introduction to data science assignment 3

Introduction to Data Science I & II

Introduction, introduction #.

Dan L. Nicolae , Michael J. Franklin , Amanda R. Kube Jotte , Evelyn Campbell, Susanna Lange, Will Trimble, and Jesse London

Forthcoming…

Acknowledgements #

Jupyter Books was originally created by Sam Lau and Chris Holdgraf with support of the UC Berkeley Data Science Education Program and the Berkeley Institute for Data Science .

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.

Data Science

Ds statistics, ds advanced, ds certificate, data science introduction.

Data Science is a combination of multiple disciplines that uses statistics, data analysis, and machine learning to analyze data and to extract knowledge and insights from it.

What is Data Science?

Data Science is about data gathering, analysis and decision-making.

Data Science is about finding patterns in data, through analysis, and make future predictions.

By using Data Science, companies are able to make:

• Better decisions (should we choose A or B)
• Predictive analysis (what will happen next?)
• Pattern discoveries (find pattern, or maybe hidden information in the data)

Where is Data Science Needed?

Data Science is used in many industries in the world today, e.g. banking, consultancy, healthcare, and manufacturing.

Examples of where Data Science is needed:

• For route planning: To discover the best routes to ship
• To foresee delays for flight/ship/train etc. (through predictive analysis)
• To create promotional offers
• To find the best suited time to deliver goods
• To forecast the next years revenue for a company
• To analyze health benefit of training
• To predict who will win elections

Data Science can be applied in nearly every part of a business where data is available. Examples are:

• Consumer goods
• Stock markets
• Logistic companies

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How Does a Data Scientist Work?

A Data Scientist requires expertise in several backgrounds:

• Machine Learning
• Programming (Python or R)
• Mathematics

A Data Scientist must find patterns within the data. Before he/she can find the patterns, he/she must organize the data in a standard format.

Here is how a Data Scientist works:

• Ask the right questions - To understand the business problem.
• Explore and collect data - From database, web logs, customer feedback, etc.
• Extract the data - Transform the data to a standardized format.
• Clean the data - Remove erroneous values from the data.
• Find and replace missing values - Check for missing values and replace them with a suitable value (e.g. an average value).
• Normalize data - Scale the values in a practical range (e.g. 140 cm is smaller than 1,8 m. However, the number 140 is larger than 1,8. - so scaling is important).
• Analyze data, find patterns and make future predictions .
• Represent the result - Present the result with useful insights in a way the "company" can understand.

Where to Start?

In this tutorial, we will start by presenting what data is and how data can be analyzed.

You will learn how to use statistics and mathematical functions to make predictions.

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Top Tutorials

Top references, top examples, get certified.

Browse Course Material

Course info, instructors.

• Prof. Eric Grimson
• Prof. John Guttag
• Dr. Ana Bell

Departments

• Electrical Engineering and Computer Science

As Taught In

• Computer Science
• Probability and Statistics

Learning Resource Types

Introduction to computational thinking and data science, assignments.

Please review the Style Guide (PDF) before attempting the problem sets. We have compiled a list of other Python resources that you may find helpful in this Additional Python Resources document (PDF) . It contains links to other online textbooks on Python, debugging tools, and fun online coding challenges.

Solutions for the problems sets are not available.

Problem Set 1: Space Cows Transportation (ZIP) (This ZIP file contains: 1 .pdf file, 2 .txt files, and 3 .py files)

Problem Set 2: Fastest Way to Get Around MIT (ZIP) (This ZIP file contains: 1 .pdf file, 1 .txt file, and 2 .py files)

Problem Set 3: Robot Simulation (ZIP) (This ZIP file contains: 1 .pdf file, 4 .pyc files, and 4 .py files)

Problem Set 4: Simulating the Spread of Disease and Bacteria Population (ZIP) (This ZIP file contains: 1 .pdf file and 2 .py files)

Problem Set 5: Modeling Global Warming (ZIP - 2.3MB) (This ZIP file contains: 1 .pdf file, 1 .csv file, and 2 .py files)

You are leaving MIT OpenCourseWare

Introduction to Data Science

A Python Approach to Concepts, Techniques and Applications

• © 2024
• Latest edition
• Laura Igual 0 ,
• Santi Seguí 1

Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain

You can also search for this author in PubMed   Google Scholar

• Describes tools and techniques that demystify data science
• Discusses Python extensions, techniques and modules to perform statistical analysis and machine learning
• Includes case studies, and supplies code examples and data at an associated website

Part of the book series: Undergraduate Topics in Computer Science (UTICS)

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Table of contents (12 chapters)

Front matter, introduction to data science.

Laura Igual, Santi Seguí

Data Science Tools

• Eloi Puertas

Descriptive Statistics

• Statistical Inference

Supervised Learning

Regression analysis, unsupervised learning, network analysis, recommender systems, basics of natural language processing, deep learning, responsible data science, back matter.

• Data Science
• Parallel Computing
• Python Programming
• Graph Analysis

About this book

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or sentiment analysis. 

Topics and features:  

• Provides numerous practical case studies using real-world data throughout the book 
• Supports understanding through hands-on experience of solving data science problems using Python 
• Describes concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data science
• Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data 
• Provides supplementary code resources and data at an associated website 

This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.

Authors and Affiliations

About the authors.

Dr. Laura Igual  is an Associate Professor at the Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Spain.  Dr. Santi Seguí  is an Associate Professor at the same institution.

The authors wish to mention that some chapters were co-written by Jordi Vitrià, Eloi Puertas, Petia Radeva, Oriol Pujol, Sergio Escalera.

Bibliographic Information

Book Title : Introduction to Data Science

Book Subtitle : A Python Approach to Concepts, Techniques and Applications

Authors : Laura Igual, Santi Seguí

Series Title : Undergraduate Topics in Computer Science

DOI : https://doi.org/10.1007/978-3-031-48956-3

Publisher : Springer Cham

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

Softcover ISBN : 978-3-031-48955-6 Published: 13 April 2024

eBook ISBN : 978-3-031-48956-3 Published: 12 April 2024

Series ISSN : 1863-7310

Series E-ISSN : 2197-1781

Edition Number : 2

Number of Pages : XIV, 246

Number of Illustrations : 4 b/w illustrations, 78 illustrations in colour

Topics : Data Structures and Information Theory , Artificial Intelligence , Data Mining and Knowledge Discovery , Python

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Programming for Data Science

Teaching data scientists the tools they need to use computers to do data science

Advanced Python for Data Science Assignment 3

All assigments beginning with Assignment 3 are to be submitted via GitHub. Create a new repository for each assignment using your NetID, an underscore ‘_’, the word ‘assignment’ and the assignment number, all in lower case. For example, if your NetID is aaa11 then the repository for this assignment would be aaa11_assignment3 . Clone this repository to your local computer. Commit any files that you are required to create as part of the assignment, then push the changes back to GitHub. Whatever is in the repository at the due date will be graded.

An astrophysicist colleague was recently complaining about how long it was taking to run an N-body simulation. “It’s really just a simple calculation, and I’m only simulating four planets, but it takes nearly a minute and a half to run one simulation. I really need it done in under 30 seconds.” You kindly offer to take a look at code to see if it is possible to speed it up. Your colleague provides you with a link to the source .

Although your colleague said the code was simple, it is still fairly complex, so you decide to tackle the problem in stages. A first scan of the code reveals a number of potential areas that could be improved. These include:

• Reducing function call overhead
• Using alternatives to membership testing of lists
• Using local rather than global variables
• Using data aggregation to reduce loop overheads

As you’re a cautious programmer, you decide to address each of these in turn. This will ensure that it is possible to check the program is still working correctly after each change, and to assess the performance improvement that the change achieved. You are also aware that the program has to be maintained by others in the future, so you want to make sure that the changes do not make this more difficult, especially if the performance improvement is only minor.

For each of these areas, create a new version of nbody.py (call them nbody_1.py , nbody_2.py , etc.) and commit them to the repository. You may also add a file with any other optimizations that you find. At the beginning of each file, put a comment indicating if the change made the most improvement, second most, etc. Finally, create another file called nbody_opt.py that contains all of the optimizations you made. Put a comment at the top indicating the relative speedup of the optimized version compared to the original version. Calculate the relative speedup (R) as follows:

Are you able to get it to run in under 30 seconds?

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This repository contains project assignments for GE 461 (Introduction to Data Science) course taken at Bilkent University [2022-2023 Spring]

esattok/introduction-to-data-science

Folders and files, repository files navigation, bilkent university ge 461 project (2022-2023 spring).

This Repository contains the project assignments for GE 461 (Introduction to Data Science) course taken at Bilkent University in 2022-2023 Spring semester.

Project Assignments:

• project-01 : Given a dataset perform linear regression analysis using R programming language
• project-02 : Develop a handwritten digit recognition system
• project-03 : Implement and test an artificial neural network (ANN) regressor
• project-04 : Analyzing wearable-sensor data from a group of subjects performing either a fall action (F) or non-fall action
• Each assignment is independent from the others
• Each assignment targets a specific area in Data Science
• Python 1.4%