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Unlike generic tech programs, Coding Temple’s Data Analytics Bootcamp blends structured coursework with real-world data projects, hands-on tools like SQL, Python, and Tableau, and analytical challenges to build job-ready skills.
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:
You’ll learn the tools and languages data analysts use, like:
AI & LLM Foundations
Start strong by learning how to use AI effectively throughout the program.
Foundations of Data Analytics
Build the mindset and core concepts of a data analyst.
Microsoft Excel for Analytics
Master the most widely used analytics tool.
SQL & Databases
Learn how to extract insights from databases like a professional.
Python for Data Analytics
The full modern analytics workflow using Python.
AI in Data & Machine Learning
Close the loop by mastering AI-powered workflows.
Machine Learning – Regression
Predict numerical outcomes using machine learning.
Machine Learning – Classification
Predict categories and make data-driven decisions.
Tableau & Data Storytelling
Turn data into powerful dashboards and insights.
Capstone
Get hands on experience as a data professional
Career Camp
Technical and Soft Skills Training
Explore the curriculum that transforms careers.
10 hours
This foundational module introduces students to the rapidly evolving world of Generative AI and Large Language Models (LLMs), equipping them with the skills to leverage AI as a powerful productivity tool throughout the entire program and beyond. Rather than treating AI as a black box, students will learn to work with it thoughtfully, critically, and ethically — understanding both its extraordinary capabilities and its limitations.
Students will practice crafting high-quality prompts tailored specifically to analytics tasks, then evaluate the outputs they receive for accuracy, relevance, and potential bias. By the end of this module, every student will have a practical framework for integrating AI into their data workflow with confidence.
Understand how large language models work and what makes them powerful tools for analytics.
Write high-quality, context-specific prompts designed for data and analytics tasks.
Critically assess AI-generated outputs for accuracy, relevance, and potential bias.
Apply AI responsibly and ethically in professional real-world analytics scenarios.
Write and evaluate prompts using Gen AI tools to analyze and improve output quality.
10 hours
This module establishes the essential mindset, vocabulary, and thinking patterns of a data analyst. Students will discover how raw data transforms into actionable business insights through a structured analytical process. From understanding the difference between qualitative and quantitative data to interpreting complex visualizations, this module prepares students to think like analysts from day one.
Key skills covered include descriptive statistics, reading and interpreting charts, and applying logic trees to solve business problems. Through hands-on project work, including a real-world customer analysis case study, students will experience the analyst’s role firsthand — asking the right questions, structuring problems clearly, and communicating findings with precision.
Understand quantitative vs. qualitative data, structured vs. unstructured, and how data is organized.
Master measures of central tendency, variability, and distribution to summarize datasets effectively.
Read, interpret, and critically evaluate a wide range of data visualizations and charts.
Apply structured problem-solving frameworks to decompose and analyze complex business questions.
Customer analysis case study and a business problem structuring exercise with real datasets.
40 hours
Microsoft Excel remains the most universally used analytics tool in the professional world, and this module ensures students can use it with real mastery. Going far beyond basic spreadsheets, students will learn to clean messy datasets, build powerful pivot tables, and construct dynamic dashboards that communicate insights at a glance.
The module covers essential formulas and functions, advanced data transformation techniques, and how to design A/B tests in Excel. The capstone project — a full end-to-end analysis of a mortgage dataset — challenges students to apply everything they’ve learned, including detecting potential bias in the data. Graduates of this module leave with an Excel skill set that stands out in real job applications.
Remove errors, standardize formats, and transform raw data into analysis-ready datasets.
Master essential Excel functions including VLOOKUP, INDEX/MATCH, SUMIFS, and logical operators.
Build dynamic pivot tables and interactive dashboards that summarize and visualize complex data.
Design and analyze controlled experiments to test hypotheses and measure the impact of changes.
End-to-end analysis of a mortgage dataset, including bias detection — a real-world portfolio-quality project.
20 hours
SQL is the language of data, and this module ensures students can write queries confidently from scratch. Students will learn how to interact with relational databases, retrieve exactly the data they need using filtering and aggregations, and combine multiple tables through expert use of joins. The module progressively builds toward advanced logic using subqueries and complex conditional statements.
Every concept is anchored in real business questions students will learn to answer with raw data. By the end of the module, students will have worked through a full e-commerce dataset analysis, created custom SQL queries to answer business-critical questions, and developed the ability to think relationally about data — a cornerstone skill for any analyst working in industry.
Write SELECT statements, filters, and aggregations from the ground up with no prior experience required.
Extract precisely the data you need using WHERE clauses, GROUP BY, HAVING, and multiple join types.
Build nested queries and use CASE statements to handle complex, multi-condition data extraction.
Translate business questions into SQL queries that extract actionable insights from real databases.
E-commerce dataset analysis and a custom SQL project where students write queries to answer their own business questions.
25 hours
Python is the language of modern data analytics, and this double-module covers the full analyst workflow from start to finish. Students will begin by learning how to use Pandas and NumPy for powerful data handling, then move into cleaning and preprocessing real-world datasets — handling missing values, duplicates, and outliers with professional-grade techniques. Data visualization using Matplotlib and Seaborn brings data to life, turning complex datasets into clear, compelling charts.
The second half of this combined module pushes students into structured analysis notebooks, where they work through exploratory data analysis (EDA) systematically and document their process clearly. Students complete two major projects: a full EDA on real-world datasets, and an independent project featuring 1000+ rows of data with multiple visualizations. By the end, students have the Python skills that employers look for in modern analytics roles.
Use the two core Python libraries for efficient data manipulation, filtering, and numerical computation.
Handle missing values, duplicates, inconsistent formats, and transform raw data into clean, analysis-ready tables.
Detect and appropriately handle outliers using statistical methods and domain knowledge.
Create professional charts and plots using Matplotlib and Seaborn to communicate findings visually.
Full EDA on real-world datasets plus an independent project with 1000+ rows of data and multiple visualizations.
30 hours
This module bridges the gap between traditional data analytics and the AI-powered future of the field. Students will develop a nuanced understanding of when AI dramatically accelerates analytical work — and crucially, when it doesn’t. Rather than treating AI as a universal solution, students learn to identify the right use cases and integrate AI tools thoughtfully into real analytics workflows.
Topics include using AI coding assistants to write and debug Python code, integrating AI tools into machine learning pipelines, and understanding how large language models can assist with data interpretation and report generation. Students will walk away with a practical toolkit for AI-augmented analysis that makes them more efficient and competitive in the modern data job market — while maintaining the critical thinking skills that distinguish exceptional analysts.
Identify when AI tools genuinely accelerate work and when human judgment must take the lead.
Use AI-powered coding assistants to write, debug, and optimize Python code for analytics tasks.
Integrate AI tools into machine learning workflows for faster feature engineering and model iteration.
Apply AI tools within real analytics projects, from data preprocessing to insight communication.
Work through guided exercises that embed AI tools naturally into the full analytics and ML workflow.
20 hours
This module introduces students to the foundational concepts of predictive modeling through regression analysis. Students will learn how to engineer meaningful features from raw data, train and evaluate regression models, and understand the critical challenge of overfitting — a common pitfall that separates beginner models from professional-grade ones. Key evaluation metrics including RMSE (Root Mean Square Error) and R² will become second nature as students learn to interpret model performance.
The module culminates in a hands-on capstone project: building a house price prediction model from scratch. Working with real estate data, students will apply feature engineering, model training, hyperparameter tuning, and performance evaluation to create a deployable regression model. This project provides a compelling portfolio piece that demonstrates practical machine learning skills to prospective employers.
Create meaningful input variables from raw data that improve model accuracy and interpretability.
Train regression models using scikit-learn and evaluate their performance on test data systematically.
Recognize, diagnose, and address overfitting using cross-validation and regularization techniques.
Interpret Root Mean Square Error and R-squared to quantify and communicate model performance accurately.
Build a house price prediction regression model from scratch using real estate data.
20 hours
Building on the regression foundations established in the previous module, this module dives into classification — one of the most powerful and widely-used machine learning techniques in industry. Students will implement logistic regression and other classification algorithms to predict categorical outcomes, then evaluate their models using confusion matrices, precision, recall, F1-score, and other essential performance metrics.
Model selection and tuning are key themes throughout: students will learn how to compare different classification approaches, select the right model for the right problem, and fine-tune hyperparameters to maximize performance. The module’s capstone project challenges students to build a passenger satisfaction prediction model, giving them experience with end-to-end binary classification on a real-world customer dataset — a project type that appears regularly in analytics and data science roles.
Implement logistic regression and decision tree classifiers to predict categorical outcomes.
Use confusion matrices to calculate precision, recall, F1-score, and overall accuracy for classification models.
Compare multiple classification algorithms and fine-tune hyperparameters to optimize model performance.
Build a complete classification workflow from data preprocessing through model evaluation and interpretation.
Build a passenger satisfaction prediction model using real airline customer data.
40 hours
This final core module transforms technically proficient analysts into compelling communicators. Tableau is the industry-leading visualization tool, and students will learn to harness its full power — from building interactive dashboards with calculated fields to designing data stories that resonate with non-technical stakeholders. The focus throughout is not just on what the data shows, but on how to communicate it in ways that drive decisions.
Students will learn best practices in data visualization design: choosing the right chart types, avoiding common misleading visualization mistakes, layering context and narrative onto their charts, and building presentations that tell a clear story from beginning to end. The module concludes with a complete Tableau dashboard project paired with a full stakeholder presentation, giving students a portfolio-worthy piece that demonstrates both their technical and communication skills — a combination that sets top analysts apart in the job market.
Build fully interactive Tableau dashboards that allow stakeholders to explore data independently.
Create custom metrics and KPIs using Tableau's formula engine to derive new insights from existing data.
Design compelling narratives around data that guide stakeholders to clear insights and decisions.
Choose the right chart types and apply design principles to ensure clarity, accuracy, and impact.
Build a complete Tableau dashboard on a real dataset and present it in a structured stakeholder presentation.
Apply layout, color, and filtering strategies to create dashboards that are both beautiful and functionally powerful.
Practice presenting data-driven insights clearly to both technical and non-technical audiences.
That’s why we built LaunchPad, an experiential training platform that will connect you with 40,000+ employers and thousands of active real-world projects, ensuring you graduate with hands-on experience that sets you apart in the job market.
Unmatched Real-World Learning Opportunities:
Boosted Placement Rates: Hands-on experience with real-world datasets and analytics tools ensures graduates enter the job market with practical skills that employers actively seek.
Stronger Portfolios & Resumes: Students build data-driven case studies, showcasing expertise in Python, SQL, and Tableau while demonstrating their ability to analyze and interpret business-critical insights.
Networking & Direct Employer Connections: Collaborating on live data projects with industry partners helps students gain exposure to hiring managers and build relationships that lead to career opportunities.
Reduces the ‘Experience Gap’ for Career Changers: By working on real analytics challenges, students develop industry-relevant experience, making the shift into data analytics roles smoother and more accessible.
Projects
Students have continuous access to 5,000 active real-world challenges from a variety of industries, guaranteeing practical experience.
Employers
Coding Temple students collaborate with real companies, gaining exposure to potential hiring managers, increasing your chances of networking with potential hiring managers.
Employment Rate
Salary Lift
4.6
/5
Instructors
4.7
/5
Careers Services
4.8
/5
Curriculum
You can choose which payment plan works best for you. More flexibility. Faster start. Your goals, your call.
Total tuition before discount
$10,000
Discount
-$6,500
Paid at enrollment
$3,500
Pay up front and save $6.5k on tuition
$10,000
$3,500
Total tuition before discount
$280/mo
Discount
-$91/mo
Deposit
$200
Installment Period
36 months
0% interest and no credit check
$280/mo
$189/mo
Total tuition before interest
$10,000
Discount
-$4,000
Enroll now, pay later. No deposit required.
$10,000
$6,000
Data analytics is about examining and evaluating raw data to extract meaningful insights that support intelligent decision-making. You will learn how to turn raw data into meaningful insights, explore beginner and intermediate statistics, and coding basics for data manipulation and analysis techniques. Coding Temple’s data analytics course will turn you into an adept data analyst, ready to tackle the challenges of today’s data-driven world.
Data analytics is a rapidly growing field with opportunities across industries like finance, healthcare, and technology. The demand for skilled analysts continues to surge as companies rely on data-driven decisions to stay competitive. Entry-level roles like Data Analyst or Business Analyst offer competitive salaries ranging from $70,000 to $120,000, while advanced positions in data science or machine learning can exceed six figures. With Coding Temple’s hands-on curriculum, you’ll be prepared to thrive in this high-demand career path.
Coding Temple’s data analytics course is designed for people with no prior coding or data analytics experience. The program is designed to be challenging but rewarding. The instructors are supportive and always available to provide guidance. However, it is fast-paced, so students need to be prepared to work hard. The student to teacher ratio is about 1:4, which allows for more personalized attention.
The nine-module curriculum is project-based and includes a capstone project where students analyze and visualize real-life datasets. The curriculum also includes dive-deep into real-world datasets to extract valuable insights, learn advanced data visualization techniques to effectively communicate insights and trends, and work on a hands-on capstone project to solve complex data analysis challenges.
Coding Temple’s flexible learning model is designed for busy individuals. You can attend live, 1-hour daily sessions (recorded for later viewing) and dedicate around 3 hours a day to self-paced activities, including videos, assignments, and projects. This approach empowers you to learn on your terms.
We’re invested in your lifelong success! Career services include:
Yes, we’re confident in our graduates! If you don’t secure a job within 9 months of completing the program, you’ll receive a full tuition refund (terms and conditions apply).
Our curriculum blends fundamental data analytics skills with advanced tools like Tableau, Python, and SQL, while emphasizing practical application through real-world projects. You’ll graduate with a comprehensive portfolio, including a capstone project showcasing your ability to analyze complex datasets and present actionable insights effectively.
Our alumni thrive in roles such as Data Analyst, Business Analyst, Data Scientist, and more. Many come from diverse backgrounds like education, finance, healthcare, and marketing, leveraging their new skills to transition into rewarding data-driven careers.
Ready to start your tech journey? We can’t wait to meet you!
40 hours
Learn version control, repository management, and collaboration with GitHub.
Understand strings, numbers, conditional statements, loops, and functions.
Learn how to handle errors and exceptions in Python.
Solve programming challenges to reinforce Python concepts.