Data science essentials is a comprehensive practical course designed to introduce learners to the fundamental concepts, tools and techniques used to collect, prepare, analyse, visualise and interpret data for informed decision-making. The course provides a strong foundation in data science and helps learners understand how data can be transformed into useful insights for business, research, finance, education, technology and other professional fields.
Learners will explore the complete data science workflow, including data collection, data types, data cleaning, data preparation, exploratory data analysis, statistical concepts, data visualisation and interpretation.
The course introduces practical use of spreadsheets, SQL and Python for working with structured datasets and developing basic analytical solutions.
The course also covers descriptive statistics, probability fundamentals, correlation, basic regression, data visualisation, dashboards, reporting and communicating analytical findings. Learners will be introduced to important concepts in machine learning and artificial intelligence, including supervised and unsupervised learning, model evaluation and responsible use of data-driven technologies.
Practical exercises using real-world datasets will enable learners to develop skills in identifying data problems, cleaning and transforming datasets, analysing patterns, creating meaningful visualisations and presenting findings. By the end of the course, learners should be able to perform basic data analysis, interpret results, create informative reports and apply data science techniques to practical problems.
Introduction to data science
Introduces data science, its applications and the processes used to turn raw data into useful information and insights.
1 LessonsData collection and data sources
Teaches learners how data is generated, collected and obtained from different sources.
1 LessonsData management fundamentals
Introduces the organisation, storage and management of datasets for analysis.
2 LessonsExcel for data analysis
Develops practical spreadsheet skills for preparing, analysing and presenting data.
2 LessonsStatistics for data science
Introduces statistical concepts needed to understand and interpret datasets.
2 LessonsProbability fundamentals
Introduces probability concepts and their applications in data analysis and decision-making.
2 LessonsData cleaning and preparation
Teaches learners how to identify and correct problems in datasets before analysis.
2 LessonsSQL for data analysis
Introduces SQL for retrieving, filtering and analysing data stored in relational databases
1 LessonsPython for data science
Introduces Python programming and its use in practical data analysis
2 LessonsPython data analysis libraries
Introduces commonly used Python libraries for manipulating and analysing datasets.
2 LessonsExploratory data analysis
Teaches learners how to investigate datasets and identify trends, relationships and patterns.
2 LessonsData visualisation
Develops skills for presenting data through clear and meaningful charts and visual representations.
2 LessonsDashboards and data reporting
Teaches learners how to organise analytical results into dashboards and professional reports.
2 LessonsCorrelation and basic regression
Introduces techniques for analysing relationships between variables and making basic predictions.
1 LessonsIntroduction to machine learning
Provides an introduction to machine learning and explains how computers can learn patterns from data.
2 LessonsMachine learning workflow and evaluation
Introduces the basic process of preparing, training and evaluating machine learning models.
2 LessonsData ethics and professional practice
Develops awareness of responsible data use, privacy, security, fairness and ethical decision-making.
2 LessonsFinal data science project
Learners apply the knowledge gained throughout the course to analyse a real-world dataset and communicate meaningful findings.
2 Lessons
Senior ICT Academy
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- Price
USh500,000
USh600,000 - Instructor Senior ICT Academy
- Duration 120 days
- Category Data Science & Analytics
- Curriculum 18
- Lectures 32
- Enrolled 0