the best data science courses in 2022 : Data science for all

You can get two or three dozen Data Science certifications, but in my opinion, there are not that many worthwhile data science certification programs. I want to talk about a few such programs supported by reputable organizations. I estimate these programs on various grounds. In particular, by the volume of topics covered, their “weight” in the eyes of a potential employer, and by the reputation of the organization implementing the program. For the past few years, I have been both a data analyst and a data scientist. All this time I have formed an understanding of what has the strongest impact on the success of a person in the field of Data Science (DS).


Here I will look at the top Data Science certification programs on the list compiled by Indeed. From this list, I have chosen four that seem to me the most worthy. And this article is devoted to them.

if you don’t know why you have to become a data scientist read this article: data science for all: what is data science


Google Certified Professional Data Engineer.


→  Google Certified Professional Data Engineer


This certification program, which I’m considering first, may come as a surprise to some as it falls into a field other than Data Science. Even so, I am confident that the skills and responsibilities of a data engineer are similar to those of a data scientist. I also think that passing this kind of certification can be a competitive advantage in the job market, as a data scientist with this kind of certification will be able to work effectively not only in data science but also in data engineering.


Here are some of the topics whose knowledge is tested on the exam:

  • Designing data processing systems.
    • Data storage technologies, building data pipelines. Specialized tools – BigQuery, Dataflow, Apache Spark, and Cloud Composer. Migration of data warehouses.
  • Creating and commissioning data processing systems.
    • Tools like Cloud Bigtable and Cloud SQL, cost and performance analysis of different solutions, data cleansing, data source transformation, and integration.
  • Commissioning machine learning models.
    • Using pre-built models like Vision API and AutoML Vision, applying DialogFlow. Re-learning models using AI Platform Prediction. Using GPUs, features of regression and classification tasks, features of learning with and without a teacher, and ways to assess model quality.
  • Decision quality assurance.
    • Security, compliance with requirements related to data encryption, application portability, use of Data Loss Prevention API, and Cloud Monitoring.


In general, having such a certificate will tell the employer that he/she is a fully trained data scientist. I have studied Data Science on various educational platforms and I can tell you that this certification program tests knowledge and skills that are taught in a few places. As a result, this certificate will allow you to present yourself in a favorable light in the job market. Finally, this certificate is issued by Google, and Google is more than famous in the IT industry.


General information about the exam:

  • Duration: 2 hours.
  • Cost: $200.
  • Language: English or Japanese.
  • Question type: multiple-choice questions.
  • Method of delivery: online exam or regular exam with exam monitoring.
  • Recommendations: experience with Google Cloud.


Google Data Machine Learning Engineer


Google Data Machine Learning Engineer


This is another certification program, which also can’t be called a program aimed solely at Data Science. Rather, it is aimed at a fairly narrow topic that is within data science. We’re talking about machine learning. Many data scientists can get so used to working in Jupyter Notebook (because that’s what most DS-courses teach), that the need to display the models in production and the need to deploy them to a website or mobile environment can cause them serious difficulties. Therefore, those who work in the field of Data Science will be very useful to get acquainted with the issues of the practical application of models, which will broaden their horizons and make their work more effective.


Here are the topics that come up in the exam:

  • Formulating machine learning tasks.
    • Converting business tasks into machine learning tasks using tools like AutoML. Defining the type of task (e.g., a classification or clustering task), and identifying key metrics for model quality.
  • Designing architectural solutions for machine learning.
    • Scaling solutions using tools like Kubeflow, feature construction, automation, orchestration, and monitoring.
  • Designing systems for data preparation and processing.
    • Exploratory data analysis, data visualization and statistical information about data, cleaning and validating data sets, creating training data sets, dealing with missing values, values that are significantly different from others, with data leaks.
  • Development of machine learning models.
    • Using different data formats to train models, including – CSV, JSON, and Apache Parquet. Application of databases. Knowledge of specific concepts like hyperparameter tuning and distributed model learning.
  • Automation and orchestration of machine learning pipelines.
    • Designing training pipelines, using platforms like Cloud Compose and Apache Airflow.
  • Monitoring, optimization, and support for machine learning solutions.
    • Model logging strategies, model retraining, model performance optimization, optimizing machine learning pipelines.


In general – this certification is similar to classical certifications from the Data Science field. Its passing will demonstrate to the employer, and the employee himself, that the employee is able not only to create models but also deploy them in production environments.


General information about the exam:

  • Duration: 2 hours.
  • Cost: $200.
  • Language: English.
  • Question type: multiple-choice questions.
  • Mode: Online exam or regular exam with exam control.


IBM Data Science Professional Certificate

IBM Data Science Professional Certificate


This is no longer just a certification program. It is a set of training courses where you can learn what is tested during the tests. This certification program, unlike the previous ones, focuses exclusively on data science itself. And that, of course, is a topic that we are particularly interested in. Another valuable feature of this program is the fact that it is prepared by IBM, and you can take it on the Coursera platform. Both of these companies are well-known and have good reputations.


Here are 10 courses that are part of the curriculum:

  • What is data science?
  • Tools of data science.
  • Data science methodology.
  • Using Python within data science for artificial intelligence and development.
  • A Python project from the field of data science.
  • Application of databases and SQL using Python for data science purposes.
  • Data analysis using Python.
  • Data visualization using Python.
  • Machine learning using Python.
  • A final course on Applied Data Science.

As you can see, there is a large focus on Python in these courses. That’s the language I prefer to use, but some might choose R. So if you are one of those and you need R for your work, you might want to look for a curriculum that uses that language.


General information about the course syllabus:

  • Course delivery method: completely remote.
  • Level of student: Beginner level.
  • Class Schedule: Flexible.
  • Duration: Usually 11 months (it’s a long time, but we are talking not only about certification, but training as well).
  • Language: English – with subtitles in English, Arabic, French, Portuguese (European version), Italian, Vietnamese, German, Russian, Spanish, Persian, and Turkish.


Microsoft Certified Azure Data Scientist Associate

Microsoft Certified Azure Data Scientist Associate


As you can see, this review features certification programs from leading IT players. Microsoft is one of them. If you study, work, and test at any of these companies – it can benefit your career as a data scientist. The program presented here is like a mixture of those programs we talked about above. On the one hand, it is a certification, but on the other hand, before the certification, you can study here, either by yourself and for free, or with an instructor and for money.


Here are the topics that come up in the exam:

  • Managing Azure resources for machine learning.
    • Creating an Azure Machine Learning workspace, managing data, being able to perform computation for experiments, security, access management, and setting up a development environment.
  • Running experiments and training models.
    • Creating models using visuals, running model training scripts, creating metrics, and working with models.
  • Deploying machine learning solutions and commissioning them.
    • Choosing a deployment model, deploying models as services, managing models, creating pipelines, publishing pipelines as web services, and applying MLOps practices.
  • Implementing responsible machine learning.
    • Using model interpretation tools, evaluating the fairness of models, and considering privacy considerations when working with models.


I believe that this certification is, in a good sense, the easiest one discussed here. It covers well the basic issues of creating and using machine learning models. And even though it has the words “Data Scientist” in the title, it is very much focused on machine learning.



In summary, I will say that if you can pass all of the certification programs described above – I believe you will be more than ready to work as a data scientist. These certifications are designed to test your knowledge of popular platforms and tools, as well as your skills related to the practical use of models. Specifically, we’re talking about working with business tasks, data analysis, modeling, and creating and deploying models. Of course, if you try to find a job with a certification company, having a certificate will increase your chances of success. When choosing a certification program, keep in mind that the ones I discussed here, based on my view of the situation, I selected from a list of programs from the Indeed resource. There are many other similar programs. You may very well be suited to something else entirely.


How would you advise someone who wants to work in Data Science to study and get certified?

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