Mengapa python digunakan untuk data science?

Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.

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Mengapa data science menggunakan Python?
Apakah Python mudah digunakan untuk melakukan pekerjaan data science?
Mengapa bahasa pemrograman Python yang dipilih pada kebanyakan proyek sains data?
Penggunaan Python untuk apa?

4 hours

Hugo Bowne-Anderson

Data Scientist at DataCamp

Python

Intermediate Python

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

4 hours

Hugo Bowne-Anderson

Data Scientist at DataCamp

Python

Investigating Netflix Movies and Guest Stars in The Office

Apply the foundational Python skills you learned in Introduction to Python and Intermediate Python by manipulating and visualizing movie and TV data.

2 hours

Justin Saddlemyer

Justin is a Workspace Content Developer at DataCamp. He holds ...

Python

Data Manipulation with pandas

Learn how to import and clean data, calculate statistics, and create visualizations with pandas.

4 hours

Richie Cotton

Data Evangelist at DataCamp

Python

Joining Data with pandas

Learn to combine data from multiple tables by joining data together using pandas.

4 hours

Aaren Stubberfield

Manager, Supply Chain Analytics @ Ingredion Incorporated

Python

Introduction to Statistics in Python

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

4 hours

Maggie Matsui

Curriculum Manager at DataCamp

Python

The GitHub History of the Scala Language

Find the true Scala experts by exploring its development history in Git and GitHub.

2 hours

Anita Sarma

Associate Professor at Oregon State University

Python

Introduction to Data Visualization with Matplotlib

Learn how to create, customize, and share data visualizations using Matplotlib.

4 hours

Ariel Rokem

Senior Data Scientist, University of Washington

Python

Introduction to Data Visualization with Seaborn

Learn how to create informative and attractive visualizations in Python using the Seaborn library.

4 hours

DataCamp Content Creator

Course Instructor

Python

Introduction to NumPy

Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.

4 hours

Izzy Weber

Curriculum Manager, DataCamp

Python

Python Data Science Toolbox (Part 1)

Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

3 hours

Hugo Bowne-Anderson

Data Scientist at DataCamp

Python

The Android App Market on Google Play

Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.

2 hours

Lavanya Gupta

Machine Learning Engineer at PropTiger.com

Python

Python Data Science Toolbox (Part 2)

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

4 hours

Hugo Bowne-Anderson

Data Scientist at DataCamp

Python

Intermediate Data Visualization with Seaborn

Use Seaborn's sophisticated visualization tools to make beautiful, informative visualizations with ease.

4 hours

Chris Moffitt

Creator of Practical Business Python

Theory

Data Communication Concepts

No one enjoys looking at spreadsheets! Bring your data to life. Improve your presentation and learn how to translate technical data into actionable insights.

3 hours

Hadrien Lacroix

Curriculum Manager at DataCamp

Python

A Visual History of Nobel Prize Winners

Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?

2 hours

Rasmus Bååth

Data Science Lead at castle.io

Python

Introduction to Importing Data in Python

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

3 hours

Hugo Bowne-Anderson

Data Scientist at DataCamp

Python

Intermediate Importing Data in Python

Improve your Python data importing skills and learn to work with web and API data.

2 hours

Hugo Bowne-Anderson

Data Scientist at DataCamp

Python

Cleaning Data in Python

Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!

4 hours

Adel Nehme

Content Developer @ DataCamp

Python

Working with Dates and Times in Python

Learn how to work with dates and times in Python.

4 hours

DataCamp Content Creator

Course Instructor

Python

Writing Functions in Python

Learn to use best practices to write maintainable, reusable, complex functions with good documentation.

4 hours

Shayne Miel

Director of Software Engineering @ American Efficient

Python

Exploratory Data Analysis in Python

Learn how to explore, visualize, and extract insights from data.

4 hours

Allen Downey

Professor, Olin College

Python

Analyzing Police Activity with pandas

Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.

4 hours

Kevin Markham

Founder of Data School

Python

Introduction to Regression with statsmodels in Python

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

4 hours

Maarten Van den Broeck

Senior Content Developer at DataCamp

Python

Sampling in Python

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

4 hours

James Chapman

Curriculum Manager, DataCamp

Python

Hypothesis Testing in Python

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

4 hours

James Chapman

Curriculum Manager, DataCamp

Python

Dr. Semmelweis and the Discovery of Handwashing

Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.

2 hours

Rasmus Bååth

Data Science Lead at castle.io

Python

Supervised Learning with scikit-learn

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

4 hours

George Boorman

Analytics and Data Science Curriculum Manager, DataCamp

Python

Predicting Credit Card Approvals

Build a machine learning model to predict if a credit card application will get approved.

2 hours

Sayak Paul

Machine Learning Engineer at Carted

Python

Unsupervised Learning in Python

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

4 hours

Benjamin Wilson

Director of Research at lateral.io

Python

Machine Learning with Tree-Based Models in Python

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

Mengapa data science menggunakan Python?

Mudah Digunakan Karena pada dasarnya bahasa pemrograman Python hampir mirip dengan bahasa sehari-hari yang digunakan oleh manusia, namun dalam bahasa Inggris. Selama kita memiliki kemampuan di bahasa Inggris, pasti proses belajar Python juga akan menjadi lebih mudah.

Apakah Python mudah digunakan untuk melakukan pekerjaan data science?

Python juga sangat mudah dipelajari bagi data scientist atau programmer pemula, karena menggunakan sintaks yang sederhana. SQL adalah bahasa pemrograman yang memungkinkan kita untuk menyesuaikan, menemukan, dan memeriksa kumpulan data yang bervolume besar.

Mengapa bahasa pemrograman Python yang dipilih pada kebanyakan proyek sains data?

Karena Python memiliki struktur yang sederhana serta keyword yang sedikit. Selain itu juga mudah diaplikasikan karena penulisan sintaksnya lebih sederhana dibandingkan dengan bahasa pemrograman lainnya untuk masalah yang sama. Sehingga sangat cocok bagi pemula yang baru memulai belajar Python untuk data science.

Penggunaan Python untuk apa?

Python adalah bahasa pemrograman yang banyak digunakan dalam aplikasi web, pengembangan perangkat lunak, ilmu data, dan machine learning (ML). Developer menggunakan Python karena efisien dan mudah dipelajari serta dapat dijalankan di berbagai platform.