The Gist

  • Opportunities galore. The market is overflowing with opportunities for learning data literacy. There are free and paid resources available that can help improve your data modeling skills.
  • Manageable chunks. When selecting an online course, focus on the practical applications of the skills being taught and the course structure should be broken into manageable chunks of no more than 20 minutes each. The course should provide an A-to-B learning experience with a focus on the practical applications and the reasons for choosing certain techniques.

Analysts often feel extraordinarily overwhelmed when it comes to collecting data. Those feelings take on a new level when they are required to learn new skills for managing data. Marketers have more ways to process data, analyze data and report insights that support their organizational objectives. This can make it challenging to determine the best starting point for learning how to extract insights from data and develop data literacy with their current tool set.

Free and Paid Resources for Marketing Training

Fortunately, opportunities for learning data literacy are on the rise, with a growing number of online courses focusing on the key concepts that drive meaningful business outcomes. Many resources exist now for marketing training (some are covered here). There are further resources that can increase your data literacy. Some courses are free certifications, while others are training supplements for tools and mentorship programs for concepts associated with data such as cybersecurity, agile development and other cloud services.

Excel: Widely-Used Tool for Business Intelligence

Excel remains a widely used tool, so learning resources exist seemingly everywhere, such as LinkedIn’s Education Center and continuing education programs such as University of Delaware Professional & Continuing Studies. One great course for touching up your Excel skills is Analyzing Data with Excel from IBM. This five- week course covers several features in the tool. Another is from a developer bootcamp called freeCodeCamp. The course, called MS Excel Tutorial for Beginners, offers the basics for using Excel, including the latest features. 

SQL: A Workhorse for Querying Databases

When it comes to databases, marketers are more than familiar with SQL. Data has reinvigorated ways to query databases. Yet despite the overwhelming variety of database options, many firms rely on data hosted within SQL tables. SQL has been around the corporate tech block, so in a fast-paced tech world, it is a workhorse as familiar as Excel. It is no wonder people are rediscovering the logic of SQL joins and queries in their training choices. 

There are numerous platforms for learning SQL. Microsoft, for example, offers Azure SQL Fundamentals, a course featuring free certification vouchers for participants in its cloud-based SQL labs and learning paths. The course is one of five modules Microsoft offers in support of its Azure services. There are other live training and certifications available from Microsoft (You can click here for schedules and information). The free CodeCamp bootcamp also offers an SQL course online.

Python: Versatile Language for Constructing Apps, Websites, and Data Models

Python has become extremely popular in business because of its broad use for constructing apps, websites, chatbots and data models for machine learning. Created 20 years ago, it quickly caught on with programmers due to its readability and ease of use. Python is as adept at solving issues in mathematics, engineering and deep learning as it is in manipulating and visualizing data.

Like SQL, there are sources aplenty for strengthening your data modeling skills in Python. Many are aimed at developer audiences, but many more are offering courses straightforward enough to allow a marketing analyst to understand what can be done with data brought into a Python environment. They include IBM Data Science Professional Certification (which also includes SQL in its sessions) and Datacamp, which specializes in courses for data engineers, programmers and data scientists (Python for Marketing) .

R Programming: Limitless Possibilities for Data Analysis

When it comes to R Programming, the sky's the limit for what kinds of courses exist. The use of R has been dramatically extended, thanks to data practitioners who found applications for research and very academic statistics through R, and then created packages to expand the usability of the language. Now there is an R course tailored for many subjects and providing compelling applications in real world cases.

Marketers can start with a DataCamp certification program called the DataCamp Career Track, which includes hands-on projects and quizzes. Another resource is DataQuest (Data Analyst in R). Additionally, edX, which also hosts the IBM program, offers a MicroMasters Data Science program, which includes an R programming course. John Hopkins University offers a Data Science Specialization via Coursera that includes an R Programming course as well.

Marketers can also consider Posit, a development company that publishes the widely used integrated development environment (IDE), RStudio. Because RStudio is the go-to solution among data science practitioners, Posit provides resources through webinars, blogs and cheat sheets that summarize and explain functions from the most frequently used libraries like dplyr and ggplot2.

Related Article: Excel, SQL, Python: What's Your Data Flavor for Customer Experience?

Tips for Selecting Online Courses to Enhance Your Data Literacy and Business Outcomes

Picking good courses or platforms to follow involves more than learning the latest technical steps. You can learn the definition of a task, but a course should answer key questions on why certain tasks or techniques are chosen, with lessons that teach a point A-to-point-B experience.

A good course should focus on the practical applications of the functions and dependencies being taught, which should spark ideas for you to apply in your workload or datasets. You’ll learn how to bring about change to your business operations, foster improved collaboration among your team and make data-driven decisions with enhanced optimization. 

A course should be broken into reasonable chunks of no more than 20-minutes each. This allows you to focus on each specific step and delve into the details of the code, gaining a comprehensive understanding of the functions and their creation. Most projects with data are an assembly of ideas, using data and code to achieve a specific goal. Breaking the course into manageable segments helps clarify the purpose and objectives.

While developing skills, you may have a nagging feeling of knowing and not knowing when it comes to the material. That feeling is perfectly OK — there are a lot of ways to apply programming syntax, so it is easy to get into the weeds of a topic. But a small amount of initial uncertainty is not a sign that you are not learning or extending your skills. It takes time to fully absorb and internalize what you've learned, even after completing a course.

Learning Opportunities

Discovering Repositories for Hands-On Experience in Data Analysis

Many professionals seeking careers in data analysis use practice datasets to gain hands-on experience with programming languages such as Python, SQL, R and others. These datasets are typically accessible from repositories.

Repositories are websites that host datasets, allowing users to access the datasets through an API or downloading a file. The most popular ones, Kaggle and, include a preview of the datasets, helping you to plan SQL, Python or R syntax for exploring data. Kaggle also offers its members a Jupyter notebook accessible in the browser. This can be helpful if you want to explore the syntax right alongside the chosen dataset, be it from a selection within Kaggle or a dataset you have uploaded into Kaggle.

These are powerful resources for choosing examples that enhance your knowledge of data models and the underlying concepts. You can also use the Google dataset search engine to discover additional datasets to import and explore as part of your training. 

Related Article: Google's Winning Business Intelligence Pieces: Looker and BigQuery SQL

Choosing the Right Support Resources for Your Learning Journey

There is a wealth of online support resources available for data, but to maximize their benefits, it's important to choose the ones that align with your preferred mode of online engagement rather than trying to access all of them indiscriminately as if you’re at a buffet.

There are independent groups and creative data science "influencers" who have started community forums in which members demonstrate useful tactics and share tips. For example, the group All About The Data hosts a terrific weekly YouTube Live session called #SQLSaturdays. Each Saturday the group hosts an expert who demonstrates key functions and techniques for all things SQL. Another example is the Digital Analytics Association mentoring cohort, where professionals and newbies volunteer to talk to each other regarding career insights as well as training topics.

R programming and Python attract an extensive variety of informal professional communities, each tailored to specific industries or development aspects such as gaming or health sciences. These are often found with hashtags. R programming groups, for example, often use the #rstats hashtag in Twitter, while #RLadies is used by a network of meetups for women in data science.

Several universities offer excellent resources that provide overviews of data models, encompassing both mathematical and computer science concepts. These resources are valuable as they present the topics in a manner that highlights their practical applications and real-world relevance.

Harvard University has a resource site called Dataverse that includes a very popular computer course among developers, CS50. MIT has a range of online courses through Open Learning MIT — you can learn about the top 10 data science courses here. There is also the Inter-university Consortium for Political and Social Research (ICPSR), an international consortium hosted through the University of Michigan. The ICPSR consists of academic institutions and research organizations that provide data curation and analysis method training for social science researchers.

Final Thoughts on Certification and Training Choices

Although many courses and groups are targeted toward audiences other than business analysts, they provide a broader perspective on data that can foster innovation and offer the latest insights into data analysis. The more diverse your knowledge and exposure, the more effective your ideas, opinions and insights on examining data with a customer-focused approach will be.