Helpful Resources for (intending) Data Analysts

The rising influence of information technology (especially digital technology) has brought about an explosion in data generation and consumption globally. A lot more data is being collected today than at any other period in human history. In the internet alone, it has been estimated that 463 exabytes of data will be created every day all over the world by 2025.

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These days, apps and websites collect various kinds of data to gain insights about consumer behaviour and other things like SERP rankings. Just one single business may have several billion engagements with millions of customers at hundreds of branches employing thousands of workers and supplying any number of products and/or services.

What Data Analysts Do

The ever-growing trend of data generation has led businesses to search for the best ways to make use of the data they collect. One of the outcomes of that search is the emergence of the field of business intelligence, a field that provides a variety of techniques and tools which businesses can leverage for better data analysis. These businesses need professional data analysts to ensure that collected data is analysed efficiently.

Basically, data analysts use statistical techniques and tools to systematically collect, analyse, clean, transform, model, and interpret data. They help unravel insightful data patterns, and trends that assist decision-making. All these make them important assets for whatever organisations that hire them.

Courses for Data Analysts

To qualify as a data analyst, consider enrolling for degree programs in areas such as math, statistics, data science, computer science, AI & ML, physics, engineering and economics. Those who prefer studying online will find useful courses at organisations such as Coursera, Udemy, edX, DataCamp, Simplilearn and Great Learning.

Here’s a breakdown of some non-degree data analytics certifications:

• Google Data Analytics Professional Certificate: A certificate course that offers a comprehensive introduction to data analytics. Its curriculum equips students with a variety of skills including SQL and R programming, data analysis, and data visualisation. 
• IBM Data Science Professional Certificate: Includes topics in data science and analytics. Some topic examples include data analysis, data visualisation, and machine learning. The course also offers practical experience with tools like SQL, Python, and Jupyter notebooks. 
• Microsoft Certified: Power BI Data Analyst Associate: Equips individuals with the expertise needed to transform data into actionable insights via the use of Microsoft Power BI. Areas of focus include data preparation, model creation, data analysis and data visualisation. 
• Certified Analytics Professional (CAP): A vendor and technology-neutral certification that aims to ensure competence in the seven domains of analytics (business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management). 
• Associate Certified Analytics Professional (aCAP): An entry-level course with a focus on the seven domains of the analytics process.
• CompTIA Data+ Certification: An early career certification designed for professionals responsible for the development and promotion of data-driven decisions in businesses. Skills to be learnt include data concepts and environments, data analysis, data governance, data mining, and data visualisation.

Books for Data Analysts

Whether you are an aspiring or practising data analyst, there are quite a number of valuable data analysis books out there. These books can help enhance your knowledge and understanding of the subject matter. A must-have among these books is this e-book by Remote4Africa on how to become a data analyst. 

Other valuable data analysis books include: 

• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney.
• R for Data Science by Hadley Wickham and Garrett Grolemund.
• Storytelling with Data: A Data Visualisation Guide for Business Professionals by Cole Nussbaumer Knaflic.
• An Introduction to Statistical Learning: With Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
• The Hundred-Page Machine Learning Book by Andriy Burkov.
• Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce and Andrew Bruce.
• Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett.
• Data Analytics Made Accessible by Anil Maheshwari.
• Data Science for Dummies by Lillian Pierson.

Software for Data Analysts

Like in other fields of knowledge, data analysts utilise a variety of software programs that can help boost their productivity. Below are some popular data analysis software:

• Python: Versatile programming language encompassing libraries such as Pandas, NumPy, and Scikit-learn for data manipulation, cleaning, analysis, visualisation and machine learning. 
• R: Statistical computing and graphics tool that offers a variety of use cases including data cleaning, manipulation, analysis, visualisation and machine learning.
• SQL: Powerful tool that can be used to access, query, extract, clean, manipulate and analyse data stored in relational databases.
• SAS: Renowned statistical software package that utilises generative AI, machine learning, and deep learning for data management, including statistical modelling and reporting. Widely used in industries such as education, health, finance, and insurance.
• SPSS: Comprehensive and user-friendly software for data management procedures such as statistical computations, graphical illustrations and forecasting. Very popular in the education industry.
• EViews: Sophisticated statistical software package that is primarily focussed on time-series econometric analysis. Useful for statistical modelling, graphical outputs, data analyses, and forecasting.
• MATLAB: A numerical computing environment capable of organising, cleaning, and analysing complex data sets across multiple fields such as medical research, climatology, finance and predictive maintenance.
• MS Excel: Beginner-friendly Microsoft tool that offers a wide range of features such as data organisation, complex calculations, and data visualisation through charts and graphs, among others.

Data Analyst Salaries

Salaries often vary according to variables such as age, level of education, possession of specialised skills, experience and geographical location. 

The total pay range for a junior data analyst in Lagos Nigeria as of February 2025 is estimated at between ₦101,000 and ₦262,000 per month while that of senior data analysts is from ₦243,000 to ₦1.1 million per month, as of January 2025. 

In Johannesburg, South Africa, as of January 2025, a junior data analyst enjoys a total monthly pay range of between ZAR9,000 and ZAR24,000 per month while the range for a senior data analyst as of March 2025 is from ZAR21,000 to ZAR43,000.

If you are searching for a junior data analyst job in Addis Ababa, Ethiopia, expect a total pay range of ETB 12,000 - ETB 19,000 per month in August 2024. A senior data analyst in the same location is faced with a range of ETB23,000 - ETB148,000 as of November 2024

In Cairo, Egypt, the total monthly pay range for both junior and senior data analysts is from EGP7,000 to EGP45,000 (November 2024) and EGP10,000 to EGP38,000 (March 2025) respectively.

Note that the figures cited above are subject to change over time.  Always do some research to unravel the most recent salary trends. All the salary figures above were sourced from Glassdoor.