Course Objective:
This Course objective is to equip you with the essential skills and knowledge to proficiently analyze data, derive meaningful insights, and make informed decisions.
Whether you’re a novice or seeking to deepen your expertise, this course is designed to cater to all levels of learners, fostering a solid foundation and advancing your proficiency in this crucial field.
What you'll learn:
Basic/Intermediate: 2Months (N250,000)
Python Syntax & Programming Fundamentals
Data Structures
Control Flow
Basic Input/Output
Functions and Modules
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Setting Up Your Environment
NumPy for Numerical Data
Pandas for Data Manipulation
Seaborn for Advanced Visualizations
Exploratory Data Analysis (EDA)
INTERMEDIATE LEVEL
Intermediate Data Manipulation
Time Series Data
Advanced Data Visualization
Statistical Analysis
SQL and Database Interaction
Machine Learning Basics
Advance: 2Months (N250,000)
Advance
Advanced Data Cleaning and Feature Engineering: Interpolation, Multiple Imputation, and KNN Imputation.
Machine Learning with Scikit-learn: Random Forest, Gradient Boosting, XGBoost, and LightGBM.
Deep Learning: Working with TensorFlow, Convolutional Neural Networks (CNNs). Recurrent Neural Networks (RNNs)
Big Data Processing: Working with Apache Spark (PySpark)
Natural Language Processing (NLP): Tokenization, Lemmatization, Stemming Using SpaCy or NLTK.
Model Deployment: o Deploying machine learning models with Flask or FastAPI
Projects & Real-World Applications
Advanced Projects: Create a complete data pipeline from ETL (Extract, Transform, Load) to visualization or deploy an ML model as a web app.
Basic Projects: Analyze datasets from sources like Kaggle (e.g., Titanic dataset, Iris dataset).
Intermediate Projects: Build recommendation systems, customer segmentation, or predictive models for real-world applications.
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