Data Science

Lecturer
fitimanager
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Course Description

Course Fee – ₦250,000

DATA SCIENCE

PYTHON, JUPYTER NOTEBOOK, STREAMLIT

INTRODUCTION TO PYTHON

WEEK 1

  • Introduction To Python
  • Variables In Python
  • Operators In Python
  • Conditional Statements

WEEK 2

  • Data Types
  • Loops In Python
    • For Loop
    • While Loop

WEEK 3

  • Functions In Python
  • Exceptional Handling
  • Python Modules and Packages

WEEK 4

  • Object Oriented Programming
    • Classes And Objects
    • Constructors And Methods
    • Inheritance And Polymorphism

DATA ANALYSIS

WEEK 5

  • Jupyter Notebook
  • Introduction and Installation to Pandas
  • Pandas Data frame Basics
  • Creating Data Frame
  • Read Write Excel CSV file

WEEK 6

  • Data Cleaning Using Pandas
  • Handle Missing Data -Split Apply Combine
  • Concat Dataframe
  • Matplotlib and Seaborn Introduction and Installation
  • Axes labels, Legend, Grid
  • Bar Chart
  • Histograms
  • Pie chart

WEEK 7:

  • Exploratory Data Analysis – A Case Study
  • Notebook – Exploratory Data Analysis – A Case Study
  • Data Preparation and Cleaning
  • Exploratory Analysis and Visualization
  • Inferences And Conclusions

MACHINE LEARNING

WEEK 8

  • What is ML
  • Linear Regression Single Variable
  • Linear Regression Multiple Variable
  • Gradient Descent and cost function
  • Save Model using Joblib and pickle
  • Dummy Variables and one Hot Encoding
  • Training and Testing Dataset

 

WEEK 9

Machine Learning Algorithm

  • Logistic Regression (Binary Classification
  • Logistic Regression (Multiclass Classification)
  • Decision Tree

WEEK 10

  • Support Vector Machine
  • Random Forest
  • K fold cross validation

WEEK 11

  • K Means Clustering Algorithm
  • Naive Bayes Classifier
  • Cross Validation
  • Lasso, Ridge Regression

WEEK 12

  • ML Project 1
  • Introduction to Prediction Project
  • Data Cleaning
  • Feature Engineering
  • Outlier Removal

WEEK 13

  • Model Building
    • Streamlit Server
    • Website
    • Deploy ML model to Production

DA vs DE vs DS

WEEK 14

  • COMPUTER VISION
  • Introduction to Computer Vision | How to install computer vision libraries
  • Moving Object Detection and tracking using OpenCV
  • Face Detection and Tracking using OpenCV

WEEK 15

  • Object Tracking based on color using OpenCV
  • Face Recognition using OpenCV

WEEK 16

  • ML Project II
  • Introduction to Prediction Project
  • Data Cleaning
  • Feature Engineering
  • Outlier Removal
  • Model Building
  • Streamlit Server
  • Website
  • Deploy ML model to Production
  • Revision
  • Project
  • Examination