Get a fast-paced introduction to Python. In this course, you can learn what Python is and why it's become such a powerful and in-demand programming language. Python expert and trainer Lavanya Vijayan shares the fundamentals of programming in Python with data types, variables, functions, sequences, conditional statements, iteration, and recursion. The course concludes with a short introduction to object-oriented programming in Python. Learning a new programming language shouldn't be hard; with this course, you can learn the basics you need to become proficient in Python, and decide if it’s the right language for your projects.
Identify the different types of data structures.
Define and apply a user-created function.
Describe a Python sequence.
Identify control flow statements.
Analyze when to apply an iteration statement.
Describe object-oriented programming concepts.
Python for Data Science Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts—for even more hands-on experience with Python. In this course, instructor Lillian Pierson takes you step by step through a practical data science project: a web scraper that downloads and analyzes data from the web. Along the way, she introduces techniques to clean, reformat, transform, and describe raw data; generate visualizations; remove outliers; perform simple data analysis; and generate interactive graphs using the Plotly library. You should walk away from this training with basic coding experience that you can take to your organization and quickly apply to your own custom data science projects.
Why use Python for working with data
Filtering and selecting data
Concatenating and transforming data
Data visualization best practices
Visualizing data
Creating a plot
Creating statistical data graphics
Performing basic math and linear algebra
Correlation analysis
Multivariate analysis
Data sourcing via web scraping
Introduction to natural language processing
Collaborative analytics with Plotly
Python for Data Science Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts—for even more hands-on experience with Python. In this course, instructor Lillian Pierson takes you step by step through a practical data science project: building machine learning models that can generate predictions and recommendations and automate routine tasks. Along the way, she shows how to perform linear and logistic regression, use K-means and hierarchal clustering, identify relationships between variables, and use other machine learning tools such as neural networks and Bayesian models. You should walk away from this training with hands-on coding experience that you can quickly apply to your own data science projects.
Why use Python for data science
Machine learning 101
Linear regression
Logistic regression
Clustering models: K-means and hierarchal models
Dimension reduction methods
Association rules
Ensembles methods
Introduction to neural networks
Decision tree models
Knowing how to code and manage relational databases and database-driven applications is a valuable skill for any career in tech. Completely updated for 2019, SQL Essential Training is designed to help users understand the most common language for database wrangling, SQL. All the lessons have been recorded in SQLiteStudio, an intuitive and easy-to-install database manager. Instructor Bill Weinman teaches all the major features of SQL: creating tables; defining relationships; manipulating strings, numbers, and dates; using triggers to automate actions; and using subselects and views. He offers a solid working knowledge of the language and shows how to retrieve and manage data efficiently. The final chapter includes a real-world example of building a simple application using SQL.
How databases are organized
Selecting rows and columns
Creating new tables
Inserting and updating data
Sorting and filtering
Accessing related tables with JOIN
Working with strings
Understanding numeric types
Using aggregate functions and transactions
Automating data with triggers
Creating views
Using CRUD functions
Get Ben Sullins's 12 must-have SQL techniques for data science pros—engineers, DevOps, data miners, programmers, and other systems specialists. Ben's tips focus on practical applications of SQL queries for data analysis. Learn how to retrieve data, join tables, calculate rolling averages and rankings, work with dates and times, use window functions, aggregate and filter data, and much more. Each tip is short, relevant, and up to date with current industry best practices—making this the perfect course for busy analysts who normally struggle to find time to build their skills.
Are you familiar with SQL? Do you know Python? Are you interested in understanding how these two languages work together? Then join Bill Weinman in this course as he shows the power of these two languages combined. Bill starts with some basics—connecting to a database, performing simple queries, and reading rows from a table. He covers how to use prepared statements and cursors, how to build a wrapper class to streamline the SQL interface and support multiple different database engines, and how to build a CRUD class and a full-featured web application using what you've learned. Many applications require a combination of SQL and Python, and after finishing Bill’s course, you’ll have a better understanding of why and how you can leverage the power of these two languages together.
To create functional and useful Python applications, you need a database. Databases allow you to store data from user sessions, track inventory, make recommendations, and more. However, Python is compatible with many options: SQLite, MySQL, and PostgreSQL, among others. Selecting the right database is a skill that advanced developers are expected to master. This course provides an excellent primer, comparing the different types of databases that can be connected through the Python Database API. Instructor Kathryn Hodge teaches the differences between SQLite, MySQL, and PostgreSQL and shows how to use the ORM tool SQLAlchemy to query a database. The final chapters put your knowledge to practical use in two hands-on projects: developing a full-stack application with Python, PostgreSQL, and Flask and creating a data analysis app with pandas and Jupyter Notebook. By the end, you should feel comfortable creating and using databases and be able to decide which Python database is right for you.
What is a database?
Relational vs. nonrelational databases
Creating a SQLite database
Editing records in SQLite
Creating a MySQL database
Encapsulating database operations
Creating a PostgreSQL database
Interacting with databases using SQLAlchemy
Creating a stored procedure
Developing full-stack apps with Python and Flask
Developing analysis apps with pandas and SQLAlchemy
Data can tell many stories: where it came from and where it’s going. Predictive analytics gives programmers a tool to tell stories about the future: to extract usable information and make accurate predictions. These predictions, in turn, allow business to make more informed, impactful decisions. Join Isil Berkun, data scientist, to explore predictive analytics with Python. Discover how to prepare data—fill in missing values, perform feature scaling, and more—and use prebuilt Python libraries to make and evaluate prediction models. She describes what models to use when, and explains the concepts in such a way that you can immediately apply them to your own work. By the end of the course, you’ll be able to leverage Python libraries like pandas and NumPy and choose the right prediction models for your projects.
Explain how predictive analytics can assist with decision-making.
Differentiate between the types of data that are used.
Apply the correct functions to Python code to produce optimal results.
Explain why data needs to be preprocessed before using predictive models.
Distinguish between the different predictive models available.
Do you rely on IT to get the data you need? Are you often stuck waiting in line for data, and wish you could just retrieve it yourself? In this course, learn how to get the data you want by writing a bit of SQL code. You won't just be able to pull data out of the database; you'll be able to manipulate it: merging it, grouping it, and relabeling it to get just the report you want. Join Emma Saunders as she shows how to write simple SQL queries for data reporting and analysis using a publicly accessible online database. Learn how to filter, group, and sort data, using built-in SQL functions to format or calculate results. Discover how to perform more complex queries, such as joining data together from different database tables. Last but not least, she introduces views, procedures, functions, and variables.
Retrieving data with SELECT statements
Filtering and sorting your results
Transforming results with built-in SQL functions
Grouping SQL results
Merging data from multiple tables
Using variables, functions, and procedures
Many data scientists know how to work with SQL—the industry-standard language for data analysis. But as data sizes grow, you need to know how to do more than simply read and write from a database. This course provides a more sophisticated approach to designing data models and optimizing queries in SQL. Instructor Dan Sullivan begins with the logical and physical design of tables—with particular focus on very large databases—and then presents a deep dive review of indexes, including specialized indexes and when to use them. The next section introduces query optimization and shows how to optimize basic, multi-join, and more complex queries. The course also covers SQL extensions, including user-defined functions and specialized data types. The techniques taught here enable more efficient analysis of large data sets using SQL, statistics, and custom business logic.