Be ahead of everyone 🤩 by being accelerated 🚀 with better materials we researched and designed for you with love ❤️
Unlock a lucrative career in AI MLquicker than the average professional!
AI ML market value of nearly $100 billion 💰 is expected to witness explosive growth and reach almost $2 trillion by 2030
CAGR of 32.9%🔥🔥
Source: Statista
The AI ML industry’s robust growth is driving up salaries for employees, with a proportional increase to the job market growth.
$145,242
Data scientist’s average salary
Source: Glassdoor
$74,598
Data analyst average salary
Source: Glassdoor
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Why Metana?
Metana is a highly respected and successful Web3 bootcamp, with a track record of producing graduates who are well-prepared for success in the professional world. Their foundational teaching method and JobCamp™️ program provide students with the skills and support they need to thrive in their first job and beyond. Now, with the addition of their Data Analytics bootcamp, Metana is expanding their offerings to include training in the fast-growing field of analysis and interpretation of data. This new bootcamp is sure to be a great opportunity for aspiring professionals looking to gain expertise in these in-demand skills.
The limitations of self-learning for achieving professional success and landing a job!
⌛️Outdated Resources
We’ve all been there – endlessly scrolling through pages of free online resources, only to discover that most of them are outdated and useless. It’s a common problem that can hinder your learning progress.
😵💫 Distractions
Distractions can easily derail your learning journey, especially in a world filled with endless distractions. Staying motivated and on track can feel nearly impossible without some form of accountability or support system.
⚠️ Lack of clear roadmaps
The lack of clear roadmaps can be a major roadblock for learners. But finding one that actually works in practice can feel like searching for a needle in a haystack. Often, the roadmaps that look good on paper turn out to be impractical and difficult to follow.
🤷♂️ No guidance
Without the right guidance, learning can feel like an aimless and frustrating pursuit. It’s like trying to hit a target blindfolded, hoping to get lucky. It’s no wonder many learners struggle to stay motivated and make progress. The absence of a mentor or guide can leave you feeling lost and directionless, unsure of how to improve or what steps to take next. If you’re serious about achieving your goals, don’t go it alone. Seek out a mentor who can provide the support, advice, and direction you need to succeed.
😩 Finding a job
It’s frustrating when you’ve put in all the hard work to learn a new skill, only to find that landing a job in that field can be an uphill battle. You may spend countless hours tailoring your resume and cover letter, but often receive no response or feedback. And even if you do manage to snag an interview, the pressure is on to make yourself stand out as the ideal candidate, with no clear direction on how to do so. It’s no wonder that finding a job can feel like an overwhelming and demotivating experience.
🤔 Doubt in knowledge & skills
Have you ever questioned whether you’re truly ready to tackle the challenges? With new vulnerabilities and exploits constantly emerging, it’s hard to feel confident that you’ve truly mastered the skills you need to create safe and effective applications.
🫤 Staying up-to Date
Keeping up with the ever-changing landscape of technology can be a daunting task. There’s always something new to learn, and it can be hard to know where to start. Plus, with so many other responsibilities vying for your attention, finding the time to stay up to date can feel impossible. And even when you do find the time, how can you be sure you’re getting the right information from the right sources?
During our bootcamp, Here's what you'll get
🤩 Highly Curated Content
We’ve done the hard work of researching and designing our bootcamp with the help of industry professionals, so you can focus on learning and applying your new skills.
✨ Community
Our supportive community of developers, where past students and expert instructors come together to help each other succeed. Whether you’re looking for career advice, project feedback, or just want to connect with like-minded individuals, our community is here to support you every step of the way.
🛠️ Robust LMS
Access our LMS for robust online learning experience with a variety of resources, including video lectures, coding exercises, and reference materials.
🤝 Mentorship
Our mentorship program gives you access to offers one-on-one sessions with industry professionals with years of experience who can answer your questions and help you stay on track.
🎓 Hands on experience
Our program offers plenty of opportunities for you to roll up your sleeves and dive into real-world projects, so you can gain the practical experience you need to succeed.
🥳 Projects with real-world data
Our projects use real-world data to provide you with practical experience that will help you stand out in the job market.
👩💻 JobCamp
As part of our program, you’ll have free access to a job hunting bootcamp (worth 8000$) that provides training and guidance on how to effectively search for data-related jobs and stand out to potential employers
🔍 Expert Designed roadmap
We’ve vetted and designed our curriculum with the help of experts to ensure you’re not just learning something superficial, but truly mastering the subject.
🙋🏻♂️Student Driven Excellence!
Incorporating regular feedback loops and surveys to gather insights from students and continuously improve the bootcamp’s offerings.
Bootcamp Structure
Each phase of our bootcamp is carefully crafted to also stand on its own.
- Data Analytics phase = Data Analytics bootcamp
- Machine learning phase = Data Science bootcamp
Whether you choose to enroll in the full AI/ML Bootcamp or opt for a standalone bootcamp focused on either data analytics or data science, we are proud to offer a job guarantee. ✨
Most Comprehensive Data Analytics Career track
Data Analytics Phase
Start your data science career with our data analytics bootcamp! Gain hands-on experience with real-world data assignments and expert guidance from experienced instructors. Don't miss out on this opportunity to launch your career in this rapidly growing industry.
Most Comprehensive Data Science Career track
Machine Learning Phase
Embark on your journey to a rewarding career in machine learning with our Machine Learning (Data Science) Bootcamp! This comprehensive program is your gateway to the exciting world of data-driven innovation. Our bootcamp is designed to equip you with the knowledge and skills you need to thrive in the rapidly evolving field of machine learning.
Roles and Salaries
Data Analytics Phase:
Data Analyst: The role of a data analyst is to collect, process, and analyze large datasets. They work with data visualization tools to present their findings and help organizations make data-driven decisions. The average annual salary for a data analyst in the US ranges from $68,000 to $75,000.
Business Analyst: Business analysts bridge the gap between data analysis and business strategy. They analyze business processes, identify areas for improvement, and use data insights to drive decision-making. The average annual salary for a business analyst in the US ranges from $70,000 to $92,000.
Data Visualization Engineer: Data visualization engineers create visual representations of data to make it easier for stakeholders to understand and interpret. They use tools like Tableau, Power BI, or D3.js to design interactive dashboards and reports. The average annual salary for a data visualization engineer in the US ranges from $85,000 to $110,000.
Machine Learning Phase:
Data Scientist Associate: Data scientist associates work on developing and implementing machine learning models and algorithms. They analyze large datasets, build predictive models, and contribute to solving complex business problems using statistical and machine learning techniques. The average annual salary for a data scientist associate in the US ranges from $90,000 to $120,000.
Data Engineer Associate: Data engineer associates are responsible for managing and optimizing data pipelines and infrastructure. They work with large-scale databases, design data architecture, and ensure data quality and reliability for machine learning projects. The average annual salary for a data engineer associate in the US ranges from $80,000 to $105,000.
Please note that these salary ranges are approximate and can vary based on factors such as location, industry, company size, and level of experience. It’s always recommended to research specific job listings and consult reliable salary data sources for the most accurate and up-to-date information.
Curriculum
| Module | Module Content |
| Data Analytics phase | |
| Introduction to Data Analytics | Introduction, overview & basics |
| Data Collection and Cleaning | Handling Missing Data, Handling Outliers, Data Types, and Formats |
| Data Transformation and Integration | Data Normalization, Feature Scaling, Data Integration Techniques |
| Data Reduction Techniques | Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Feature Selection |
| Descriptive Statistics and Data Visualization | Measures of Central Tendency and Dispersion, Frequency Distributions, Box Plots, Histograms, and Scatter Plots |
| Hypothesis Testing and Correlation Analysis | Types of Hypothesis Testing, Correlation, and Causation, Pearson Correlation Coefficient, and Spearman’s Rank Correlation Coefficient |
| Linear and Multiple Regression | Simple and Multiple Linear Regression, Residual Analysis, Model Selection Techniques |
| Logistic Regression and Time Series Analysis | Binary and Multinomial Logistic Regression, Stationarity and Time Series Decomposition, ARIMA, and Seasonal ARIMA |
| Advanced Regression Techniques | Ridge and Lasso Regression, Elastic Net Regression, Polynomial Regression |
| Classification Techniques | K-Nearest Neighbors (KNN), Naive Bayes, Decision Trees, and Random Forests |
| Clustering Techniques | K-Means Clustering, Hierarchical Clustering, Density-Based Clustering |
| Association Rule Mining and Market Basket Analysis | Apriori Algorithm and Market Basket Analysis, Collaborative Filtering |
| Support Vector Machines (SVM) | Kernel Functions, Non-Linear SVM, SVM for Regression |
| Neural Networks and Deep Learning | Perceptron and Multi-Layer Perceptron, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) |
| Evaluation Metrics and Model Selection | Confusion Matrix and Classification Metrics, Cross-Validation and Model Selection Techniques, Bias-Variance Tradeoff |
| Ensemble Methods | Bagging and Boosting, Stacking, Gradient Boosting |
| Data Visualization with Python | Types of Visualization, Choosing the Right Chart, Design Principles |
| SQL for Data Analysis | Introduction to SQL, Selecting Data with SQL, Joins and Grouping, Advanced SQL Queries |
| Data Wrangling with Python | Introduction to Python for Data Analysis, Data Wrangling with Pandas, Data Cleaning and Transformation, Data Aggregation and Pivot Tables |
| Big Data Technologies | Hadoop and MapReduce, Spark and Spark SQL, NoSQL Databases |
| Machine learning phase | |
| Introduction to Advanced Machine Learning | 1. Review of machine learning fundamentals 2. Mathematical foundations in machine learning 3. Overview of deep learning, classical algorithms, and business applications |
| Linear Algebra for Machine Learning | 1. Vector spaces, matrices, and operations 2. Eigenvectors and eigenvalues 3. Singular Value Decomposition (SVD) and its applications 4. Principal Component Analysis (PCA) |
| Multivariable Calculus for Machine Learning | 1. Partial derivatives and gradients 2. Optimization techniques: gradient descent, stochastic gradient descent 3. Convex optimization and its role in machine learning 4. Regularization techniques: L1, L2, and elastic net |
| Probability and Statistics for Machine Learning | 1. Probability distributions: discrete and continuous 2. Statistical inference and hypothesis testing 3. Bayesian inference and probabilistic modeling 4. Expectation-Maximization (EM) algorithm |
| Feedforward Neural Networks | 1. Deep learning architectures: perceptron, feedforward neural networks 2. Activation functions and network initialization 3. Backpropagation algorithm and training neural networks 4. Optimization techniques for deep learning: Adam, RMSprop, etc. |
| Convolutional Neural Networks (CNNs) | 1. Introduction to CNNs for image analysis 2. Convolution and pooling layers 3. Object detection and image segmentation 4. Transfer learning with pre-trained CNNs |
| Recurrent Neural Networks (RNNs) and Sequence Modeling | 1. RNN fundamentals: architecture, hidden states, and memory cells 2. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) 3. Sequence generation and language modeling 4. Applications of RNNs: text generation, machine translation, speech recognition |
| Generative Models and Unsupervised Learning | 1. Generative Adversarial Networks (GANs) 2. Variational Autoencoders (VAEs) 3. Clustering algorithms: K-means, hierarchical clustering 4. Dimensionality reduction techniques: t-SNE, UMAP |
| Classical Machine Learning Algorithms | 1. Decision trees and ensemble methods: Random Forests, Gradient Boosting 2. Support Vector Machines (SVMs) and kernel methods 3. Naive Bayes and Gaussian Mixture Models (GMMs) 4. Instance-based learning: k-Nearest Neighbors (k-NN) |
| Machine Learning in Business | 1. Introduction to machine learning in business applications 2. Predictive modeling and customer segmentation 3. Recommender systems and personalization 4. Time series forecasting and anomaly detection |
| Reinforcement Learning | 1. Markov Decision Processes (MDPs) 2. Q-Learning and Deep Q-Learning 3. Policy Gradient methods 4. Model-based reinforcement learning |
| Advanced Topics in Machine Learning | 1. Transfer Learning and domain adaptation 2. Explainability and interpretability in machine learning models 3. Adversarial attacks and defenses in deep learning 4. Fairness, bias, and ethical considerations in machine learning |
| Machine Learning Deployment and Scalability | 1. Introduction to deploying machine learning models in production 2. Model serving and APIs 3. Scalability and distributed machine learning 4. Monitoring and performance optimization |
| Ethics and Responsible AI | 1. Ethical considerations in machine learning and AI 2. Bias, fairness, and transparency in algorithms 3. Privacy and data protection 4. Regulatory frameworks and guidelines |
| Project Work and Case Studies | 1. Undertake a machine learning project or work on a business case study 2. Apply the concepts and techniques learned throughout the course 3. Gain hands-on experience with real-world datasets and business scenarios |
**Note: This course outline provides an overview of the topics and structure. The actual course content may be adjusted based on the pace of learning, the specific interests of the participants, and recent advancements in the field.
Tuition
We offer three options to get your career change started. All plans include a full refund policy if you do not get a job after graduating.
This investment includes a full year of access to our AI/ML course material. Additionally, the course offers live events, AMA sessions, personalized support from the instructor, and a certificate of completion for those who complete the course.
You can pay your tuition via card, bank transfers, or with crypto.
- Non-Job-guarantee discount – $1,600 (If you choose to not have the job guarantee, you get an additional discount)
Pay Upfront
Pay upfront & save up to 32% on tuition for a limited amount of time.
| With Job-guarantee |
Without Job-guarantee
|
|
| Total tuition before discount | $18,700 | $18,700 |
| Discount | - $4,900 | - $4,900 |
| Non-job-guarantee Discount | - $1,600 | |
| Paid at enrollment | $13,800 | $12,200 |
| Total tuition | $13,800 | $12,200 |
Month-to-month
Pay monthly. Save up to 28%
| With Job-guarantee |
Without Job-guarantee
|
|
| Total tuition before discount | $18,700 | $18,700 |
| Discount | - $4,280 | - $4,280 |
| Non-job-guarantee Discount | - $1,600 | |
| Paid at enrollment | $2,500 | $2,500 |
| Monthly payments during course (8) | $1,490 | $1,290 |
| Estimated total tuition | $14,420 | $12,820 |
Personal loan
Apply for a loan & pay it off in installments.
Some students use personal loans to pay for their education. There are many personal lending options for you to research and consider.
Keep in mind that Metana does not endorse, recommend, or promote any particular lender. The payment choice is at the discretion of you, the student. If you decide to use a personal loan, make sure to choose the option that works best for you.
Below are a few options; personal loans may also be available through your personal financial institution.
Apply for Meritize loan
**Tuition will increase to $21,980 for all cohorts in Dec 2023. To lock in the current tuition rate, pay your tuition in full or the first month’s installment and enroll in a cohort that begins in 2023. You’re eligible for a 100% refund till 2 weeks after starting the cohort. 🌱
Upcoming Cohorts
We have monthly cohorts. You can always choose to pause the program and resume where you left off if it’s too fast-paced for you or if life gets in the way. There is no financial cost associated with this. We want you to succeed and won’t make you follow a schedule that doesn’t suit you.
Admission Policy and Process
You have to prove your seriousness in learning and then only you are admitted to our bootcamp. This makes our admission policy as unique as our Bootcamp.
Admission Policy
- Only programmers with at least a year of professional experience will be considered. You must be proficient in English.
- The coding test result you receive will be the most important component of your application.
- Past achievements. We want to see that you have the perseverance to work at something until you master it. We’ll talk about these in the interview call.
- It will be beneficial to have some prior knowledge. Although prior knowledge is not required, our curriculum is fairly fast-paced, so having a head start will help you succeed and show us that you are committed to the subject.
Admission Process
- After you submit your application & schedule an interview call with one of our student admissions officers, You will receive an email with a link to a coding test. (dates are available within 3-7 days from the application date)
- You need to complete the coding test within 3 days.
- Send us an email if you need more time ([email protected])
- Your application will be rejected if your score falls short of a predetermined level. Because we need to compare your application to those of the other applicants for the upcoming cohorts, we can’t always make a decision right away.
- We limit cohorts to 10 students per month to ensure maximally effective learning outcomes. If you have a great application but didn’t quite make it in, we will offer to waitlist you for the upcoming month.
Career Success - Metana's JobCamp™️
- Build a Technical Resume
- Optimize Your LinkedIn Profile
- Network within the Industry
- Prepare for Behavioral Interviews
- Stay Motivated on the Job Search
- Craft Cover Letters
- Communicate with Recruiters
First Impressions
Make a brilliant first impression. LinkedIn, GitHub and Resume templates and guidance.
The Hunt
Learn to build connections, how to look for jobs, and explore starting as a freelance.
The Interview Process
Learn about both the technical and non-technical parts of an interview. How to prepare effectively.
Technical Know-How
Learn common data structures and algorithms, and describe them during a whiteboard interview. Practice coding techniques for take home assignments.
Our students used to work at
Frequently Asked Questions
The advanced AI & Machine Learning (ML) bootcamp is a 9-month program delivered in online sessions. Participants will have access to online course materials and lectures.
Still have a question? Send us an email at [email protected]
