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- Environments: The use of python environments, Anaconda, and Jupyter Notebooks
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Data Science Bootcamp
BY
Code Labs Academy
With the help of this course, students will get to discover the core concepts and efficient uses of data science, data ...Read more
Full time, Online
12 Weeks
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Course overview
Students will be guided by Data Science Bootcamp certification course experts through the fundamental knowledge and practical skills necessary to start a successful career in data science. Learners will study data science's foundational theory and applications in the Data Science Bootcamp training.
This course is offered by the Code Labs Academy which will provide deep learning in data analytics, machine learning, and data science to the participants. The boot camp focuses on providing students with hands-on experience with real-world datasets and industry-relevant projects, teaching participants how to extract insights, build predictive models, and make data-driven decisions.
The highlights
- Fast-paced
- Small class sizes
- Virtual classroom
- Hands-on activities
Program offerings
- Certification
- Two weeks worth of preparational study
- Forum
- Live sessions per week
- 1:1 career coaching
- Remote-first learning
Course and certificate fees
The Data Science Bootcamp certification fee is €5,999.00 (€166.00/month).
Fee structure
Course | Amount |
Data Science Bootcamp | €5,999.00 (€166.00/month) |
certificate availability
Yes
certificate providing authority
Code Labs Academy
Who it is for
Students who complete a data science boot camp are prepared for careers where they can use their knowledge of data to solve challenging challenges, drive corporate development, and extract insightful information from it. Those are:
Eligibility criteria
Certification Qualifying Details
Data Science Bootcamp certification by Code Labs Academy will be given to participants who successfully complete the training.
What you will learn
Natural language processing
Machine learning
Knowledge of deep learning
After completing the Data Science Bootcamp certification syllabus students will learn:
Foundation
- SQL, Python, Jupyter Notebook, Git and GitHub, Linear Algebra, Probabilities and Statistics.
Data Analytics
- Data Analysis, Data Preparation, Data Visualization and Data Exploration.
Classic Machine Learning
- Machine Learning, Supervised and Unsupervised learning, ML model enhancement, Naive Bayes, SVM, Random Forests, ML Pipelines and Classification.
Deep Learning
- Neural Networks (implementation, troubleshooting & optimisation), CNN Architectures, Autoencoder Architecture, Data Augmentation, Tensorflow, Keras and Scikit-Learn.
Natural Language Processing
- Text coding for NLP, Recurrent Neural Networks (RNN), LSTM, Attention Mechanisms, Transformer Model and chatbot building.
The syllabus
Chapter 0: Prework
Chapter 1: Data Analytics
- Data Terminology: Basic terminology used in data science
- Data Exploration: Fundamentals of exploring different types of data
- Data Cleaning and Preparation: An introduction to data cleaning. Including; missing values, imbalanced data, outliers and type mismatches
- Data Visualization: Basic data visualization for both univariate and multivariate analyses
- Linear Regression: The theory and uses of linear regression
- Feature Extraction and Dimensionality Reduction: Fundamentals of selecting features and the different techniques used to reduce the dimensionality of the data
- Data Acquisition: Discover the different methods of data acquisition including web scrapping, web crawling, and data parsing
Chapter 2: Classical Machine Learning
- Classical Machine Learning Intro: Introduction to classical machine learning approaches, the notations used, and the different learning and evaluation techniques
- Supervised Learning Classical Methods: Introduction to different supervised learning techniques such as; KNNs, Naive Bayes, SVM and Decision Trees
- Unsupervised Learning: Introduction to different unsupervised learning techniques including Kmeans, Expectation Maximisation, and association rules
- Hyperparameter Selection: Introduction to hyperparameters and their selection and optimization
Chapter 3: Deep Learning
- Introduction to Neural Networks: History and foundations of neural networks
- Neural Networks In-Depth: A deep dive into neural networks including the math behind them, weight initialization, activation functions, forward and backward passes, and native implementation of a neural network
- Introduction to Tensorflow and Keras: Introduction to building deep learning models with TensorFlow and Keras including some common tools such as Tensorboard.
- Advanced Concepts in Deep Learning: An overview of multiple advances deep learning techniques including batch normalisation, data augmentation, dropout, and solutions to some of the challenges faced when doing deep learning
Chapter 4: Deep Learning for Computer Vision
- Introduction to Computer Vision: Introduction to convolution, CNNs, and the different techniques used with them
- Advanced Concepts in CNNs: A deep dive into how CNNs work CNN Tasks: An overview of the different
- CNN tasks including image classification, segmentation, and generation
Chapter 5: Deep Learning for Natural Language Processing
- Introduction to Natural Language Processing: History and introduction to NLP and its foundations
- Recurrent Neural Networks: A deep dive into RNNs and how they work including the different GRU and LSTM gates
- Representations and Embeddings: Fundamentals about learning representations and embeddings using different techniques such as Bag of words, TF-IDF, Word2Vec, Glove, ElMo, and more
- Transformers: An introduction to attention and transformers, their architecture and how they can be used with self-supervising learning
Chapter 6: Deployment
- Cloud Services AWS, Azure, GCP: Introduction to the major cloud providers
- Web Hosting: Introduction to hosting a web application
- Deploy on AWS: Introduction to deploying models on AWS
- Model Monitoring: Introduction to monitoring live models and their usage
- Updating a Live Model: Learn how to update your deployed model
Admission details
To enrol in Data Science Bootcamp classes students must follow the given steps:
Step 1: Browse the official URL: https://codelabsacademy.com/courses/data-science
Step 2: Click the apply now option, fill in all the required information and submit the application.
Step 3: Applicants will receive a call from the learning specialist after which they can make the payment and start their course.
Filling the form
Students first need to browse the official website, register for the course by submitting the required details and then make the payment to start with the Data Science Bootcamp online course.
How it helps
The Data Science Bootcamp certification benefits students by offering them thorough, hands-on instruction in the fundamental theories, methods, and equipment of data analysis, machine learning, and statistical modelling. Working on actual projects gives participants practical experience, which helps them build skills and a portfolio that demonstrates their talent.
FAQs
Where can students access this course?
Yes, everyone can access the course from anywhere, but only those who follow the Berlin, Germany, time zone will find it useful.
What is the duration of the live lessons?
Live lessons will take place up to 34 hours a week in the full-time course and up to 17 hours a week in the part-time course.
What is the mode of payment available for the Data Science Bootcamp online course?
There are several funding platforms, including GoCardless, Knoma, and Quotanda. There are additional plans for circumstantial discounts available.
What is the duration of this course?
The students can choose between taking the course full- or part-time, depending on their preferences.
How many topics are covered per week?
1-3 topics will be covered in the full-time course, and 1-2 topics will be addressed in the part-time course each week
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Online Data Science Course Eligibility Criteria: Syllabus, Skills & Subjects
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