Throughout this certificate program, students develop essential programming capabilities enabling them to access, refine, and perform multiple levels of data investigation and evaluation. This comprehensive curriculum positions graduates for entry-level positions in data science and Python software engineering roles.
Master Programming Fundamentals
Master the essentials of Python programming and develop practical proficiency with Numpy, Pandas, and Matplotlib libraries designed for data analysis applications. Explore the primary data science libraries and gain experience constructing predictive analytical models utilizing machine learning software packages, including Sci-Kit Learn for advanced data analysis.
Read and Write Complex Queries
Database query interpretation and creation comprise critical competencies for data science professionals. During this program, students practice preparing and refining data for implementation within Python analytical workflows.
Automate Tasks
Leverage Python programming to streamline and automate everyday operations, including data consolidation, modification, and formatting tasks.
What You'll Learn
- Analyze tabular data using NumPy and Pandas libraries
- Create visualizations and graphical representations using Matplotlib
- Construct predictions using linear regression techniques
- Applying machine learning algorithms to data for pattern discovery
- Cleaning and balancing datasets within Pandas for quality improvement
- Evaluating performance metrics of machine learning model predictions
- Combine information across multiple tables using join statements
- Advanced techniques, including subqueries and stored procedures for complex queries
- Learn Python programming to automate routine everyday tasks efficiently
Courses in the Certificate Program
Python for Data Science Immersive:
- Programming foundations, including object creation, looping structures, and function definitions
- Object-oriented programming concepts and methodologies
- Data type manipulation for strings, lists, integers, and other formats
- Conditional statement logic to direct and control program execution flow
- Analyze tabular data using Python libraries NumPy and Pandas for data investigation
- Create data visualizations with Matplotlib for presentation and analysis
- Predict outcomes using linear regression methodology with Scikit-Learn tools
Python Machine Learning Immersive:
- Data cleaning and balancing approaches utilizing the Pandas library
- Implementing machine learning algorithms, including logistic regression and random forest, using Scikit-learn
- Selecting and engineering important features for algorithm input
- Dividing datasets appropriately into training, test, and cross-validation portions
- Understanding key theoretical principles, including overfitting, variance, and bias in modeling
- Assessing the performance effectiveness of your machine learning models
Python for Automation:
- Learn the Python syntax and programming structure conventions
- Learn how to execute your programs on a regularly scheduled basis
- Learn error handling and exception management techniques
SQL Bootcamp:
- Explore and modify data utilizing a graphical user interface
- Create queries to search database tables in a programmatic manner
- Understand various data types and learn conversion techniques
- Combine information across multiple tables through join operations
- Advanced techniques, including subqueries and timestamp function applications
- Translate business questions and requirements into SQL logic
Learn more about the Data Science Certificate at Practical Programming.
This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.
Upon registration, the instructor will send along additional information about how to log-on and participate in the class.
Also available in-person in NYC
