Best Data Analytics Bootcamps Online

This course guides students through a practice-based curriculum designed to help them enter careers in data analytics. Students start by learning about structured thinking for data analysis and how businesses solve economic, financial, and statistical problems with data analytic solutions. By gaining skills in SQL, Python, and Tableau, students learn to analyze and visualize data and present their findings and conclusions. Students graduate with a porfolio of work, including two capstone data analytics projects.

Through this sequence of two courses, students prepare for careers in data analytics. Students begin by learning about the theory and applications of data analytics, gaining skills in using pivot tables and statistics in Excel to understand, analyze, and visualize data. Then, students dig into practicing data analysis, developing proficiency with Python, SQL, and Tableau before learning about big data, machine learning, and ethical issues in data analysis. Students complete the course with a portfolio of professional data analytics work.

These eight sequential courses prepare beginner data analysts for careers in the field. Students start by learning about data and the kinds of problems data analysis can solve, then develop skills in SQL in order to extract, store, and clean data for analysis. Students also gain experience with data visualization and programming with R for data analysis. In the final course, students develop a data analysis case study for their capstone project and benefit from lessons in job hunting before they graduate with a certificate from Google.

Through this bootcamp, students with experience in programming and statistics can jumpstart their careers in data science. Students learn to use Python, SQL, Pandas, and APIs for data science as they develop skills for data wrangling and data analysis. In addition, students gain experience with inferential statistics, machine learning, deep learning, and natural language processing as they advance through the course and begin to scale up their work. By the end of the course, students will have produced two capstone projects.

After studying this course, students with a background in Python, machine learning, SQL, and statistics will have the essential skills for doing work with data science. The course starts by teaching students data visualization and presentation before diving into data pipelines with a unit on data engineering. Students then discover best practices for designing and testing experiments. At the end of the course, students develop a capstone project to practice their new data science skills.

Students with some experience in Python and SQL can specialize in data engineering with this course. The course introduces students to data modeling, lakes, and pipelines. Along the way, students develop skills with PostgreSQL, NoSQL, Spark, and Apache Airflow. Five hands-on activities and a capstone project help students practice their new data engineering skills.

Designed for students who have no prior programming experience, this course helps complete beginners gain the necessary experience to join the Data Science Career Track bootcamp. Students learn to code in Python and gain familiarity with data visualization, probability, and algorithms. Students also work with Pandas and matplotlib as they prepare to use those skills in the bootcamp. At the end of the course, students develop a capstone project to put their new skills into practice.

Machine learning students can extend their education by learning to make models in the Microsoft Azure program. The course begins with an orientation constructing pipelines in the program. Then, students learn about DevOps and work with the Automated ML feature and HyperDrive. Students complete the course with a capstone machine learning project built in Azure.

Beginners in this course learn the tools and concepts necessary for expert database management. Students work primarily with SQL as they gain experience with data manipulation before moving on to work with tables, clauses, queries, indexes, data warehouses, and schemas as they learn to structure queries. Students also learn to use industry-standard database management tools including Microsoft SQL Server and Visual Studio and Oracle Database. When they complete the course, students graduate with a certification issued by Austin Peay State University continuing education, recognized by the global CPD Certification Service.

With this course, machine learning students can take the next step in their deep learning education with deep reinforcement learning. The course starts with an introduction to the field, sharing insights into value-based and policy-based methods. Students build their own algorithms and train agents before learning about multi-agent reinforcement learning. By the end of the class, students will have completed three projects.

After studying this course, students of deep learning will have the skills for working with natural language processing. Students learn about text processing and sentiment analysis. Then, they gain experience with recurrent and deep neural networks. By the end of the course, students will have developed three practical projects for natural language processing.

With this course, machine learning students with a Python background can advance their skills by learning Amazon SageMaker to engineer models. Students learn to build, monitor, and evaluate machine learning workflows. Then, students discover natural language processing, including neural networks, as well as computer vision. By the end of the course, students will be able to build and operationalize their machine learning model capstone project.