Become a Data Scientist in 12 Weeks.

Join our intensive data science training and gain the critical skills for driving innovation and advancement across industries.

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Next Cohort Starts

February 1st, 2025

Our alumni work here

Who can enrol for this course

Zyonel Academy’s Data Science course was designed for individuals with a background in Python Programming, Random Variables, Probability Distribution and Basic Calculus.

Class Duration

This course is designed for both novice and intermediate learners

Course duration

This course will last for a duration of 16 weeks. 

Training delivery mode

This course can be accessed both online and on-site.

Learning model

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Instructor-Led Lectures

Learn in real-time from industry-expert and enjoy interactive sessions from the comfort of your home.

Project-Based Learning

Build your portfolio by working on complex projects that will challenge your problem solving skills and prepare you for your first job.

Mentorship

In addition to live classes, students receive full support from a personal mentor throughout their
project phase.

Peer-to-Peer-Learning

Learning with your peers boosts your confidence and collaborative skills. 

Course module

  • Overview of Data Science
  • Data Science Process
  • Tools and Technologies in Data Science
  • Applications of Data Science

  • Data Types and Sources
  • Data Collection and Cleaning
  • Descriptive Statistics
  • Data Visualization Techniques
  • Identifying Patterns and Anomalies

  • Principles of Data Visualization
  • Tools for Data Visualization (Matplotlib, Seaborn, etc.)
  • Creating Various Types of Plots (line, bar, scatter, histogram)
  • Interactive Visualizations (Plotly, Bokeh)
  • Best Practices for Effective Visualizations

  • Understanding K-Nearest Neighbors Algorithm
  • Implementing KNN in Python
    • Linear Regression Concepts
  • Least Squares Method
  • Evaluating Regression Models (R-squared, RMSE)

  • Multiple Linear Regression
  • Interaction Terms
  • Polynomial Regression
  • Overfitting and Underfitting
  • Model Interpretation

  • Model Evaluation Metrics
  • Bias-Variance Trade-off
  • Train-Test Split
  • Cross-validation Techniques (k-fold, LOOCV)
  • Model Selection Criteria (AIC, BIC)

  • Introduction to Regularization
  • Ridge Regression (L2 Regularization)
  • Lasso Regression (L1 Regularization
    • Elastic Net
  • Impact of Regularization on Model Performance
  • Linear Regression Concepts
    • Logistic Regression I and II
  • Binary Classification Problems
  • Logistic Regression Model
  • Odds and Log Odds
  • Model Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
  • ROC and AUC

  • Introduction to Dimensionality Reduction
  • PCA Algorithm
  • Eigenvalues and Eigenvectors
  • Interpreting Principal Components
  • Applications of PCA
  • Missing Data and Imputation
  • Types of Missing Data (MCAR, MAR, MNAR)
  • Handling Missing Data (Listwise Deletion, Pairwise Deletion)
  • Imputation Techniques (Mean/Median Imputation, KNN Imputation)
  • Advanced Imputation Methods (Multiple Imputation, MICE)

  • Ethical Considerations in Data Science
  • Data Privacy and Security
  • Bias and Fairness
  • Ethical Decision Making
  • Case Studies in Data Science Ethics

  • Storytelling with Data
  • Simplifying Complex Concepts
  • Creating Effective Reports and Dashboards
  • Visualization Tools for Communication
  • Best Practices for Presentations

  • Introduction to Decision Trees
  • Splitting Criteria (Gini, Entropy)
  • Tree Construction
  • Pruning Techniques
  • Overfitting in Decision Trees

  • Advanced Tree Methods
  • CART Algorithm
  • Handling Categorical Features
  • Interpretability of Decision Trees
  • Applications of Decision Trees

  • Ensemble Methods Overview
  • Bagging Technique
  • Random Forest Algorithm
  • Out-of-Bag Error
  • Feature Importance in Random Forest

  • Boosting Techniques Overview
  • Gradient Boosting Machines (GBM)
  • Hyperparameter Tuning in GBM
  • AdaBoost Algorithm
  • Comparing Boosting Methods

  • Introduction to Time Series Data
  • Decomposition of Time Series
  • Autoregressive (AR) Models
  • Moving Average (MA) Models
  • ARIMA and Seasonal ARIMA

  • Introduction to Unsupervised Learning
  • Clustering Techniques (K-means, Hierarchical)
  • Cluster Evaluation Metrics
  • Dimensionality Reduction for Clustering
  • Applications of Clustering

  • Basics of Reinforcement Learning
  • Markov Decision Processes (MDP)
  • Policy and Value Functions
  • Q-Learning
  • Applications of Reinforcement Learning

  • Project Proposal
  • Data Collection and Preparation
  • Model Development and Evaluation
  • Results and Interpretation
  • Presentation Skills

Learning tools

Below are the tools that form the foundation of data science practices and are essential for transforming data into innovations.

Vscode

Jupyter Notebook

Anaconda

Git

Github

Python

Seaborn

TensorFlow

Scikit-learn

Keras

PyMC3

OpenAi

Numpy

Matplotlib

SQL

Real Success Stories from Zyonel Academy Alumni

Meet our alumni who’ve turned their passion for tech into thriving careers. From landing dream jobs to launching innovative startups, their success starts with Zyonel Academy

Ademola

Data analysis

I have had the privilege of undergoing training with Zyonel Academy, and I must say, it has been an incredible journey! The training has not only honed my language abilities but has also enabled me to understand the nuances of human communication. The expertise and guidance provided have been invaluable, and I am now equipped to assist and provide helpful responses to a wide range of queries. Thank you for the opportunity to learn and grow with you!

Victor

Product Design

It boosted my skills across coding, teamwork, communication, & problem-solving, standing out with its supportive community & real-world approach.

Ayanfe

Frontend Developer

Learning web development at Zyonel Academy was like stepping into a world of endless creativity and problem-solving. Starting out, I felt overwhelmed—especially coming from a public health background—with so many languages and frameworks to learn. But the journey was incredible. Read More

Ronke

Backend Development

Zyonel Academy provided me with the skills and knowledge I needed to excel as a backend developer. The training was practical, the instructors were supportive, and the experience was truly transformative. I highly recommend it to anyone pursuing a career in tech.

Queen

Data Analysis

Zyonel Academy's Data Analysis course was eye-opening, offering crucial insights & practical skills for a data analyst career. Grateful for the invaluable experience.

Dideoluwa

QA

My journey at Zyonel Academy as a Frontend Development and Quality Assurance student was nothing short of transformative. The academy’s hands-on approach and industry-relevant curriculum gave me the skills and confidence to face real-world challenges head-on. Read More

Tamilore

Backend Development

Zyonel Academy broadened my perspective on backend development, especially as a woman. The structured learning, mentorship, collaboration, and projects greatly improved my problem-solving skills. Thankful to Zyonel!"

Irene

FrontEnd Programming

When I joined Zyonel Academy, I was in the middle of maternity with a four-month-old baby and a toddler. I had almost no programming experience or knowledge, and I honestly doubted my ability to keep up. But enrolling in the frontend programming course at Zyonel turned out to be one of the best decisions I’ve ever made. The lectures, hands down, were the most engaging I’ve ever attended. Read More

“95% of our graduates secure meaningful tech employment within six months of completing their programs”

Ready to take the next bold step?

Start your journey today!

Payment Plan

$367/month

Regular: $1,366/month

Get 73% off your payment

Promo expires 28th January, 2025.

Pay 3 equal installments before the commencement of the training. 

Enrol now

One-off payment

$999

Regular: $4,000

Get 75% off your payment

Promo expires 25th January, 2025

 Pay 100% upfront.

Frequently
asked questions

The training is strictly online. However, students are allowed to meet physically with their private mentors during the project phase.