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Self-paced Full Stack Data Science Bootcamp

 

The comprehensive full stack data science program that is designed for everyone.

 
For whom: For those who have no previous programming knowledge

Language: English

Course Type: Self-paced Bootcamp

Price: 100€ –>> 50€

 Organisors Horizon Global AcademyOrganisers

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 Organisors Horizon Global AcademyCurriculum

The comprehensive full-stack data science program that is designed for everyone. No previous programming knowledge is required! Make your own way into data science with this comprehensive curriculum. Study the lessons, make the exercises, complete the projects and get certified. And it’s all 50€.

This course includes:

1. Unlimited lifetime access
2. Continuous content updates
3. Hands-on exercises
4. Capstone projects

Introduction to Python
  • Python Basics
  • Basic Data Types
  • What are Variables?
  • Lists
  • Dictionaries
  • Tuples and Sets
Further into Python
  • Conditional Statements
  • Loops
  • Functions
  • Classes
  • Errors and Exception Handling
  • File Operations
Python Data Science Libraries
  • Introduction to NumPy
  • Basics of NumPy Array
  • Indexing and Slicing of NumPy Arrays
  • Mathematical Operations on NumPy Arrays
  • Introduction to Pandas
  • DataFrame Basics
  • Filtering DataFrames
  • Grouping and Aggregation
  • Operations on DataFrames
  • Combining DataFrames
Data Visualization with Python
  • Basic Chart Types
  • Introduction to Matplotlib
  • Visualization with Matplotlib
  • Plotting Basic Charts With Matplotlib
  • Introduction to Seaborn
  • Visualization with Seaborn
  • Introduction to Plotly
  • Visualization with Plotly Express
Introduction to Statistics
  • Main Statistical Concepts
  • Probability
  • Statistical Distributions
  • Population, Sampling and Related Theorem
Explorotary Data Analysis
  • What is Exploratory Data Analysis?
  • Data Cleaning – Variable Types
  • Data Cleaning – Missing Values
  • Data Cleaning – Outliers
  • Data Exploration – Univariate Analysis
  • Data Exploration – Multivariate Analysis
  • Feature Engineering – Part 1
  • Feature Engineering – Part 2
  • The Concept of Statistical Modeling
Project 1 (Explorotary Data Analysis)
  • Project Details
Introduction to Databases and SQL
  • Introduction to Databases
  • SELECT – FROM – WHERE
  • Grouping and Aggregating Data
  • CASE Statements
  • Joining Data
  • Operators and Functions
  • SQL Queries in Python (SQLAlchemy)
  • MySQL Installation and Configuration
Introduction to Machine Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning
Regression Problems
  • What is Regression
  • Simple Linear Regression Models
  • Assumptions of Linear Regression
  • Understanding the Relationship
  • Evaluating Goodness of Fit
  • Making Predictions
  • Overfitting and Regularization
Project 2 (Regression)
Project Details
Classification Problems
  • What is Classification
  • Logistic Regression
  • Performance Metrics
  • Imbalanced Data
  • Cross Validation
Project 3 (Classification)
Project Details
Supervised ML Algorithms
  • K-nearest Neighbors
  • Decision Trees
  • Random Forest
  • Support Vector Machine
  • Boosting Methods
  • XGBoost
  • LightGBM
Clustering Algorithims
  • K-means
  • Evaluating Clusters
  • Hierarchical Clustering
  • DBSCAN
  • GMM
Dimension Reduction Problems
  • What is Dimension Reduction
  • PCA
  • TSNE
  • UMAP
Project 4 (Unsupervised Machine Learning)
Project Details
Introduction to Deep Learning
  • What Is Deep Learning
  • Artificial Neural Network
  • Introduction to Keras and TensorFlow
  • Convolutional Neural Networks
  • Recurrent Neural Network
Graduation Project
Project Details

Enrolment Process:

1. Application on Horizon Global Academy (HGA)

2. Get a confirmation email

3. Registration to requested program on Bootrain

 

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