Data Science Certificate

In this certificate, students learn necessary programming skills to manipulate databases and perform various levels of analysis on the data. This program prepares students for entry-level data science and Python engineering positions.

Master Programming Fundamentals

Learn Python programming fundamentals and how to use Numpy, Pandas, and Matplotlib to analyze data. Discover the three main data science libraries, and learn how to create predictive models from the data using machine learning packages such as Sci-Kit Learn.

Read and Write Complex Queries

Interpreting and creating queries are essential skills for data scientists. Students learn to prepare and clean data for Python analysis.

Automate Tasks

Use Python to automate everyday tasks such as aggregating, updating, and formatting data.

What You’ll Learn:

• Analyze tabular data with NumPy and Pandas
• Create graphs and visualizations with Matplotlib
• Make predictions with linear regression 
• Applying Machine learning algorithms to the data
• Cleaning and balancing data in Pandas
• Evaluating the performance of machine learning models
• Combine information across tables with join statements
• Advanced techniques such as subqueries and stored procedures 
• Learn how to write programs in Python to automate everyday tasks 

Courses in the Certificate Program

Python for Data Science Immersive:

• Programming foundations including objects, loops, and functions
• The object-oriented programming paradigm
• How to work with different types of data such as strings, lists, and integers
• Selectively alter the control flow of your programming with conditional statements 
• Analyze tabular data using Python libraries NumPy and Pandas
• Create data visualizations with Matplotlib
• Predict outcomes using linear regression with Scikit-Learn

Python Machine Learning Immersive:

• How to clean and balance your data using the Pandas library
• Applying machine learning algorithms such as logistic regression and random forest using the scikit-learn library
• Choosing good features to use as input for your algorithms
• Properly splitting data into training, test and cross-validation sets
• Important theoretical concepts like overfitting, variance and bias
• Evaluating the performance of your machine learning models

Python for Automation:

• Learn the syntax of Python and how to construct programs
• Learn how to run your programs on a regular schedule
• How to handle errors

SQL Bootcamp:

• Explore and alter data using a graphical user interface
• Write queries to search through tables programmatically
• Understand various data types and convert between them
• Combine information across tables with join statements
• Advanced techniques like subqueries and timestamp functions
• Translate business questions to SQL logic

Learn more about Data Science Certificate at Practical Programming.