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Learn Data Analytics Using Python Training in Singapore
Data analytics is all about examining raw data to uncover meaningful, actionable insights that help businesses make smarter decisions. Our Data Analytics Using Python course takes you through every step of the process—covering powerful Python libraries for data analysis, exploratory data analysis, model building, and data visualization—so you can turn data into decisions with confidence.
Schedule
- Days: 2 Days
- Duration: 16 Hours
- Timings: 9:30 AM - 5:30 PM
- 10 Anson Road, 26-08A International Plaza, Singapore 079903
Why Choose Us
- Expert Local Trainers
- Hands-on Training by experts
- Certificate of Achievement
Who Should Attend This Data Analytics Using Python Course
Data analytics using Python is ideal for those who want to become a data analyst or a data scientist.
Business professionals
Analyst
Entrepreneur
Training Professionals
Student
Course Completion Requirements
Minimum 75% attendance is required in order to complete the course and receive the course completion certificate
Above 16 years old
High School Level English
Basic internet & computer operation skills
Learn How to Turn Information into Insights About the Course
These are the important outcomes that learners will get:
- Techniques to get data from different sources
- How to analyze and make sense of data from a business perspective
- How to clean data in Python
- The functionalities of the pandas library
- How to create data models required for analysis
- Exploratory data analysis using Python
- How to formulate KPIs based on business cases
- How to plot complex visualizations
- How to get key insights from data
Begin your Data Analytics career with us
Data Analytics Using Python is a mid-level course that teaches you how to work with data like a pro. You’ll learn how to clean, manipulate, and wrangle datasets, perform statistical calculations, and create powerful visualizations—all using Python. After successful completion of this course, you will be able to learn advanced modules.
Course Requirements:
Basic Python knowledge is compulsory, and recommended to take Basic Knowledge of Python for Data Science before joining this course.
Unsure if this course is suitable for you? Take this Quick test to help you decide.
Data Analytics Using Python Course Outlines
Module 1
The objective of this module is to learn how to extract data from external sources into the Python environment and manipulate and analyse data. You should use statistical algorithms and methods to analyse data before creating any data analytics project.
- Learn Python packages for data manipulation
- Learn Importing and Exporting datasets
- Learn the Basics of analyzing the data
Module 2
The main objective of this module is to learn Data Wrangling. Python libraries can enable you to deal with data inconsistencies and issues. Almost 80% of data scientists and engineers spend their time. This is where 80% of today’s data scientists and engineers spend their time on this.
- Learn how to deal with data issues and preparation
- Learn Data Formatting and conversions in Python
- Learn how to work with Pandas
Module 3
The goal of this module is to learn exploratory data analysis. Exploratory Data Analysis (EDA) is a process to analyse the data using visual techniques.
- Execute detailed statistics with Python
- Learn Correlations, Scatter-plots, and charts with Matplotlib
- Learn data analysis with respect to various business scenarios
Module 4
The main goal of this module is to learn Data Visualisation, metrics, KPIs of different business cases for data analysis.
- Learn basic metrics and KPIs
- Learn Visualizations using Python (Seaborn, Folium)
Frequently Asked Questions (FAQs)
Who should take this course?
This mid-level course is perfect for aspiring data professionals—such as data analysts, data scientists, IT pros, or students—who want to level up from basic Python and work with real data.
What skills you will learn?
You’ll learn importing and cleaning data, performing exploratory analysis, wrangling and manipulating datasets, running statistical calculations, building data models, and crafting advanced visualizations using Python libraries like Pandas, Matplotlib, Seaborn, and Folium.
How long is the course?
The course spans 2 days
What are the completion requirements?
A minimum of 75% attendance is required to complete the course and receive the course completion certificate