Data Science Classes Live Online

In this comprehensive data science program, individuals will learn a variety of critical skills to become a data scientist. Students will learn to code with Python, create machine learning models, automate tasks like web scraping, and interact with databases using SQL. Topics include data analysis with Python's Numpy and Pandas packages, data visualization, predictive analytics, machine learning, SQL querying, Python automation, and web scraping. Students will learn hands-on by working in Python and SQL and will complete the program with the skills they need to enter the field of data science. This program offers flexible scheduling and provides a free retake for students to refresh the material.

This online data science program is meant to get students ready for a career in data science. The program covers the key components for data science, including SQL, Python, statistics, and machine learning. Students will start coding in Python using Jupyter notebooks, learn the statistics behind machine learning, apply machine learning techniques, and work on data science projects in this data science course. Students will also receive career coaching and job support during the program for help finding a job in data science. The online program offers flexible scheduling options, including full-time over 5 months, part-time over 10 months, and self-paced for up to 15 months.

This full-time, accredited data science bootcamp covers data wrangling and exploratory data analysis using Python's statistical and data visualization packages. It also teaches regression techniques, web scraping, classification techniques using supervised and unsupervised models, and natural language processing. Finally, the courses finishes off with a capstone data science project where students can showcase their knowledge and talent.

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 bootcamp offers the ability to learn online with a 16-week full-time schedule or a 24-week part-time schedule. Students will learn how to use tools such as Python, R, Hadoop, and AWS to facilitate data visualization and analysis, machine and deep learning, and building statistical models. This program culminates in a professional capstone experience.

This 35-hour program offers a comprehensive introduction to R. This course teaches the skills needed to process, manipulate, and analyze data, create visualizations, and generate reports. The program begins with an overview of programming with R. It then moves on to the treatment of basic data elements, the use of “dplyr” to manipulate data, and how to create basic and advanced visualizations such as violin and mosaic plots, and time-series diagrams.

This six-week course teaches students how to use key components of Apache Hadoop, Spark, MapReduce, and more. Using platforms such as Databricks, Amazon Web Services, and Docker, students will learn how to use Python and cloud computing to run exercises. Students will complete five units on topics such as Hadoop, MapReduce, Apache Hive, Apache Pig, Apache Spark, and Amazon Web Services.

Data Science Immersive is a 5-month full-time program that will prepare you to be a data scientist. You don't have to pay anything until you receive a job that is more than $40,000. To be accepted into this program, you will have to complete 3 weeks of prepwork and pass an assessment test before you start. Once you're accepted, you'll learn how to clean data using Python, query data using SQL, learn how to train and evaluate supervised and unsupervised machine learning models, and choose a track to specialize. You can deepdive in natural language processing, big data and spark, and deep learning with TensorFlow and Keras.

The Remote Data Analytics Bootcamp is a 9-week full-time program that will start by teaching you the basics of MySQL, Python, and Git. Once you've mastered the basics, it will introduce web scraping, API, and data cleaning concepts. Afterwards, you will learn about inferential statistics, probability, topics in business intelligence. Finally, the course will teach you how to build, train, and evaluate supervised and unsupervised machine learning models. You will complete three projects after each module and showcase your last project to your peers in a "Hackshow".

The Remote Data Analytics Part-Time Bootcamp is 24 weeks long that will start by teaching you the basics of MySQL, Python, and Git. Once you've mastered the basics, it will introduce web scraping, API, and data cleaning concepts. Afterwards, you will learn about inferential statistics, probability, topics in business intelligence. Finally, the course will teach you how to build, train, and evaluate supervised and unsupervised machine learning models. You will complete three projects after each module and showcase your last project to your peers in a "Hackshow".

Online Data Science Certificate is a 16-week comprehensive that will teach you teach you the entire data science workflow from start to finish. The program will start with data cleaning, visualization, and exploration concepts. Afterwards, it will teach you how to use various predictive algorithms and how to evaluate their performance. It also introduces boosting, ensemble, ranking, and linear regression methods that are important tools for data scientists. Finally, you will have an opportunity to use your knowledge and deploy your models using a dataset from Kaggle competitions.