Top 10 LinkedIn Learning Data Science Certificates
By: Frederik Bussler
Tags: Data Analytics, Certification, LinkedIn, Data Science, Career Advice

Top 10 LinkedIn Learning Data Science Certificates

#9: Pitching your ideas strategically.

Photo by Mateus Campos Felipe on Unsplash

Background

Since DJ Patil and Jeff Hammerbacher coined the term “data scientist” in 2008, the field has exploded in popularity. Data scientists power our daily applications, including Netflix’s personalization system, Amazon’s recommendation engine, Spotify’s AI-powered playlists, and more.

Every day, incredible advancements are being made. Tesla is nearing level 5 self-driving cars, while OpenAI’s GPT-3 model can write articles that fool humans. From the FAANGs to SMEs, companies want in on “the new oil,” resulting in attractive job prospects for data scientists. Even amidst economic turmoil, many companies are hiring data scientists.

That being said, data science isn’t easy. It’s a multidisciplinary field, requiring skills in computer science, statistics, and business at the high-level. Companies ask for a wide range of skills, including Excel, Python or R, SPSS, R, statistical modeling, machine learning, Jupyter Notebooks, and more.

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That being the case, constant learning is a must. Here are 10 LinkedIn Learning courses — which offer certificates — to teach you these skills.

Preface: Why LinkedIn Learning

I’m a huge LinkedIn fan. It’s the world’s biggest professional network, by far. Most tech CEOs and hiring managers are accessible with one message. LinkedIn Learning has thousands of courses on every major subject.

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I’ve been featured by LinkedIn Learning, having now taken 176 LinkedIn Learning courses. Of course, quality of learning trumps quantity, and practical experience trumps certificates, but keeping that in mind, these are powerful tools.

1. Data Science Foundations: Fundamentals

Before you dive into specific skills like Python or R, it’s useful to understand the multidisciplinary nature of the field and what exactly it entails.

A large part of data science is domain expertise, so you’ll also want to figure out what domain you’re most interested in. For example, a cliodynamicist applied data science to history to accurately predict 2020’s chaos with less than 100 rows of data.

If you just blindly apply tools like machine learning to a dataset you don’t understand, you’ll get poor results. As the saying goes:

If all you have is a hammer, everything looks like a nail. — Abraham Maslow

2. Statistics Foundations

Some critics quip that “data scientists is just statistics.” While data science is more multidisciplinary, there is some truth to that saying. Statisticians have been working with data, forming hypotheses, creating predictive models, and generating insights for decades.

To become a successful data scientist, you need to understand statistics. Without this fundamental knowledge, you might build models that look accurate, but are actually overfitting, or you might even be misunderstanding the data and creating a “99.9% accurate” fraud detection model that just guesses “no fraud” every time.

Your results could also be statistically insignificant, or you might be using the wrong tools for the job. You don’t want to just throw deep learning at every problem, especially when you need explainable models.

Ultimately, statistical knowledge is a must-have.

3. Data Science Foundations: Data Mining

Data mining is all about extracting patterns from data. In the real world, you won’t have a neat .csv file with all the clean data you need to work with. Data mining, therefore, also includes collecting the necessary data, and manipulating it into formats that enable further analysis.

4. Data Visualization: Storytelling

People are irrational. Most people first make emotional decisions and then back them up with logical-sounding conclusions.

To actually inspire change at your organization, sharing Python models and showing a complex dashboard won’t cut it. You need to know how to make high-quality data visualizations and tell a story with your data. After all, we’re wired for stories, not numbers.

5. Learning Data Analytics

Data analytics, unlike data science, is more focused on visualizations and reporting over modeling. Still, these are critical skills to have, as we’re all visual learners, and data analytics is a pre-requisite to more involved work.

6. Python for Students

Python is the #1 programming language for data scientists, by far. This extremely versatile language is relatively easy to learn, as much of the code almost reads like English.

For example, this is the code to read the first 10 rows of a CSV.

pd.read_csv(‘sample.csv’, nrows = 10)

7. NumPy Essential Training

Python packages like NumPy, or numerical python, are used to extend its functionality. With NumPy, you have access to scientific computing tools that make it easy to deal with numbers.

For example, this code computes the sum of all elements in an array.

np.sum(x)

8. Pandas for Data Science

Pandas is another indispensable Python package, letting you work with data in the form of DataFrames and Series without having to write much code. It also provides many useful exploratory data analysis functions, including built-in plot functions.

9. Pitching Your Ideas Strategically

Communication is a vital skill for data scientists, who need to explain their insights to often non-technical decision-makers. For your ideas to gain a foothold, you need to be able to read the room, understand agendas, and be persuasive.

10. Spark for Machine Learning & AI

While Python is a great tool for data analysis, you’ll want to use frameworks fit for the purpose when you’re dealing with big data. Spark is a great tool for efficiently processing large amounts of data in parallel. Spark includes built-in libraries for machine learning that you can apply to almost any field.

Conclusion

Data science is an exciting field to be in, but it’ll take a lot of work. LinkedIn Learning courses are a great place to get started, and if you have this knowledge already, you might want to move on to data science internships or data science competition platforms.

For more on entering the field, check out this data science job guide: