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.

Make enquiry

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

Enrol now

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