Statistical Techniques
General statistical analysis techniques and their use for data inspection, exploration, analysis and visualisation (as supporting activity for more complex data analysis)
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Professional Membership
IDSA peer reviewed professional membership is recognised by leading international organisations. IDSA membership validates your skills, knowledge and experience and can help you take the next steps in your career.
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Profesional Certification
If you are not yet ready for certification, we have identified a learning path of online courses with our partners, which are mapped to our requirements for qualification. Should you choose to expand your skills and knowledge...
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IDSA Africa
The focus of the Institution of Data Scientists and Analysts in Africa aligns with the specific needs and challenges of the region. Here are some key areas of focus .
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10 Million Data Science/Tech Experts
IDSA Digital Skills for Africa
Our program is designed for IT and non-IT professionals who are interested to transition their career into data science, analytics, machine learning and artificial intelligence
The Institution of Data Scientists and Analysts (IDSA) through our partners provide opportunities for over 10 million interested individuals across Africa to get needed skills and necessary knowledge to succeed in today’s information, entrepreneurial and knowledge economy. Our free courses, programs and resources are from industry experts and reputable institutions across the globe.
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Membership
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Competence and Skills
Discover new relations
and realise new possibilities
Computational Thinking
Apply information technology, computational thinking, and utilize programming languages and software and hardware solutions for data analysis.
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Programming Languages
Use general and specialised statistical and data analysis programming languages and tools to develop specialised data analysis processes and applications.
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Data Visualization
Create and communicate compelling and actionable insights from data using visualization and presentation tools and technologies.
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Data Management
Data collection, data entry and annotation, data preparation, data and files versioning, Data Management Plan (DMP), metadata, Open Data, data repositories
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Research Methods
Create new understandings and capabilities by using the scientific method to discover new approaches to create new knowledge and achieve business goals
MOREDelivers new capabilities
Bringing transformative benefits
to organizations, businesses and societies
What’s the difference between data science, artificial intelligence, and machine learning?
To better understand data science—and how you can harness it—it’s equally important to know other terms related to the field, such as artificial intelligence (AI) and machine learning. Often, you’ll find that these terms are used interchangeably, but there are nuances. Here’s a simple breakdown:
- AI means getting a computer to mimic human behaviour in some way.
- Data science is a subset of AI, and it refers more to the overlapping areas of statistics, scientific methods, and data analysis—all of which are used to extract meaning and insights from data.
- Machine learning is another subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
How data science is transforming business
Organizations are using data science to turn data into a competitive advantage by refining products and services. Data science and machine learning use cases include:
- Determine customer churn by analyzing data collected from call centers, so marketing can take action to retain them.
- Improve efficiency by analyzing traffic patterns, weather conditions, and other factors so logistics companies can improve delivery speeds and reduce costs.
- Improve sales by creating recommendations for customers based upon previous purchases.
- Detect fraud in financial services by recognizing suspicious behaviours and anomalous actions.
Tools for Data Science
Building, evaluating, deploying, and monitoring machine learning models can be a complex process. That’s why there’s been an increase in the number of data science tools. Data scientists use many types of tools, but one of the most common is open source notebooks.
- Some of the most popular notebooks are Jupyter, RStudio, and Zeppelin. Notebooks are very useful for conducting analysis, but have their limitations when data scientists need to work as a team. Data science platforms were built to solve this problem.
- To determine which data science tool is right for you, it’s important to ask the following questions: What kind of languages do your data scientists use? What kind of working methods do they prefer? What kind of data sources are they using?
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Upcoming Event
UN World Data Forum
Medellín, Colombia provisional dates are 18 to 21 November 2024
The aim of the UN World Data Forum is to spur data innovation, nurture partnerships, mobilize high-level political and financial support for data, and build a pathway to better data for sustainable development.
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Fellowship
IDSA honours individuals who have made outstanding contributions to the development of data science, data analytics, artificial intelligence, machine learning, and software development and information technology in Nigeria, Africa and across the globe. There are three different categories of fellowship of the Institution of Data Scientists and Analyst.
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Understand how to improve data quality, security, privacy, and governance. Know how to embed analytics in your organisation culture – supporting every action, business process and decision.
