Launch your Data Analytics career

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  • Graduate in 6 months or less
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Overview

Become a Data Analyst

Discover the process of examining and evaluating raw data in order to bring meaning and insight that empowers intelligent decision-making, leading you to rewarding careers as:

  • Data Analyst
  • Operations Analyst
  • Data Engineer
  • Learn how to turn raw data into meaningful insights
  • Explore beginner and intermediate statistics
  • Inclusive of coding basics for more understanding of data manipulation and analysis techniques
  • Engage in hands-on exercises that replicate the challenges faced by data professionals in various industries

You’ll learn the tools and languages analysts use, like:

Microsoft Excel
Python
Tableau
PostgreSQL
Pandas
dplyr
NumPy
ggplot2
Matplotib
Jupyter
MongoDB
tidyverse
Linux
Githhub
Git
R
DBeaver
Learning Schedule

Learning schedules that align with your lifestyle and career goals

Fully online, project-based curriculum

Flexible learning hours to fit your schedule, allowing you to balance work, life, and study.

Regular assessments to track your progress and ensure you’re on the right path.

Flex: Self-paced 6 month access

Curriculum

What you’ll learn in this data analytics bootcamp

Download your Coursebook

Intro to Coding Basics (10 hours)

Discover various data roles and their distinctions. Gain insight into data analyst duties versus those of data engineers, scientists, and machine learning engineers. Learn fundamental statistics and tabular data concepts. Understand the significance of data types and collection methods. Familiarize with the course structure, including CT Self Paced model, Slack, Google Classroom, and software installations.

Key Elements of the module include:

  • Understanding Data Roles: Gain insight into various data roles such as data analyst, data engineer, data scientist, and machine learning engineer, including their responsibilities and differences.
  • Statistics and Tabular Data: Learn foundational concepts in statistics and tabular data analysis, including the importance of data in decision-making processes.
  • Data Types and Collection Methods: Understand the differences between continuous and discrete data types, as well as experimental and observational data collection methods.
  • Course Orientation and Tools: Get oriented to the full course structure and curriculum, including an introduction to the CT Self-Paced model of education. Learn how to use communication platforms like Slack and Google Classroom, adhere to course etiquette and norms, and install required software for course participation.

Learning Excel(10 hours)

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.

 

Key Elements of the module include:

  • Introduction to Tabular Data: Understand the fundamentals of tabular data and tables within Microsoft Excel, including how to organize and manipulate data effectively.
  • Spreadsheet Creation and Data Adjustment: Learn how to create spreadsheets and manipulate data values efficiently within Excel, including techniques for organizing data and performing basic calculations.
  • Anomaly Detection: Gain skills in identifying anomalies or irregularities within datasets using Excel’s features and functions.
  • Efficiency Techniques: Explore shortcuts and best practices to increase efficiency and productivity while working with Excel, enabling students to work faster and more effectively.

Basic Statistics(4 hours)

In this module, students will cover beginner and intermediate statistics, including common errors, data types review, and creating a graph selection guide. Topics include distributions, subpopulations, normal distribution, probability, percentiles, correlation, causation, inferential and descriptive statistics, samples, experiments, and evaluating experiments. Also, students will explore hypothesis formation and statistical tools for data analysis.

Key Elements of the module include:

  • Common Statistical Errors: Understand common errors encountered in statistical analysis and how to avoid them, ensuring accuracy in data interpretation and decision-making.
  • Data Types and Visualization: Revisit different data types and learn how to choose the appropriate graph or visualization method for displaying different types of data effectively.
  • Fundamental Concepts: Gain knowledge of key statistical concepts such as distributions, subpopulations, normal distribution, probability, percentiles, correlation and causation, inferential and descriptive statistics, samples, experiments, and evaluation of experiments.
  • Hypothesis Formation and Statistical Tools: Learn how to form hypotheses based on data observations and utilize statistical tools to analyze data and draw meaningful conclusions, empowering students to become proficient data detectives.

SQL (20 hours)

In module 4, learn SQL for building, querying, sorting, and updating relational databases. Students will develop custom functions for automating SQL tasks. Topics include entity relationship diagrams, database planning, and data relationships. Since much tabular data resides in SQL databases, familiarity with SQL is essential. Students will also explore data transfer between SQL and Excel using CSV files. The focus is on establishing a strong foundation in relational databases and gaining intermediate SQL skills through repeated practice. Additionally, advanced SQL techniques and an overview of PostgreSQL and other relational databases will be covered.

 

Key Elements of the module include:

 

  • Building and Querying Relational Databases: Learn how to create, query, sort, and update relational databases using SQL, including the development of functions to automate processes.
  • Entity Relationship Diagrams (ERDs): Understand the importance of designing databases with entity relationship diagrams, emphasizing the significance of proper planning and data relationships.
  • Data Manipulation and Transfer: Explore techniques for moving data between SQL databases and Excel using CSV files, enabling seamless integration and analysis of tabular data.
  • Foundational and Intermediate SQL Proficiency: Develop a strong foundational understanding of relational databases and SQL, practicing SQL queries repeatedly to achieve intermediate fluency. Additionally, delve into advanced SQL techniques to enhance data manipulation and analysis capabilities. Gain insights into the workings of PostgreSQL and other relational database management systems.

 

Python(40 hours)

The first part of this module covers basic Python programming, mirroring our full-stack self-paced course. Topics include data types, custom functions, and scripting, providing a comprehensive introduction to Python’s syntax. You’ll explore looping, conditional statements, object-oriented programming, and more, creating Python applications such as shopping carts and interactive games. The latter part focuses on data analysis using NumPy, Pandas, Matplotlib, and introduces Regular Expressions.

 

Key Elements of the module include:

     

  • Basic Python Programming: Learn fundamental concepts of Python programming, including data types, writing custom functions, looping, and conditional statements, to build a solid foundation in programming.
  • Scripting and Application Development: Dive into scripting with Python, exploring its elegant syntax and applying core programming concepts to develop small applications such as shopping carts and interactive games.
  • Data Analysis with Python Libraries: Explore data analysis capabilities using Python libraries such as NumPy, Pandas, and Matplotlib, enabling manipulation, visualization, and interpretation of data for analytical insights.
  • Regular Expressions: Gain proficiency in using regular expressions within Python for pattern matching and text processing tasks, enhancing your ability to extract and manipulate data efficiently.

R(40 hours)

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.

Key Elements of the module include:

  • Introduction to R Programming: Learn the fundamentals of the R programming language, including basic operations, data structures, and syntax, tailored specifically for statistical analysis.
  • R Libraries and Packages: Explore R’s counterpart to Python’s libraries, including ggplot2, tibbles, dplyr, and the tidyverse, for data manipulation, visualization, and analysis tasks. Exploratory
  • Data Analysis (EDA): Dive into exploratory data analysis techniques using R, leveraging its powerful libraries and tools to gain insights into datasets and identify patterns and trends.
  • Application of Statistical Concepts: Apply statistical concepts learned throughout the course, particularly in the realm of statistics, to real-world data analysis tasks using R, reinforcing understanding and proficiency in statistical analysis.

MongoDB, Shell, and more(10 hours)

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.

Key Elements of the module include:

 

  • MongoDB and NoSQL Fundamentals: Introduction to managing non-relational data with MongoDB.
  • Querying with MongoDB Compass: Hands-on querying and data manipulation using MongoDB’s graphical interface.
  • Python Integration and Cloud Concepts: Utilizing Python for MongoDB interaction and an overview of cloud-based databases.
  • Intro to Coding Basics

    Intro to Coding Basics (10 hours)

    Discover various data roles and their distinctions. Gain insight into data analyst duties versus those of data engineers, scientists, and machine learning engineers. Learn fundamental statistics and tabular data concepts. Understand the significance of data types and collection methods. Familiarize with the course structure, including CT Self Paced model, Slack, Google Classroom, and software installations.

    Key Elements of the module include:

    • Understanding Data Roles: Gain insight into various data roles such as data analyst, data engineer, data scientist, and machine learning engineer, including their responsibilities and differences.
    • Statistics and Tabular Data: Learn foundational concepts in statistics and tabular data analysis, including the importance of data in decision-making processes.
    • Data Types and Collection Methods: Understand the differences between continuous and discrete data types, as well as experimental and observational data collection methods.
    • Course Orientation and Tools: Get oriented to the full course structure and curriculum, including an introduction to the CT Self-Paced model of education. Learn how to use communication platforms like Slack and Google Classroom, adhere to course etiquette and norms, and install required software for course participation.
  • Microsoft Excel

    Learning Excel(10 hours)

    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.

     

    Key Elements of the module include:

    • Introduction to Tabular Data: Understand the fundamentals of tabular data and tables within Microsoft Excel, including how to organize and manipulate data effectively.
    • Spreadsheet Creation and Data Adjustment: Learn how to create spreadsheets and manipulate data values efficiently within Excel, including techniques for organizing data and performing basic calculations.
    • Anomaly Detection: Gain skills in identifying anomalies or irregularities within datasets using Excel’s features and functions.
    • Efficiency Techniques: Explore shortcuts and best practices to increase efficiency and productivity while working with Excel, enabling students to work faster and more effectively.
  • Basic Statistics

    Basic Statistics(4 hours)

    In this module, students will cover beginner and intermediate statistics, including common errors, data types review, and creating a graph selection guide. Topics include distributions, subpopulations, normal distribution, probability, percentiles, correlation, causation, inferential and descriptive statistics, samples, experiments, and evaluating experiments. Also, students will explore hypothesis formation and statistical tools for data analysis.

    Key Elements of the module include:

    • Common Statistical Errors: Understand common errors encountered in statistical analysis and how to avoid them, ensuring accuracy in data interpretation and decision-making.
    • Data Types and Visualization: Revisit different data types and learn how to choose the appropriate graph or visualization method for displaying different types of data effectively.
    • Fundamental Concepts: Gain knowledge of key statistical concepts such as distributions, subpopulations, normal distribution, probability, percentiles, correlation and causation, inferential and descriptive statistics, samples, experiments, and evaluation of experiments.
    • Hypothesis Formation and Statistical Tools: Learn how to form hypotheses based on data observations and utilize statistical tools to analyze data and draw meaningful conclusions, empowering students to become proficient data detectives.
  • SQL

    SQL (20 hours)

    In module 4, learn SQL for building, querying, sorting, and updating relational databases. Students will develop custom functions for automating SQL tasks. Topics include entity relationship diagrams, database planning, and data relationships. Since much tabular data resides in SQL databases, familiarity with SQL is essential. Students will also explore data transfer between SQL and Excel using CSV files. The focus is on establishing a strong foundation in relational databases and gaining intermediate SQL skills through repeated practice. Additionally, advanced SQL techniques and an overview of PostgreSQL and other relational databases will be covered.

     

    Key Elements of the module include:

     

    • Building and Querying Relational Databases: Learn how to create, query, sort, and update relational databases using SQL, including the development of functions to automate processes.
    • Entity Relationship Diagrams (ERDs): Understand the importance of designing databases with entity relationship diagrams, emphasizing the significance of proper planning and data relationships.
    • Data Manipulation and Transfer: Explore techniques for moving data between SQL databases and Excel using CSV files, enabling seamless integration and analysis of tabular data.
    • Foundational and Intermediate SQL Proficiency: Develop a strong foundational understanding of relational databases and SQL, practicing SQL queries repeatedly to achieve intermediate fluency. Additionally, delve into advanced SQL techniques to enhance data manipulation and analysis capabilities. Gain insights into the workings of PostgreSQL and other relational database management systems.

     

  • Python

    Python(40 hours)

    The first part of this module covers basic Python programming, mirroring our full-stack self-paced course. Topics include data types, custom functions, and scripting, providing a comprehensive introduction to Python’s syntax. You’ll explore looping, conditional statements, object-oriented programming, and more, creating Python applications such as shopping carts and interactive games. The latter part focuses on data analysis using NumPy, Pandas, Matplotlib, and introduces Regular Expressions.

     

    Key Elements of the module include:

         

    • Basic Python Programming: Learn fundamental concepts of Python programming, including data types, writing custom functions, looping, and conditional statements, to build a solid foundation in programming.
    • Scripting and Application Development: Dive into scripting with Python, exploring its elegant syntax and applying core programming concepts to develop small applications such as shopping carts and interactive games.
    • Data Analysis with Python Libraries: Explore data analysis capabilities using Python libraries such as NumPy, Pandas, and Matplotlib, enabling manipulation, visualization, and interpretation of data for analytical insights.
    • Regular Expressions: Gain proficiency in using regular expressions within Python for pattern matching and text processing tasks, enhancing your ability to extract and manipulate data efficiently.
  • R

    R(40 hours)

    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.

    Key Elements of the module include:

    • Introduction to R Programming: Learn the fundamentals of the R programming language, including basic operations, data structures, and syntax, tailored specifically for statistical analysis.
    • R Libraries and Packages: Explore R’s counterpart to Python’s libraries, including ggplot2, tibbles, dplyr, and the tidyverse, for data manipulation, visualization, and analysis tasks. Exploratory
    • Data Analysis (EDA): Dive into exploratory data analysis techniques using R, leveraging its powerful libraries and tools to gain insights into datasets and identify patterns and trends.
    • Application of Statistical Concepts: Apply statistical concepts learned throughout the course, particularly in the realm of statistics, to real-world data analysis tasks using R, reinforcing understanding and proficiency in statistical analysis.
  • MongoDB, Shell, and more

    MongoDB, Shell, and more(10 hours)

    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.

    Key Elements of the module include:

     

    • MongoDB and NoSQL Fundamentals: Introduction to managing non-relational data with MongoDB.
    • Querying with MongoDB Compass: Hands-on querying and data manipulation using MongoDB’s graphical interface.
    • Python Integration and Cloud Concepts: Utilizing Python for MongoDB interaction and an overview of cloud-based databases.

Here’s why students choose Coding Temple

Dylan and Christian created a collaborative and inspiring learning environment. Their supportive and interactive teaching style significantly enhanced the in-class experience, making the intensive learning process not just bearable, but utterly enjoyable.

 

Coding Temple

Oackland T.

I had an incredible experience at Coding Temple. The instructors (Flex team—Brandt, Donovan, and Saint) were not only highly knowledgeable but also incredibly supportive and always available to provide guidance. Their continuous feedback and reassurance boosted my confidence and made me feel like I was making great progress.

Coding Temple

Melibeth M.

I’ve always been intrigued to learn coding and to chance to work remotely. I chose Coding Temple based on the review and now, I can say that was a best choice!    I did the self-paced program and did my lessons and assignments and projects at my own pace-that was the beauty of it.

Coding Temple

Melisse Z.

I was still in my senior year of highschool, but I wanted a head start on my career! Coding Temple made that possible with their flex/self-paced schedule! With this I was able to complete lessons on time whenever it was convenient! Though the work was challenging and humbling, I had the help of fellow peers and instructors who I got to know very well, helping me with whatever issues one may face with code or issues with payment/deadlines! The experience is something that I will never forget and am forever grateful to have taken part in!

Coding Temple

Augustine T.

Coding Temple exceeded my expectations and left me feeling well-equipped for my career in coding. I highly recommend it to anyone seeking to dive into the world of software development or looking to enhance their existing skills. I had fun learning from my instructors, who were always happy to help when I was stuck and willing to explain things in different ways if needed. They made learning the material easy.

Coding Temple

John C.

The best part about Coding Temple is the alumni resources, which include career and tech advisors for life!

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Ibsy M.

I started CT in February and just graduated yesterday. I tried out a couple of bootcamps before CT and knew they just weren’t for me. The culture, the way the curriculum was set up. I LOVED that the student to teacher ratio was about 1:4 (compared to others bootcamps where its about 1:15). Our instructors made it known it is a very fast paced course and set us up for success every step of the way. Id recommend this course to anyone that wants to learn how to code!

Coding Temple

Monique V.

The coding experience and confidence I gained from this boot camp cannot be overstated. In the span of 12 weeks I feel I reached a deep understanding of back end and front end development. The road map was clearly laid out and the staff was around anytime I needed help. The lectures were well structured and the pacing allowed for more in depth coverage on anything that I felt I didn’t understand at first.

Coding Temple

Mason B.

4.97/5

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Instructors

4.96/5

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Career Services

4.86/5

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Curriculum

Boost your skills learning from passionate software experts and mentors

Project Based Curriculum

Dive-deep into real-world datasets to extract valuable insights and drive data-informed decisions

Learn advanced data visualization techniques to effectively communicate insights and trends

Work on a hands-on capstone project to solve complex data analysis challenges

How it Works

See inside our program

Become Proficient in Data Analytics

Dive deep into the world of data analytics with our comprehensive program. Coding Temple’s Data Analytics course is meticulously designed to turn you into an adept data analyst, ready to tackle the challenges of today’s data-driven world. From the basics of data manipulation to advanced analytics techniques, we cover it all.

Analyze and visualize real-life datasets

Our data analytics course transcends theory, culminating in a capstone project where you can tackle real-world challenges. Through hands-on practice, you’ll be able to master diverse data tools, converting raw data into actionable insights, ultimately preparing you for success in the dynamic data analytics field.

Stand Out from the Rest

Our digital badges offer a powerful platform to showcase your journey and acquired competencies. Coding Temple credentials provide detailed insights into each student’s proficiencies, enhancing overall understanding of learner achievements. Utilize these badges to effortlessly communicate your expertise, opening new pathways for growth and overcoming job market challenges.

Master the Art of Technical Interviews with Expert Guidance

Our program includes specialized coaching focused on preparing you for technical interviews and assessments. This crucial training involves mock interviews, coding challenges, and problem-solving exercises tailored to mimic real interview scenarios. Our experienced coaches provide personalized feedback, share strategies for tackling complex questions, and offer tips on communicating your thought process effectively. This targeted preparation ensures you enter every technical interview with confidence, well-equipped to impress potential employers.

Equip Yourself with the Full Suite of Professional Skills

Our program goes beyond technical skills, focusing also on the softer aspects critical to your professional success. Learn effective communication, teamwork, problem-solving, and time management. We prepare you for the realities of the tech workplace, ensuring you’re not just a great coder, but a well-rounded professional.

Your Success, Our Commitment

We are committed to your career success post-graduation. Our comprehensive job support includes resume building, interview preparation, networking strategies, and access to our vast network of industry contacts.

Future-proof your career with our Lifetime Career Support

Career Services and support doesn’t have an expiration period. We are here for your career transformation, not just your first new role in tech

90%

Employment Rate

$23k

Salary Lift

Logo

We exist to get our learners jobs.

  • Benefit from 2:1 Career Mentors
  • Get recruited by our hiring partners
  • Mock Technical Interviews
  • Access exclusive resources and tools

Technical Training

  • CoderPad Technical Assessments
  • Mock Technical Interviews
  • Technical Interview Coaches
  • Technical Workshops
  • Coding Challenges
  • Guest Lectures from Industry Experts

Soft Skills Coaching

  • Mock Interviews
  • Behavioral Training
  • Career Coaching
  • Salary Negotiation
  • LinkedIn Optimization
  • Resume Refinement
Tuition & Financing Options

Invest in your Future

Access to flexible payment options to make a life-changing education.

BEST VALUE

Pay Up Front

Pay up front and save 30% on tuition

$12,995

$8,995

  • Total Tuition before discount $12,995
  • Discount -$4,000
  • Paid at enrollment $8,995
  • Total Cost $8,995
MOST FLEXIBLE

Installment plan

0% interest and no credit check

$12,995

  • Total Tuition before deposit $12,995
  • Deposit $1,000
MOST POPULAR

Deferred payments

Enroll now, pay later. No deposit required.

$12,995

  • Total Tuition before interest $12,995
Admissions Process

How to apply to our web development bootcamp

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.

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.

Apply
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.

See Payment Plans

Become a Data Analyst

Take the first step towards your future success by applying to one of our programs today!

FAQ

Frequently asked questions