Flex Data Analytics Bootcamp

Become a Data Analyst using the most sought-after curriculum in the industry!

  • 6 month access | 100% online | Live group sessions 6 month access | 100% online | Live group sessions
  • Beginner & advanced friendly Beginner & advanced friendly
  • Job guaranteed Job guaranteed

Get the Flex Data Analyst coursebook

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300+

PLACEMENT PARTNERS

86%

GRADUATION RATE

$81,310

AVERAGE SALARY

$23k

SALARY INCREASE

Skills & technologies covered

Curriculum

Skills & technologies covered

1
Intro to Data
2
Excel
3
Statistics
4
SQL
5
Python
6
Shell Bash Scripting
7
MongoDB / JSON
8
Data Analysis with R
9
Capstone Projects

Module 1

Intro to Data

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.

Technologies Used:

Basic statistics, career orientation, course goals

MODULE 2

Excel

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.

Skills Learned

  • Entering and manipulating data
  • Creating formulas
  • Charts and graphs
  • Import and export CSV
  • VLOOKUP
  • Pivot tables
  • Split and concatenate text

Technologies Used:

Excel

MODULE 3

Statistics

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.

Skills Learned

  • Mean, median
  • Mode
  • Standard deviation
  • Skew
  • Distributions
  • Subpopulations
  • Normal distribution
  • Probability
  • Percentiles
  • Correlation and causation
  • Inferential and descriptive statistics
  • Samples
  • Experiments
  • Evaluation of experiments

Technologies Used:

Excel

MODULE 4

SQL

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.

Skills Learned

  • Entity Relationship Diagrams
  • Relational Databases
  • Advanced Querying
  • Database Management

Technologies Used:

DBeaver, PostgreSQL

MODULE 5 & 6

Python

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.

Skills Learned

  • Python, Data analysis with python
  • Automation
  • Web scraping

Technologies Used:

Python, Numpy, Pandas, Matplotlib, SciPy, RegEx, Beautiful Soup, Automations, Jupyter Notebook

MODULE 7

Shell Bash Scripting

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.

Skills Learned

  • Scripting, SQL
  • Python
  • Automation

Technologies Used:

Linux, terminal, CLI

MODULE 8

MongoDB / JSON

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.

Skills Learned

  • NoSQL
  • Intro to cloud data

Technologies Used:

MongoDB Atlas, MongoDB Compass, JSON, Python, Cypher

MODULE 9

Data Analysis with R

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.

Skills Learned

  • Data analysis
  • R
  • Rstudio
  • dplyr
  • ggplot2
  • tidyvers

Technologies Used:

R, RStudio, Jupyter Notebook, dplyr, ggplot2, tidyverse

MODULE 11

Capstone Projects

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

Project 1: Parallel Analysis Report Project 1: Parallel Analysis Report

In this project, students will be expected to find a data set to analyze, and then do so in Python, R, and SQL/Excel if possible. Students will perform the same analysis in all three languages with the goal of understanding the subtle differences between the three tools. After forming a hypothesis, they will explore the data, and this work will culminate in a written report detailing the exploration process, as well as pros and cons of each platform for the analysis. The report will be written as if it is being sent in to a business or research entity as a cumulative response to the hypothesis.

Project 2: Analysis Presentation Project 2: Analysis Presentation

A huge part of analysis will be explaining and communicating data to people who don’t speak data. In this project, students will choose a database and language (R, Python, or SQL/Excel) and explore a data set after creating a hypothesis about it. After spending time in the data, they will create a presentation, and then use Loom or similar software to explain the results of the search in a 5-10 minute pre-recorded presentation.

1
Intro to Data

Module 1

Intro to Data

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.

Technologies Used:

Basic statistics, career orientation, course goals

2
Excel

MODULE 2

Excel

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.

Skills Learned

  • Entering and manipulating data
  • Creating formulas
  • Charts and graphs
  • Import and export CSV
  • VLOOKUP
  • Pivot tables
  • Split and concatenate text

Technologies Used:

Excel

3
Statistics

MODULE 3

Statistics

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.

Skills Learned

  • Mean, median
  • Mode
  • Standard deviation
  • Skew
  • Distributions
  • Subpopulations
  • Normal distribution
  • Probability
  • Percentiles
  • Correlation and causation
  • Inferential and descriptive statistics
  • Samples
  • Experiments
  • Evaluation of experiments

Technologies Used:

Excel

4
SQL

MODULE 4

SQL

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.

Skills Learned

  • Entity Relationship Diagrams
  • Relational Databases
  • Advanced Querying
  • Database Management

Technologies Used:

DBeaver, PostgreSQL

5
Python

MODULE 5 & 6

Python

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.

Skills Learned

  • Python, Data analysis with python
  • Automation
  • Web scraping

Technologies Used:

Python, Numpy, Pandas, Matplotlib, SciPy, RegEx, Beautiful Soup, Automations, Jupyter Notebook

6
Shell Bash Scripting

MODULE 7

Shell Bash Scripting

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.

Skills Learned

  • Scripting, SQL
  • Python
  • Automation

Technologies Used:

Linux, terminal, CLI

7
MongoDB / JSON

MODULE 8

MongoDB / JSON

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.

Skills Learned

  • NoSQL
  • Intro to cloud data

Technologies Used:

MongoDB Atlas, MongoDB Compass, JSON, Python, Cypher

8
Data Analysis with R

MODULE 9

Data Analysis with R

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.

Skills Learned

  • Data analysis
  • R
  • Rstudio
  • dplyr
  • ggplot2
  • tidyvers

Technologies Used:

R, RStudio, Jupyter Notebook, dplyr, ggplot2, tidyverse

9
Capstone Projects

MODULE 11

Capstone Projects

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

Project 1: Parallel Analysis Report Project 1: Parallel Analysis Report

In this project, students will be expected to find a data set to analyze, and then do so in Python, R, and SQL/Excel if possible. Students will perform the same analysis in all three languages with the goal of understanding the subtle differences between the three tools. After forming a hypothesis, they will explore the data, and this work will culminate in a written report detailing the exploration process, as well as pros and cons of each platform for the analysis. The report will be written as if it is being sent in to a business or research entity as a cumulative response to the hypothesis.

Project 2: Analysis Presentation Project 2: Analysis Presentation

A huge part of analysis will be explaining and communicating data to people who don’t speak data. In this project, students will choose a database and language (R, Python, or SQL/Excel) and explore a data set after creating a hypothesis about it. After spending time in the data, they will create a presentation, and then use Loom or similar software to explain the results of the search in a 5-10 minute pre-recorded presentation.

Dive deeper into our Flex curriculum

Schedule

Daily schedule

Flex learning allows you to get the coding bootcamp experience on your own time. You are able to move at your own speed, free from deadlines and class schedules. You’ll learn to program through a mix of recorded lectures, coding exercises, and projects.

Support

We set students up for success

1:1 Live support sessions with an instructor

1:1 Live support sessions with an instructor

Weekly peer programming and code wars sessions

Weekly peer programming and code wars sessions

Real projects and graded assignments

Real projects and graded assignments

Dedicated Student Success Manager

Dedicated Student Success Manager

Slack community

Slack community

Fine-tune your communication

Fine-tune your communication

Build your personal brand

Build your personal brand

Sharpen your technical skills

Sharpen your technical skills

Leverage our employer network

Leverage our employer network

Build a meaningful community

Build a meaningful community

Utilize resources and templates

Utilize resources and templates

Careers

We offer lifetime career services

Our post-graduation services provide each student with the necessary resources, tools, and guidance to build a meaningful career. We provide 1-1 support for the entirety of your professional journey.

Learn more

Want quick answers to your questions?

Quick Questions

Our Admissions Representatives are ready to help

We’ll work 1-1 to get your questions answered.

We’re here to help you understand our curriculum and financing, as well as give you information about post-graduation services.

Quick Questions

Attend one of our Weekly Webinars

See what our program is like and learn how to get started.

Ask questions during our live Q&A.

Curriculum Reviews

Don’t just take our word for it. Hear from our

Flex Data Analytics graduates

Five Stars

"Committing to a boot camp without much knowledge of what you are getting into can be scary. The Coding Temple creates a fun, engaging environment to help you feel comfortable. The instructors are extremely knowledgeable and patient while willing to help you learn. Coding temple has many resources to assist in your learning experience and career coaches to help carve your career path! Also, it doesn’t end when you graduate, you become part of a community!"

Sean

Five Stars

"They teach you the most up-to-date technology and practices. The instructors are extremely knowledgeable, and the support staff are great! You will be expected to put in the work and effort, but they provide a lot of support and help for you when you need it. Everyone is easily accessible, and they actually respond to you when you reach out to them. They provide a ton of job support after you graduate. You have interview support, job search support, resume writing support, weekly coding post-grad class, and continued coding challenges to help you stay up to date on your coding skills."

Kia

Five Stars

"I highly recommend this course if you want to push yourself to learn something new, change careers, or improve on whatever coding skills you already have and come out the other side prepared to tackle the job search."

Thumbelina W.

Five Stars

"If you're looking for a bootcamp that not only provides you with the resources to be successful but also gives you a sense of belonging, look no further! I am beyond grateful for the experience Coding Temple provided me with and I would highly recommend it to anyone! You will get out of this bootcamp what you put into it."

Yesenia V.

Tuition + payment plans breakdown

Program cost

Save $1000

Pay Up Front

Save $500 on program cost by paying upfront. No additional payments required.

$ 8,995

Pay With a Loan

Pay With a Loan

Pay with Climb. Secure payment platform. Seamless payment experience.

$ 9,995

Secure Payment

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Admissions process

How to get started

1

Explore our career paths and courses

Ready to start your new career in tech? Explore our different career tracks and see which path interests you the most!

Explore Courses
2

Application and basic skills assessment

Submit your application – it takes less than 5 minutes. After you apply you will be sent a basic skills assessment. Our assessment is meant to test your cognitive skills. Don’t stress! We want to know if you can think like a programmer, if you can we will take it from there!

Apply Now
3

Schedule an admissions call

Talk with our admissions team so we can get to understand your career goals and answer any questions you have about our program.

Schedule a Call
4

Secure your seat and enroll

Finalize your payment plan to secure your seat! Once you’re enrolled, you will gain instant access to our preparatory work, slack channels, and 1:1 support prior to class.

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