Python for Data Scientist and Data Analyst Professional Test

This Course about Python Data Science online test evaluates a candidate's proficiency in using Python and its data science libraries (Pandas, NumPy, Scipy, and Scikit-learn) through questionnaire challenges. The test focuses on:

• Introduction to Python Programming

• Python Data Structures

• Python Object-Oriented Programming

• Exploratory Data Analysis using Python

• Classifying data with various algorithms.

• Performing data operations such as aggregating, grouping, sorting, and cleaning.

• Constructing machine learning models.

Bonus Part:

Git And GitHub

• • Git Basics

  • Git Commands

  • Clone a Repository

  • Git Branching

  • Merging

  • Git Stash

  • Git Add Interactive

  • Reflog

  • Cherry Picking

  • Git Rebase

  • Git Bisect

  • Fetching and Pulling Content

  • Working With Multiple Repositories

  • Pushing Code

  • Pull Requests

  • Git Log

  • Squashing Commits

  • Cherry-Picking and Three-Way Merges

  • Git Hooks

  • Advanced: Beyond the Basics

  • GitHub Overview

  • SSH Authentication

  • GitHub Repository

  • GitHub Repository Branches

  • GitHub Tags and Releases

  • Comparing Differences

  • Social Coding

  • GitHub Issues

  • GitHub Gists

  • GitHub Organisations

All are expected to apply best of their knowledge to address data science problems and Basic Python problems.

This course is built in such a way that every individual's proficiency in various Python data science techniques, including data acquisition, cleaning, manipulation, modeling, analysis, and visualization. It confirms expertise in data analytics, such as:

Decision-making under uncertainty

Data-based decision-making

Predictive modeling

Model selection

Basic Python Data Structure

Additionally, it measures skills in using Python for file processing and programming operations with NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn libraries.

Bonus part is added as Git and GitHub so that everyone can learn about versioning, and how software is released, automated pipelines and much more.