Writer : Mr. George Miguel

In this Coursera course, you’ll learn about deep learning. As one of the internet’s most popular data science courses, this course was developed by Andrew Ng, the man behind Stanford Machine Learning. This course series is for those who want to learn more about neural networks in Python and how to implement them.

**CodeWithMosh: Complete SQL Mastery**

This is a fantastic course for those who want to learn the basics of SQL as well as more advanced topics. More choices and reviews can be found on my list of the best SQL courses.

**Courses on edX that teach computational thinking with Python**

If you’re new to computer science or Python, this is a great series to check out if you get the chance, even if it only happens once every few months. I found the lecturers to be enthusiastic about what they were teaching, which made the classes enjoyable.

**Coursera’s Math for Machine Learning**

ML-specific mathematics is one of the most highly rated courses available. Taking this course will save you time if you’re apprehensive about linear algebra and calculus, which are necessary for machine learning.

**There is a Coursera course called Bayesian Statistics: From Concept to Data Analysis.**

Data scientists should be familiar with both frequentist and Bayesian statistics. We all learned Frequentist statistics in college without realizing it, and this course does a great job of comparing and contrast the two so that we can better understand the Bayesian approach.

**PySpark: Big Data Spark and Python Tutorial — Udemy**

You’ll learn how to use Spark and Python on an AWS cluster for data analysis and machine learning from the same instructor who taught the Python for Data Science and Machine Learning Bootcamp listed above. Mock consulting projects are given to students, and then the instructor walks them through the solution step-by-step.

**Learning Guide**

**Learning data science can be challenging.**

A college-level commitment to learning is required when enrolling in any of these programs. One of the primary objectives of online data science education is to induce as much mental discomfort as possible. For many people, it’s easy to fall into a routine of signing in to watch a few videos and feeling like they are gaining knowledge, but this is a false sense of accomplishment.

Here’s some advice from Dataquest’s Vik Paruchuri on how to get the most out of your data science education:

Do what you’re learning, i.e. immediately apply what you’ve learned from a course to a real-world project when you finish it. In order to solidify your understanding and demonstrate your competence with real-world projects, you should work on them.

It’s one of the most frustrating aspects of learning data science online is that you never know when you’ve learned enough. When learning online, there aren’t as many tangible markers of success as there are in a traditional classroom, such as passing or failing tests or entire courses. This can be remedied with projects because they first demonstrate what you don’t know and then serve as a record of knowledge once they are completed.

In general, the project should be the primary focus, with courses and books serving as a supplement.

The same as many others, I began my study of data science and machine learning by making stock market predictions. I researched courses, books, and papers that would teach me what I needed to know, and then applied what I learned to my project while I was doing so. A curriculum would have been impossible for me to complete in such a short period of time.

Being passionate about what you’re doing is a powerful motivator. My desire to predict the market made it easy for me to put in long hours and study constantly.

**Essential skills and knowledge are necessary.**

No matter what industry they work in, all data scientists must have a certain set of fundamental skills and knowledge. It is not enough to be a master of data science math, but you must also have the ability to analyze and interpret data.

The math you should be able to understand:

- Algebra
- Statistics (Frequentist and Bayesian)
- Probability
- Linear Algebra
- Basic calculus
- Optimization

In addition, you should be familiar with the following programming skills:

- SQL can be implemented in Python or R.
- In order to gather information from various sources, such as a SQL database or JSON or CSV or XML
- Organizing and cleaning up jumbled, unorganized data
- Insightful Display of Information
- Regression, Clustering, kNN, SVM, Trees and Forests, Ensembles, Naive Bayes, and other machine learning techniques

Many critical soft skills, such as self-awareness and empathy, are not taught in courses. Among them:

- Inquisitiveness and originality
- Skills in public speaking and presentation, as well as the ability to explain difficult concepts to team members who aren’t experts in the field.
- Analytical solutions to business problems are referred to as problem solving.

**Python vs. R**

You may have noticed that each course is focused on a single programming language: Python or R. You’ve got a choice, then.

In a nutshell, you can either learn Python or both Python and R.

Data science, machine learning, and statistics all benefit greatly from Python’s wide range of capabilities. In addition, you can create web apps, automate tasks, scrape the web, create graphical user interfaces (GUIs), create a blockchain, and create games.

I believe Python should be your language of choice due to its versatility. It doesn’t really matter which programming language you learn for a career in data science because there are plenty of jobs available for both. Because it can do almost anything, why not use it?

In the long run, learning R is beneficial because many statistics and machine learning textbooks use R examples and exercises. It’s actually true that both books I mentioned at the beginning use R, and you won’t get the full benefit of the book unless someone translates everything to Python and posts it to Github Having learned Python, you’ll be able to quickly learn R.

This StackExchange answer provides an excellent comparison of the strengths and weaknesses of Python and R for machine learning.

**Are certificates worth the money?**

Udemy and other platforms like edX, Coursera, and Metis, on the other hand, don’t offer certificates upon completion and are usually taught by university instructors.

There are some certificates, such as those from edX and Metis, that can even be used for continuing education credit. If you don’t upgrade, you won’t be able to access graded homework and tests, as well as many other benefits. If you’re having trouble staying motivated to finish a course, consider enrolling in a certificate program, which puts money on the line if you drop out. Certificates have a lot of personal value, but employers don’t seem to value them nearly as much.

**A comparison of Coursera, edX, and Udemy**

In general, I think Udemy courses are better for more practical learning because they do not currently have a way to offer certificates. Coursera and edX, on the other hand, tend to be better for theoretical material.

First, whenever I need a course on a specific tool like Spark or Hadoop or Postgres or Flask web apps, I go to Udemy because the courses favor an actionable, applied approach to learning. edX and Coursera, on the other hand, are my go-to sources when I’m looking for a deeper understanding of a topic like NLP, Deep Learning, or Bayesian Statistics.

**Wrapping Up**

Studying and working in the field of data science opens up a world of possibilities. As a data scientist, you’ll need a wide range of skills, a passion for data, and a lot of time.

Check out the Top 5 Machine Learning Courses for 2022 as a supplement to this article if you’re more interested in machine learning. Check out Best Python Courses According to Data Analysis if you’re just getting started with Python programming.

Please feel free to ask any questions or make any suggestions in the space provided below.

Please enjoy your time here, and thank you for reading.

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