Data Analytics Career Path: Degree, Training, Jobs, and Salary in 2022

A data analytics career path involves rigorous training in data analysis models and analytical tools. A technical professional can start from an entry-level analyst job and progress to becoming a manager, senior manager and eventually obtain a seat at the C-suite. This article discusses the top courses to explore on a data analytics career path, with key jobs and salaries in 2022. 

Top 10 Data Analytics Degrees in 2022

A data analytics career path involves rigorous training in data analysis models and analytical tools. A technical professional can start from an entry-level analyst job and progress to becoming a manager and then a senior manager, to eventually obtain a seat at the C-suite. 

Data analysis has become a massive part of the 21st-century economic hub. It involves accessing, cleaning, transforming, and processing data into a helpful state that one can use to draw valuable conclusions and support vital decisions. Institutions and organizations of every type now receive a massive influx of data daily. These organizations, ranging from a large industrial farms to city hospitals, all use data as a crucial part of daily activities. The ability to transform that data into a form where it can advise future decisions and increase efficiency is known as data analysis. 

This has made data analysis such an essential skill in today’s world, and the data analyst is highly valued in the job market. The good news is that there are certifications that an individual can get after being trained and being recognized as a professional data analyst. 

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1. Google Data Analytics Professional Certificate 

This Google-certified course is a 6-month training on data analysis with a shareable, valid, and highly recognized certificate offered at the end of the course. With absolutely no previous degree or experience in data analysis, you can learn how to check for data integrity, use spreadsheets to discover data cleaning techniques, develop and apply Structured Query Language (SQL) queries for databases to clean and transform data, understand how to verify results, and visualize data findings and explore reports. This data analytics degree costs about $39 per month on Coursera.

2. Microsoft Certified Power BI Data Analyst Associate 

The Power BI data analyst associate certification is an online certification by Microsoft. Taking the exam and earning the certification verifies you to be a professional data analyst who can prepare, model, adequately visualize and analyze data using Power BI and Microsoft Azure and deploy and maintain assets. Before applying for the exam, you should have previous experience and fundamental knowledge of data repositories and processes. 

However, there are also tutor modules (online free versions or instructor-led versions), that can help you adequately prepare for the certification. The cost of this Microsoft certification exam is $165. 

3. Data Analytics Graduate Certificate from Harvard 

This data analytics graduate certificate course is offered by the Harvard Extension School for both beginners and experienced data analysts. The program runs for a maximum of three years but can be completed in under 18 months, either online or offline. The graduate certificate course teaches how to apply statistical analyses and data mining methods to real problems, business analytics programming skills, data visualization, and application to various fields in real life. To earn the certificate, each student must complete four certificate courses costing $3100 and totaling the graduate certificate expense of $12,400. 

4. CompTIA Data+ 

The Computing Technology Industry Association (CompTIA) has prepared the Data+ certifications for individuals seeking to further their data analytics careers. Those who take the exam are certified as experts in data mining, manipulation, visualization, reporting, statistics, and analysis. There is also the e-learning platform to not just prepare you for the exam but enable you to master all these aspects of data analysis. With $239 and some background in data analysis, you can become a CompTIA certified professional and compete with peers globally.

5. Tableau Certified Data Analyst 

Tableau is a data visualization company (now part of Salesforce) that also offers certifications for data analysts. The certifications exam requires minimum prerequisites but recommends an average of six months of product experience. In addition, candidates can also explore and access the multiple training options through their website. The certifications exam is made up of both knowledge and performance-based tests. The cost of taking the exam and getting certified is $250, with an extra rescheduling fee of $25 if applicable. 

6. IBM’s Data Analyst Professional Certificate 

IBM Data Analyst Professional Certification course can be undertaken on the EDX platform, among others. The self-paced certification spans up to 10 months. It covers nine individual courses for both beginners without a college degree and experts seeking to further their knowledge to become IBM-certified analysts. Anyone that completes the course and gets certified can qualify for an entry-level data analyst role in top companies. The course covers data analysis, Excel, Python for data science, analyzing and visualizing data, data visualization, and SQL. You can enroll in the 10-month program and complete the nine courses for $783.90.

7. Data Science Certification from SAS 

The data certification offered by SAS is a fully comprehensive and inclusive testing module for data analysts. SAS-certified analysts should be able to use various tools to manipulate and gain insight from big data and make valuable business recommendations with machine learning models. The data analytics course teaches you how to combine data management with advanced analytics, artificial intelligence, and machine learning. You can also master these skills by enrolling in the SAS academy; the certification costs $970. 

8. The Columbia University’s Certification of Professional Achievement in Data Sciences 

The Columbia University Data Science Institute offers this professional certification to university students and non-students. The program consists of four courses – algorithms, probability and statistics, machine learning, and exploratory data analysis and visualization for data science. If taken online, the total cost of the certification will amount to $1580, with $395 paid per course. 

9. Six Sigma Data Analytics Certification 

SSGI or Six Sigma Global Institute provides an online data analytics training and certification program. It can be taken by both students and professionals, or employees seeking to expand their skill set in data analysis. Designed as a self-paced course, students have access to the materials for life after a one-time payment of $199 that covers both the training, examination and final certification. 

10. Associate Big Data Analyst by DASCA 

The Associate Big Data Analyst certification, by the Data Science Council of America (DASCA), is a certification exam for professionals already in the field of data analytics, or other related subjects. Therefore, those already working in roles that require lots of research and data analysis, or graduates from computer science, business, management, statistics, etc., can apply for this certification process and become globally recognized. A one-time fee of $585 gives students the required learning texts, practice tests, and six months prep time for the online exam. 

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Data Analytics Training

Becoming a data analyst is not something one can achieve over the weekend or even a few weeks. It is a highly professional job that requires a particular skill set. However, an individual who is determined can successfully learn the necessary skills from scratch. The steps below give a detailed outline of how to become a trained data analyst. 

1. Learning programming languages

One of the fundamental skills every data analyst must have is knowledge of programming languages. There are several programming languages, but some common ones useful in data analysis include python, SQL, and R. These languages help you carry out advanced analysis by writing programs to clean, visualize and analyze data efficiently. They also help the analyst understand every other aspect of data analysis. 

Python programming language helps analyze, manipulate, clean, and visualize data. SQL is necessary to interact with database management systems, and R plays a vital role in statistical analysis and visualization. 

The idea of learning a programming language might immediately seem daunting to some. However, multiple platforms simplify this learning process and give you the level of knowledge required to use this skill when necessary. From commercial online course sites to open source YouTube videos, anybody can learn Python, R, or any other programming language. Acquiring these skills brings you one step closer to being a data analyst.

2. Knowing math and statistics 

The age-long discipline of mathematics and also statistics are indispensable to a data analyst. For starters, the data analyst deals with numbers regularly and can only be successful with a strong background in certain mathematical and statistical principles. Strong knowledge of statistics will help you decide the suitable algorithm, estimate events’ probabilities, interpret the implication of collected data, identify trends and patterns, and produce reliable results. As much as it might be tempting to jump into data analysis directly, learning statistics if your knowledge is limited will make you stand out among other data analysts. 

Some key concepts in statistics that data analysts should master include Bayesian thinking, probability, hypothesis testing, regression, distribution, and significance. There is a strong likelihood that some aspiring data analysts might be deficient in these areas. An easy way to correct this is by taking refresher courses from multiple online resources like Coursera, Udemy, Khan Academy, etc.

3. Getting familiar with data wrangling 

Data wrangling, from the name, describes the processing of data. It is the process of cleaning data, eliminating errors, and combining data sets to increase accessibility. Data wrangling is a very vital skill that every data analyst must have. After data wrangling, raw, unrefined data is made to yield a form of data that can work with algorithms and formulas to give good results. The data wrangling process is responsible for over 70% of the work time of a data analyst.

Data wrangling involves cleaning, organizing, and transforming raw data. One can use multiple tools in this process of data wrangling. Spreadsheets, Parsehub, Scrapy, and Talend, are examples of tools that save time and cost. Knowledge of SQL and NoSQL is also essential as data is stored in a database, and to access or use the data, the analyst must be able to query it using these languages. 

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4. Understanding the principles of data visualization 

As a data analyst, your job goes beyond getting insights from collected data. One crucial aspect of a data analyst’s duty is to be able to present data in an easily consumable fashion. Stakeholders and other individuals working in your organization also need to understand the conclusions you have drawn as an analyst. By mastering data visualization techniques, you can present your work in the best way possible. Data visualization uses charts, graphs, maps, and other methods used to graphically represent data to facilitate understanding of specified research or study. 

To become a good data analyst, you must develop your data visualization skills. Luckily, several data visualization tools can help you learn and improve as an analyst. They include Tableau, Excel, Power BI, Plotting, Bokeh, or InfoGram. You should also work on your presentation skills needed during meetings, where you must explain your findings to the rest of the group. 

5. Developing a data analyst portfolio

Becoming a data analyst or any professional today requires you to be able to showcase your past work. So even though you are just starting in the field, you are still expected to showcase some projects you have successfully completed. This is quite understandable as it helps to instill confidence in you as a prospective employee. How can a novice without job experience create an enticing and inspiring professional portfolio?

One good way is by working on projects dedicatedly. By projects I mean jobs for friends and family that may be willing to try you out for a little token. It also includes projects, codes, and tests written at the end of online courses and competitions from data analysis communities. All these can be put together in a well-arranged and organized folder using any cloud storage platform that you can easily share with prospective employees. Some platforms you can try out include GitHub, Topcoder, and Kaggle. 

6. Finding the right job 

There is a wide variety of jobs you can apply for with the data analyst skill set. Therefore, finding the one that appeals to you the most requires consideration. By researching the job roles or asking directly, you will get a sense of what you are expected to do. Some standard job titles for a data analyst include data analyst, data scientist, statistician, data phase administrator, business analyst, systems analyst, data engineer, data architect, quantitative analyst, business intelligence analyst, etc. 

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Data Analytics Jobs and Salaries

Data analytics is a versatile (and often transferable) competency you can use throughout your career. It is beneficial for the following jobs:

1. Data analyst 

A data analyst in any organization is tasked with collecting, cleaning, organizing, and analyzing or processing data. In analyzing the data, the data analyst draws insights that will help make specific decisions in the organization, such as pricing, marketing, etc. A data analyst should have a degree in related fields or a certification course. The average pay for a data analyst in the U.S. is about $63,486, excluding bonuses and commissions, per Payscale data last updated on July 2, 2022. 

2. Business intelligence analyst 

A business intelligence analyst had the essential function of providing valuable insight from the company’s data to push the company forward. The business intelligence analyst analyzes all industry data, internal or external to find trends and patterns that the organization can capitalize on. This role requires a minimum bachelor’s degree and may even ask for advanced degrees. The average pay of a business intelligence analyst in the US is about $71,395 yearly, as per Payscale data last updated on July 2, 2022. 

3. Data scientist 

The role of a data scientist in an organization is to create the framework for data analysis. It involved data testing, developing data products, and running algorithms. The data scientist is in a more technical position than the data analyst who runs the show from the front end. On the other hand, the data scientist ensures that the business decisions taken are as accurate as possible. The salary of a data scientist is $99,398 per year with additional bonuses, as per Payscale data last updated on July 2, 2022. 

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4. Marketing analyst 

An organization’s marketing analyst determines the best marketing options strategically. That is, they must be a good data analyst and also understand the science behind marketing. The marketing analyst is part of the marketing team and helps them make the best decisions to minimize the amount of money spent on campaigns with maximal results. The annual salary for a marketing analyst is about $58,277 as a base salary without bonuses, as per Payscale data last updated on July 2, 2022. 

5. Data architect 

A data architect in an organization is tasked with designing the framework needed for a successful analysis. They draw the blueprint for enterprise data management (EDM) systems and work with other team members to help them easily access company data. A data architect’s average take-home salary without bonuses is $123,486, as per Payscale data, last updated on July 2, 2022. 

6. Data engineer 

A data engineer must be able to build, maintain and scale data structures. The data engineer is expected to optimize database infrastructure for each part of the analytical process. They work closely with other developers in the company. The average salary of a data engineer is $97,658 base pay, as per Payscale data, last updated on July 2, 2022. 

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Takeaway 

According to the Bureau of Labor and Statistics, the demand for data analytics professionals will increase by 25% between 2020 and 2030. This is much higher than the cross-industry average, promising a bullish job market for candidates with analytics training. Fortunately, there are plenty of data analytics courses to help you get started and job opportunities in nearly every major company. With time, these professionals can demand six-figure salaries and eventually become part of the C-suite. 

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