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It can be hard to start a new learning journey when you have little to no experience or even understanding of what route to go down. Do you take a boot camp? But maybe you can’t commit to the time constraints. Do I go back to university? But that’s a hefty cost, which many people are unwilling to do. How about online courses where you can learn at your own pace and not hurt your back pocket?

This blog is aimed at beginners looking to transition into the data science world. A world that is only getting more popular by the day. Although these courses have provided details on how long it will take you to complete based on the hours you commit - I truly believe the more you commit, the faster you can complete the course. 

You can complete all these courses in a year if you commit to it!

Data Analytics

Link: Google Data Analytics Professional Certificate

A course that is very popular for those in the data science world. I personally have taken this course myself and I believe it is one of the best courses for any beginner! This course will take you 6 months to complete if you commit 10 hours a week. I was able to complete it in a month as I had free time and was able to get it done quicker!

Consisting of 8 sections, this course will dive into the day-to-day use of data, best practices and processes for your new data analytics job. You will learn how to clean and organize data for the analysis process and make calculations using spreadsheets, SQL and R programming. It doesn’t stop there, you will further your analytical skills by creating data visualizations and also learning about tools such as Tableau. 

At the end, you will get a certificate and also have exclusive access to career resources such as resume review, interview prep and career support. 

Data Science

Link: IBM Data Science Professional Certificate

Go that step further and take your analytical skills to the next level with a data science professional certificate from IBM. With no experience required, you can complete this course in 5 months if you commit 10 hours a week. Remember, the more hours you commit - the faster you can complete the course. 

In the course, you will learn the most up-to-date practical skills and knowledge that data scientists use in their day-to-day tasks. You will dive into learning about tools, languages and libraries such as Python and SQL which are very popular. Not only will you learn about cleaning data, analyzing it and then visualizing it. You will also learn how to build machine learning models and pipelines. 

Take the skills you learn in this course and apply them to real-world projects and build yourself a portfolio of data projects to show off at interviews. 

Machine Learning

Link: Machine Learning Specialization

With how things are moving at the moment with chatbots being the hot topic - having machine learning under your belt is more important than ever. This beginner-friendly course is offered by Stanford University and DeepLearning.AI was created to help people break into the world of AI. You can complete this course in 2 months if you commit 10 hours a week.

This course will help you master the fundamentals of AI concepts and have practical machine learning skills under your belt. Learn how to build machine learning models with NumPy and scikit-learn, such as supervised models for prediction. You will also learn how to build and train a neural network with TensorFlow. Decision trees, ensemble methods, clustering, anomaly detection, deep reinforcement learning - this course has it all!

Deep Learning

Link: Deep Learning Specialization

Another course is provided by DeepLearning.AI where you will transition from being a beginner in machine learning to an expert. This course is continuously updated with cutting-edge techniques to help you break into the world of AI and will take you 3 months to complete if you commit 10 hours a week. 

Learn how to build and train deep neural networks as well as identify key architecture parameters. You will also learn how to use standard techniques and optimize algorithms to train/test and analyze deep learning applications. You will build a convolution neural network (CNN) and apply it to detection and recognition tasks, where you can use neural style transfer to generate art content - cool right?

Wrapping it up

When it comes to learning something new, we often find ourselves overcomplicating the learning process. With these 4 courses, you can transition from a beginner to an expert by the end of the year. 

But it is important to remind yourself that the data science sector is always about learning, so make sure you’re prepared to learn new things as they come. If you are looking more into Generative AI as one of your goals, have a look at the Top 5 DataCamp Courses for Mastering Generative AI.
 
 

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.