Average Salary For Data Analyst In South Africa

A data analyst is a person whose job is to gather and interpret data in order to solve a specific problem. The role includes plenty of time spent with data but entails communicating findings too. Here’s what many data analysts do on a day-to-day basis: Gather data: Analysts often collect data themselves.

Advertisement

Data Analyst Salary In South Africa

The average data analyst salary in South Africa is R 746 630 per year or R 383 per hour. Entry-level positions start at R 324 000 per year, while most experienced workers make up to R 7 702 500 per year.

What does a Data Analyst do in South Africa?

A data analyst uses data to acquire information about specific topics.

This usually starts with the survey process, in which data analysts find survey participants and gather the needed information.

The data is then interpreted and presented in forms such as charts or reports.

How much does a Junior Data Analyst earn in South Africa?

The salary trajectory of a Junior Data Analyst ranges between locations and employers.

The salary starts at ZAR 53,417 per year and goes up to ZAR 81,415 per year for the highest level of seniority.

Is Data Analyst in demand in South Africa?

To drive their data initiatives, people highly skilled in data analytics are in high demand today. MyJobMag South Africa, the publisher of the top jobs in South Africa, reached out to several data analysts particularly those who just got their jobs recently.

How do I become a data analyst in South Africa?

A degree in a quantitative field (Physics, Maths, Statistics, Finance, Economics, Computer Science, etc.)

A higher degree/research (Masters, Ph.

A direct entry through a four-year Bachelor’s degree in Data Science is becoming common.

6 Skills Needed to Become a Data Scientist

Statistics:
Prepares the Data Scientist to analyze data in a scientific way. This includes but is not limited to distributions, linear regression, probability theory, Bayesian statistics, statistical tests, etc.

Programming:
It is a necessary tool in the Data Scientist’s toolbox. It helps the data scientist to analyze data and use different algorithms to deliver a solution in code. The two most commonly used languages in use by the Data Science community are Python and R. Modules/Packages are available in both languages, which make it easy to do data analysis, visualization, machine learning, etc. Other languages such as C++, Scala, and Java also come in handy, especially when creating data science products that integrate with existing systems.

Curiosity:
One of the most important skills of a Data Scientist is curiosity. He is eager to find patterns in data, not afraid to investigate ‘anomalies’ and come up with a sound reason why such patterns or anomalies exist.

Data Wrangling:
This is the ability to extract data from various sources, clean up the data, and transform it into a required format.

Story Telling:
This covers the ability to explain one’s findings to a non-technical audience and business to aid them with their decision-making. Great communication skills are needed to translate findings into insights. Not only visuals but also a language that can be understood by a business must be employed that will eventually lead to action that, in turn, leads to growth.

Domain Knowledge:
Data Science is applicable in many industries and, therefore, knowledge in these business domains is important. It can be acquired over a period of time as one works in a particular field.

Advertisement