Data Science Courses for Working Professionals

by | Feb 22, 2024 | Data Science

Want to know more about data science? Have limited time? Don’t know where to begin? Here, we’ve curated a list with data science courses for working professionals like you, that will help you give your first steps into data science.

This article is suited for all of you who are currently employed full-time in a field completely unrelated to data science, and

  • want to transition to a career in data science in the future.
  • are interested in learning more about data science, perhaps as a hobby or to tackle a few side projects at work.


Data Science for Working Professionals

If you’ve ever wondered whether you stand a chance to work in data science, the answer is a resounding yes! The fact that you’re here indicates that you already possess two important traits for success in data science: a strong desire and curiosity.

We won’t sugarcoat it – your journey to work in data science won’t be easy. However, let us share a brief true story about our friend, whom we’ll call Paul.

Paul is a mechanical engineering graduate who worked in the automotive industry. He became interested in data science and deep learning after seeing their applications in autonomous vehicles. Knowing he has limited knowledge in both fields, Paul took several data science programs after work and worked on personal projects in his spare time. These projects eventually led him to interviews for data scientist positions, and he ultimately accepted a job offer as a data scientist.

We share this story to emphasize that we were once in a similar position as you! If you’re currently seeking suitable data science courses to prepare for your transition, then this article is perfect for you. Without further ado, let’s dive in!


What is Data Science?

Data science is an interdisciplinary field focused on one big objective: extracting insights and knowledge from data. While this objective might sound straightforward, the exponential growth in data volume causes a significant challenge in uncovering the right patterns from it. On the other hand, we know that by unlocking the right patterns within data, we can drive innovation, solve complex problems, and generate value across various business sectors. Because of this, the demand for people skilled in data science within companies has skyrocketed in recent years.

At its core, a typical data science project involves several important steps, such as data ingestion, data preprocessing, data analysis, data visualization, and the development of advanced predictive models for forecasting unseen data. Each of these steps has its own set of challenges and it’s important for us to know the fundamental theories of data science to effectively address them.

Furthermore, data science also involves effective communication and storytelling. People who work in data science often find themselves tasked with presenting their findings and insights to non-technical stakeholders in a clear and understandable manner. Therefore, we can say that if you want to be successful in data science, you need both technical and interpersonal skills.


What Can You Do Within Data Science?

As data science is a broad field, you will find several roles commonly associated with it, such as:

  • Data Engineer: Data engineers are responsible for building and maintaining the infrastructure necessary to process and store large volumes of data. Their tasks typically involve designing databases and setting up data pipelines to ensure data quality and reliability.
  • Data Analyst: Data analysts work in the field of data analytics, primarily focusing on analyzing data to drive data-driven decision-making within a company. They require skills in data preprocessing, data mining, data wrangling, data visualization, as well as presentation and communication.
  • Business Intelligence Analyst: Business intelligence analysts perform tasks similar to data analysts, focusing on analyzing past data and current trends to provide insights for strategic decision-making within a company. This role often leans more towards the business side, utilizing tools like Microsoft Excel and Tableau rather than programming languages.
  • Data Scientist: Data scientists are often described as the jack-of-all-trades in data science. They work on various aspects of the data science lifecycle, including data ingestion, statistical analysis, and implementing advanced machine learning techniques.
  • Machine Learning Engineer: Machine learning engineers specialize in developing complex machine learning models. Their role involves training and testing various algorithms, as well as deploying and monitoring model performance in production environments.

Data science roles

Skills You Need to Learn to Step into Data Science

As mentioned earlier, data science is a broad field of study, which means you need to grasp the fundamentals of several subjects. Here’s a breakdown of key areas essential for you to excel as a data science professional:


If there’s one subject that deserves more attention, it’s statistics. People who work in data science are essentially modern-day statisticians, and many sophisticated concepts in data science and machine learning stem from statistics. A solid understanding of statistics greatly enhances your ability to conduct comprehensive data analysis.


Calculus and Linear Algebra

In addition to statistics, proficiency in linear algebra and calculus is crucial for comprehending the inner workings of various machine learning algorithms. Concepts like matrices, vectorization, integrals, and partial differential equations are essential for understanding algorithms like back-propagation in neural networks.



While statistics, calculus, and linear algebra are foundational, programming skills are equally important. Proficiency in Python programming or R programming allows you to apply mathematical concepts to code, enabling comprehensive data analysis. Python is the preferred language in data science due to its extensive libraries like numpy and pandas, making data analysis more efficient.


Database Management System

Data analysis often requires retrieving data from databases, whether in-house or on the cloud. Understanding structured and non-structured databases is essential, along with proficiency in SQL to query and fetch data for analysis.


Machine Learning

To develop systems capable of predicting future events from historical data, understanding the theory of machine learning is important. This involves learning two key components:

  1. Machine learning algorithms, in which we need to learn the theory of various models such as linear regression, logistic regression, decision trees, support vector machines, and deep neural networks.
  2. Machine learning concepts, in which we need to learn how to assess and optimize machine learning models, including concepts like bias and variance, underfitting and overfitting, performance metrics, regularization, activation functions, optimizers, and more.

What can you do as data scientist?

How Can I Learn Data Science?

To learn data science efficiently, we recommend dividing your learning process into two sequential categories: first, mastering the fundamental building blocks of data science, and second, learning and practicing data science methodologies and algorithms.


Learn the Fundamental Blocks of Data Science

When we want to learn about data science, it’s essential to start with the fundamentals. Before delving into the intricacies of data science applications and machine learning algorithms, it’s wise to begin by mastering the building blocks of data science: statistics, calculus, and linear algebra.

Why? Because without a solid understanding of these concepts, comprehending the inner workings of various algorithms and selecting appropriate methods for data analysis becomes challenging.

Once you have a solid grasp of these concepts, you can proceed to the next crucial step: programming. Proficiency in programming is essential for translating your knowledge of statistics, calculus, and linear algebra into practical implementation.

In this step we advise you to focus your attention on two things: Python and SQL. Python is by far the most popular programming language for data science, and almost every online data science courses out there is using Python. Meanwhile, SQL skill is essential to fetch or filter data, especially if your company uses database for data storage.

To learn statistics, calculus, linear algebra, and programming, you can take courses that are available online. We know that it’s overwhelming to choose which course will worth your time as there are a lot of courses out there. Therefore, in the last section of this article, we will give you list of course recommendations that we think will equip you best with the fundamental blocks of data science.

learn new skills and practice with data science projects

Learn and Practice the Data Science Methodologies and Algorithms

At this stage, with a solid understanding of the fundamental blocks of data science, you’re ready to delve into applied data science concepts such as data preprocessing, data analysis, data visualization, and basic machine learning.

To familiarize yourself with these concepts, we recommend you to enroll yourself in self-paced online courses, which we’ll list in the final section of this article. It’s crucial not only to take these courses but also to begin practicing solving data science problems independently.

There are two best ways for practicing data science problem-solving:

  1. Do personal hands-on projects that are aligned with your interests. By pursuing projects in fields you’re passionate about, you leverage your domain knowledge to assess the validity of your analysis. As an example, if you’re a football fan, you likely understand which features are relevant for football analysis and the appropriate metrics to use. If you couldn’t get the data, you can find a lot of public datasets available for free on platforms like Kaggle.Once you’ve completed a personal project, don’t let it sit only in your local computer. Share it with others by making it available as open source on GitHub or by writing a blog about it and sharing it on your personal blog or on Medium. Establishing an online presence is advantageous when seeking a data science job, giving you a competitive edge over other candidates.
  2. Solve data science problems on online platforms such as LeetCode, Stratascratch, InterviewQuery, or DataLemur to hone your problem-solving skills. Many of the problems on these platforms mirror those encountered during data science interviews, making them excellent place to practice your data science skills.


Is a Course Really Going to Make Me Succeed in Data Science?

First, let’s address the elephant in the room: your success in transitioning to a career in data science depends entirely on your motivation and determination. It’s essential to recognize that working in data science is a long-term goal, requiring consistent effort to develop your skills bit by bit. Taking a data science course is one effective way to do this.

At the beginning of your journey into data science, you may feel uncertain about where to gain the necessary skills, especially if you’re juggling a full-time job which limit your time to learn data science. Online courses offer a flexible solution, allowing you to gain practical skills at your own pace. You can revisit lectures as needed anytime, ensuring you fully grasp the material.

If you have limited time to learn, you might believe that skipping a course and diving straight into real-world projects is the better option. However, this approach can backfire in the long run. Consider this scenario: you decide to tackle statistical modeling projects related to healthcare right away. While you may complete the tasks, you may struggle to interpret the results accurately. Similarly, if you attempt to build a machine learning model without understanding concepts like overfitting, you risk misinterpreting your model’s performance.

Therefore, taking a course is an important first step before diving into real-life data science projects. Courses provide the foundational knowledge needed to address real-world challenges effectively. While the learning curve may seem steep at first, rest assured that the investment will pay off in the long run.

Data science and machine learning course

Do I Need a Certification?

While many courses offer professional certificates upon completion, it’s crucial not to make certificates your primary motivation when enrolling in an online course or a bootcamp. Some individuals pursue data science courses solely to obtain certificates to enhance their credentials. However, we strongly advise against this approach.

In today’s job market, hiring managers and data science managers don’t really care about certificate programs because they cannot accurately assess the depth and complexity of the course material. Instead, they prioritize practical skills and accomplishments, such as the projects you’ve completed, your blog posts, and your portfolio. Therefore, it’s essential to view a course as a means to develop your skills and build a personal portfolio.

Furthermore, during technical interviews, data science certifications are unlikely to provide significant advantages. What truly matters is the knowledge you’ve acquired, which can only be gained by approaching the course with the right mindset – focusing on skill development and practical application.


Data Science Courses for Working Professionals

In this section, we will give you a list of course recommendations that can help you in your journey of learning data science from zero to hero. The list of courses below will follow the structure of what has been presented in the former sections ‘Skills you need to learn to step into data science’ and ‘how to learn data science’ above.


Fundamental Blocks of Data Science

All of the following recommended courses are aimed to upskill your knowledge in the fundamentals of data science which include mathematical concepts (statistics, linear algebra, and calculus) as well as programming concepts (Python and SQL). You can take all of them online on Coursera.

Data Analysis with R Specialization by Duke University (Statistics)


Although the specialization name is ‘Data Analysis’ and all of the practical examples are written in R, you’ll learn all of the relevant concepts regarding probability and statistics in this specialization. The good thing is, you don’t need to have any prerequisite knowledge of statistics to be able to understand each chapter presented in this course, as the instructor will teach you the concepts without technical jargon.


Bayesian Statistics Specialization by UC Santa Cruz (Statistics)


If the previous specialization mainly deals with statistics from a frequentist perspective, this specialization will introduce you to the concept of statistics from a Bayesian perspective. Specifically, you will learn what are the differences between frequentist and Bayesian statistics, and how we can apply Bayesian statistics to draw scientific conclusions.


Mathematics for Machine Learning and Data Science Specialization by (Calculus, Linear Algebra, and Statistics)


This course is for beginners and is designed for you who want to get started with calculus, linear algebra, and statistics. You’ll be introduced with the concept of vectors, matrices, matrix operations, derivations, integrals, etc. The teaching style is also fun and interactive with lots of practical examples which makes this specialization perfect as a starting point for everyone of you who want to learn more about the mathematics of data science.


Modern Big Data Analysis with SQL Specialization by Cloudera (SQL and Database System)


If you don’t know anything about SQL or database and want to learn it for the first time, then we recommend you to take this specialization. You’ll learn all of the important SQL clauses and how you can use them to fetch the data that you want. They also teach you how to ingest data from cluster databases or an S3 bucket, which is highly relevant in modern day where data is not sufficient anymore to be kept internally within a company database.


Python for Everybody by the University of Michigan (Python)


This course is for everyone who has never heard or used Python before. So, if you’ve never programmed before, this course will be a perfect starting point. In this course you’ll learn all of the basic concepts of programming commonly found in a computer science degree from A to Z, and how to implement each of the concepts using Python. At the end of the course, you need to complete a capstone project, where you have to implement Python concepts you’ve learned to solve real-life problems.


Data Science Methodologies and Algorithms

At this point, you should already know the foundations needed to learn data science any further. The following courses will teach you about the methodologies and algorithms commonly applied in data science. After taking these courses, you should be able to build your own data science portfolio independently and eventually apply for a data science job.


Data Science Specialization by IBM (General Data Science Concepts)


This specialization is the perfect entry point for all of you who learn data science for the first time. Finishing this course wouldn’t get you to the level of mastering data science as the course will not get deep into the theory of each concept. However, this course provides a very good overview of the end-to-end data science lifecycle from ingesting the data until machine learning model building.


Data Science Specialization by John Hopkins (General Data Science Concepts)


To supplement the above data science specialization from IBM and to enhance your knowledge of fundamental blocks, we would recommend you to take this specialization. This specialization will be helpful for you because of two things. First, it provides a recap to strengthen your knowledge regarding fundamental blocks such as statistics and probability. Second, it enhances your understanding of each stage of the data science lifecycle as it goes deeper into the theory for each data science methodologies.


Machine Learning Specialization by (Machine Learning Concepts)


By taking the two courses above, you should now have a very good knowledge regarding general data science concepts such as data ingestion, data preprocessing, exploratory data analysis, and a little bit of machine learning. To deepen your understanding of machine learning, this is the best data science course that you could choose. Andrew Ng’s engaging and easy to understand teaching method will spark your interest in machine learning even more after finishing the specialization.


Machine Learning Track by Train in Data (Machine Learning Concepts)


At this point you should now have a very good understanding of different machine algorithms and it’s not fair to call you a beginner anymore. What we recommend you to do next is taking various courses from Train in Data, as we have many modules that will deepen your understanding of the inner workings of machine learning algorithms.

machine learning courses at train in data.

Many researchers say that the performance of a machine learning model will only be as good as the data it’s trained on. Therefore, in our course, we will teach you how to perform feature selection and feature engineering in the data to boost the performance of our machine learning model. You’ll also learn how to effectively train our model in a case study where the data is imbalanced. Addressing data imbalance is important to mitigate bias in the model when predicting unseen data.


Deep Learning Specialization by (Deep Learning Concepts)


While they’re very powerful when dealing with tabular data, machine learning algorithms like decision tree or regression wouldn’t cut it to solve complex problems, especially when we have text, image, or audio as input data. Thus, normally we need to look into deep neural networks to solve the problem. This course is a perfect entry point for you who want to learn deep neural networks from scratch, as Andrew Ng’s teaching style makes the theory easy to understand. Also, if you feel like you are stuck on certain problems, there will be several mentors ready to help you on the course’s forums.


Natural Language Processing Specialization by or Generative Adversarial Networks Specialization by (Deep Learning Concepts)

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After completing Deep Learning specialization above, now you should have a very good understanding of deep learning concepts. By the time you’re completing that specialization, chances are that you have grown some interest in either one or both of these categories: Natural Language Processing (NLP) and Computer Vision (CV).

If you’re interested in NLP, then we recommend you to take the NLP specialization by In this specialization, learners will learn about the evolution of language models and you’ll get a chance to get your hands dirty by implementing the Transformers model to solve textual problems.

Meanwhile, if you’re interested in CV, then we recommend you to take the GANs specialization also by In this specialization, learners will learn about one of the most famous models useful to generate images and you’ll also get hands-on experience to code the architecture as well as the training process of GANs.


Series of Short Courses by (Generative AI concepts)


At this point, you should already have a Master’s degree level understanding of machine learning and deep learning. To keep your skill set up-to-date with the advancements of deep learning models, then we recommend you to check out a series of short courses by They will usually partner with industry experts in Artificial Intelligence to create the course and you can typically finish it in one day. There you’ll find generative AI related topics such as how state-of-the art methods like RAG works, how to fine-tune LLMs, how we can integrate vector databases with LLMs, etc.



In this article, we’ve highlighted some of the best data science courses available to enhance your data science skills from beginner to advanced levels. We began with courses covering the fundamental building blocks of data science, including statistics, linear algebra, calculus, and programming. Then, we delved into more advanced courses focusing on data ingestion, data preprocessing, exploratory data analysis, machine learning, and deep learning.

We hope this article serves as a helpful guide for your data science learning journey. It’s essential to approach learning as a long-term process, as mastering every data science concept takes time. Taking it step-by-step allows you to measure your progress and enjoy the learning experience. Happy learning!