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Python Course For Data Science

What will I learn?

Become a Python expert for Data Science and boost your Business Intelligence skills with our focused, top-notch course. Learn crucial data cleaning methods, including managing duplicates and outliers, and delve into sales performance analysis with insights into regions and product categories. Gain expertise in data loading, preprocessing, and calculating key performance indicators (KPIs). Improve your ability to communicate insights clearly using Jupyter Notebook, and discover practical recommendations to improve sales strategies and stock management.

Apoia's Unique Features

Lifetime access to online courses
Certificate adhering to educational standards
Printable PDF summaries
Online support available at all times
Select and arrange the chapters you wish to study
Customize your course schedule
Instant feedback on practical activities
Study at your convenience, without needing an internet connection

Develop skills

Strengthen the development of the practical skills listed below

Become proficient in data cleaning: Remove duplicates, normalise data, and handle outliers effectively.

Analyse sales data: Conduct sales analysis by region, product, and time series.

Pre-process data: Convert data types, manage missing values, and load CSV files using Pandas.

Calculate key metrics: Determine total revenue, revenue by region, and average number of units sold.

Communicate insights: Organise findings and present them using Jupyter Notebook.

Suggested summary

Workload: between 4 and 360 hours

Before starting, you can change the chapters and workload.

  • Choose which chapter to start with
  • Add or remove chapters
  • Increase or decrease the course workload

Examples of chapters you can add

You will be able to generate more chapters like the examples below

This is a free course focused on personal and professional development. It is not equivalent to a technical, undergraduate, or postgraduate course, but it offers practical and relevant knowledge for your professional journey.