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Enugu Coding School Classes Lesson Tutor Visual basic programming Data Analytics Online in Enugu
Visual Basic (VB) is a programming environment from Microsoft in which a programmer uses a graphical user interface (GUI) to choose and modify preselected sections of code written in the BASIC programming language

Below is a break down of what the student would learn

Week

Topics

Objectives

Week One

Introduction to programming

Student will learn the following:

Understanding the meaning of programming language
Various frame works in program
Be able to write a little program
Week Two

Data types, reserve word and programming error handling.

The student will be able to master:

Data types exist in the course of study.
The meaning of variables and variable name.
How to use it in writing a program
Understand types of programming errors and how to handle it.
Be able to write more than five different program with various ways mentioned above
Week four

Control structure

The student will be able to master:

How to use all types of control structures in a program.
Write different program using it.
Week four

Functions

The student will be able to master:

How to create a function
Know when a function suppose to return a data
How function makes use of data types
Know how to use recursive and iterative method
Week five

Connecting to database

The student will be able to master:

to know how to create a simple database
how to link the database to the program
Week Six and Seven

project

The student must have created a project.

This course is originally 20,000(naira) but for our partner institutions we are charging 10000 naira per student

Data Analytics Curriculum

The intensive program will give you the analytical skills to help you further your personal and professional goals.

Unit 1:

Intro to Data Analytics 1.1 Introduction to Big Data and Analytics: Fundamental Concepts, Industry Practice and Applications, Technical Domains and Roles

Unit 2:

Probability Theory and Statistics Review with Excel 2.1 Probability Theory Fundamentals 2.2 Statistics Review: Descriptive Statistics 2.3 Introduction to Excel 2.4 Statistics

Review : Inferential Statistics 2.5 Prob/Stats and Data Problem Solving with Advanced Excel

Unit 3: Introduction to R and R Analytics 3.1 Introduction: RStudio, Syntax and File I/O 3.2 R Data Types and Data Objects, Basic Data Operations and Manipulation Utilities 3.3 Effective Use of Conditional Arguments, Loops and Functions 3.4 Statistical Analysis and Regression in R 3.5 Basic R Plotting and Parameter Setting 3.6 Stattleship XCase

Unit 4: Predictive Analytics and Case Studies 4.1 Introduction to End-to-end Predictive Modeling Method 4.2 Introduction to Logistic Regression and Case Study I: Dental Visit 4.3 Data Exploration and Pre-processing Demonstration with Case Study II: Fraud Detection 4.4 Problem-solving Approach and Classification Model Evaluation Criteria in Case Study II: Fraud Detection Unit 5: Introduction to Tableau 5.1 Introduction to Tableau: Applications, Architecture and Working Elements 5.2 Building Data Views and Effective Data Manipulation in Tableau 5.3 Effective Use of Analytics Functions and Calculations in Tableau 5.4 Tableau Case Studies

Real Projects : Each student is paired with an industry-leading employer partner on a 1:1 capstone project, where students apply data skills to real world cases and give final presentations to employers.

Unit 6: Introduction: Relational Database and SQL 6.1 Relational Database, SQL Working Environment and MySQL Workbench 6.2 Relational Data Modeling - EntityRelationship Modeling (ERM) 6.3 MySQL Fundamentals: Basic Database Management Operations, Data Types, Basic Table Operation Commands and Functions 6.4 Essentials of MySQL Queries and Data Problem Solving 6.5 Burning Glass XCase

Unit 7: Introduction to Machine Learning: Concepts, Algorithms and Applications 7.1 Introduction to Machine Learning 7.2 Unsupervised learning 7.3 Supervised learning 7.4 End-to-end ML in practice

Unit 7: Introduction to Machine Learning: Concepts, Algorithms and Applications 7.1 Introduction to Machine Learning 7.2 Unsupervised learning 7.3 Supervised learning 7.4 End-to-end ML in practice

Unit 8: Data Visualization in R 8.1 Advanced R plotting

Optional Topics : Basics of NoSQL, Applications and Fundamentals Introduction: Use of RStudio Shiny, File Structure, Share On Web Input, Output and Render functions in Shiny Reactivity in Shiny Format and layout of Shiny apps Experiential: Capstone Project One-on-one Project with Employer Partner Use Data Sets to help Solve Business Problem Present to Class & Employer