Data Science Foundations
R3 840.00
Data Science is one of the most in-demand skillsets across many job roles. This Aspire Journey will teach you the basics of cleaning, analyzing, and visualizing data. You will learn industry-standard libraries in Python, including pandas and seaborn. Along the way, you will create real-world projects to practice and demonstrate your analytics skills.
 Digital learning |
 Start anytime |
 Months course access |
 Skillsoft LMS access |
 Online Skillsoft mentor |
 Certified by College SA |
- Course fee
- About the course
- Admission requirements
- Accreditation status
- Assessment details
- What to expect
- Enquire now
Course fee
Cost: R3 840.00
Deposit: R960.00
Monthly instalments: R960.00 x 3
Duration: You will have Skillsoft access to this course for 12 months. The average time required to work through the syllabus is:
- 11 courses (6h 35m) / 22 labs (22h) / 26 others (25h 10m)
- Optional additional resources are available to enhance your learning in your own time.
About the course
Course code: C01354
Course overview:
- Track 1: Python for Data Science
- Track 2: Exploratory Data Analysis with Python
- Track 3: Data Visualization with Python
- Track 4: Data Cleaning with Python
- Track 5: Introductory Statistics
Modules and topics covered:
Track 1: Python for Data Science
- Case Studies In Data Literacy
- Exploring Data With Python
- Sorting And Filtering Rows With Python
- Cleaning And Transforming Columns With Python
- Merging and Aggregating Data
- Handling Complex Datasets
- Custom Data Methods
Track 2: Exploratory Data Analysis with Python
- Data Types And Quality
- What is EDA?
- Python Variable Types with Pandas
- EDA Data Cleaning and Validation
- Statistical Thinking
- EDA Data Summaries
- EDA Associations: Quantitative And Categorical Variables
- EDA Associations: Two Quantitative Variables
- EDA Associations: Two Categorical Variables
Track 3: Data Visualization with Python
- Data Visualization Basics
- Misleading And Confusing Graphs
- Make A Line Chart with Matplotlib
- Making A Visual Argument with Matplotlib
- Seaborn Make Charts
- Seaborn Modify Chart Parameters
Track 4: Data Cleaning with Python
- Introduction to Regular Expressions
- Data Cleaning with pandas
- Handling Missing Data with Pandas: Deletion
- Handling Missing Data with Pandas: Single Imputation
- Missing Data Lab: Stack Overflow Survey Trends
- Python Data Transformations
Track 5: Introductory Statistics
- Analyzing Data
- Rules of Probability
- Probability Distributions with Python
- Sampling Distributions
- Hypothesis Testing: One-Sample t-Tests
- Hypothesis Testing: Simulating a Binomial Test
- Significance Thresholds in Hypothesis Testing
- Intro to Linear Regression
Admission requirements
Academic grade: No minimum school pass requirements or formal prerequisites.
Language: Proficiency in English (course material and support only available in English).
Expertise level: Beginner
Equipment: Access to a PC or laptop with a reliable internet connection.
Effort: Self-paced learning online.
Accreditation status
Course type: Short course
Industry partner: Skillsoft
Certification: Certificate confirming course completion.
Certification issued by: College SA
Assessment details
Each track concludes with a final internal exam that will test your knowledge and application of the topics presented throughout that specific track.
There are no external certification exams for this course.
What to expect
Dedicated support team
We understand that students may require guidance and support to navigate the learning journey, and our Client Services team is always ready to assist them in every possible way. Our team is readily available during office hours and can be contacted via email, phone, WhatsApp and social media.
Skillsoft Learner Management System (LMS) access
Skillsoft is an online learning management system that offers all students enrolled for any of our IT Academy courses compelling content, interactive videos, quizzes, mentoring and practical simulations/virtual labs. The platform allows students to learn at their own pace.
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