Our social responsibility drives our training objective to enhance access to world-class data analysis skills and open access tools to the less reached groups.
Introducing Coding for Kids
Empowering Young Minds with ISADS Jr
Welcome to ISADS Jr, your ultimate destination for empowering children through coding! We understand the importance of equipping the younger generation with essential skills that will shape their future. *With our comprehensive coding program, we aim to inspire creativity, foster critical thinking, and provide a solid foundation in programming for kids. Read More…
Why ISADS?
Get World Class Quality Research Design, Analysis, Training and Access to Diverse Datasets
Analysis and Training
Our social responsibility drives our training objective to enhance access to world-class data analysis skills and open access tools to the less reached groups.
Research design and analysis
A team of analysts brings to you more than 15 years of teaching, academic supervision, and research work together to ensure world-class data analysis for small reports to large academic projects.
Data and Statistics Repository
Technology use in every sector is increasingly generating huge amounts of information that can yield valuable insights.
What they are saying about us
Our Advisory Team
Barbara Wangari (BSc, Mathematics and Computer Science)
● Strategic and results oriented professional with over 10 years of experience successfully identifying opportunities and building healthy pipelines to support growing business objectives.
● Detail Oriented and Insightful Data Analyst with a demonstrated history of converting raw data into practical intelligence that guides corporate strategic choices
● Enthusiastic and Seasoned Data Analytics trainer specializing in R programming language
Dr. Faith Milkah Wakonyo Muniale
She is an environmental scientist who has been doing interdisciplinary research in various sectors including agriculture, water, natural resources, livelihoods and climate adaptation. This involves research in the laboratory, social surveys and field experiments.
Dr. P Njage
Dr. Njage is a Statistician working as a Genomic Epidemiologist at the Technical University of Denmark. His passion and ability to teach both common and advanced statistical topics with great simplicity in open access language R stems from his background in two worlds. He has a background training in both natural sciences (BSc, MSc, PhD (Microbiology and Biotechnology) as well as Statistics (Master of Statistics, University of Hasselt, Belgium). He is experienced in the analysis of complex, unstructured data..
James Orwa, Ph.D.
Currently a Biostatistician/ Instructor at Aga-Khan University, Kenya
Beatrice Muchiri (BA, MA, PhD)
She has over 10 years experience in data analysis and modelling of qualitative and quantitative data in humanities and social sciences
Annrose Mwangi (BSc, MSc, PhD Fellow)
Geospatial data scientist, livelihood consultant in rangeland landscape in Kenya ASALs.
Peter Wangome
Specialized in Dryland research in East Africa
Gallery
Frequently Asked Questions
Are Data Science Jobs in Demand?
With the fast development of modern technologies, data science is currently in extremely high demand, and this is only going to grow. For verification, just type “data science jobs” into Google or search for them on any job-hunting website such as LinkedIn, Glassdoor, or Indeed. You will be overwhelmed by the number of job opportunities in this sphere. There are many explanations for such popularity. The amount of data produced around the world is rapidly accumulating every day, and every business requires data analyzing and predictive modeling to remain alive and successful in today’s highly competitive market. Scientific research in any field can be conducted only if enough historical data is collected. In other words, the more data an organization or science has gathered, the more reliable data-derived forecasts it can make. That said, as with any other sphere, there were (and are) various “fashionable” trends in data science in different periods of its existence: machine learning, deep learning, data engineering, big data, and even Covid-19 data science.
What Does a Data Science Job Usually Entail?
Broadly speaking, data scientists gather and investigate the data relevant to a certain business or scientific task, and extract from it meaningful insights and hidden trends. Using machine learning and deep learning algorithms to build predictive models, they then create reports of their findings and communicate their results to non-technical shareholders. In turn, shareholders can then make strategic, data-driven decisions to improve the business. All these steps require data scientists to be multi-skilled professionals. In particular, they should have sufficient knowledge of coding tools, be familiar with the mathematical principles behind various machine learning algorithms, understand the nuances of the business domain of a particular sphere of interest, follow data ethics, and have excellent communication skills so as to clearly explain complex ideas to a non-technical audience. The above is an integral description of a classical data scientist’s role. Since this profession is relatively new, however, different companies can have their own understanding of what the role of a data scientist should include. For example, in some cases, data scientists are rather data analysts focused on the investigation of historical and current data without predicting future scenarios. In other companies, data scientists are supposed to use graphical user interface (GUI) applications to build machine learning models, so they practically don’t need to write any scripts. Finally, sometimes data scientists are meant to be data engineers whose main tasks are converting raw data to a usable form and designing and maintaining data storage infrastructure.
What Are the Prerequisites to Start Learning Data Science?
While it is true that for mathematicians, statisticians, and programmers, the process of learning data science could be smoother and quicker, it doesn’t necessarily mean that a career in data science is completely inaccessible to people with different qualifications. Indeed, there are plenty of inspiring stories of the success of people who have entered this sphere from completely unrelated professions, made fast progress, and are now happily employed. However, it is not correct either to claim that there are no prerequisites at all for a person to start learning data science. To succeed in your studies, you will need to be fascinated by the data and what is hidden behind it, have an exploratory mindset, a certain amount of creativity, and a high motivation to learn data science.
Do I Need a University Degree to Learn Data Science, or Can I Learn Online?
While there is nothing wrong with a university degree in data science, you have to keep in mind one important thing: time matters. If you have recently graduated from college and are deciding on your further education, then a solid, well-grounded university degree in data science could be a great choice. If you are a career-changer instead, you probably won’t want to spend at least two more years on your studies before being employed. Fortunately, if you are from the second category of people, there is good news for you: you can learn data science on an online Bootcamp at a sufficient level to be employed as a data scientist. Moreover, this approach gives you much more freedom to organize your learning process, manage your time, practice a lot, and accelerate your progress whenever you feel ready. In the world of work, it doesn’t matter how much time it takes for you to learn data science or whether or not you have a world-class certificate. What a potential employer really wants to see in a tech-competent candidate is a set of proven skills (confirmed by a portfolio of projects) relevant to a job position of interest.
How Long Does it Take to Learn Data Science?
The answer to this question depends on many factors, such as the way of learning you choose (book-based or video-based self-tuition, in a school, a boot camp, a master’s program, etc.), the curriculum you follow, how many hours you are ready to dedicate to learn data science, your initial background, etc. On average, to a person with no prior coding experience and/or mathematical background, it takes from 7 to 12 months of intensive studies to become an entry-level data scientist. It is important to keep in mind that learning only the theoretical basis of data science may not make you a real data scientist. Whatever program you choose, you should pay attention to practicing your skills, making data science projects, creating your project portfolio, exploring data science use cases in various spheres, and experimenting with alternative approaches to solving the same data science task. All these activities, if conducted with diligence and persistence, can be rather time-consuming. However, this is the best way to master your data science skills and gain job-ready proficiency. To accelerate your learning process, consider opting for an online self-study program with a well-balanced curriculum that covers the most important techniques and aspects of data science. This will help you efficiently manage your time, decide on the most comfortable and productive approach to learning the materials, and allow you to learn at your own pace from wherever you have a computer and Internet access. With Datacamp, you can select from fully-packed career tracks for very beginners, specialized skill tracks to sharpen particular skills, and short courses to explore narrow-focused topics.
How Proficient Should a Data Scientist Be in Coding?
While coding is an essential skill for any data science job, expertise in programming is not mandatory to get started in this sphere. No doubt, a person who wants to land a job in data science should be familiar with certain programming languages and related technical tools, and the companies that hire data scientists usually require such skills. However, the coding toolkit of a data scientist is definitely not as extensive as that of, say, a software developer or a computer scientist. The choice of programming languages relevant to solving data science tasks is also quite limited, and learning the basic data-related methods and techniques of only one of them can be a great place to start. Rather than being a purely programming-focused discipline, data science is a vast field of study that requires a diverse set of skills and competencies apart from codings, such as having an analytical mindset, understanding statistics, probability, linear algebra, efficient storytelling, and business domain knowledge.