Immersive Data Analytics bootcamp

Learn the big picture of data roles, and how they fit together. Understand what a data analyst does specifically and how it differs from data engineers, scientists, and machine learning engineers. Gain a cursory understanding of statistics and tabular data, and the importance of data. Students will be able to explain the difference between continuous and discrete data, as well as the differences between experimental and observational data collection. They’ll also be oriented to the full course and how to use the CT Self Paced model of education. This section will also include how to use slack, google classroom, course etiquette and norms, as well as the installs required to make the course work.

The first of these two modules will be focused on basic python programming. This will be the same curriculum as our full-stack self-paced course, where we learn data types, writing and executing custom functions, we will get started with scripting in module 2 with Python. This module will get you up to speed on the core concepts of programming using Python’s elegant, easy-to-learn syntax. You’ll learn about looping, conditional statements, data types, object-oriented programming, and more – culminating in a series of small Python applications like shopping carts and interactive games. The second module of Python will focus on doing data analysis with Numpy, Pandas, Matplotlib, and Scipy. We’ll also pick up Regular Expressions, Beautiful Soup for web scraping, and actually automating Excel with Python.

Build, query, sort and update relational databases in module 4 with SQL. We’ll make our own functions to help automate SQL processes as well. Students will learn about making entity relationship diagrams, the importance of planning ahead when it comes to databases, and how certain data relates to other data. Most tabular data lives in SQL databases, so we will need to be very familiar with this language to explore our data. We’ll look at moving data back and forth between SQL and Excel as well using CSV files. We will build a very good foundational understanding of relational databases, and then practice SQL repeatedly to achieve intermediate SQL fluency. We will also look at a few advanced SQL techniques. We’ll also look a bit under the hood at how PostgreSQL works, and what other kinds of relational databases exist.

Module 6 equips learners with skills in data visualization using Tableau and Streamlit. In Tableau, learners master dashboard creation, diverse charting, and data connectivity, including SQL. Streamlit training encompasses Python-based dashboard development for effective, polished online presentation. The module also introduces MongoDB, providing insights into non-relational databases.

A programming language specifically meant for use in statistics, we will review most of the concepts in the rest of the course, particularly statistics, in R. Students will learn the basics of the programming language, as well as R’s counterparts to Python’s libraries – Learners will be engaged with ggplot2, tibbles, dplyr, and the tidyverse as well as basic operations, data structures, and more. This module will primarily be used to discuss exploratory data analysis as well.

There will be two capstone projects for this course with two different focuses.

You graduated, Congratulations! Now, the real work begins. This is where you will work with our Career Services team to make sure you are ready to start applying for jobs. They will be there to support you every step of the way through coding assessments, mock interviews, mock whiteboard sessions, resume reviews, LinkedIn reviews, workshops, AI learning, and more. Once you complete the needed requirements, you will be given access to Huntr to support your job search and have access to the team for any job search-related needs.