Data Science Bootcamp by Code Labs Academy: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom +1 more	Mode of Delivery Video and Text Based

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.

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

Data analyst Data scientist Machine learning engineer Business intelligence analyst

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

Python Fundamentals: Full language overview
Environments: The use of python environments, Anaconda, and Jupyter Notebooks
SQL and Databases: Introduction to databases and the SQL language
Linear Algebra: Basic linear algebra including scalar, vector, and matrix operations
Git/Github: Basic understanding of Git and GitHub for code versioning

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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Data Science Bootcamp

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12 Weeks

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