Data Analysis for Data Science Part 3

Python Programming for Data Science

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

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
Loops
Define the motivation behind using a loop
For, While, Do-While, For Each loops
Error Handling
Course Syllabus | Python for Data Science Bootcamp 1
Functions
Identify when to use a function
Syntax & Implementation
Arguments & Return values.

Lessons
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
Packages

Introduction to Data Science
Review Python Fundamentals
Understanding the data science discipline
Pandas
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
Debugging/profiling
Statistical simulation
Data Visualizations
& EDA
Shell Workshop
Purpose &
Terminology
Create a Git Repo
Review a Repo’s
History
Add commits to a
Repo
Tagging, Branching,
and Merging
Undoing Changes
Working with
Remotes
Working on Another
Repository
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)

₦ 10,000

  • Days: Sat and Sun
  • Time: 6pm-8pm
  • Duration: 1 Month
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