Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

Discover how to use Python for data science, learning about the ways to store and manipulate data in the environment to begin conducting your own analyses.

Introduction to Python

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

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

In this intermediate Python course, learn to visualize real data with Matplotlib's functions and get to know new data structures such as the pandas DataFrame.

Intermediate Python

pandas is the world's most popular Python library, used for everything from data manipulation to data analysis. In this course, you'll learn how to manipulate DataFrames, as you extract, filter, and transform real-world datasets for analysis. Using pandas you’ll explore all the core data science concepts. Using real-world data, including Walmart sales figures and global temperature time series, you’ll learn how to import, clean, calculate statistics, and create visualizations—using pandas to add to the power of Python!

Use the world’s most popular Python data science package to manipulate data and calculate summary statistics.

Data Manipulation with pandas

As a data scientist, you'll often find that the data you need is not in a single file. It may be spread across a number of text files, spreadsheets, or databases. You’ll want to be able to import the data you’re interested in as a collection of DataFrames and combine them to answer your central questions. This course is all about the act of combining—or merging—DataFrames, an essential part of any data scientist's toolbox. You'll hone your pandas skills by learning how to organize, reshape, and aggregate multiple datasets to answer your specific questions.

This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox. 

Hone your pandas skills by learning how to organize, reshape, and aggregate multiple data sets to answer your specific questions.

Merging DataFrames with pandas

The role of a data scientist is to turn raw data into actionable insights. Much of the world's raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. To be an effective data scientist, you must know how to wrangle and extract data from these databases using a language called SQL . This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.

Learn the basics of syntax in SQL, how to extract and wrangle data, and use querying tables in relational databases like MySQL, SQL Server, and PostgreSQL.

Introduction to SQL

Coding interviews can be challenging. You might be asked questions to test your knowledge of a programming language. On the other side, you can be given a task to solve in order to check how you think. And when you are interviewed for a data scientist position, it's likely you can be asked on the corresponding tools available for the language. In either of the cases, to get a cool position as a data scientist, you need to do a little work to perform the best. That's why it's very important to practice in order to prove your expertise! This course serves as a guide for those who just start their path to become a professional data scientist and as a refresher for those who seek for other opportunities. We'll go through fundamental as well as advanced topics that aim to prepare you for a coding interview in Python. Since it is not a normal step-by-step course, some exercises can be quite complex. But who said that interviews are easy to pass, right?

Prepare for your next coding interviews in Python.

Prepare for Python coding interviews by refreshing your knowledge on data structures, iterable objects, lambda functions, NumPy, Pandas DataFrames and more

Practicing Coding Interview Questions in Python

This course will provide you an understanding of how to use built-in PostgreSQL functions in your SQL queries to manipulate different types of data including strings, character, numeric and date/time. We'll travel back to a time where Blockbuster video stores were on every corner and if you wanted to
watch a movie, you actually had to leave your house to rent a DVD! You'll also get an introduction into the robust full-text search capabilities which provides a powerful tool for indexing and matching keywords in a PostgreSQL document. And finally, you'll learn how to extend these features by using PostgreSQL extensions.

Learn the most important PostgreSQL functions for manipulating, processing, and transforming data. 

Learn how to use built-in PostgreSQL functions in your SQL queries to manipulate different types of data like strings, character, numeric and date/time.

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