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 in 2020?
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
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
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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⚠️ Level: Beginner
Prerequisite: Basic knowledge of Python
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.
✅ 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.
✅ 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
✅ 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.
✅ Understanding Machine Learning – main components, applications
✅ Types of Machine Learning Algorithms – learning: supervised, unsupervised, semi-supervised, reinforcement
✅ 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.
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:
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You can check out Grokking Data Science here.
Is Grokking Data Science worth it? Conclusion
If you’re seriously considering a career in data science, then Grokking Data Science 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.
- What is data science in simple words?
Simply put, data science is the process of extracting meaningful insights from data. And then interpreting it. It uses a combination of domain expertise, programming skills, math and statistics. So from there, using statistics and computation, you interpret complex data for decision-making purposes. Educative.io has a course for beginners called Grokking Data Science. If you have an understanding of Python, you can get started with this course.
- Where can I find examples of data science projects?
If you need a project walkthrough, then check out The Kaggle Challenge in the course Grokking Data Science on Educative.io. If you're looking for more projects, you can also check out Alex Attia's repo on GitHub: https://github.com/alexattia/Data-Science-Projects
- Is there a need for data science?
Yes. The more companies streamline their data, the more data scientists they need. And the industry is booming. And there's actually a shortage of data scientists out there. So the salary for a data scientist is generally pretty high. Educative.io has a course called Grokking Data Science which is a beginner course for anyone interested in learning data science.