Become a Data Scientist in 4 months.

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

Make enquiry

Next Cohort Starts

November 16th, 2024

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

3 Hours/Week

Course duration

16 weeks

Training delivery mode

Online

Learning model

Instructor-Led Lectures

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

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. 

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.

Enrol Now

Curriculum

  • 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

Alumni success story

Training at Zyonel was transforming for me.

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

Victor

I gained invaluable insight into the tech industry.

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

Queen

My problem solving skills improved tremendously.

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!"

Tamilore

Ready to take the next bold step?

Start your journey today!

₦300,000

Full tuition payment

₦400,000

Get 25% off your payment

Promo expires 16th November 2024.

Pay 100% upfront. 

₦160,000 x 2

Instalment tuition payment

₦400,000

Get 20% off your payment

Promo expires 9th November 2024

Pay 50% upfront and 50% balance before the 6th week of the training. 

Enrol now

Frequently
asked questions

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