QUALIFI
Level 3
QN: 610/1950/1
Curriculum Active
Qualifi Level 3 Diploma in Data Science
Official qualification regulated by Ofqual, awarded by QUALIFI. Classified as a Vocationally-Related Qualification. Total guided learning hours: 455 hours.
Qualification Level
Level 3
Awarding Body
QUALIFI
Syllabus Units
14 Units
Guided Hours
455 hrs
Instant access to interactive lessons, quizzes, and spaced revision.
Course Syllabus & Study Modules
Complete breakdown of verified curriculum topics and learning objectives.
1
Creating and Interpreting Visualisations in Data Science
Verified
Key Learning Objectives:
- Understand the role and importance of visualizing data.
- Understand basic plots and charts.
- Be able to create and interpret plots and charts.
2
Data Analytics with Python
Verified
Key Learning Objectives:
- Be able to load and save data
- Be able to perform basic data wrangling and exploratory analysis.
- Be able to perform basic data cleaning tasks.
- Be able to perform basic data transformation tasks.
3
Data and Descriptive Statistics in Data Science
Verified
Key Learning Objectives:
- Understand the different types of data and their characteristics.
- Understand measures of centre.
- Understand measures of spread.
- Understand measures of symmetry and peakness.
- Understand measures of joint variability and linear relation.
4
Decision Trees in Data Science
Verified
Key Learning Objectives:
- Understand what a decision tree is in data science.
- Understand how to construct a decision tree in data science.
- Be able to perform calculations using decision tree metrics in data science.
- Be able to build a decision tree model in data science.
5
Fundamentals of Data Analytics
Verified
Key Learning Objectives:
- Understand the processes and types of data analytics.
- Understand the data analytics ecosystem.
- Understand the issues and methods for dealing with data quality issues.
- Understand the issues and methods of basic data transformations.
6
Graphs and Graph Data Science
Verified
Key Learning Objectives:
- Understand different types of graphs and their properties.
- Understand the core types of graph data models.
- Understand the graph ecosystem.
- Understand the types of graph data science and graph algorithms.
7
K-means Clustering in Data Science
Verified
Key Learning Objectives:
- Understand the theory of k-means clustering.
- Understand how to evaluate k-means clusters
- Be able to create and evaluate a k-means model.
8
Linear Regression in Data Science
Verified
Key Learning Objectives:
- Understand the basic theory of linear regression.
- Understand regression metrics and how to evaluate a regression model.
- Be able to perform regression calculations and analysis.
- Be able to create linear regression models.
9
Logistic Regression in Data Science
Verified
Key Learning Objectives:
- Understand the basic theory of logistic regression.
- Be able to perform logistic regression calculations.
- Be able to create logistic regression models.
10
Machine Learning Methods and Models in Data Science
Verified
Key Learning Objectives:
- Understand the concepts of basic supervised machine learning models
- Understand the concepts of basic unsupervised machine learning models
- Understand the concepts of basic reinforcement learning.
11
Python for Data Science
Verified
Key Learning Objectives:
- Understand the design philosophy and features of Python.
- Understand Python’s basic data types.
- Be able to create and manipulate lists and tuples.
- Be able to create and manipulate sets and dictionaries.
- Be able to write Python functions and flow statements.
12
Synthetic Data for Privacy and Security in Data Science
Verified
Key Learning Objectives:
- Understand the core issues of data privacy and security.
- Understand the basics of differential privacy.
- Understand the core issues of synthetic data.
- Understand the synthetic data ecosystem.
- Be able to create anonymised or fake data.
13
The Field of Data Science
Verified
Key Learning Objectives:
- Understand the core issues of data science.
- Understand the core issues of data and big data.
- Understand the core issues of artificial intelligence.
- Understand the core issues of machine learning.
- Understand the core issues of deep learning.
14
The Machine Learning Process
Verified
Key Learning Objectives:
- Understand the machine learning process.
- Understand the data preparation process for machine learning models.
- Understand how to evaluate machine learning models.
- Be able to evaluate classification models.
- Understand the issues of bias and variance in models.
Regulatory & Verification Details
- Qualification Number (QN)
- 610/1950/1
- Regulated Status
- Available to learners
- Awarding Body
- QUALIFI
- Qualification Type
- Vocationally-Related Qualification
- Operational Start
- Jan 2023