Which is the best data science course for working professionals?
Choosing a data science course can be a daunting task. It isn't easy to know where to start and how to choose the right course for you. Nowadays, there are many options to choose from. Which program is right for you? Are you a student who wants to become a data scientist? Do you want to jumpstart your career as a data scientist? Are certain features preventing you from choosing one program over another? Aspiring data scientists must consider some of the most important things about their future careers before taking courses or attending lectures. It can help them to make an informed decision on what to study and focus on.
If you want to master data science, you'll want to find an online course. But many options make it difficult to choose the best course. Luckily, we at Edyst have done all the legwork for you and found the best online data science courses accessible via in-person or online training.
Data Science Specialization
It is a popular and well-received course series by many data science enthusiasts. The course is divided into various parts systematically. It offers statistics knowledge required for Data Science. You will comprehend the broad directions of statistical inference and use this knowledge to make informed decisions when analyzing the data.
The course also contains the regression analysis and special cases of the various regression models, such as ANOVA and ANCOVA. You can learn machine learning and apply the knowledge to create a data product there. Moreover, this data Science specialization Program is perfect for both theoretical and practical learning.
Python for Machine Learning and Data Science Bootcamp
It is a comprehensive course that will teach you how to use Python's power to analyze data, create beautiful visualizations, and use powerful machine-learning algorithms. The purpose of this course is intended for both beginners with some programming experience and experienced developers interested in transitioning to data science. This comprehensive course is comparable to other data science bootcamps, typically costing thousands of dollars. Still, you can now learn all that information for a fraction of the price.
Data Analysis with R
It is an introductory course that can help working professionals learn to use R programming to analyze data. The course contains several fundamental learnings of R by installing RStudio and packages.
Following it, you can learn about exploratory data analysis (EDA). With EDA, you can investigate and understand the distribution of a variable and even figure out anomalies and outliers. In addition, you will learn how to quantify and visualize individual variables within a data set to make sense of a fictitious data set of users.
SQL Basics for Data Science Specialization
A Specialized designed professional course for learners with no prior coding experience who want to improve their SQL query fluency. You will learn SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, Delta Lake, and more through four more difficult SQL projects with data science applications. The topics listed in it will allow you to use SQL creatively. Furthermore, you can analyze and explore data, demonstrate efficiency in creating data analysis datasets, and conduct feature engineering. Also, you can do SQL with unstructured data sets.
Conclusion
The courses mentioned above are the best data science courses for working professionals. If you are a beginner or are interested in becoming a data scientist, you can enroll in them with Edyst. Here, we offer the best experts and learning bootcamps to ensure candidates duly prepare themselves for the data scientist interview and jobs.
Frequently Asked Questions
Is enrolling in data science courses beneficial?
Yes, they are extremely beneficial as with such courses, you can learn about data science and even prepare yourself for jobs.
Does Edyst provide Data science courses for working professionals?
Yes, we have several courses for working professionals.