Data Scientist Salary in Uganda
A data scientist is an analytics professional who is responsible for collecting, analyzing, and interpreting data to help drive decision-making in an organization.
How much does a Data Scientist earn in Uganda?
A person working as Data Scientist in Uganda typically earns around 4,280,000 UGX per month. Salaries range from 1,970,000 UGX (lowest) to 6,810,000 UGX (highest).
How much is a Data Analyst paid in Uganda?
Data Analyst in Kampala, Uganda Salaries
| Job Title | Location | Salary |
|---|---|---|
| Mantrac Data Analyst salaries – 1 salary reported | Kampala, Uganda | UGX 600,000,000/yr |
| Compuscan Data Analyst salaries – 1 salary reported | Kampala, Uganda | UGX 1,000,000/yr |
| Compuscan Data Analyst salaries – 1 salaries reported | Kampala, Uganda | UGX 1,000,000/mo |
How to Become a Data Scientist in Uganda
Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software. Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets. Visualize, interpret, and report data findings. May create dynamic data reports
Daily Tasks
1. Analyze, manipulate, or process large sets of data using statistical software.
2. Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
3. Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
4. Clean and manipulate raw data using statistical software.
5. Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
6. Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
7. Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users
8. Design surveys, opinion polls, or other instruments to collect data.
9. Identify business problems or management objectives that can be addressed through data analysis.
10. Identify relationships and trends or any factors that could affect the results of research.
11. Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.
12. Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.
13. Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.
14. Recommend data-driven solutions to key stakeholders.
15. Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.
16. Write new functions or applications in programming languages to conduct analyses.
Technical Skills
Python Coding
Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists. This is why 40 percent of respondents surveyed by O’Reilly use Python as their major programming language.
Because of its versatility, you can use Python for almost all the steps involved in data science processes. It can take various formats of data and you can easily import SQL tables into your code. It allows you to create datasets and you can literally find any type of dataset you need on Google.
Hadoop Platform
Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial. A study carried out by CrowdFlower on 3490 LinkedIn data science jobs ranked Apache Hadoop as the second most important skill for a data scientist with 49% rating.
As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly convey data to various points on a system. That’s not all. You can use Hadoop for data exploration, data filtration, data sampling and summarization.