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Introduction to Data Science in Python

This course covers the basics of the Python programming environment and data manipulation using the Pandas library. It introduces fundamental techniques such as lambdas and numpy and teaches data cleaning, manipulation and basic statistical analysis. By the end, students will be able to work with tabular data and run inferential analyses. It is recommended to take this course before other Applied Data Science with Python courses.


Per Person




4 Day


Face-to-face (F2F) / Virtual Class


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Course structure

Course Overview

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library.

The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively.

This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

Course Objectives

By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Course Objectives

What You Will Learn

  • Python Programming
  • Numpy
  • Pandas
  • Data Cleansing

Course Prerequisite

Ideal if you have experience with basic Python

Course Content

Module 1: Fundamentals of Data Manipulation with Python

In this module you’ll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus.

  • Data Science
  • The Coursera Jupyter Notebook System
  • Python Functions
  • Python Types and Sequences
  • Python More on Strings
  • Python Demonstration: Reading and Writing CSV files
  • Python Dates and Times
  • Advanced Python Objects, map()
  • Advanced Python Lambda and List Comprehensions
  • Advanced Python Demonstration: The Numerical Python Library (NumPy)

Module 2: Basic Data Processing with Pandas

In this module of the course you’ll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing — pandas. You’ll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed.

  • Introduction
  • The Series Data Structure
  • Querying a Series
  • The DataFrame Data Structure
  • DataFrame Indexing and Loading
  • Querying a DataFrame
  • Indexing Dataframes
  • Missing Values

Module 3: More Data Processing with Pandas

In this module you’ll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We’ll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis

  • Merging Dataframes
  • Pandas Idioms
  • Group by
  • Scales
  • Pivot Tables
  • Date Functionality

Module 4: Answering Questions with Messy Data

In this module of the course, you’ll be introduced to a variety of statistical techniques such a distributions, sampling, and t-tests.

  • Introduction
  • Distributions
  • More Distributions
  • Hypothesis Testing in Python

Who Should Attend

  1. Beginners in Python: If you have little to no experience with Python programming, this course is an excellent choice. It provides a solid introduction to Python and covers essential programming techniques, making it accessible for beginners.
  2. Aspiring Data Analysts: If you are interested in becoming a data analyst, this course is highly relevant. It focuses on data manipulation and cleaning techniques using pandas, which are essential skills for data analysts. By completing this course, you will gain the foundational knowledge needed to work with data and perform basic analysis.
  3. Professionals Transitioning to Data Science: If you are currently working in a related field and looking to transition into data science or data analysis roles, this course can help you acquire the necessary skills. Python is widely used in the data science field, and proficiency in data manipulation using pandas is highly sought after by employers.

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Per Person




4 Day


Face-to-face (F2F) / Virtual Class


Frequently asked questions

Is this course suitable for beginners?

Yes, this course is designed for learners who are new to Python programming and data analysis. It provides a solid foundation for further courses in Applied Data Science with Python.

Will I be proficient in Python programming and data manipulation after completing this course?

This course provides a solid foundation in Python programming and data manipulation using pandas. However, proficiency comes with practice and experience. It is recommended to apply the learned concepts in real-world scenarios and continue learning through practical projects and further courses.

What are the benefits of taking the course?

The course covers essential libraries such as numpy and pandas. These libraries are widely used in the data science ecosystem and are valuable tools for data analysis and manipulation tasks. By learning these libraries, you gain practical skills that can be directly applied in real-world projects.