Data science is an exciting and highly-paid field that offers many opportunities for tech career advancement. To kickstart your career in this field, you should choose the best courses from Coursera, one of the leading online education platforms.
Data is critical in whether you want to innovate for innovation and better decisions. We live in a data-driven world. As digitalisation accelerates, the quantity of data is snowballing. Globally, data creation, collection, copying, and consumption are estimated to reach 149 zettabytes by 2024, up from two zettabytes in 2010.
The proliferation of data-related job roles and opportunities worldwide does not surprise. Harvard Business Review has labelled data scientists “the sexiest jobs of the 21st century.”
This guide focuses on some of the most highly rated data science courses on Coursera. Each course includes extensive course content, online videos, quizzes, capstone projects, and virtual classes by the top educators in the field.
The Coursera courses are generally free-to-audit, meaning that to enrol, you can learn for free, but to be certified, take a test, and do exercises, you must pay.
1. Google Data Analytics Professional Certificate
The Google Data Analytics Professional Certificate is an eight-course certification program that teaches you all you need to know to land an entry-level role in data analytics. The course will give you an immersive understanding of what junior data analysts do daily. The course will also teach you concepts like working with stakeholders, maintaining data integrity, cleaning and visualising data and using specialised software such as SQL and R.
In this program, you’ll receive over 180 hours of training and hundreds of practice assessments, which will let you simulate the real-world data analytics scenarios you’ll encounter in the workplace. Google’s data analytics experts have contributed decades of experience to highly interactive and exclusive content.
The skills you will gain include:
- Spreadsheet
- Data Cleansing
- Data Analysis
- Data Visualisation (DataViz)
- SQL
- Questioning
- Decision-Making
- Problem Solving
- Metadata
- Data Collection
- Data Ethics
- Sample Size Determination
2. IBM Data Science Professional Certificate
The “IBM Data Science Professional Certificate” is one of the most popular and widely recognised certificate courses for data scientists. More than 12 data scientists with either an IBM affiliation or a PhD in data science created the course. The course includes a total of nine beginner-friendly modules as well as a capstone project.
Starting at the beginning of the course, students are introduced to topics such as data science, data visualisation, Python programming, data mining, machine learning algorithms, and more. In all modules, beginners are encouraged to complete the capstone project after completing the modules.
After completing the course, students will be able to comprehend the following learning outcomes:
- An overview of a data scientist’s roles and responsibilities
- Utilise data scientists’ tools, libraries, and languages to develop hands-on skills.
- The import and cleaning of data sets and the analysis and visualisation of data.
- Use Python to build machine learning models.
- Share your data science capstone project with potential employers based on your learning.
The skills you will gain include:
- Data Science
- Deep Learning
- Machine Learning
- Big Data
- Data Mining
- Github
- Python Programming
- Jupyter notebooks
- Rstudio
- Methodology
- Data Analysis
- Pandas
3. Deep Learning Specialisation
There is probably no machine learning course more famous than the Deep Learning specialisation course on the web. There will be comprehensive coverage of Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and strategies such as Dropout, BatchNorm, Xavier/He initialisation, and more.
Upon completion of the course, participants can create and train deep neural networks, build DL applications using standard NN techniques, integrate vectorised neural networks, and use end-to-end, transfer, and multitask learning strategies to reduce ML error systems. More so, you will be able to build a Convolutional Neural Network, use it for visual recognition and detection, and at the same time create a train and operate Recurrent Neural Networks (RNNs), working with NLPs and Word Embeddings, use HuggingFace tokenisers and transformers.
The skills you will gain include:
- Artificial Neural Network
- Convolutional Neural Network
- Tensorflow
- Recurrent Neural Network
- Transformers
- Deep Learning
- Backpropagation
- Python Programming
- Neural Network Architecture
- Mathematical Optimisation
- hyperparameter tuning
- Inductive Transfer
4. Supervised Machine Learning: Regression and Classification
The course introduces you to one of the main supervised machine learning model types: regressions and classifications. In this course, you will learn how to build machine learning models using NumPy and sci-kit-learn, two of the most popular machine learning libraries.
Furthermore, you will be trained on supervised machine learning models that can be used to predict and classify binary data, including linear regression and logistic regression. During the hands-on section of the course, students will be taught how to use best practices for classification, including how to split training and testing data sets and how to prepare unbalanced data sets.
The skills you will gain include:
- Regularisation to Avoid Overfitting
- Gradient Descent
- Supervised Learning
- Linear Regression
- Logistic Regression for Classification
5. IBM Data Analyst Professional Certificate
If you want to become a data analyst professional in 2022, the IBM Data Analyst course is one of the best online courses available. This course allows you to learn all about data analysis from nine of IBM’s expert trainers.
You will get practical experience with data manipulation and apply analytical techniques by working with data sources, project scenarios, and analysis tools, including Excel, SQL, Python, Jupyter Notebooks, and Cognos Analytics. In addition to practical experience, you will develop technical skills, such as how to collect, manage, mine, and visualise data, plus soft skills, such as working with stakeholders and using data stories to engage your audience.
The skills you will gain include:
- Microsoft Excel
- Python Programming
- Data Analysis
- Data Visualisation (DataViz)
- SQL
- Data Science
- Spreadsheet
- Pivot Table
- IBM Cognos Analytics
- Dashboard
- Pandas
- Numpy
6. Learn SQL Basics for Data Science Specialisation
Another excellent Coursera course is designed for learners with no previous coding experience interested in improving their SQL query skills.
SQL fundamentals, data manipulation, SQL analysis, AB testing, distributed computing with Apache Spark, Delta Lake, and other topics will be covered. These topics will teach you how to use SQL to analyse and explore data in new ways, demonstrate query efficiency, and work with unstructured data sets. Students will work on four SQL projects involving data science applications.
The skills you will gain include:
- Data Analysis
- Apache Spark
- Delta Lake
- SQL
- Data Science
- Sqlite
- A/B Testing
- Query String
- Predictive Analytics
- Presentation Skills
- creating metrics
- Exploratory Data Analysis
7. Applied Data Science with Python Specialisation
Applied Data Science with Python is another excellent specialisation course offered by the University of Michigan. It is a five-course skills-based specialisation designed for learners who understand basic Python and want to apply machine learning statistically, information visualisation, text analysis, and social network analysis techniques using Python tools like pandas matplotlib scikit-learn, nltk, and networkx.
Students will learn Python programming fundamentals through an Introduction to Data Science in Python, including lambdas, reading and manipulating CSV files, and using NumPy. Participants are introduced to information visualisation basics in the second course using the matplotlib library. In contrast, the third course, Applied Machine Learning in Python, focuses more on the methods and techniques than the statistics of applied machine learning.
The skills you will gain Include:
- Text Mining
- Python Programming
- Pandas
- Matplotlib
- Numpy
- Data Cleansing
- Data Virtualisation
- Data Visualisation (DataViz)
- Machine Learning (ML) Algorithms
- Machine Learning
- Scikit-Learn
- Natural Language Toolkit (NLTK)
8. Machine Learning Engineering for Production (MLOps) Specialisation
For those interested in becoming machine learning experts, this is a great course to learn from. Students learn how to conceptualise, design, and build integrated systems that run continuously in production. The need for production systems to deal with constantly evolving data differs dramatically from standard machine learning models. Additionally, the production system must operate non-stop at the lowest cost while producing the highest quality. You will learn how to do this effectively and efficiently using well-established tools and methodologies.
Once you’ve completed this course, you’ll be able to
- Design ML production systems from the ground up: defining project scope, identifying data needs, choosing modelling strategies, and implementing them
- Develop, deploy, and continuously improve a production-sized ML application by establishing a model baseline, addressing concept drift, and prototyping how to develop, deploy, and constantly improve.
- Collect, clean, and validate datasets to build data pipelines.
- Use TensorFlow Extended for feature engineering, transformations, and selections.
- Implement enterprise data schemas and lineage tools to establish the data lifecycle and follow data evolution.
- Develop techniques for managing modelling resources and serving inference requests offline and online.
- Use analytics to mitigate bottlenecks and ensure model fairness.
- Deliver model, operating pipelines that require a variety of infrastructures
The skills you will gain include:
- Managing Machine Learning Production Systems
- Deployment Pipelines
- Model Pipelines
- Data Pipelines
- Machine Learning Engineering for Production
- Human-level Performance (HLP)
- Concept Drift
- Model baseline
- Project Scoping and Design
- ML Deployment Challenges
- ML Metadata
- Convolutional Neural Network
9. Data Science Specialisation
Developed by John Hopkins University and hosted by Coursera, the Data Science Specialisation covers R programming and reproducible research topics. Beginners can take the course, but they should have some programming experience and mathematical knowledge. As part of the program, you will complete a capstone project in which you will have the opportunity to produce a real-world data product.
The skills you will gain include:
- Github
- Machine Learning
- R Programming
- Regression Analysis
- Data Science
- Rstudio
- Data Analysis
- Debugging
- Data Manipulation
- Regular Expression (REGEX)
- Data Cleansing
- Cluster Analysis
10. Natural Language Processing
You will learn cutting-edge NLP techniques through four hands-on courses in this project-based course!
After taking this specialisation course, participants can develop NLP applications for question answering, sentiment analysis, language translation, text summarisation, and chatbots.
Building cutting-edge NLP systems requires the following machine learning basics and state-of-the-art deep learning techniques:
To carry out sentiment analysis, complete analogies, translate words, and compute nearest neighbours, using logistic regression, naive Bayes, and word vectors.
You can automatically correct misspelt words, complete partial sentences, and identify part-of-speech tags by combining dynamic programming, hidden Markov models, and word embeddings.
Using TensorFlow and Trax, perform advanced sentiment analysis, text generation, named entity recognition, and duplicate question detection using dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks.
Encoder-decoders, causality, and self-attention are used to translate complete sentences, summarise text, answer questions, and design chatbots. Learn more about transformers, BERTs, transformers, and reformers.
The skills you will gain include:
- Word2vec
- Machine Translation
- Sentiment Analysis
- Transformers
- Attention Models
- Word Embeddings
- Locality-Sensitive Hashing
- Vector Space Models
- Parts-of-Speech Tagging
- N-gram Language Models
- Autocorrect
- Word Embedding
Final Thoughts
Data science is becoming increasingly lucrative with an increasing demand for your skills. Practice makes perfect. Learning doesn’t end at the end of an online course in data science. Keep building your skills by undertaking a personal data science project or participating in a Kaggle competition.
Projects are also great for getting immediate feedback on areas that could be improved. For instance, if you’re working on a project and get stuck on which package you need to use, you know you should probably do more research in that area.
Another advantage of competitions and personal data projects is building a data science portfolio. A comprehensive portfolio will demonstrate to potential employers that you are both knowledgeable and enthusiastic about data science.
As a data scientist, you will always have options at your disposal. Almost every industry makes use of the power of data. As a result, you can work as a data scientist in various industries and sectors, from healthcare and pharmaceuticals to manufacturing and entertainment.
A data science certification will provide you with valuable transferable skills, allowing you to work as a business intelligence analyst, database manager, or machine learning engineer in the future. But that’s just a taste of what you could do; the possibilities are limitless.