Analyse Business Data and Create Interactive Dashboards using Python (SF)
Training Provider: NTUC LEARNINGHUB PTE. LTD.
Course Reference: TGS-2023020075
S$375
Original: S$1,250
Save S$875
About This Course
With the understanding and analyzing data as one of the key skills required in the industry today, this course is focused on the various aspects of data analytics and creating interactive dashboards using Python.
Participants will be taught on the use of the key libraries for data ingestion and manipulation, exploratory data analysis, model building and data visualisation as well as the basic statistics knowledge required to understand the concepts of data analysis and visualisation.
What You'll Learn
MODULE 1: Understanding Data
To learn how to get data from external sources into the Python environment and manipulate and analyze data. Before any data analytics project, it is very important to use statistical algorithms and methods to analyze data as part of the data analytics process.
• Introduction to python packages for data manipulation
• Importing and exporting data
• Importing datasets and understanding data
• Basics of analyzing the data
MODULE 2: Data Wrangling
To learn how python libraries can be leveraged to deal with data inconsistencies, issues with data and making them fit for data analytics. This is where 80% of today’s data scientists and engineers spend their time and it is very important to know how to do it.
• Dealing with data issues and preparation in Python
• Data Formatting and conversions in Python
• Data Wrangling
• Working with Pandas
MODULE 3: Exploratory Data Analysis
The objective of this module is to understand how to use appropriate statistical methods and visualizations for descriptive analytics.
• Performing descriptive statistics with Python
• Correlations, Scatter-plots and charts with matplotlib in Python
• Understanding data analysis with respect to various business scenarios
MODULE 4: Model Development for Analysis
This module gives the participants the very first steps towards forming hypothesis and doing predictive analytics. It aims to equip participants with the basics of how supervised machine learning models work and how evaluation and optimization can be carried out.
• Hypothesis Testing
• Linear Regression and Multiple Regression Models
• Model Evaluation Methods
• Model selection
MODULE 5: Data Visualization
The objective of this module is to understand basic data metrics and KPIs (Key Performance Indicators) of different business use cases and plotting advanced interactive visualizations for data analysis to gain insights from the data.
• Understanding basic metrics and
To learn how to get data from external sources into the Python environment and manipulate and analyze data. Before any data analytics project, it is very important to use statistical algorithms and methods to analyze data as part of the data analytics process.
• Introduction to python packages for data manipulation
• Importing and exporting data
• Importing datasets and understanding data
• Basics of analyzing the data
MODULE 2: Data Wrangling
To learn how python libraries can be leveraged to deal with data inconsistencies, issues with data and making them fit for data analytics. This is where 80% of today’s data scientists and engineers spend their time and it is very important to know how to do it.
• Dealing with data issues and preparation in Python
• Data Formatting and conversions in Python
• Data Wrangling
• Working with Pandas
MODULE 3: Exploratory Data Analysis
The objective of this module is to understand how to use appropriate statistical methods and visualizations for descriptive analytics.
• Performing descriptive statistics with Python
• Correlations, Scatter-plots and charts with matplotlib in Python
• Understanding data analysis with respect to various business scenarios
MODULE 4: Model Development for Analysis
This module gives the participants the very first steps towards forming hypothesis and doing predictive analytics. It aims to equip participants with the basics of how supervised machine learning models work and how evaluation and optimization can be carried out.
• Hypothesis Testing
• Linear Regression and Multiple Regression Models
• Model Evaluation Methods
• Model selection
MODULE 5: Data Visualization
The objective of this module is to understand basic data metrics and KPIs (Key Performance Indicators) of different business use cases and plotting advanced interactive visualizations for data analysis to gain insights from the data.
• Understanding basic metrics and
Entry Requirements
This course requires participants to have a basic understanding of Python. Participants who do not have the required basic knowledge are encouraged to take up Basics of Python prior to this course.
Course Details
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Note: To apply for this course, visit the SkillsFuture website or contact the training provider directly.
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