**Today we’re looking at Grokking Data Science, a course on Educative.io.**

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.

*This post contains affiliate links. I may receive compensation if you buy something. Read my disclosure for more details.*

## Is there a need for data scientists?

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

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.

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.

*Want to know more about the Grokking series on Educative.io?**Check out our full review list.*

## Grokking Data Science

⚠️ *Level: Beginner*

*Prerequisite: Basic knowledge of Python*

Lessons | Quizzes | Challenges | Playgrounds | Code Snippets |

59 | 5 | 15 | 45 | 97 |

*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

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, attributes, concatenation, arithmetic and statistics, etc.

✅ **Pandas** – core components, DataFrame operations, cheat sheet

✅ **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 Plots** – interpretation, anatomy, five-number summary

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

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

✅ **Bayesian Statistics** – Bayes’ 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

✅ **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.

✅** 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

✅ **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 **Educative.io**, it might be worth getting a subscription:

Grokking DataScience | MonthlySubscription | YearlySubscription | |

Cost | $79 per year | $59 per month | $199 per year |

Access to330+ Courses | ❌ | ✅ | ✅ |

Certificate of Completion | ✅ | ✅ | ✅ |

## Is Grokking Data Science worth it? Conclusion

If you’re seriously considering a career in data science, then we think **Grokking Data Science** by **Educative.io** 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.

## Grokking Data Science students are also reading:

- 9 Best Data Science Courses for Beginners [+4 Data Science Learning Paths]
- 12 Best Data Science Books for Beginners [Learn Data Science ASAP]
- Top 11 Python Books for Data Science [Learn Data Science using Python]
- Data Science for Non-Programmers [Educative Course Review 2022]
- 8 Must-Have Data Science Interview Books