Course Details

This Data Science course will equip you with the tools and techniques to turn raw data into actionable insights and predictive models. You’ll learn the end-to-end data science process, from data collection and cleaning to building and evaluating machine learning models. With hands-on projects and real-world applications, you will gain the skills needed to tackle complex data challenges and make data-driven decisions from exploratory analysis to deploying machine learning models in production.

DATA SCIENCE

This Data Science course will equip you with the tools and techniques to turn raw data into actionable insights and predictive models. You’ll learn the end-to-end data science process, from data collection and cleaning to building and evaluating machine learning models. With hands-on projects and real-world applications, you will gain the skills needed to tackle complex data challenges and make data-driven decisions from exploratory analysis to deploying machine learning models in production.

Course Fee

₦ 120,000

Duration

16weeks

APPLY
  • Overview of data science and its applications.
  • Data science tools and environments (Python, Jupyter Notebooks).
  • The data science lifecycle: from problem definition to deployment.
  • Data sources: APIs, web scraping, databases.
  • Data cleaning and preprocessing techniques (handling missing values, outliers).
  • Feature engineering: creating new features from raw data.
  • Data transformation techniques (scaling, normalization, encoding).
  • Descriptive statistics for summarizing data.
  • Visualizing distributions, relationships, and trends.
  • Uncovering patterns and correlations in data.
  • Tools for EDA (Matplotlib, Seaborn, Pandas).
  • Supervised vs. unsupervised learning.
  • Overview of key algorithms (linear regression, decision trees, KNN, etc.).
  • Training, testing, and validating models.
  • Overfitting and underfitting concepts.
  • Linear and logistic regression for predictive modeling.
  • Decision trees, random forests, and ensemble methods.
  • Support vector machines (SVM).
  • Model evaluation metrics (accuracy, precision, recall, F1-score).
  • Clustering techniques (K-means, hierarchical clustering).
  • Principal Component Analysis (PCA) for dimensionality reduction.
  • Anomaly detection.
  • Applications of unsupervised learning.
  • Time series decomposition and analysis.
  • Forecasting techniques (ARIMA, exponential smoothing).
  • Visualizing and interpreting time series data.
  • Real-world applications of time series analysis.
  • Introduction to neural networks and deep learning.
  • Understanding deep learning frameworks (TensorFlow, Keras).
  • Building and training simple neural networks.
  • Applications of deep learning in image and text analysis.
  • Text preprocessing techniques (tokenization, stemming, lemmatization).
  • Bag of Words, TF-IDF, and word embeddings.
  • Sentiment analysis and text classification.
  • Introduction to transformers and BERT for NLP tasks.
  • Introduction to model deployment (Flask, FastAPI).
  • Using cloud platforms for model deployment (AWS, Google Cloud).
  • Monitoring and updating models in production.
  • Case studies from different industries (finance, healthcare, retail).
  • End-to-end data science project.
  • Problem definition, data collection, and model building.
  • Model evaluation and final presentation.
  • Applying data science techniques to real-world problems