Data Science Classes Melbourne

This data science program is designed to help individuals with a math background and some familiarity with Python programming bolster their skills and get up to speed and launch their data career. Through their blended learning model, students will be able to hone their data skills through fundamental preparatory work, in-class project work, and though the support of a devoted career coach. Students in this course will start their data science journeys by learning fundamental concepts around Python programming and leveraging programming tools like GitHub. As the course progresses, students will explore additional programming concepts like NumPy, utilizing Unix commands, using Pandas to clean and extract data, and web scraping tools. The course also covers statistical modeling and regressions, machine learning models, NLP, and a few other advanced topics and trends that are helpful to data scientists of all levels.

This data science course is a 10-week, part-time program for individuals with backgrounds in programming or a quantitative field. Utilizing a hands-on approach, students learn by doing to build vibrant skills that are essential in any field that leverages big data. The course curriculum centers around Python programming and data exploration, data modeling, and machine learning. Graduates of the Data Science course will be able to showcase their strong understanding of Python programming and applied statistics through their capstone projects, which are also a great springboard to a career in data science.

This nine-week bootcamp gives participants the skills they need to join data science teams or accelerate their current career growth. Learn how to convert raw data into insightful information. Specific course topics include understanding basic statistical theories, using Python to write code, using seaborn and matplotlib to visualize data, using SQL to develop databases, publishing findings, data processing, using performance metrics, overfitting, unsupervised learning, managing images and text, understanding neural networks, developing computer vision, machine learning, and a capstone project.

This data analytics course is a 40-hour in-person or online program for individuals with no prior experience with data analytics. Utilizing a hands-on approach, students learn by doing in order to build vital and dynamic skills that are applicable in a wide range of industries. The course curriculum centers around the core processes all data analysts must know, including data collection, cleaning, analyzing, and data visualization best practices. Students also come away with hands-on experience using some of the most common tools in the industry, including Microsoft Excel, SQL, and Tableau. By the end of the course, students will have a better understanding of how to gain insights from data and feel more prepared for a possible career in data analytics.

In this part-time bootcamp, 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.

This 1-week data analytics course provides a deep-dive into using Python for data analysis. Students will get comfortable with the basics of Python programming and start working with critical data analysis libraries like Numpy, Pandas, and Matplotlib to perform data analysis and create data visualizations. This 35 hour intensive is meant to quickly get beginners in Python up to speed on performing data analysis and visualization in Python.

This Python and machine learning course is a 2-day bootcamp designed for individuals with some familiarity with Python programming looking to advance their skill set. In this hands-on bootcamp, students will learn how to use some Python data science libraries and various machine learning techniques. Day one of the bootcamp focuses on introducing students to two data science libraries (NumPy and Pandas), best practices for preparing data, an intro to machine learning, and regression analysis. Day two starts off with cross validation and regularization of data, concludes with classification, and brings everything learned in days one and two together.

Introduction to Data Analysis is a 17.5-hour course open to beginners. Students learn data analysis through programming, machine learning, data wrangling, and data visualization.

This machine learning course is meant for learners looking to master the concepts needed for machine learning. Students will learn how machine learning can be used for decision-making, the different machine learning techniques available, and how to tackle machine learning projects. The course covers various techniques in machine learning, including regression, classification, k-means, neural networks, and more.

In this introductory, 2-day SQL course, participants will learn the basics of databases and start querying databases with SQL. Participants will get comfortable with databases and start performing basic querying techniques like SELECT, ORDER, SUM, and GROUP BY. This course is intended to give students a fundamental understanding of the use of SQL for database management.