Course Summary

The Data Analyst Program is designed to provide learners with comprehensive knowledge and practical skills required to analyze data, make informed business decisions, and communicate insights effectively. Covering a range of topics from data cleaning and visualization to statistical analysis and predictive modeling, this course equips participants with the expertise to work confidently with data across various industries.

The course includes hands-on projects, case studies, and real-world scenarios to ensure practical application and mastery of key analytical tools and techniques.

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Data Collection & Cleaning
- Understanding different data sources and types
- Data wrangling techniques: handling missing data, data transformation, and formatting
- Tools: Microsoft Excel, SQL, and Python (Pandas library)
Exploratory Data Analysis (EDA)
- Descriptive statistics and summary statistics
- Data visualization techniques: histograms, scatter plots, and heatmaps
- Tools: Power BI, Tableau, and Matplotlib
Statistical Analysis & Hypothesis Testing
- Basic probability concepts and distributions
- Inferential statistics: hypothesis testing, t-tests, chi-square tests
- Confidence intervals and p-values
Data Visualization & Reporting
- Creating visual representations of data trends and patterns
- Effective storytelling with data
- Designing dashboards and reports in Power BI and Tableau
Predictive Modeling & Machine Learning (Introductory Level)
- Introduction to regression analysis and classification techniques
- Building simple machine learning models
- Tools: Python (scikit-learn)
Data-Driven Decision Making
- Techniques for interpreting data and extracting actionable insights
- Presentation skills for communicating findings to stakeholders

Quizzes and Tests: Short, formative assessments after each module to evaluate understanding of concepts.
Practical Exercises: Hands-on tasks using datasets to clean, analyze, and visualize data.
Case Studies: Industry-based case studies requiring the application of learned techniques to solve business problems.
Capstone Project: A final project where participants analyze a large dataset from start to finish, presenting findings through a comprehensive report and dashboard.

By the end of this course, learners will:

Understand how to collect, clean, and prepare data for analysis.
Perform exploratory data analysis to identify trends, patterns, and outliers.
Apply statistical techniques to analyze data and validate hypotheses.
Create impactful data visualizations and reports using advanced tools like Tableau and Power BI.
Build introductory predictive models using machine learning algorithms.
Communicate data-driven insights effectively to stakeholders for business decision-making.

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: 3 sessions (9 months)
Session Length: 1 session = 3 months
DWTA Qualification: Professional Certificate
CTVET’s National TVET Qualification Framework (NTVETQF): National Certificate II
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 pm
Teaching Method: Online & Offline / Online only (eg. Google meet video call)
Course Duration: 3 sessions (9 months)
Session Length: 1 session = 3 months
DWTA Qualification: Professional Certificate
CTVET’s National TVET Qualification Framework (NTVETQF): National Certificate II
Tuition & Guidance Fees:
- Ghc 2600 per session or
- Ghc 900 per month

Fill Application Form

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Data Analyst

Course Summary

Regular & Intensive Learning Option

Slow-Paced & Relaxed Learning Option

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