL-107 Category:

Python with Data Science


This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.


To receive the certificate for this course, you’ll need to submit one of the projects for the course. After successful evaluation by the course advisor, you’ll receive the certification.


Core Python With Data Science

Python Introduction

  • What is Python
  • History and context of Python
  • Who uses Python,
  • Python 2 versus Python 3(Different Versions)
  • Installing python
  • Lots of Python basics (using the interpreter)
  • Comments, variables types, numbers


Python Fundamentals

  • Objects, variables, and types
  • Additional useful string methods, string formatting (using .format(), file I/O (using a context manager).
  • Running Python as a script, and the basics of imports.

Introduction to Data Structures

  • Dictionaries, tuples and sets, along with all of their common operators and even a few uncommon ones.
  • We briefly look at a few looping techniques (enumerate, zip), and then dive into comprehensions.
    • Python allows us to write list, dictionary, and set comprehensions, and we’ll explore these tools as well as their connection to higher-level reasoning about problem-solving.

IPython: An Interactive Computing and Development Environment

  • IPython Basics
    • Tab Completion
    • Introspection
    • The %run Command
    • Executing Code from the Clipboard
    • Keyboard Shortcuts
    • Exceptions and Tracebacks
    • Using the Command History
    • Searching and Reusing the Command History
    • Input and Output Variables
    • Logging the Input and Output

Learn Scientific libraries in Python – NumPy, Pandas and Matplotlib

  • NumPy Basics: Arrays and Vectorized Computation
  • The NumPyndarray: A Multidimensional Array Object
  • Universal Functions: Fast Element-wise Array Functions
  • Data Processing Using Arrays
  • File Input and Output with Arrays
  • Linear Algebra
  • Random Number Generation

Getting started with Pandas

  • Introduction to pandas Data Structures
  • Essential functionality
  • Summarizing and computing descriptive statistics
  • Handling missing data
  • Hierarchial indexing

Data Loading, Storage, and File Formats

  • Reading and Writing Data in Text Format
  • Binary Data Formats
  • Interacting with HTML and Web APIs
  • Interacting with Databases

Data Wrangling: Clean, Transform, Merge, Reshape

  • Combining and Merging Data Sets
  • Reshaping and Pivoting
  • Data Transformation
  • String Manipulation

Plotting and Visualization

  • A Brief matplotlib API Primer
  • Plotting Functions in pandas
  • Python Visualization Tool Ecosystem

Data Aggregation and Group Operations

  • GroupBy Mechanics
  • Data Aggregation
  • Group-wise Operations and Transformations
  • Pivot Tables and Cross-Tabulation

Time Series

  • Date and Time Data Types and Tools
  • Time Series Basics
  • Date Ranges, Frequencies, and Shifting

Financial and Economic Data Applications

  • Data Munging Topics
  • Group Transforms and Analysis
  • More Example Applications

Advanced NumPy

  • ndarray Object Internals
  • Advanced Array Manipulation
  • Broadcasting

Realtime data set problems