Top Data Science Certifications For 2024 (Data Science Roadmap)

If you are looking to get ahead in the field of data science, then staying updated with the right skills is paramount. Data science certifications can provide a structured learning path and demonstrate your expertise whether it is to an employer or you want to pave your way into the data science industry. In this post we will be looking at the top data science certifications for 2024. This is a roadmap especially aimed at beginners to follow to become a data scientist.

Let’s start with the most important question, why should I even learn data science? That is a very important question to answer and something that you should really think about before going on this journey as it is going to require a lot of effort from you.

Why You Should Learn Data Science:

1. Empower Your Career: Data science skills are in high demand and command high salaries. By learning data science, you become a key player in making data-driven decisions, setting the stage for a robust career trajectory.
2. Versatility Across Industries: Data science principles apply to nearly every sector from healthcare to finance, offering you a vast landscape of career opportunities.
3. A Critical Role in Innovation: Data scientists are at the forefront of unlocking new insights and fostering innovation, making you an integral part of the future tech landscape.

Why Data Science Might Not Be For You:

1. A Long-Term Commitment: The field requires a substantial investment in learning and staying current with new methods and technologies. If immediate results are your goal, this might not be the path for you.
2. Complex Problem-Solving: If unraveling complex, data-driven problems doesn’t excite you, data science can be more of a chore than a passion.
3. Technical Proficiency: A strong background in mathematics, statistics, and programming is essential. Without a willingness to delve into these areas, you might find the field challenging.

Data Science vs Machine Learning

Before exploring certifications, there’s another crucial question that some might consider: Should I learn data science or machine learning? Although these fields are similar and share considerable overlap, data science is a broader discipline encompassing many more aspects compared to machine learning. Machine learning primarily focuses on building and training models. This is a simplification, but it captures the essence of the difference.

To put it very simply, machine learning is like teaching a computer to learn from experience while data science is like being a detective with data. It’s not just about finding patterns (like machine learning) but also involves collecting data, cleaning it up, exploring it to find interesting insights, and then explaining these insights to others.

Stage	Description	Primarily Data Science	Includes Machine Learning
Data Collection	Gathering data from various sources like databases, sensors, or online sources.	Yes	No
Data Cleaning	Preprocessing and cleaning the data to make it suitable for analysis (handling missing values, etc.)	Yes	No
Exploratory Data Analysis (EDA)	Analyzing the data to find patterns and insights, often using statistical methods and visualization.	Yes	No
Feature Engineering	Selecting and transforming the most relevant features from the data for use in machine learning models.	Yes	Yes
Model Building	Developing machine learning models to make predictions or decisions based on the data.	No	Yes
Model Evaluation	Assessing the performance of the machine learning models.	Yes	Yes
Deployment	Implementing the model in a real-world environment for practical use and monitoring its performance.	Yes	Yes
Decision Making	Interpreting the results of the analysis and machine learning models to make informed decisions.	Yes	Yes

Here is another way to think about it. If your goal is to develop models which power ChatGPT, then learning machine learning is the better choice, as it involves building and training models. On the other hand, data science could involve analyzing the types of queries ChatGPT receives, understanding user interactions, and deriving insights to enhance the model’s effectiveness.

For those leaning towards machine learning, I’ve prepared a detailed roadmap for 2024, which you might find useful.

Now that you have made up your mind to learn data science. Let’s look at some of the top data science certifications currently available.

Data Science Specialization

The first certification on our list is the Data Science Specialization. This basic introductory course, taught by professors from Johns Hopkins University, covers the fundamentals of data science. It’s ideal for individuals who have no prior experience in the field.

This specialization consists of the following courses to receive a certificate:

1. The Data Scientist’s Toolbox.
2. R Programming.
3. Getting and Cleaning Data.
4. Exploratory Data Analysis
5. Reproducible Research.
6. Statistical Inference.
7. Regression Models.
8. Practical Machine Learning.
9. Developing Data Products.
10. Data Science Capstone.

However, if you already possess some basic knowledge of data science, you might consider advancing to the next certification on our list.

Overview and Relevance: This specialization starts with the absolute basics of data science, that is setting up the required tools and then heading into R programming, which is one of the essential programming languages for data science. Towards the end, everything you have learnt is translated into a capstone project.

Prerequisites: Basic level in Python.

Time to Complete: 285 hours or seven months at 10 hours a week. The actual time to completion will be faster if you have some basic programming experience.

Skills Gained: Github, Regression, Statistical Analysis, R Programming.

Learning format: Video, quizzes, assignments and capstone project.

Cost: Free to audit but Coursera charges $49 USD/month until you complete the specialization to receive the certificate. It is also included as a part of Coursera Plus which gives you access to all the courses on Coursera.

Next Steps: This specialization introduced individuals to data science, ending with a capstone project. In fact if you have prior data science experience you will complete this with a breeze.

Applied Data Science with Python Specialization

If you already have basic data science experience and are looking to enhance your skills, the Applied Data Science with Python Specialization is an excellent choice. Although it begins with the basics, the course quickly progresses to more advanced topics such as data representation and text mining. Offered by the University of Michigan, this specialization emphasizes practical applications of data science.

This specialization consists of the following courses to receive a certificate::

1. Introduction to Data Science in Python.
2. Applied Plotting, Charting & Data Representation in Python.
3. Applied Machine Learning in Python.
4. Applied Text Mining in Python.
5. Applied Social Network Analysis in Python.

Overview and Relevance: It emphasizes practical application in data science, leveraging Python’s powerful libraries like Pandas, Matplotlib, and Scikit-learn. This specialization is highly relevant in today’s data-centric world, catering to professionals and enthusiasts aiming to harness data for insightful decision-making across various sectors. It offers a deep dive into data analysis, machine learning, and data visualization, equipping learners with the tools to tackle real-world data challenges effectively.

Prerequisites: Python, ability to use libraries. These courses consists of intermediate level difficulty.

Time to Complete: 140 hours or four months at 10 hours a week.

Skills Gained: Text Mining, Pandas, Matplotlib, Networkx, Machine Learning.

Learning format: Video, quizzes and assignments.

Cost: Free to audit but $49 USD/month until you complete specialization for the certificate. It is also included as part of Coursera Plus.

Next Steps: By completing this specialization, you will have a solid understand of data science. Next you can move onto the mathematics behind data science.

Mathematics for Machine Learning and Data Science Specialization

Math is at the heart of data science and machine learning. If you truly want to understand the ‘science’ part of data, then understanding the mathematics behind data science and machine learning will make you stand out amongst other people who take data science courses. The courses are offered by DeepLearning.ai.

This specialization consists of the following courses to receive a certificate:

1. Linear Algebra for Machine Learning and Data Science.
2. Calculus for Machine Learning and Data Science.
3. Probability & Statistics for Machine Learning & Data Science.

Overview and Relevance: This specialization is particularly relevant for those new to the field or seeking to strengthen their mathematical skills in linear algebra, calculus, probability, and statistics, which are critical for understanding and developing machine learning algorithms. With its practical approach, incorporating Python labs for real-world application, this program is ideal for anyone looking to build a robust mathematical foundation in the rapidly evolving fields of AI and data science.

Prerequisites: High school level math, basic python and a basic understanding of machine learning. Although the prerequisites might seem high, this is a beginner level specialization.

Time to Complete: 74 hours or five hours a week for three months.

Skills Gained: Probability, Machine Learning, Bayesian Statistics, Linear Regression.

Learning format: Video, quizzes and assignments.

Cost: Free to audit but $49 USD/month until you complete specialization for the certificate.

Next Steps: This is probably the last specialization/certification you will do in data science. Courses after this are all optional as they focus on a specialised applications of data science.

Data Science for Health Research Specialization

One of the most significantly impacted fields by data science and machine learning is health research. This specialization caters specifically to that area, particularly benefiting those already involved in the medical health sector. It’s important to note that this specialization, while valuable, is entirely optional and specifically targets health research. These courses are provided by the University of Michigan.

This specialization consists of the following courses to receive a certificate:

1. Arranging and Visualising Data in R.
2. Linear Regression Modeling for Health Data.
3. Logistic Regression and Prediction for Health Data.

Overview and Relevance: It’s highly relevant for health professionals, researchers, and data analysts who seek to utilize data science for impactful health research and policy-making. The specialization’s practical approach, including real-world project applications, makes it a valuable resource for those looking to make data-driven decisions in public health and healthcare analytics.

Prerequisites: Basic programming but is a intermediate level course.

Time to Complete: 33 hours or 10 hours a week for three months.

Skills Gained: Logistic Regression, Exploratory Data Analysis, Health Data.

Learning format: Video, quizzes and assignments

Cost: Free to audit but $49 USD/month until you complete specialization for the certificate. It is also included as part of Coursera Plus.

Next Steps: As mentioned earlier, this is an optional certification for people who want to get jobs within the field of health research. By getting this certification, you should be applying for roles within the medical research field.

By completing all of the specialisations above, you will have a very strong understanding of data science. One of the biggest things that you can take away from doing any course is practical knowledge. Especially building projects. That’s why School of Machine Learning believes in project-based learning. We encourage you to visit our homepage to discover any new projects that have been released. Happy learning!