Introduction to Data Science with Python

Learn python for data analysis.

Join Harvard University Instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.

Harvard John A. Paulson School of Engineering and Applied Sciences

What You'll Learn

Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?

Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI). 

Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.   Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python , and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.

The course will be delivered via edX and connect learners around the world. By the end of the course, participants will learn:

  • Gain hands-on experience and practice using Python to solve real data science challenges
  • Practice Python coding for modeling, statistics, and storytelling
  • Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn
  • Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems
  • Build a foundation for the use of Python in machine learning and artificial intelligence, preparing you for future Python study

Your Instructor

Pavlos Protopapas is the Scientific Program Director of the Institute for Applied Computational Science(IACS) at the Harvard John A. Paulson School of Engineering and Applied Sciences. He has had a long and distinguished career as a scientist and data science educator, and currently teaches the CS109 course series for basic and advanced data science at Harvard University, as well as the capstone course (industry-sponsored data science projects) for the IACS master’s program at Harvard. Pavlos has a Ph.D in theoretical physics from the University of Pennsylvania and has focused recently on the use of machine learning and AI in astronomy, and computer science. He was Deputy Director of the National Expandable Clusters Program (NSCP) at the University of Pennsylvania, and was instrumental in creating the Initiative in Innovative Computing (IIC) at Harvard. Pavlos has taught multiple courses on machine learning and computational science at Harvard, and at summer schools, and at programs internationally.

Course Overview

  • Linear Regression
  • Multiple and Polynomial Regression
  • Model Selection and Cross-Validation
  • Bias, Variance, and Hyperparameters
  • Classification and Logistic Regression
  • Multi-logstic Regression and Missingness
  • Bootstrap, Confidence Intervals, and Hypothesis Testing
  • Capstone Project

Ways to take this course

When you enroll in this course, you will have the option of pursuing a Verified Certificate or Auditing the Course.

A Verified Certificate costs $299 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate. 

Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums.  Please note that this track does not offer a certificate for learners who earn a passing grade.

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HarvardX: Introduction to Data Science with Python

Learn the concepts and techniques that make up the foundation of data science and machine learning.

introduction to data science in python assignment

There is one session available:

Introduction to data science with python, about this course.

Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?

Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).

Using Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.

Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.

At a glance

  • Institution: HarvardX
  • Subject: Data Analysis & Statistics
  • Level: Intermediate

Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python , and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.

  • Professional Certificate in Learning Python for Data Science
  • Professional Certificate in Python for Data Science and Machine Learning
  • Language: English
  • Video Transcripts: اَلْعَرَبِيَّةُ, Deutsch, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
  • Associated skills: Parsing, Artificial Intelligence, R (Programming Language), Scikit-learn (Machine Learning Library), Data Science, NumPy, Python (Programming Language), Matplotlib, Algorithms, Pandas (Python Package), Scientific Methods, Machine Learning

What you'll learn

  • Gain hands-on experience and practice using Python to solve real data science challenges
  • Practice Python programming and coding for modeling, statistics, and storytelling
  • Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn
  • Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems
  • Build a foundation for the use of Python in machine learning and artificial intelligence, preparing you for future Python study

Course Outline:

  • Linear Regression
  • Multiple and Polynomial Regression
  • Model Selection and Cross-Validation
  • Bias, Variance, and Hyperparameters
  • Classification and Logistic Regression
  • Multi-logstic Regression and Missingness
  • Bootstrap, Confidence Intervals, and Hypothesis Testing
  • Capstone Project

Who can take this course?

This course is part of learning python for data science professional certificate program, ways to take this course, interested in this course for your business or team.

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Introduction to Data Science in Python

Description.

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

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.

based on 23190 ratings

introduction to data science in python assignment

Applied Data Science with Python

U-M Credit Eligible

introduction to data science in python assignment

Christopher Brooks

Assistant Professor of Information

School of Information

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Introduction to Data Science with Python

Arvind Krishna, Lizhen Shi, Emre Besler, and Arend Kuyper

September 20, 2022

This book is developed for the course STAT303-1 (Data Science with Python-1). The first two chapters of the book are a review of python, and will be covered very quickly. Students are expected to know the contents of these chapters beforehand, or be willing to learn it quickly. Students may use the STAT201 book (https://nustat.github.io/Intro_to_programming_for_data_sci/) to review the python basics required for the STAT303 sequence. The core part of the course begins from the third chapter - Reading data .

Please feel free to let the instructors know in case of any typos/mistakes/general feedback in this book.

introduction to data science in python assignment

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  1. Introduction to Data Science in Python

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  2. Introduction to Data Science in Python Week 3 || Assignment 3 Programming Assignment Coursera

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  3. Introduction to Data Science with Python

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  4. Data Science Tutorial

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  5. NPTEL Programming Data Structures And Algorithms Using Python Week 1

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VIDEO

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  3. Python for Data Science Week 2 (NPTEL)

  4. Lecture_9: Python Essentials

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  6. Python For Data Science

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  3. Introduction to Data Science with Python

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    Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you'll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).

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  16. Introduction to Data Science with Python

    Preface. This book is developed for the course STAT303-1 (Data Science with Python-1). The first two chapters of the book are a review of python, and will be covered very quickly. Students are expected to know the contents of these chapters beforehand, or be willing to learn it quickly. Students may use the STAT201 book (https://nustat.github ...

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  24. GitHub

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