Course Summary

The Data Scientist course provides a comprehensive introduction to the world of data science. Designed for aspiring data professionals, this course covers essential tools, techniques, and methodologies used in data analysis, machine learning, and statistical modeling.

Throughout the program, learners will gain hands-on experience in extracting insights from raw data, building predictive models, and effectively communicating data-driven results. The course focuses on real-world applications, enabling students to develop the necessary skills to thrive in today’s data-driven industry.

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Data Analysis & Visualization:
- Exploratory Data Analysis (EDA)
- Data cleaning and preprocessing
- Data visualization using tools like Matplotlib, Seaborn, and Power BI
Programming & Scripting:
- Python for Data Science (NumPy, Pandas, Scikit-learn)
- SQL for database management and querying
- R for statistical computing (optional)
Statistics & Probability:
- Descriptive and inferential statistics
- Hypothesis testing
- Probability theory and distributions
Machine Learning:
- Supervised learning (Regression, Classification)
- Unsupervised learning (Clustering, Dimensionality Reduction)
- Model evaluation and tuning (Cross-validation, Hyperparameter tuning)
Big Data & Cloud Platforms:
- Introduction to big data concepts
- Working with cloud platforms like AWS, Google Cloud, or Azure
- Handling and processing large datasets
Data Ethics & Governance:
- Data privacy and security
- Ethical considerations in AI and machine learning
- Data governance frameworks

Quizzes and Assignments:
- Weekly quizzes to test understanding of core concepts.
- Short coding assignments focusing on real-world data problems.
Capstone Project:
- End-to-end data science project where students choose a dataset, perform exploratory analysis, apply machine learning models, and present findings.
- Presentation of findings in the form of a report or slide deck, simulating real-world business communication.
Case Studies:
- Work on industry-specific data problems such as financial forecasting, customer segmentation, or sentiment analysis.
Peer Reviews:
- Students review each other’s projects to foster collaborative learning and improve presentation skills.

By the end of this course, students will be able to:

Identify and explain the functions of key computer hardware components.
Assemble, disassemble, and upgrade computer systems.
Install, configure, and troubleshoot major operating systems.
Diagnose hardware and software issues and apply effective repair techniques.
Set up basic network configurations and resolve connectivity problems.
Perform data recovery, backups, and virus/malware protection.
Communicate technical solutions to users effectively and professionally.
Demonstrate problem-solving skills in real-world computer repair scenarios.

Regular & Intensive Learning Option

Class Schedule:
Morning Students – Monday to Thursday, 09:00 am – 03:00 pm
Afternoon Students – Monday to Thursday, 03:30 pm – 08:00 pm
Teaching Method: Online & Offline / Online only (eg. Google meet video call)
Course Duration: 5 sessions (15 months)
Session Length: 1 session = 3 months
DWTA Qualification: Academic Diploma
CTVET’s National TVET Qualification Framework (NTVETQF): Higher National Diploma
Tuition & Guidance Fees:
- Ghc 2600 per session or
- Ghc 900 per month

Slow-Paced & Relaxed Learning Option

Class Schedule:
Weekend Evening Students – Friday to Sunday, 06:00 pm – 08:00 p
Teaching Method: Online & Offline / Online only (eg. Google meet video call)
Course Duration: 6 sessions (18 months)
Session Length: 1 session = 3 months
DWTA Qualification: Academic Diploma
CTVET’s National TVET Qualification Framework (NTVETQF): Higher National Diploma
Tuition & Guidance Fees:
- Ghc 2600 per session or
- Ghc 900 per month

Fill Application Form

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Data Scientist

Course Summary

Regular & Intensive Learning Option

Slow-Paced & Relaxed Learning Option

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