Catalog cursuri

Data Analytics with Python

16000,00
RON
26700,00
RON
Trimite o cerere
This course equips learners with the foundational and intermediate skills required for data analytics using Python. Ideal for aspiring data analysts and business professionals, it covers essential tools like NumPy and Pandas, text processing, data gathering, and visualization. The curriculum includes real-world exercises such as the Absenteeism project, ensuring practical, hands-on experience in every module. With expanded beginner sessions, this version is perfect for learners with little or no prior programming experience.
Course Structure
Specifications

Course Structure

Module 1: Foundations of Data Analytics (8 sessions)

  • Introduction to the Course
  • Introduction to Data Analytics
  • Setting up the Environment (Python, Jupyter, VSCode)
  • Python Basics: Syntax, Variables, and Data Types
  • Control Structures (if, loops)
  • Functions and Modules
  • Working with Built-in Python Functions
  • Mathematics for Python (Basic Statistics and Operators)

Module 2: Data Manipulation with NumPy (6 sessions)

  • NumPy Basics and Installation
  • Understanding Arrays and DataTypes
  • Array Operations and Indexing
  • Generating and Reshaping Data
  • Statistics with NumPy (mean, median, std)
  • Preprocessing with NumPy (Normalization, Scaling)

Module 3: Data Management with Pandas (6 sessions)

  • Introduction to Pandas: Series and DataFrames
  • Importing and Exporting Data (CSV, Excel, JSON)
  • Working with Text Files and Text Data
  • Data Selection and Filtering
  • Combining and Merging DataFrames
  • Descriptive Statistics with Pandas

Module 4: Data Collection & APIs (4 sessions)

  • Web Data Gathering Techniques
  • Working with Open APIs (GET Requests)
  • Intro to API Tools: Postman Overview
  • Collecting and Structuring API Data

Module 5: Data Cleaning and Preprocessing (4 sessions)

  • Handling Missing and Inconsistent Data
  • Data Transformation and Encoding
  • Data Types Conversion and Scaling
  • The "Absenteeism" Exercise and Solution

Module 6: Data Visualization (4 sessions)

  • Principles of Data Visualization
  • Plotting with Matplotlib and Seaborn
  • Customizing Charts and Plots
  • Building Dashboards and Insight Reports

Module 7: Applied Projects and Capstone (8 sessions)

  • Mini Project 1: Exploratory Data Analysis
  • Mini Project 2: Cleaning and Structuring Real-World Data
  • Mini Project 3: Building and Visualizing Reports
  • Business Problem Framing with Data
  • KPI Design and Metric Tracking with Python
  • Working in Data Analytics Teams: Tools and Roles
  • Final Capstone Project (Individual or Group)
  • Capstone Presentation & Feedback

Specifications

Format: 1:1 & Group

Recommended Sessions: 40

Level: Intermediate

See also