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The Process
STEP 2 STEP 3 STEP 4 STEP 5 STEP 6STEP 1
Get paired with a 1-on-1 expert mentor
Complete exercises online
Your mentor reviews your work within hours
Perfect your skills over 3-6 months
Create a portfolio, hone your CV, interview prep
Begin your new career in data science
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Overview
Going beyond the essentials
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Our 1-on-1 code review centric approach works
Here’s why learning through code review is smarter
Be exposed to the industry standards from day one
Don’t make the same mistakes as computers
Industry experts tailored to your goals
Join a community of career-changers
Free of fear of failure
We layer a proven 1-on-1 personalised mentorship approach over our code review
Get unstuck with technical help
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Why choose data science as a lucrative career?
How we get you hired
Technical CV and portfolio
Interview preparation Join our hiring network
Responsibilities include:
Career paths
The Data Analyst
Average salaries
Average salaries
Responsibilities include:
The Machine Learning Engineer
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Structure of the bootcamp
Free trial
Before you start
Python for Data Science
Beginner level
Data Analytics and Exploration
Intermediate level
Machine Learning and AI
Advanced level
Interview and getting hired
Post graduation
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Tasks: Capstone Projects:
Breakdown of syllabus
12Remember, with HyperionDev, you’re never alone. You can contact your mentor for 1:1 support whenever you need help with a task. The code you submit for each task is reviewed by your mentor
Python for Data Science
Tasks
1
Description
Thinking logically: Pseudocodeand algorithm design
Introduction to Python
Numerical data types
The string data type
Control Structures: If else
The boolean data type and logical operators
Control Structures: While & For Loops
Working with external data sources: Input
Control Structures: Elif Statements
Capstone Project I: Nested loops
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3
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5
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Discover the different types of errors that might occur in your programs and how to handle them.
Defensive programming: Error handling
12
Beginner data structures: Lists
Working with external data sources: Output
Functions: Using built-in functions
Introduction to ObjectOriented Programming
Capstone Project II:Consolidation
Introduction to Natural Language Processing (NLP)
13
14
15
16
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Semantic Similarity (NLP)
Capstone Project III: Introductory NLP
19
20
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Data visualisation I7
1
Introduction to databases
Design and build relational database
Sources of data
Working with SQL
SQLite
Capstone Project I: Databases
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3
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6
Tasks Description
Tasks: Capstone Projects:
Data Analytics & Exploration
Understand basic data visualization and how to choose the best form of visualisation based on aspects such as nature of dataset and expectations from the visualisation exercise.
21
Data Structures:Lists & Dictionaries
Get introduced to principles of object oriented programming to use them later with machine learning libraries.
Learn about sentiment similarity, a popular application of NLP widely used for social media analysis.
Use all the knowledge you have gained throughout this level to create a useful program.
Data visualisation II
Python packages for Data Science
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Working with datasets11
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Exploratory Data Analysis
Data analysis II
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Capstone Project III: Data Visualization
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Dive into more complicated data visualisation. Scatterplot matrix. Network visualization.
Data Visualization III Explore popular data visualization tools such as Tableau.
Get introduced to some of the most popular Python packages like pandas, NumPy, SciPy.
Understand how to deal with Missing Values and turn categorical variables into quantitative variables. Explore data normalisation.
Learn about descriptive statistics and concepts such as GroupBy, Correlation, Analysis of Variance ANOVA.
Build an analysis report based on a dataset.
Learn how to import and export data in Python. Start importing and manipulating datasets.
Set up Matplotlib and understand how to start loading data from a CSV and NumPy + Panda. Create basic visualisation using Matplotlib, such as pie charts and bar graphs.
Learn about cleaning data, dataframe manipulation, and summarising data.
Data Visualization IV
Data analysis I
Capstone Project II: Data Analysis
Create advanced visualization using Matplotlib, including scatter-plots, time-series plotting, area charts, and 3D plots.
16 Build an analysis report based on a dataset.Capstone Project II: Data Analysis
Introduction to versioncontrol and Git
19
21
Explore the Git version control system and the GitHub collaboration platform.
20 Git basics
Build your brand I
Dive into using Git and discover how to set up a repository, use common Git commands, commit a modified file, view your project's history and branch.
Use GitHub to start building a portfolio of work that you can share with others to showcase your skills.
Data visualisation V
Create or update your LinkedIn profile to connect with a network of professionals and let people know about your skills.
Tasks: Capstone Projects:
Supervised learning V: Decision Trees II
Build Your Brand III
Unsupervised Learning II: Clustering II
Capstone Project I:Image Processing
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8
9
Unsupervised Learning II:
Unsupervised Learning III: PCA
Clustering I10
11
12
Capstone Project II:Unsupervised Machine Learning
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Neural Networks I: Introduction14
Tasks
2
Description
Supervised learning I: Simple Linear Regression
Introduction to Machine Learning
Build your Brand II
Learn different techniques to train neural networks using backpropagation.
Test your knowledge of unsupervised machine learning in this challenging task.
Neural Networks II:Training Neural Networks
Supervised learning III: Logistic Regression
Supervised learning II: Multiple Linear Regression
Supervised learning IV: Decision Trees I
3
1
4
5
6
Machine Learning and AI
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Learn what linear regression is and when to apply it.
Explore more concepts such as multiple linear regression, and Training vs. Test Set.
Introduces the notion of classification, and the application of logistic regression to binary classification.
Join the Hyperion Connect community to make yourself visible to Hyperion hiring partners.
Become familiar with the fundamental concepts and terminology used in neural networks. Understand backpropagation and learn how to validate your models.
15
Add to your knowledge of unsupervised learning by studying dimensionality reduction.
Understand how to work on clustering algorithms such as k-means, a commonly used unsupervised learning algorithm.
Explore more unsupervised learning algorithms such as hierarchical clustering.
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Neural Networks III:CNNs, Yelp reviews Practical
Neural Networks IV:Recurrent Neural Networks: Sample Project
Reinforcement Learning:Practical example of AlphaGo
Get introduced to Convolutional Neural Networks with sample projects.
Delve into the applications of Recurrent Neural Networks with a simple project to follow and work on.
Describe the concept of reinforcement learning including Markov Decision Processes, Q-Learning.
Machine Learning in the IndustryLearn about industry-relevant applications of machine learning in finance and healthcare.
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20Capstone Project III:Consolidated Machine Learning
Challenge your knowledge of machine learning gained throughout this bootcamp in this final capstone project.
22 Build your brand VMake your mark by ensuring that all components of your online presence related to your professional brand are finalised.
Build your brand IVGet some pointers for a successful technical interview and connect with your Hyperion Connect team to arrange a mock interview.