Introduction to Python
Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.
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Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.
Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.
Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.
Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.
Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.
Master the complex SQL queries necessary to answer a wide variety of data science questions and prepare robust data sets for analysis in PostgreSQL.
Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Continue to build your modern Data Science skills by learning about iterators and list comprehensions.
Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.
Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.
Learn to use SQL Server to perform common data manipulation tasks and master common data manipulation tasks using this database system.
Learn to use best practices to write maintainable, reusable, complex functions with good documentation.
Learn to implement distributed data management and machine learning in Spark using the PySpark package.
Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().
Learn how to create one of the most efficient ways of storing data - relational databases!
Dive in and learn how to create classes and leverage inheritance and polymorphism to reuse and optimize code.
This course is an introduction to version control with Git for data scientists.
Learn how to work with dates and times in Python.
The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds.
Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.
Learn the fundamentals of working with big data with PySpark.
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
Take your R skills up a notch by learning to write efficient, reusable functions.
Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges.
Learn how to write unit tests for your Data Science projects in Python using pytest.
In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.
Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.
Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Begin your journey with Scala, a popular language for scalable applications and data engineering infrastructure.
Master SQL Server programming by learning to create, update, and execute functions and stored procedures.
In this course, students will learn to write queries that are both efficient and easy to read and understand.
Learn efficient techniques in pandas to optimize your Python code.
Learn to create your own Python packages to make your code easier to use and share with others.
Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.
Prepare for your next coding interviews in Python.
Learn how to design and implement triggers in SQL Server using real-world examples.
Learn how to pull character strings apart, put them back together and use the stringr package.
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Learn how to structure your PostgreSQL queries to run in a fraction of the time.
Learn to upscale your Python workflows to efficiently handle big data with Dask.
Use your knowledge of common spreadsheet functions and techniques to explore Python!
Learn to easily summarize and manipulate lists using the purrr package.
Learn to automate many common file system tasks and be able to manage and communicate with processes.
This course is for R users who want to get up to speed with Python!
Create and share your own R Packages!
Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.
Learn how to write recursive queries and query hierarchical data structures.
Learn to develop a set of principles for your data science and software development projects.
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
Transition from MATLAB by learning some fundamental Python concepts, and diving into the NumPy and Matplotlib packages.
Learn defensive programming in R to make your code more robust.
This course covers in detail the tools available in R for parallel computing.
Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.
Use C++ to dramatically boost the performance of your R code.
Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.