-39% English

Python for Data Analysis Certificate

كانون الأول 25, 2025

4.38 /5

(8)

0 already enrolled

Overview
Curriculum
Instructors

Course Prerequisites

Please note that this course has the following prerequisites which must be completed before it can be accessed

About This Course

A hour practical module that introduces learners to Python programming for data analysis Participants gain handson experience with pandas and NumPy perform data cleaning and exploratory data analysis create visualizations with MatplotlibSeaborn and work with CSV Excel and SQL data sources The module includes two realworld projects and a final assessment focused on producing a complete EDA report

Learning Objectives

Learning Objectives Python for Data Analysis

Target Audience

This module is designed for Beginners transitioning into data analysis roles Professionals looking to upskill in Pythonbased analytics Students or researchers handling dataheavy projects Business analysts seeking to automate or enhance data workflows Anyone interested in learning EDA data cleaning and visualization with Python No advanced Python experience required basic computer literacy is sufficient

CURRICULUM MATERIAL
Introduction to Python for Data Analysis Overview of Python ecosystem Working with Jupyter Notebook Data types variables loops and functions Importing and using essential libraries NumPy pandas Data Structures Manipulation Series and DataFrame fundamentals Indexing slicing filtering Handling missing data Merging joining concatenating datasets Data Cleaning Wrangling Detecting and handling outliers Data type conversion Feature engineering basics Working with dates and categorical data Exploratory Data Analysis EDA Descriptive statistics Distribution analysis Correlation and relationships between features Identifying trends and anomalies Data Visualization Essentials Introduction to Matplotlib Introductory Seaborn plots Creating line bar scatter histogram and box plots Styling and customizing visualizations Working With External Data Reading CSV and Excel files Querying SQL databases Exporting cleaned and processed data Practical Projects Agriculture Yield Analysis Exploring crop yield trends identifying patterns cleaning inconsistent data and generating visual insights Social Media Sentiment Exploration Loading text data preprocessing generating sentiment scores visualizing engagement and sentiment distribution Assessment Components Jupyter Notebook project submission Final EDA report with visualizations and insights

CURRICULUM METHODOLOGY

The module applies a handson practicefirst learning approach designed for immediate workplace application Methodologies include Projectbased learning Real datasets used throughout the course Demonstration Guided Practice Instructorled coding examples followed by learner replication Incremental Skill Building Each lesson builds on previous concepts for continuous skill progression Interactive Notebook Activities Learners work directly in Jupyter Notebooks to test and apply concepts FeedbackOriented Assessment Projects are reviewed with feedback to strengthen analytical thinking

Your Instructors

Loai Medhat Awni al-nemer

42 Courses

200 Jod 300 JOD

39% off

Level

Advanced
Duration

Semester
Lectures

15 lectures
Language

English

Material Includes

Videos
Booklets