Data Science Bootcamp

Learn data science with Approved America’s award winning curriculum and top notch instructors. Data Analyst Instructor : Rhonda Coleman Albazie and 12 Additional, Data Science Instructors

20 Hours of Study per Week

Starting at $6,500

24 Weeks

Module 1: Introduction to Data Science Fundamentals 

Interested in starting a new career in data science? Get approved. Approved America Data Science course delivers the key foundational skills to develop command mastery of data science for entry level and advanced careers as a data scientist. You’ll discover what data science is, what data scientists do, which tools data scientists use and how to leverage data to make strategic data driven decisions to best serve invested stakeholders. 

Week 1: Welcome To Approved America Data Science with Course Primer  

  • What Data Science Is, Why Data Science Is Important and What Data Scientists Do
  • Approved America’s ‘hands-on’ Jupyter activities 
  • Approved America’s Python Primer
  • Data Science Glossary of Terms 
  • Approved America Bibliophile’s Data Science Reading List 
  • Approved America Data Scientist Interlocked Networks 

Week 2: Introduction to Data Science Core Concepts, Technologies and Trends 

  • The Data Science Process
  • Approved America’s Data Science ToolKit 
  • Types of Data and Applications Plus Examples 

Week 3: Data Collection and Data Management

  • Sources of Data
  • Data Collection and APIs
  • Exploring and Fixing Data
  • Data Storage and Management
  • Using Multiple Data Sources

Week 4: Data Analysis

  • Introduction to Statistics
  • Basic Machine-Learning Algorithms

Week 5: Data visualisation

  • Types of Data Visualization
  • Data for Visualization 
  • Technologies for Visualization 

Week 6: The Future of Data Science Plus Data Trends 

  • A Comprehensive Overview and Exploration of the Future of Data Science

No previous data science or computer programming experience required. 

Get approved and start today.

Approved America Technology Training Data Science Certificate Upon Completion. 

Additional Advanced Data Analytics Certificates Available For Approved America Alumni. 

Offering  interlocked networking internships and job placement for graduates. 


1.1 What Data Science Is and Why It’s Important

1.2 Hands-on Jupyter Primer Activities 

1.3 Approved America Python Primer 

Module 2: Mathematical and Statistical Skills 

The Approved America Data Science Mathematical and Statistical Module covers

  • Introduction to Statistics 
  • Terminologies in Statistics 
  • Categories in Statistics 
  • Understanding Descriptive Analysis 
  • Descriptive Statistics in R 
  • Understanding Inferential Analysis 
  • Inferential Statistics in R 


1.1 Introduction to Statistics 

1.2 Terminologies In Statistics 

1.3 Categories In Statistics 

Module 3: Machine Learning 

Gain command mastery of supervised and unsupervised machine learning. 

Intro to Supervised/Unsupervised Learning

  • Decision Trees
  • Linear Regression: OLS, Regularization, Linear Classifiers
  • Logistic Regression, Multi-Class Logistic Regression Ranking Support Vector Machines
  • Feature Selection Latent Factor Models (PCA)
  • Clustering (k-means, soft k-means)
  • Ensemble Methods such as Random Forest and Ada Boost
  • Probabilistic methods (Bayesian view)
  • Model evaluation and model selection
  • Introduction to Neural Networks and Convolutional Neural Networks
  • Autoencoders 


1.1 Supervised and Unsupervised Learning 

1.2 Decision Trees 

1.3 Linear Regression : OLS, Regularizatin and Linear Classifiers 

About The Data Science Bootcamp

Learn Path

Career Resources

The Approved America Data Science curriculum includes : 

  • Introduction to Data Science
  • Mathematical & Statistical Skills
  • Machine Learning
  • Coding
  • Algorithms used in Machine Learning
  • Statistical Foundations for Data Science
  • Data Structures & Algorithms
  • Scientific Computing
  • Optimization Techniques
  • Data Visualization
  • Matrix Computations
  • Scholastic Models
  • Experimentation, Evaluation and Project Deployment Tools
  • Predictive Analytics and Segmentation using Clustering 
  • Applied Mathematics and Informatics
  • Exploratory Data Analysis
  • Business Acumen & Artificial Intelligence



Introduction To Data Science Fundamentals


Mathematical and Statistical Skills


Machine Learning




Algorithms Used In Machine Learning


Statistical Foundations for Data Science


Data Structures and Algorithms


Scientific Computing


Optimization Techniques


Data Visualization


Matrix Computations


Scholastic Models


Experimentation, Evaluation and Project Deployment Tools


Predictive Analytics and Segmentation Using Clustering


Applied Mathematics and Informatics


Exploratory Data Analysis


Business Acumen & Artificial Intelligence

Module 4: Coding 

Learn coding skills, the secrets of writing well tested simple to improve programs to develop command mastery of programming languages and advance your career as a data scientist. 

The Approved America Data Science Coding Module will cover : 

  • How to represent information as data
  • How to focus each part of your program on a single task
  • How to use examples and tests to clarify what your program should do
  • How to simplify the structure of your program using common patterns
  • Recognize and represent more complicated information
  • R Programming 
  • Python Primer 
  • Hadoop Platform 
  • SQL Database/Coding 
  • Java
  • Perl 
  • C/C++ 
  • Analyze Data With Python 
Additional Primers On 
  • Apache Spark 
  • Machine Learning and AI 
  • Data Visualization 
  • Unstructured Data 


1.1 How To Represent Information As Data 

1.2 Python 

1.3 Analyze Data with Python

Module 5: Algorithms Used In Machine Learning 

Introduction to Algorithms Used In Machine Learning

Algorithm Descriptions – A comprehensive overview of the linear, nonlinear and ensemble algorithm descriptions:

  • Algorithm 1: Gradient Descent.
  • Algorithm 2: Linear Regression.
  • Algorithm 3: Logistic Regression.
  • Algorithm 4: Linear Discriminant Analysis.
  • Algorithm 5: Classification and Regression Trees.
  • Algorithm 6: Naive Bayes.
  • Algorithm 7: K-Nearest Neighbors.
  • Algorithm 8: Learning Vector Quantization.
  • Algorithm 9: Support Vector Machines.
  • Algorithm 10: Bagged Decision Trees and Random Forest.
  • Algorithm 11: Boosting and AdaBoost.
  • How To Talk About Data In Machine Learning
  • Algorithms Learning Mapping From Input to Output
  • Parametric and Non-Parametric Algorithms In Machine Learning
  • Supervised, Unsupervised and Semi-Supervised Learning 
  • The Bias Variance Trade-Off 
  • Overfitting and Underfitting 
Linear Algorithms 
  • Crash Course In Spread Sheet Math 
  • Gradient Descent For Spread Sheet Math 
  • Linear Regression 
  • Simple Linear Regression Tutorial 
  • Linear Regression Tutorial Using Gradient Descent 
  • Logistic Regression 
  • Logistic Regression Tutorial 
  • Linear Discriminant Analysis 
  • Linear Discriminant Analysis Tutorial 

Non-Linear Algorithms

  • Classification and Regression Trees
  • Classification Trees and Regression Tutorials 
  • Naive Bayes 
  • Naive Bayes Tutorial 
  • Gauassian Naive Bayes Tutorial 
  • K-Nearest Neighbors
  • K-Nearest Neighbors Tutorial 
  • Learning Vector Quantization 
  • Learning Vector Quantization Tutorial 
  • Support Vector Machines
  • Support Vector Machines Tutorial 

Ensemble Algorithms 

  • Bagging and Random Forest
  • Bagged Decision Trees Tutorial 
  • Boosting and AdaBoost
  • AdaBoost Tutorial 


1.1 Grouping Algorithms by Learning Styles 

1.2 Grouping Algorithms By Similarity In Form and Function 

1.3 How To Talk About Data In Machine Learning 

Web Development 
Software Development 
FrontEnd Development 
BackEnd Development 
UI UX Design

Al and Machine Learning 

iOS Mobile Development 

Cyber Security 



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