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Our Data Analytics Bootcamp is designed to equip you with the skills and tools used by data professionals, blending live instruction, hands-on projects, and expert mentorship to ensure you gain real-world experience. This program provides structured learning, guiding you from data fundamentals to advanced analytics techniques.
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:
Foundations of Data Analytics
Module 1 provides a strong foundation in data analysis, covering the basics of data analysis, statistical theory, and essential tools...
Microsoft Excel
Module 2 immerses students in Microsoft Excel, starting from basic functionalities to advanced data analysis features...
SQL and Relational Databases
In this module, the student will be introduced to SQL and relational databases, essential for data storage, retrieval, and manipulation...
R Programming
Module 4 focuses on R programming, a language widely used for statistical analysis and data visualization...
Python Fundamentals
This module provides a comprehensive introduction to Python programming...
Python for Data Analysis
This module teaches coding and data analytics skills including command line basics, GitHub version control, Python environment setup...
Machine Learning - Regression
This module explores machine learning basics, focusing on linear regression for predicting outcomes...
Machine Learning - Classification
This module covers machine learning classification, including model evaluation with confusion matrices and metrics like accuracy, precision, recall, and specificity...
Tableau
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...
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 module builds a strong base in the core principles of data analysis, including statistical theory, data ethics, and essential analytical tools. Students explore the full analytics process—from managing raw data to drawing insights—while also being introduced to modern tools that streamline and enhance analysis through automation and intelligent recommendations.
Understanding the basics, applications, and ethics of data analytics.
Distinguishing between Data Analysts, Data Scientists, and Data Engineers.
Managing datasets, calculating descriptive statistics, and analyzing variable relationships.
Visualizing data distributions, interpreting probability distributions, and conducting hypothesis testing.
Explore how smart tools assist with practice generation, insight validation, and critical evaluation—while reinforcing when and why manual analytical skills still matter.
10 hours
This module immerses students in Excel, starting with foundational tasks and advancing toward real-world data analysis applications. From data cleaning and formatting to statistical modeling and visual storytelling, students gain hands-on experience using Excel’s core capabilities—alongside emerging tools that bring intelligent automation into the workflow.
Navigate Excel’s interface, apply functions for calculations and formatting, and manage missing data and duplicates.
Use logical functions, create basic charts and graphs, and perform statistical analysis including linear regression.
Utilize XLOOKUP, construct pivot tables and charts, and execute A/B tests for data-driven decision-making.
Standardize data formats, perform text manipulation, and create detailed visual representations like histograms and boxplots.
Explore how integrated tools like Excel Ideas, Flash Fill, and data types can automate analysis, enhance accuracy, and simplify complex tasks—bringing a layer of intelligence into everyday spreadsheet work.
40 hours
This module introduces students to the structure and function of relational databases, focusing on how SQL is used to store, query, and manipulate data. Students learn how to build databases from the ground up, write complex queries, and work with large datasets in modern environments—while also exploring the evolving role of intelligent systems in query generation and validation.
Understand database concepts, the relational database model, and normalization techniques for efficient data organization.
Write and execute SQL queries (SELECT, INSERT, UPDATE, DELETE) and utilize advanced techniques like JOIN operations, subqueries, and aggregate functions.
Execute SQL queries and manipulate large datasets within cloud-based platforms like BigQuery.
Create and modify database tables, define data types, set constraints, and apply SQL for comprehensive data analysis and insight extraction.
Explore how AI tools can help generate, validate, and refine queries—while identifying the core logic, strategy, and critical thinking skills that remain uniquely human and irreplaceable in the data analysis process.
20 hours
This module introduces students to R, a powerful language for statistical analysis and data visualization. Through hands-on coding and real-world projects, students learn to manipulate data, conduct complex analyses, and create insightful visuals—while also gaining exposure to modern tools that support code generation, optimization, and debugging.
R Set up the R environment and understand R syntax, including installing and utilizing packages for additional functionalities.
R Use dplyr and tidyr for data manipulation, cleaning, filtering, sorting, and transformation.
R Perform descriptive statistics, hypothesis testing, correlation analysis, regression analysis, ANOVA, and time-series analysis, and interpret results for real-world tasks.
R Create diverse types of plots and customize visualizations using ggplot2.
Learn how modern tools assist with code generation, package selection, and debugging—while developing the judgment to validate outputs and understand which skills remain uniquely human in statistical programming.
25 hours
This module offers a comprehensive introduction to Python programming, covering everything from basic data types to more advanced coding techniques. Students build a strong foundation in writing clean, efficient code and preparing for more complex programming and data analysis tasks—while also learning to navigate the balance between manual problem-solving and automated support.
Use the command line for file and system management, and set up the Python environment with Anaconda.
Understand Python data types and structures, implement control flow structures, and handle errors.
Write and modularize functions, master list comprehensions, and streamline loops with zip, enumerate, and advanced loop types.
Explore lambda functions for concise and functional programming.
Understand when and how to use automation wisely in coding—avoiding common pitfalls like over-reliance, blind trust, or shortcuts that hinder your creativity and deeper learning.
30 hours
This module builds on Python fundamentals and introduces students to practical data analysis workflows using powerful tools like Pandas and Matplotlib. From data cleaning to exploratory analysis and visualization, students learn how to turn raw data into meaningful insights—while also using intelligent assistants to accelerate and validate their process.
Understand Pandas data structures, perform data cleaning and preprocessing, and handle missing values and categorical data.
Conduct basic and advanced EDA, select data subsets, and compute descriptive statistics.
Create and customize diverse visualizations, including bar graphs, histograms, scatter plots, line plots, heat maps, and box plots.
Craft informative README files detailing project purpose, functionality, installation instructions, and usage guidelines.
Use assistants like Pandas AI, Jupyter AI, and other generative tools to enhance EDA, automate repetitive tasks, and validate your approach—while sharpening your judgment on what requires manual precision and human insight.
20 hours
This module introduces students to machine learning fundamentals with a focus on linear regression. Through real-world prediction tasks and hands-on practice—including Kaggle competitions—students develop both the technical and collaborative skills required to build, refine, and communicate predictive models in modern data environments.
Understand core concepts of machine learning and differentiate between types, with a focus on supervised learning.
Apply linear regression for predictive modeling, interpret assumptions and coefficients, and make predictions using regression models.
Apply machine learning techniques to real-world challenges, such as predicting concrete strength and home sale prices.
Handle multicollinearity, fine-tune model parameters, and understand advanced techniques like polynomial regression and regularization (Ridge and Lasso).
Work effectively on team projects that involve automation and AI tools, maintain clear documentation, and practice communicating the role and limitations of AI to both technical and non-technical stakeholders—fostering collaboration and informed decision-making.
20 hours
This module introduces classification algorithms and model evaluation, empowering students to build predictive tools for real-world decision-making. From customer behavior analysis to deploying interactive apps with Streamlit, students also begin using intelligent assistants to support modeling and visualization—while developing a clear understanding of where human expertise remains essential.
Understand classification concepts and evaluate models using confusion matrices and metrics like accuracy, precision, recall, and specificity.
Implement the KNN algorithm for classification tasks and analyze real-world datasets such as the Iris and breast cancer datasets.
Extract insights from customer behavior data to inform decisions in industries like telecommunications.
Create and deploy interactive web applications using Streamlit for seamless sharing and user accessibility.
Use platforms like PyCaret and AI-powered visualization libraries to accelerate model building and insight generation—while reinforcing critical machine learning skills that require human judgment, domain knowledge, and contextual reasoning.
40 hours
This module focuses on mastering Tableau to turn raw data into clear, interactive dashboards that support data-driven storytelling. Students learn to design compelling visuals, apply best practices, and create dynamic dashboards—while also gaining exposure to built-in intelligent features that accelerate analysis and insight delivery.
Get started with the basics of Tableau and its interface.
Learn techniques for creating clear and effective visualizations.
Create your first dashboards to display data insights.
Explore a variety of chart types to present data in more dynamic ways.
Develop interactive dashboards that allow users to explore data.
Learn how to embed dashboards in external platforms and share them with stakeholders.
Use built-in tools like Ask Data, Explain Data, and AI-assisted data prep to speed up analysis and uncover trends—while learning to validate results and apply design logic that AI can’t replicate.
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.
Coding Temple ensures students don’t just learn, but also apply their knowledge in professional settings.
Our instructors bring years of industry experience and a passion for teaching to every session. As experts in software development, data analytics, cybersecurity, and AI, they provide hands-on guidance, real-world insights, and personalized support in our interactive hybrid learning environment.
Employment Rate
Salary Lift
4.6
/5
Instructors
4.7
/5
Careers Services
4.8
/5
Curriculum
Access to flexible payment options to make a life-changing education.
Total Tuition before discount
$9,995
Discount
-$3,000
Paid at enrollment
$6,995
Pay up front and save 30% on tuition
$9,995
$6,995
Total Tuition before discount
$7,495
Deposit
$1,000
0% interest and no credit check
$7,495
Total Tuition before interest
$9,995
Enroll now, pay later. No deposit required.
$9,995
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!
Coding Temple believes in the power of education. We have built our curriculum around the most in-demand technologies and paired it with the most effective learning styles to prepare our students to be tomorrow’s leaders in technology.
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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.
Othmane Benyoucef
Data Analytics Instructor
Who am I
I am a versatile professional with a unique blend of expertise in data science, education, and industry leadership. My ability to bridge complex data challenges with clear insights has been shaped through a robust academic foundation and hands-on experience across diverse industries.
With a proven track record in strategic decision-making, curriculum development, and instructional design, I bring a powerful combination of technical acumen and pedagogical expertise to my role as a Data Analytics Curriculum Developer and Instructor. My work is centered around creating engaging, real-world learning experiences that empower students to excel in today’s data-driven environment.
Spearheading innovative projects at IT&B has allowed me to leverage advanced data analytics techniques, machine learning algorithms, and visualization tools to solve multifaceted challenges. These experiences have not only enhanced my technical skills but also honed my ability to communicate complex concepts effectively.
My diverse experience—ranging from founding a technology business to conducting cutting-edge research in data science—equips me with the unique perspective needed to lead students through transformative learning journeys. Whether I’m mentoring future data scientists or designing curricula that align with industry trends, my goal is to empower individuals with the tools and confidence necessary to thrive in today’s rapidly evolving landscape.
My Certifications
Education:
Awards & Recognitions:
Technical Expertise:
My Experience
Owner, IT&B (April 2009 – Present):
Data Analytics and Visualization Instructor, edX Boot Camps (October 2023 – February 2025):
Research Data Scientist, Database and Signal Analysis Lab at USTO MB-Oran (October 2010 – July 2015):
Academic Roles:
Teaching Philosophy
I believe that mastering data science is not just about acquiring knowledge but also about applying it effectively. My teaching approach is rooted in bridging the gap between theoretical concepts and practical application, ensuring students are well-equipped to tackle real-world challenges.
By incorporating current industry trends, case studies, and interactive projects that mirror actual business scenarios, I create a dynamic learning environment where critical thinking and innovation thrive. I am committed to fostering collaboration, encouraging students to explore creative solutions and apply analytical techniques to solve complex problems.
My dedication to continuous learning and effective communication helps inspire students to excel, driving success in the ever-evolving field of data science.
Hamza El-Husseiny
Data Analytics Instructor
Who am I
I am a data analytics and business intelligence instructor with extensive experience in data analysis, business strategy, and technical education.
My expertise spans Python, SQL, data visualization, machine learning, and business analytics, enabling me to guide students through hands-on projects and real-world applications.
As a dedicated mentor and educator, I am committed to making complex concepts accessible and fostering an engaging learning environment.
My Certifications
Academic Background:
Certifications & Specializations:
My Experience
Data Analytics & Business Intelligence Training
Technical & Business Analysis Expertise
Instructional Design & Mentorship
Teaching Philosophy
I am dedicated to bridging the gap between data analytics and business intelligence through real-world applications, hands-on learning, and personalized mentorship.
My expertise in data visualization, Python, SQL, and AI concepts ensures that students develop industry-ready skills to analyze, interpret, and leverage data for strategic decision-making.
Ken Wood
Data Analytics Instructor
Who am I
I am a seasoned data scientist specializing in machine learning, data analytics, and AI-driven business strategies.
With a strong background in cloud-based and SaaS technology solutions, I have developed and optimized machine learning models to transform datasets into actionable insights that drive decision-making.
I specialize in supervised and unsupervised learning techniques, feature engineering, and building machine learning pipelines on AWS SageMaker.
My Certifications
Academic Background:
Certifications & Specializations:
My Experience
Data Science & Machine Learning
Data Analytics & Mentorship
Teaching Philosophy
I leverage my deep expertise in data science, machine learning, and cloud-based analytics to bridge the gap between theory and practical applications.
My courses emphasize hands-on learning, real-world projects, and industry-aligned skill development to prepare students for successful careers in data analytics and AI-driven decision-making.
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