Our alumni are now working at:
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
DBeaverLearning 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.
Blended Learning Model: Choose from 4-month, 5-month, or 6-month pacing to fit your schedule.
What you’ll learn in this data analytics bootcamp
Download your Coursebook
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Foundations of Data Analytics (10 hours)
Module 1 provides a strong foundation in data analysis, covering the basics of data analysis, statistical theory, and essential tools. Students will gain an understanding of the data analytics process, basic statistical concepts, and practical skills for exploring relationships between variables.
Key Elements of the module include:
- Data Analytics Fundamentals: Understanding the basics, applications, and ethics of data analytics.
- Role Differentiation: Distinguishing between Data Analysts, Data Scientists, and Data Engineers.
- Data Handling and Analysis: Managing datasets, calculating descriptive statistics, and analyzing variable relationships.
- Data Visualization and Interpretation: Visualizing data distributions, interpreting probability distributions, and conducting hypothesis testing.
Learning Excel(20 hours)
Module 2 immerses students in Microsoft Excel, starting from basic functionalities to advanced data analysis features. Students learn about data cleaning, transformation, and statistical analysis in Excel. The module includes practical exercises, quizzes, and lab assignments, culminating in a project that applies Excel in a real-world data analysis scenario.
Key Elements of the module include:
- Excel Proficiency: Navigate Excel’s interface, apply functions for calculations and formatting, and manage missing data and duplicates.
- Data Analysis and Visualization: Use logical functions, create basic charts and graphs, and perform statistical analysis including linear regression.
- Advanced Excel Features: Utilize XLOOKUP, construct pivot tables and charts, and execute A/B tests for data-driven decision-making.
- Data Standardization and Manipulation: Standardize data formats, perform text manipulation, and create detailed visual representations like histograms and boxplots.
SQL and Relational Databases(20 hours)
In this module, the student will be introduced to SQL and relational databases, essential for data storage, retrieval, and manipulation. The module covers database creation, SQL queries, and advanced SQL features, culminating in a project utilizing SQL in a real-world data analysis context.
Key Elements of the module include:
- Database Concepts and Normalization: Understand database concepts, the relational database model, and normalization techniques for efficient data organization.
- SQL Query Proficiency: Write and execute SQL queries (SELECT, INSERT, UPDATE, DELETE) and utilize advanced techniques like JOIN operations, subqueries, and aggregate functions.
- Cloud-Based SQL Execution: Execute SQL queries and manipulate large datasets within cloud-based platforms like BigQuery.
- Database Management and Analysis: Create and modify database tables, define data types, set constraints, and apply SQL for comprehensive data analysis and insight extraction
R Programming (20 hours)
Module 4 focuses on R programming, a language widely used for statistical analysis and data visualization. Students learn about data manipulation, basic and inferential statistical analysis, and data visualization in R. The module includes hands-on exercises and a project applying R in a data analysis scenario.
Key Elements of the module include:
- R Environment and Syntax: Set up the R environment and understand R syntax, including installing and utilizing packages for additional functionalities.
- Data Manipulation and Cleaning: Use dplyr and tidyr for data manipulation, cleaning, filtering, sorting, and transformation.
- Statistical Analysis and Interpretation: Perform descriptive statistics, hypothesis testing, correlation analysis, regression analysis, ANOVA, and time-series analysis, and interpret results for real-world tasks.
- Data Visualization: Create diverse types of plots and customize visualizations using ggplot2.
Python Fundamentals(25 hours)
This module provides a comprehensive introduction to Python programming. It covers fundamental data types, control flow structures, functions, and advanced techniques like list comprehensions. Students gain proficiency in string manipulation, loop iterations, modular code organization, and efficient list manipulation, preparing them for more complex programming tasks.
Key Elements of the module include:
- Command Line and Python Setup: Use the command line for file and system management, and set up the Python environment with Anaconda.
- Python Fundamentals: Understand Python data types and structures, implement control flow structures, and handle errors.
- Efficient Coding Techniques: Write and modularize functions, master list comprehensions, and streamline loops with zip, enumerate, and advanced loop types.
- Advanced Python Features: Explore lambda functions for concise and functional programming.
Python for Data Analysis(30 hours)
This module teaches coding and data analytics skills including command line basics, GitHub version control, Python environment setup, Pandas for data manipulation, data visualization techniques, exploratory data analysis, and creating project README files for effective project documentation and communication.
Key Elements of the module include:
- Pandas Data Manipulation: Understand Pandas data structures, perform data cleaning and preprocessing, and handle missing values and categorical data.
- Exploratory Data Analysis (EDA): Conduct basic and advanced EDA, select data subsets, and compute descriptive statistics.
- Data Visualization: Create and customize diverse visualizations, including bar graphs, histograms, scatter plots, line plots, heat maps, and box plots.
- Project Documentation: Craft informative README files detailing project purpose, functionality, installation instructions, and usage guidelines.
Machine Learning - Regression(20 hours)
This module explores machine learning basics, focusing on linear regression for predicting outcomes. We apply our skills to real-world challenges like predicting concrete strength and refine our techniques with Kaggle submissions. Through practical exercises, we develop a strong understanding of machine learning’s applications in data analysis and prediction tasks.
Key Elements of the module include:
- Machine Learning Fundamentals: Understand core concepts of machine learning and differentiate between types, with a focus on supervised learning.
- Linear Regression Modeling: Apply linear regression for predictive modeling, interpret assumptions and coefficients, and make predictions using regression models.
- Real-World Applications: Apply machine learning techniques to real-world challenges, such as predicting concrete strength and home sale prices.
- Advanced Regression Techniques: Handle multicollinearity, fine-tune model parameters, and understand advanced techniques like polynomial regression and regularization (Ridge and Lasso).
Machine Learning - Classification(20 hours)
This module covers machine learning classification, including model evaluation with confusion matrices and metrics like accuracy, precision, recall, and specificity. It also includes implementing K-Nearest Neighbors (KNN) for classification and analyzing real-world datasets. Additionally, it focuses on extracting insights from customer behavior data for industry decision-making and creating interactive web applications using Streamlit for seamless sharing and accessibility.
Key Elements of the module include:
- Classification in Machine Learning: Understand classification concepts and evaluate models using confusion matrices and metrics like accuracy, precision, recall, and specificity.
- K-Nearest Neighbors (KNN): Implement the KNN algorithm for classification tasks and analyze real-world datasets such as the Iris and breast cancer datasets.
- Data-Driven Decision Making: Extract insights from customer behavior data to inform decisions in industries like telecommunications.
- Streamlit Applications: Create and deploy interactive web applications using Streamlit for seamless sharing and user accessibility.
Tableau(15 hours)
This module focuses on mastering Tableau for data visualization and storytelling. You’ll learn how to transform raw data into interactive, insightful dashboards that drive decision-making. By the end of this module, you’ll be able to apply data visualization best practices and build complex dashboards, empowering you to effectively communicate insights.
Key Elements of the module include:
- Introduction to Tableau: Get started with the basics of Tableau and its interface.
- Data Visualization Best Practices: Learn techniques for creating clear and effective visualizations.
- Building Simple Dashboards: Create your first dashboards to display data insights.
- Advanced Chart Types in Tableau: Explore a variety of chart types to present data in more dynamic ways.
- Building Interactive Dashboards: Develop interactive dashboards that allow users to explore data.
- Embedding and Sharing Dashboards: Learn how to embed dashboards in external platforms and share them with stakeholders.
-
Foundations of Data Analytics
Foundations of Data Analytics (10 hours)
Module 1 provides a strong foundation in data analysis, covering the basics of data analysis, statistical theory, and essential tools. Students will gain an understanding of the data analytics process, basic statistical concepts, and practical skills for exploring relationships between variables.
Key Elements of the module include:
- Data Analytics Fundamentals: Understanding the basics, applications, and ethics of data analytics.
- Role Differentiation: Distinguishing between Data Analysts, Data Scientists, and Data Engineers.
- Data Handling and Analysis: Managing datasets, calculating descriptive statistics, and analyzing variable relationships.
- Data Visualization and Interpretation: Visualizing data distributions, interpreting probability distributions, and conducting hypothesis testing.
-
Microsoft Excel
Learning Excel(20 hours)
Module 2 immerses students in Microsoft Excel, starting from basic functionalities to advanced data analysis features. Students learn about data cleaning, transformation, and statistical analysis in Excel. The module includes practical exercises, quizzes, and lab assignments, culminating in a project that applies Excel in a real-world data analysis scenario.
Key Elements of the module include:
- Excel Proficiency: Navigate Excel’s interface, apply functions for calculations and formatting, and manage missing data and duplicates.
- Data Analysis and Visualization: Use logical functions, create basic charts and graphs, and perform statistical analysis including linear regression.
- Advanced Excel Features: Utilize XLOOKUP, construct pivot tables and charts, and execute A/B tests for data-driven decision-making.
- Data Standardization and Manipulation: Standardize data formats, perform text manipulation, and create detailed visual representations like histograms and boxplots.
-
SQL and Relational Databases
SQL and Relational Databases(20 hours)
In this module, the student will be introduced to SQL and relational databases, essential for data storage, retrieval, and manipulation. The module covers database creation, SQL queries, and advanced SQL features, culminating in a project utilizing SQL in a real-world data analysis context.
Key Elements of the module include:
- Database Concepts and Normalization: Understand database concepts, the relational database model, and normalization techniques for efficient data organization.
- SQL Query Proficiency: Write and execute SQL queries (SELECT, INSERT, UPDATE, DELETE) and utilize advanced techniques like JOIN operations, subqueries, and aggregate functions.
- Cloud-Based SQL Execution: Execute SQL queries and manipulate large datasets within cloud-based platforms like BigQuery.
- Database Management and Analysis: Create and modify database tables, define data types, set constraints, and apply SQL for comprehensive data analysis and insight extraction
-
R Programming
R Programming (20 hours)
Module 4 focuses on R programming, a language widely used for statistical analysis and data visualization. Students learn about data manipulation, basic and inferential statistical analysis, and data visualization in R. The module includes hands-on exercises and a project applying R in a data analysis scenario.
Key Elements of the module include:
- R Environment and Syntax: Set up the R environment and understand R syntax, including installing and utilizing packages for additional functionalities.
- Data Manipulation and Cleaning: Use dplyr and tidyr for data manipulation, cleaning, filtering, sorting, and transformation.
- Statistical Analysis and Interpretation: Perform descriptive statistics, hypothesis testing, correlation analysis, regression analysis, ANOVA, and time-series analysis, and interpret results for real-world tasks.
- Data Visualization: Create diverse types of plots and customize visualizations using ggplot2.
-
Python Fundamentals
Python Fundamentals(25 hours)
This module provides a comprehensive introduction to Python programming. It covers fundamental data types, control flow structures, functions, and advanced techniques like list comprehensions. Students gain proficiency in string manipulation, loop iterations, modular code organization, and efficient list manipulation, preparing them for more complex programming tasks.
Key Elements of the module include:
- Command Line and Python Setup: Use the command line for file and system management, and set up the Python environment with Anaconda.
- Python Fundamentals: Understand Python data types and structures, implement control flow structures, and handle errors.
- Efficient Coding Techniques: Write and modularize functions, master list comprehensions, and streamline loops with zip, enumerate, and advanced loop types.
- Advanced Python Features: Explore lambda functions for concise and functional programming.
-
Python for Data Analysis
Python for Data Analysis(30 hours)
This module teaches coding and data analytics skills including command line basics, GitHub version control, Python environment setup, Pandas for data manipulation, data visualization techniques, exploratory data analysis, and creating project README files for effective project documentation and communication.
Key Elements of the module include:
- Pandas Data Manipulation: Understand Pandas data structures, perform data cleaning and preprocessing, and handle missing values and categorical data.
- Exploratory Data Analysis (EDA): Conduct basic and advanced EDA, select data subsets, and compute descriptive statistics.
- Data Visualization: Create and customize diverse visualizations, including bar graphs, histograms, scatter plots, line plots, heat maps, and box plots.
- Project Documentation: Craft informative README files detailing project purpose, functionality, installation instructions, and usage guidelines.
-
Machine Learning - Regression
Machine Learning - Regression(20 hours)
This module explores machine learning basics, focusing on linear regression for predicting outcomes. We apply our skills to real-world challenges like predicting concrete strength and refine our techniques with Kaggle submissions. Through practical exercises, we develop a strong understanding of machine learning’s applications in data analysis and prediction tasks.
Key Elements of the module include:
- Machine Learning Fundamentals: Understand core concepts of machine learning and differentiate between types, with a focus on supervised learning.
- Linear Regression Modeling: Apply linear regression for predictive modeling, interpret assumptions and coefficients, and make predictions using regression models.
- Real-World Applications: Apply machine learning techniques to real-world challenges, such as predicting concrete strength and home sale prices.
- Advanced Regression Techniques: Handle multicollinearity, fine-tune model parameters, and understand advanced techniques like polynomial regression and regularization (Ridge and Lasso).
-
Machine Learning - Classification
Machine Learning - Classification(20 hours)
This module covers machine learning classification, including model evaluation with confusion matrices and metrics like accuracy, precision, recall, and specificity. It also includes implementing K-Nearest Neighbors (KNN) for classification and analyzing real-world datasets. Additionally, it focuses on extracting insights from customer behavior data for industry decision-making and creating interactive web applications using Streamlit for seamless sharing and accessibility.
Key Elements of the module include:
- Classification in Machine Learning: Understand classification concepts and evaluate models using confusion matrices and metrics like accuracy, precision, recall, and specificity.
- K-Nearest Neighbors (KNN): Implement the KNN algorithm for classification tasks and analyze real-world datasets such as the Iris and breast cancer datasets.
- Data-Driven Decision Making: Extract insights from customer behavior data to inform decisions in industries like telecommunications.
- Streamlit Applications: Create and deploy interactive web applications using Streamlit for seamless sharing and user accessibility.
-
Tableau
Tableau(15 hours)
This module focuses on mastering Tableau for data visualization and storytelling. You’ll learn how to transform raw data into interactive, insightful dashboards that drive decision-making. By the end of this module, you’ll be able to apply data visualization best practices and build complex dashboards, empowering you to effectively communicate insights.
Key Elements of the module include:
- Introduction to Tableau: Get started with the basics of Tableau and its interface.
- Data Visualization Best Practices: Learn techniques for creating clear and effective visualizations.
- Building Simple Dashboards: Create your first dashboards to display data insights.
- Advanced Chart Types in Tableau: Explore a variety of chart types to present data in more dynamic ways.
- Building Interactive Dashboards: Develop interactive dashboards that allow users to explore data.
- Embedding and Sharing Dashboards: Learn how to embed dashboards in external platforms and share them with stakeholders.
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.
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.
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.
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!
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.
John C.
The best part about Coding Temple is the alumni resources, which include career and tech advisors for life!
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!
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.
Mason B.
Instructors
Career Services
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
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
- 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
Invest in your Future
Access to flexible payment options to make a life-changing education.
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.
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 CoursesApplication and basic skills assessment
Submit your application – it takes less than 5 minutes. After you apply you will be sent a basic skills assessment.
ApplySchedule 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 CallSecure 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 PlansBecome a Data Analyst
Take the first step towards your future success by applying to one of our programs today!