Data Analytics
Join our dynamic team as a Data Analyst! We are looking for a passionate individual who can turn data into actionable insights. If you have a knack for numbers and love solving complex problems, we want to hear from you! Apply now to be part of our innovative journey
LOCATION
Kigali, RW
EMPLOYMENT TYPE
Permanent
What You’ll Do
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Data Collection: Gather relevant data from various sources to ensure a comprehensive analysis.
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Data Processing: Organize clean the collected data for accurate insights and findings.
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Data Visualization: Present the analyzed data through charts and graphs to facilitate better understanding and decision-making.
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Present: Communicating the results of your findings will be a key part of your job. You do this by putting together visualizations like charts and graphs, writing reports, and presenting information to interested parties.
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Clean data: Raw data might contain duplicates, errors, or outliers. Cleaning the data means maintaining the quality of data in a spreadsheet or through a programming language so that your interpretations won’t be wrong or skewed.
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Model data: This entails creating and designing the structures of a database. You might choose what types of data to store and collect, establish how data categories are related to each other, and work through how the data actually appears.
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Interpret data: Interpreting data involves finding patterns or trends in data that could answer the question at hand.
Who You are | Data analyst technical skills
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Database tools: Microsoft Excel and SQL should be mainstays in any data analyst’s toolbox. While Excel is ubiquitous across industries, SQL can handle larger sets of data and is widely regarded as a necessity for data analysis.
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Programming languages: Learning a statistical programming language like Python or R will let you handle large sets of data and perform complex equations. Though Python and R are among the most common, it’s a good idea to look at several job descriptions of a position you’re interested in to determine which language will be most useful to your industry.
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Data visualization: Presenting your findings in a clear and compelling way is crucial to being a successful data analyst. Knowing how best to present information through charts and graphs will make sure colleagues, employers, and stakeholders will understand your work. Tableau, Jupyter Notebook, and Excel are among the many tools used to create visuals.
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Statistics and math: Knowing the concepts behind what data tools are actually doing will help you tremendously in your work. Having a solid grasp of statistics and math will help you determine which tools are best to use to solve a particular problem, help you catch errors in your data, and have a better understanding of the results.