Flex Data Analytics Bootcamp

Learn tabular data with Microsoft Excel! Students will be introduced to the concept of tables and tabular data, how to work in tables, organize data, and more. Students will be able to create spreadsheets and adjust data values with ease, find anomalies, and do basic calculations. We will focus on ease-of-access and shortcuts as well to help make Excel work faster for students.

In this module, students will learn beginner and intermediate statistics. We will look at some common statistical errors, revisit data types, and create a ‘cheat sheet’ for ourselves to use when we are asking ‘What kind of graph should I use to display this data?’ Students will also learn about distributions, subpopulations, normal distribution, probability, percentiles, correlation and causation, inferential and descriptive statistics, samples, experiments, and evaluation of experiments. We’ll also start looking into forming hypotheses and how to use statistical tools to be data detectives.

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.

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.

Similar to python scripting, we will write a few scripts in our command line interfaces. This will be a lighter module, but we will get very used to writing in the shell, and write a little Python and PostgreSQL as well. We will look at ways of executing scripts in the bash shell and learn basic Linux commands as well.

In this module, students will pick up MongoDB and NoSQL data. We’ll get familiar with writing non-relational data, look at some of the advantages and disadvantages, and get querying using MongoDB’s Compass software. We’ll also briefly look at graph data, which is not part of MongoDB, but is something data heads should be aware of. Students will use Python to pull data from MongoDB and JSON documents hosted on the internet. We’ll also discuss how the cloud works, since MongoDB is a cloud-based database.

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.