grokking data science in pink and blue letters with faded background of cartoon computer parts

Is Grokking Data Science worth it? [ Course Review]

Today we’re looking at Grokking Data Science, a course on

Grokking data science can seem larger than life. Algorithms, statistics, structured and unstructured data, computation…

That’s already a mouthful. And now you have to interpret it?

Getting into data science isn’t for the faint of heart. And with countless resources out there, getting started can be confusing.

“So what is data science in simple words?”

Simply put, data science is the process of extracting meaningful insights from data. And then interpreting them.

It uses a combination of domain expertise, programming skills, math and statistics.

Using statistics and computation, you then interpret complex data for decision-making purposes.

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Is there a need for data scientists?

You bet there is. In fact, there’s a shortage of data scientists.

Data scientist shortage graph 3x job postings vs job searches
Image courtesy of Quanthub

The more companies streamline their data, the more data scientists they need. And the industry is booming.

Plus with a data science background, you’ll be able to market yourself to in-demand careers such as:

✅ data scientist

✅ machine learning engineer

✅ applications architect

✅ data engineer

✅ statistician

And more.

Many of these include 6-figure salaries.

“I’m ready to learn data science. Where do I start?”

If you like the interactive approach, the course Grokking Data Science could be a good choice for you.

Using a combination of lessons, quizzes, challenges, code snippets and playgrounds, you’ll learn:

✅ Python fundamentals for data science

✅ the fundamentals of statistics

✅ machine learning

And more.

Machine learning in the course Grokking Data Science on

You’ll build a complete machine learning project.

Bonus: You’ll get some tips on how to get hired as a data scientist.

So if you’re seriously considering a career in data science, this course is a good starting point.

Now let’s take a closer look at this detailed course.

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Check out our full review list.

Grokking Data Science

⚠️ Level: Beginner

Prerequisite: Basic knowledge of Python

LessonsQuizzesChallengesPlaygroundsCode Snippets

Estimated completion time: 10 hours

Grokking Data Science is broken down into 5 sections:

🔷 Python Fundamentals for Data Science

🔷 The Fundamentals of Statistics

🔷 Machine Learning 101

🔷 End-to-End Machine Learning Project

🔷 The Real Talk

Student environment in Grokking Data Science – Python Libraries

None of these are light topics, so let’s take a look at the finer details.

✨ 1. Python Fundamentals for Data Science

In data science, Python is a must-know language. So buckle up, because this section is huge.

It covers:

Jupyter Notebook – installation, uses, tips

Python libraries – NumPy, Pandas, scikit-learn, Matplotlib, Seaborn

NumPy arrays example in Grokking Data Science course on

NumPy – arrays, attributes, concatenation, arithmetic and statistics, etc.

Pandas – core components, DataFrame operations, cheat sheet

Multiple rows Pandas dark mode code with graph of movie stats beneath
Slicing in Pandas library in Grokking Data Science on

Data Visualization – introduction and tips, quiz

And much, much more.

✨ 2. The Fundamentals of Statistics

Statistics is a key component of data science. Therefore, this module covers the technical analysis of data.

It covers:

Statistical Features: Basics – mean, median, standard deviation, correlation coefficient

Working with Box Plotsinterpretation, anatomy, five-number summary

Probability – conditional, events: independent, dependent, mutually exclusive, inclusive

Four probability distribution types in Grokking Data Science on

Probability Distributions – uniform, Bernoulli, binomial, Gaussian, Poisson, exponential

Bayesian StatisticsBayes’ Theorem

Statistical Significance – hypothesis testing, normal distribution, p-value

And then you’ll take a brief quiz to assess your understanding.

✨ 3. Machine Learning 101

This module is paced with a delicate combination of machine learning fundamentals, algorithms and concept understanding.

This includes:

Understanding Machine Learning – main components, applications

Types of Machine Learning Algorithms – learning: supervised, unsupervised, semi-supervised, reinforcement

Machine Learning Algorithms I – linear and logistic regression, decision trees, Naive Bayes, support vector machine

Example of decision trees in Grokking Data Science on

Machine Learning Algorithms II – K-nearest neighbors and means, random forest, dimensionality reduction, artificial neural networks

Evaluating a Model – precision, recall and confusion matrix, accuracy trap, AUC-ROC curve

And far beyond.

In addition, there are multiple quizzes on machine learning concepts.

✨ 4. End-to-End Machine Learning Project

Why spend countless hours looking for examples of data science projects? Grokking Data Science has one right here.

This section goes over the steps of the Kaggle Challenge:

Exploratory Data Analysis – understanding data structure, numerical and categorical attributes, correlations among numerical attributes, etc.

Numerical Attributes for the Kaggle Challenge in Grokking Data Science on

Data Preprocessing – deal with missing values, outliers, correlated attributes, feature scaling, etc.

Data Transformation – transformation pipelines

Machine Learning Models – create and evaluate models on the training set

Fine Tune Parameters – grid search, randomized search, ensemble methods

Grid search in Kaggle Challenge on Grokking Data Science on

Present, Launch and Maintain the System – present solution, launch, monitor and maintain system

Plus, you’ll walk away with some handy data science and machine learning study materials.

✨ 5. The Real Talk

This is a short, yet invaluable section of this data science course.

More of a sit-back-and-read section, you’ll get some insight on 2 common roadblocks:

i. How to Get That High-Paying Job

There are a few golden recommendations for acquiring data science jobs in this section.

ii. Imposter Syndrome

This brief section will help you eliminate doubts about being new to the field.

💰 Cost

You can get this course for $79.

But with all the other programming language and FAANG interview prep courses on, it might be worth getting a subscription:

Grokking Data
Cost$79 per
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$199 per year
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Is Grokking Data Science worth it? Conclusion

If you’re seriously considering a career in data science, then we think Grokking Data Science by is worth it.

Because of its combination of well-explained concepts, illustrations, snippets and a project, this is an ideal course for beginners.

You’ll gain an understanding of machine learning, statistics, and data science.

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