Python & R Programming

Course Overview

In this course, you will learn how to use Python Programming and Data virtualization using R Programming to transform data into actionable insights that inform an organization’s business decisions.

Data analysis is the process of extracting information from data. Data analysis can involve data mining, descriptive and predictive analysis, statistical analysis, business analytics and big data analytics.

Courses Included are:

Python for Data Science  

R Programming

Python for Data Science

History of Computers
Understanding Hardware
Writing First Program (“Hello World”)
Variables & Data Types
Strings, Integers, Integers, Floats, Boolean, etc.
Assigning Variables

Define motivation behind control flow
If, If-Else, Elif, Switch Statements
Complex Data Types
Initializing Lists
Printing Lists
List functions such as length, append, pop, etc.
Introduction to Dictionaries & their structures
Define the motivation behind using a loop
For, While, Do-While, For Each loops
Error Handling
Course Syllabus | Python for Data Science Bootcamp 1
Identify when to use a function
Syntax & Implementation
Arguments & Return values.

Data Sources
Storage Modes
Query Performance
Data Profiling
Lab : Preparing Data in Power BI Desktop
Prepare Data

Introduction to O.O.P paradigm
Introduction to Objects, Classes, Instances
Inheritance, Abstraction, and Sets

File Input
User Input
List Comprehension

Introduction to Data Science
Review Python Fundamentals
Understanding the data science discipline
Data set reading
Filtering, Cleaning, Manipulating Data
Excel vs Python
Data Visualization
Matplotlib Package
Understanding motivations between different graphs
Machine Learning
Sci-Kit Learn package
Understand motivation and definition of machine learning
Statistical simulation
Data Visualizations
Shell Workshop
Purpose &
Create a Git Repo
Review a Repo’s
Add commits to a
Tagging, Branching,
and Merging
Undoing Changes
Working with
Working on Another
Staying in Sync with
a Remote Repository

Data Visualization using R Programming

  • Introduction to R Programming
  • History and overview of R
  • Install and configuration of R programming environment
  • Basic language elements and data structures
  • R+Knitr+Markdown+GitHub
  • Data input/output
  • Data storage formats
  • Subsetting objects
  • Vectorization
  • Control structures
  • Functions
  • Scoping Rules
  • Loop functions
  • Graphics and visualization
  • Grammar of data manipulation (dplyr and related tools)

₦ 90,000

  • Days: Tue & Thur
  • Time: 7 pm
  • Start Date: 29th September, 2023
  • Duration: 4 weeks
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