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

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

Begin your journey into Data Science! Even if you've never written a line of code in your life, you'll be able to follow this course and witness the power of Python to perform Data Science. You'll use data to solve the mystery of Bayes, the kidnapped Golden Retriever, and along the way you'll become familiar with basic Python syntax and popular Data Science modules like Matplotlib (for charts and graphs) and pandas (for tabular data).

Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.

Dive into data science using Python and learn how to effectively analyze and visualize your data using Pandas and Matplotlib. No coding experience needed.

Introduction to Data Science in 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

Now that you've learned the basics of SQL in our <a href="https://learn.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a> course, it's time to supercharge your queries using joins and relational set theory. In this course, you'll learn all about the power of joining tables while exploring interesting features of countries and their cities throughout the world. You will master inner and outer joins, as well as self joins, semi joins, anti joins and cross joins—fundamental tools in any PostgreSQL wizard's toolbox. Never fear set theory again after learning all about unions, intersections, and except clauses through easy-to-understand diagrams and examples. Lastly, you'll be introduced to the challenging topic of subqueries. You will be able to visually grasp these ideas by using Venn diagrams and other linking illustrations.

Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.

Discover all the fundamental in PostgreSQL, like inner, outer self, semi, anti, and cross joins, as well as unions, intersections, and except clauses.

Joining Data in SQL

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

SQL is an essential skill for data scientists, and Microsoft SQL Server is one of the world's most popular database systems. In this course, you'll start with simple SELECT statements, and refine these queries with ORDER BY and WHERE clauses. You'll learn how to group and aggregate your results, and also how to work with strings.  You'll also cover the most common way to join tables, how to create tables, and inserting and updating data. You'll mainly work with the Chinook digital media database, representing sales of various artists and tracks. The music theme continues with the classic rock and Eurovision datasets, while we also look at trends in power outages in the US. Overall, you'll become proficient in the most common data manipulation tasks in SQL Server and build a solid foundation for the upcoming T-SQL courses here on DataCamp!

Become proficient at using SQL Server to perform common data manipulation tasks.

Learn how to use SQL Server to do common data manipulation tasks, like insert data, joining tables,  group and aggregate results, and work with strings.

Introduction to SQL Server

So you've learned how to aggregate and join data from tables in your database—now what? How do you manipulate, transform, and make the most sense of your data? This intermediate-level course will teach you several key functions necessary to wrangle, filter, and categorize information in a relational database, expand your SQL toolkit, and answer complex questions. You will learn the robust use of CASE statements, subqueries, and window functions—all while discovering some interesting facts about soccer using the European Soccer Database.

Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.

Intermediate SQL

It's time to push forward and develop your Python chops even further. There are tons of fantastic functions in Python and its library ecosystem. However, as a data scientist, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first Python Data Science Toolbox course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python and be able to write lambda functions and handle errors in your function writing practice. And you'll wrap up each chapter by using your new skills to write functions that analyze Twitter DataFrames.

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

Python Data Science Toolbox (Part 1)

There’s a lot more to Microsoft Excel than SUM and COUNT. Companies and institutions around the world use Excel’s powerful functions to efficiently transform mountains of raw data into clear insights, and now you can too.

 

In this course, you’ll develop employable analyst skills as you learn how to use time-saving keyboard shortcuts, convert and clean data types including text, times and dates, and build impressive logic functions and conditional aggregations. Through hands-on practice, you’ll learn over 35 new Excel functions, including CONCATENATE, VLOOKUP, and AVERAGEIF(S), and work with real-world Kickstarter data as you use your new-found Excel skills to analyze what makes a successful project.

Discover time-saving shortcuts in Excel to help you convert and clean your data. You will learn 35+ new Excel functions including CONCAT, VLOOKUP, and more.

Data Analysis in Excel

Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you communicate your data to others, and are useful to data analysts and other consumers of the data. In this course, you will learn how to use Matplotlib, a powerful Python data visualization library. Matplotlib provides the building blocks to create rich visualizations of many different kinds of datasets. You will learn how to create visualizations for different kinds of data and how to customize, automate, and share these visualizations.

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

Introduction to Data Visualization with Matplotlib

Being able to combine and work with multiple datasets is an essential skill for any aspiring Data Scientist. pandas is a crucial cornerstone of the Python data science ecosystem, with Stack Overflow recording 5 million views for pandas questions. Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using pandas. You'll work with datasets from the World Bank and the City Of Chicago. You will finish the course with a solid skillset for data-joining in pandas.

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

Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using Pandas. You'll gain a solid skillset for data-joining.

Joining Data with pandas

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

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

Python Data Science Toolbox (Part 2)

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

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

You'll learn the many ways to read data into Python: from flat files such as CSVs to Excel spreadsheets and relational databases in SQLite & PostgreSQL.

Introduction to Importing Data in Python

Seaborn is a powerful Python library that makes it easy to create informative and attractive visualizations. This course provides an introduction to Seaborn and teaches you how to visualize your data using plots such as scatter plots, box plots, and bar plots. You’ll do this while exploring survey responses about student hobbies and the factors that are associated with academic success. You’ll also learn about some of Seaborn’s advantages as a statistical visualization tool, such as how it automatically calculates confidence intervals. By the end of the course, you will be able to use Seaborn in a variety of situations to explore your data and effectively communicate the results of your data analyses to others.

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

Learn how to visualize in scatter plots, box plots, and bar plots, to create informative and attractive visualizations using the Python libary, Seaborn.

Introduction to Data Visualization with Seaborn

You’ve already used SQL to query data from databases. But did you know that there's a lot more you can do with databases? You can model different phenomena in your data, as well as the relationships between them. This gives your data structure and consistency, which results in better data quality. In this course, you'll experience this firsthand by working with a real-life dataset that was used to investigate questionable university affiliations. Column by column, table by table, you'll get to unlock and admire the full potential of databases. You'll learn how to create tables and specify their relationships, as well as how to enforce data integrity. You'll also discover other unique features of database systems, such as constraints.

Learn how to create one of the most efficient ways of storing data - relational databases! 

Learn how to create tables and specify their relationships in SQL, as well as how to enforce data integrity and other unique features of database systems.

Introduction to Relational Databases in SQL

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In the prequel to this course, you learned many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL. In this course, you'll extend this knowledge base by learning to import data from the web and by pulling data from Application Programming Interfaces— APIs—such as the Twitter streaming API, which allows us to stream real-time tweets.

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

Learn how to import data into Python from sources like the web and by pulling data from APIs, such as the Twitter streaming API to stream real-time tweets.

Intermediate Importing Data in Python

This course will dig deeper into some of the core functionality of Google Sheets. There's a whole bunch of predefined functions we'll cover, like `SUM()` and `AVERAGE()`, and `VLOOKUP()`. We'll apply these techniques to do some analysis on your grades in school, look at performance statistics within a company, track monthly sales, and look at some real geographical information about the countries of the world.

Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().

Data Analysis in Spreadsheets

Do you want to make beautiful, informative visualizations with ease? If so, then you must learn seaborn! Seaborn is a visualization library that is an essential part of the python data science toolkit. In this course, you will learn how to use seaborn's sophisticated visualization tools to analyze multiple real world datasets including the American Housing Survey, college tuition data, and guests from the popular television series, The Daily Show. Following this course, you will be able to use seaborn functions to visualize your data in several different formats and customize seaborn plots for your unique needs.

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

Learn how you can use Seaborn functions to visualize your data in several different formats and customize Seaborn plots for your unique needs in Python.

Intermediate Data Visualization with Seaborn

A good database design is crucial for a high-performance application. Just like you wouldn't start building a house without the benefit of a blueprint, you need to think about how your data will be stored beforehand. Taking the time to design a database saves time and frustration later on, and a well-designed database ensures ease of access and retrieval of information. While choosing a design, a lot of considerations have to be accounted for. In this course, you'll learn how to process, store, and organize data in an efficient way. You'll see how to structure data through normalization and present your data with views. Finally, you'll learn how to manage your database and all of this will be done on a variety of datasets from book sales, car rentals, to music reviews.

Learn to design databases using star and snoflake schemas, and OLTP and OLAP. You will gain essential skills for creating and managing databases in SQL.

Database Design

A majority of data is stored in databases and knowing the necessary tools needed to analyze and clean data directly in databases is indispensable. This course focuses on T-SQL, the version of SQL used in Microsoft SQL Server, needed for data analysis. You will learn several concepts in this course such as dealing with missing data, working with dates, and calculating summary statistics using advanced queries. After completing this course, you will have the skills needed to analyze data and provide insights quickly and easily.

In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.

Learn concepts for working in  T-SQL, such as dealing with missing data, working with dates, and calculating summary statistics using advanced queries.

Intermediate SQL Server

This course will expand your Google Sheets vocabulary. You'll dive deeper into data types, practice manipulating numeric and logical data, explore missing data and error types, and calculate some summary statistics. As you go, you'll explore datasets on 100m sprint world records, asteroid close encounters, benefit claims, and butterflies.

Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.

Learn about Google Sheets by practicing manipulating numeric and logical data, exploring missing data and error types, and calculating summary statistics.

Intermediate Spreadsheets

Working with large quantities of data in spreadsheets can be difficult and time-consuming. Have you ever wished there was a quick and efficient way to organize and evaluate your data within seconds? Pivot Tables are your answer! In this course we will explore the world of Pivot Tables within Google Sheets, and learn how to quickly organize thousands of datapoints with just a few clicks of the mouse. We will analyze the Average rainfall across multiple US cities, the Top 10 of the Fortune Global 500, and a selection of Films released between 2010 and 2016. You will learn techniques such as sorting, subtotaling, and filtering your data using these real world examples. By the end of the course, you will be able to create your own custom pivot tables with datasets of any size!

Explore the world of Pivot Tables within Google Sheets, and learn how to quickly organize thousands of data points with just a few clicks of the mouse.

Pivot Tables in Spreadsheets

Statistics is the science that deals with the collection, analysis, and interpretation of data. Having a solid foundation in statistics will help you effectively work with your data to test hypotheses and uncover insights that can help solve your problems. This course is designed to give you that foundation in statistics. Using Spreadsheets functions, you'll dive into averages, distributions, hypothesis testing, and conclude the course by applying your newfound knowledge in a case study. Along the way, you'll work with a variety of datasets ranging from eBay auctions to train ridership to historical presidential approval ratings. Enjoy!

Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

Introduction to Statistics in Spreadsheets

SQL Server has a robust set of tools to prepare, aggregate, and query time series data. This course will show you how to build and work with dates, parse dates from strings (and deal with invalid strings), and format dates for reporting. From there, you will see how SQL Server's built-in aggregation operators and window functions can solve important business problems like calculating running totals, finding moving averages, and displaying month-over-month differences using realistic sample data sets. You will also see how taking a different perspective on your data can solve difficult problems.

Explore ways to work with date and time data in SQL Server for time series analysis

Time Series Analysis in SQL Server

A picture can tell a thousand words - but only if you use the right picture! This course teaches you the fundamentals of data visualization with Google Sheets. You'll learn how to create common chart types like bar charts, histograms, and scatter charts, as well as more advanced types, such as sparkline and candlestick charts. You will look at how to prepare your data and use Data Validation and VLookup formulas to target specific data to chart. You'll learn how to use Conditional Formatting to apply a format to a cell or a range of cells based on certain criteria, and finally, how to create a dashboard showing plots and data together. Along the way, you'll use data from the Olympics, sharks attacks, and Marine Technology from the ASX.

Learn the fundamentals of data visualization using spreadsheets. 

Learn how to create different chart types, from bar charts, histograms, and scatter charts, to advanced sparkline and candlestick charts in Google Sheets.

Data Visualization in Spreadsheets

In this course, you will learn how to make use of the most important functions for manipulating data provided by SQL Server.  You can use these functions for processing and transforming data to get the results you want.

Learn the most important functions for manipulating, processing, and transforming data in SQL Server.

Learn how to use the most important functions for manipulating data in SQL Server to manipulate, process, and transform data to get the results you want.

Functions for Manipulating Data in SQL Server

A mission critical assignment is depending on your SQL coding skills. You’ve been given some code to fix.  It is giving the results you need but it’s running too slow, and it’s poorly formatted making it hard to read.  The deadline is tomorrow.  You’ll need to reformat the code and try different methods to improve performance.  The pressure is on!!!  In this course we’ll be using SQL on real world datasets, from sports and geoscience, to look at good coding practices and different ways how we can can improve the performance of queries to achieve the same outcome.

In this course, students will learn to write queries that are both efficient and easy to read and understand.

Discover how you can reformat code and try different methods to improve the performance of queries in SQL. Then apply your skills to real-world datasets.

Improving Query Performance in SQL Server

Take your SQL Server programming to the next level. First, we demystify how to manipulate datetime data by performing temporal exploratory data analysis with the Washington DC BikeShare transactional dataset. Then, you’ll master how to create, update, and execute user-defined functions and stored procedures. You will learn the proper context for each modular programming tool and best practices. In the final chapter, you will apply all of your new skills to solve a real-world business case identifying the New York City yellow taxi utilization for each borough, and which pickup locations should be scheduled for each driver shift.

Master SQL Server programming by learning to create, update, and execute functions and stored procedures.

Writing Functions and Stored Procedures in SQL Server

It is critical to know how to handle errors and manage transactions when programming SQL scripts. Unhandled errors can be very harmful and can cause unexpected situations, such as inconsistent data in your database, or even worse, errors can lead you to make wrong business decisions.
In this course, you will learn how to handle errors and discover how to manage transactions in case of an error. Additionally, you will study what happens when two or more people interact at the same time with the same data. You will practice all these concepts using two datasets, one of them based on bank accounts and the other one on an electric bike store.

Learn to write scripts that will catch and handle errors and control for multiple operations happening at once.

Transactions and Error-Handling in SQL Server

Transactions and Error Handling in SQL Server

Auditing your SQL Server database and keeping data integrity can be a challenging task for DBAs and database developers. SQL Server triggers are special types of stored procedures designed to help you achieve consistency and integrity of your database. This course will teach you how to work with triggers and use them in real-life examples. Specifically, you will learn about the use cases and limitations of triggers and get practice designing and implementing them. You will also learn to optimize triggers to fit your specific needs.

Learn how to design and implement triggers in SQL Server using real-world examples.

Building and Optimizing Triggers in SQL Server

Spreadsheets are an essential tool for any marketing professional, but how does one keep these spreadsheets clean and accurate - especially when multiple parties contribute data? Data validation and regular expressions are powerful tools for marketing analysts, but having clean data is only half the battle. After we learn how to clean the data, we will visualize it by building charts! Throughout the course, we will explore a dataset that includes the kind of information you will encounter in the world of digital marketing. We will spot errors in metrics using data validation, use regular expressions to aggregate campaign metrics, build charts to analyze campaign performance, and use everything we've learned to build a dynamic dashboard!

Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.

Marketing Analytics in Spreadsheets

You rely on predictions every day: you might check the weather app before choosing your outfit or peek at the traffic before starting your commute. Perhaps you are responsible for setting your organization’s strategy in the future. Do you find yourself wondering how accurate predictions are, how you can see into the future, and why the weatherman always seems to be wrong? In our Error and Uncertainty course, you’ll make some predictions yourself, learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making. You will uncover patterns in Seattle crime data, predict students’ final grades, prevent Nashville traffic accidents, and determine whether a bakery’s menu needs to change. Join us! We’re certain you’ll enjoy learning about error and uncertainty.

Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.

Error and uncertainty in spreadsheets

Error and Uncertainty in Spreadsheets

Do you want to query complex data structures in an iterative way? Do you have access to hierarchical data structures that need to be queried? This course will teach you the tools required to solve these questions. You will learn how to write recursive queries and query hierarchical data structures. To do this, you will use Common Table Expressions (CTE) and the recursion principle on a wide variety of datasets. You will, for example, dig into a flight plan dataset and learn how to find the best and cheapest connection between two airports. After completing this course, you will understand the principle of recursion, and be able to identify and create hierarchical data models.

Learn how to write recursive queries and query hierarchical data structures.

Learn how to write recursive queries as well as query hierarchical data structures using Common Table Expressions (CTE) and the recursion principle.

Hierarchical and Recursive Queries in SQL Server

The best marketers and growth hackers are data-driven. They quickly pull data about marketing campaigns, customers, ... from databases on their own and use this information to run more profitable campaigns. In this interactive tutorial by DataCamp, you will learn through fun interactive exercises how you can use the powerful Structured Query Language or SQL to communicate with a relational database. Step-by-step, you will learn how to retrieve and filter data from a database that contains data on bank clients and direct marketing campaigns.

SQL Tutorial for Marketers

Learn the basics of database design and the different SQL data types. Then master the core concepts for the exam. In this track, you’ll learn how to write queries, functions, and stored procedures. You’ll also find out how to manage transactions, handle errors, and improve query performance. 

Gain the SQL Server skills you need to write, troubleshoot, and optimize your queries using SQL Server. 

with SQL Server

 Data Analyst

Join millions of people using Google Sheets and Microsoft Excel on a daily basis and learn the fundamental skills necessary to analyze data in spreadsheets! The skills you learn in these courses will empower you to join tables, summarize data, and answer your data analysis and data science questions.

Become a spreadsheet master. Learn the core skills you need to analyze data using Google Sheets and Microsoft Excel.

Spreadsheet Fundamentals

Save yourself hours when working with Microsoft Excel and Google Sheets and gain spreadsheet skills you can use every day. 

In this track, you’ll learn time-saving methods such as data validation and gain a foundation of statistical concepts to quickly calculate averages and analyze data. Through hands-on exercises, you’ll expand your spreadsheet mastery and create impressive visualizations, including histograms, scatter plots, and bar plots. You’ll also leverage your data validation skills and more advanced techniques such as regular expressions to create your own marketing dashboard using real-world digital marketing data.

Expand your spreadsheet skills. Learn how data validation, statistics, and regular expressions can save you time with Excel and Sheets!

Gain the starting SQL Server skills you need to summarize data, join tables, and analyze data using built-in SQL Server functions. 

In this track, you’ll discover how SQL Server is like a supercharged Microsoft Excel. Through interactive exercises, you’ll learn how to access, query, and extract huge amounts of data from multiple databases. Using real-world data you’ll also learn how to perform powerful cross-database analyses that you can then apply in your own role. 

Gain the starting SQL Server skills you need to summarize data, join tables, and analyze your data using SQL Server functions. 

SQL Server Fundamentals

Data is one of your business’s most valuable commodities. In this track you’ll learn the key skills you need to set-up, design, maintain, and monitor your team’s database. Dive in and learn the key SQL Server skills you need to securely manage access and monitor database performance using SQL server.

Dive in and learn the key SQL Server skills you need to securely set-up, design, and maintain your business’s database.

SQL Server for Database Administrators

In this finance-oriented introduction to R, you will learn essential data structures such as lists and data frames and have the chance to apply that knowledge to real-world financial examples. By the end of the course, you will be comfortable with the basics of manipulating your data to perform financial analysis in R.

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Learn the essential data structures, including lists and data frames and have the chance to apply that knowledge directly to financial examples using R.

Intro to R for Finance

Introduction to R for Finance

Have you got your basic Python programming chops down for Data Science but are  yearning for more? Then this is the course for you. Herein, you'll consolidate and practice your knowledge of lists, dictionaries, tuples, sets, and date times. You'll see their relevance in working with lots of real data and how to leverage several of them in concert to solve multistep problems, including an extended case  study using Chicago metropolitan area transit data. You'll also learn how to use many of the objects in the Python Collections module, which will allow you to store and manipulate your data for a variety of Data Scientific purposes. After taking this course, you'll be ready to tackle many Data Science challenges Pythonically.

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

Data Types for Data Science in Python

Spreadsheet software is one of the most popular and powerful tools in data analysis. Millions of people use tools like Google Sheets or Microsoft Excel on a daily basis. Even the most experienced data scientists often started their careers with spreadsheets and still use it to test assumptions or to look at data for the first time. In this course, you will learn the basics of spreadsheets by working with rows, columns, addresses, and ranges. You will create your own formulas and learn how to use references.

Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges. 

Discover the basics of spreadsheets by working with rows, columns, addresses, and ranges, then create your own formulas and learn how to use references.

Introduction to Spreadsheets

You have access to a database. Now what do you do? Building on your existing skills joining tables,  using basic functions, grouping data, and using subqueries, the next step in your SQL journey is  learning how to explore a database and the data in it.  Using data from Stack Overflow, Fortune 500 companies, and 311 help requests  from Evanston, IL, you'll get familiar with numeric, character, and date/time data types. You'll use  functions to aggregate, summarize, and analyze data without leaving  the database. Errors and inconsistencies in the data won't stop you!  You'll learn common problems to look for  and strategies to clean up messy data. By the end of this course, you'll be ready to start exploring your  own PostgreSQL databases and analyzing the data in them.

Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.

Learn what's available in a database in SQL, including the tables, relationships, and data stored in them so you can explore your own PostgreSQL databases.

Exploratory Data Analysis in SQL

As a Data Scientist, the majority of your time should be spent gleaning actionable insights from data -- not waiting for your code to finish running. Writing efficient Python code can help reduce runtime and save computational resources, ultimately freeing you up to do the things you love as a Data Scientist. In this course, you'll learn how to use Python's built-in data structures, functions, and modules to write cleaner, faster, and more efficient code. We'll explore how to time and profile code in order to find bottlenecks. Then, you'll practice eliminating these bottlenecks, and other bad design patterns, using Python's Standard Library, NumPy, and pandas. After completing this course, you'll have the necessary tools to start writing efficient Python code!

Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.

Writing Efficient Python Code

Have you ever wondered how data professionals use SQL to solve real-world business problems, like generating rankings, calculating moving averages and running totals, deduplicating data, or performing time intelligence? If you already know how to select, filter, order, join and group data with SQL, this course is your next step. By the end, you will be writing queries like a pro! You will learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon! Using flights data, you will discover how simple it is to use window functions, and how flexible and efficient they are.

Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon! 

PostgreSQL Summary Stats and Window Functions

Become a master at building complex reports!  In this course, you will apply all the SQL concepts and functions you have learned in previous courses to build out your very own dashboard.  By navigating through an Olympics database, you will become an expert data explorer and learn how to understand novel database quickly and effectively.  Since data is never perfect, you will gain valuable strategies to deal with real-world issues commonly found with SQL, including how to remove data duplication and how to turn messy data into clean, organized reports.  Lastly, you’ll conquer complex calculations using window functions and layered calculations, all within the same report.  This is a perfect class for anyone who will be commonly pulling data from databases and is a great complement for those who use R or Python for data science.

Learn how to build your very own dashboard by applying all the SQL concepts and functions you have learned in previous courses.

Reporting in SQL

Now that you’ve learned the basic tools of SQL you are ready to synthesize them into practical, real-world skills. In this course, you will work with a database of a fictional movie rental company. The size and complexity of this database will allow you to experience the challenges of working with databases firsthand. Throughout this course, you will use SQL to answer business-driven questions. You will learn new skills that will empower you to find the tables you need. You will then learn how to store and manage this data in tables and views that you create. Best of all you will also learn how to write code that not only clearly conveys your intent but is also legible.

Find tables, store and manage new tables and views, and write maintainable SQL code to answer business questions.

Applying SQL to Real-World Problems

Spreadsheets often suffer from having too much data. If you want to tell the underlying story that is in the data without creating additional reports, conditional formatting can help! Whether it's showing the age of your inventory by highlighting the items using a color scale, or accentuating the largest variances in year over year financial data, conditional formatting has built-in options that can be used without any complex code. It can be used instead of sorting or filtering since it works with the data that is already there! By the end, you will be creating your own report using conditional formatting to analyze a company's payroll.

Learn how to use conditional formatting with your data through built-in options and by creating custom formulas. 

Conditional Formatting in Spreadsheets

Oracle SQL is one of the most widely used database management system around the world. It's well suited to power primarily commercial and enterprise applications, and is particularly popular at large companies with mission-critical databases. Mastering Oracle SQL is therefore guaranteed to open exciting opportunities.
 
In this course, you will get a complete introduction to Oracle SQL solving business problems for a digital media store. You will first learn how to access, retrieve and restrict data. You will then perform aggregation and combine tables. The last chapter will have you handle missing values and convert data. By the end of this course, you will understand how Oracle processes SQL queries and will have a solid foundation in Oracle SQL.

Learn how to import and manipulate data with Oracle SQL.

Discover Oracle SQL, what it is, and how you can use it to access, retrieve, convert, and restrict data to perform aggregation and handle missing values.

Introduction to Oracle SQL

Tableau is a widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever to explore, visualize, and securely share data in the form of Workbooks and Dashboards. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data.

You’ll learn how to navigate Tableau’s interface and connect and present data using easy-to-understand visualizations. By the end of this training, you’ll have the skills you need to confidently explore Tableau and build impactful data dashboards. Let’s dive in.

Get started with Tableau, a widely used business intelligence (BI) and analytics software to explore, visualize, and securely share data.

Learn to navigate Tableau’s interface, connect and present data, and build easy-to-understand visualizations to build impactful dashboards for business.

Introduction to Tableau

Gain a 360° overview of exploring and using Power BI to build impactful reports. In this course, you’ll go from zero to hero as you discover how to use this popular business intelligence platform through hands-on exercises. Before diving into creating visualizations using Power BI's drag-and-drop functionality, you’ll first learn how to confidently load and transform data using Power Query and the importance of data models. You’ll also learn to drill down into reports and make your reports fully interactive. You've got the power!

Gain a 360° overview of how to explore and use Power BI to build impactful reports.

Learn how to use the Power BI platform to load and transform data, create visualzations, run DAX formulas, and create reports through hands-on exercises.

Introduction to Power BI

In 2019, the average salary for data engineers overtook data scientists. How did this happen? Companies wanting to find the gold within their data realized it wasn’t possible if they hadn’t yet built the mine. Data engineers lay the foundations that make data science possible.
 
In this course, you’ll learn about a data engineer’s core responsibilities, how they differ from data scientists, and facilitate the flow of data through an organization. Through hands-on exercises you’ll follow Spotflix, a fictional music streaming company, to understand how their data engineers collect, clean, and catalog their data. By the end of the course, you’ll understand what your company's data engineers do, be ready to have a conversation with a data engineer, and have a solid foundation to start your own data engineer journey.

Discover how data engineers lay the groundwork that makes data science possible. No coding involved!

Data Science Courses

Data Science Tracks

Our Learning Experience

@aa0o0

Workspace Publications (0)

Completed Tracks(5)

Completed courses (35)

Completed projects (5)