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Data Science has been one of the most highly sought-after technological job opportunities. Rarely does their ever-increasing demand match the supply of data scientists, leaving a load of novices ready and willing to venture into this field if given the opportunity.
Fortunately, there are a good number of free online courses that provide a specific, elaborate learning path to take up an internship in this field. This article will almost serve as a roadmap of basic modules that any data science beginner in this field should master, with pointers to other free courses that can be leveraged into this field.
Now let’s take a deep dive into which modules you can master under free data science courses as a beginner to kickstart your career.
The foundation of higher-level topics must be properly laid. This module introduces the learner to what data science is, typical problems solved by methods in data science, and the typical workflow in a data science project.
Recommended Free Course
What is Data Science? by IBM on Coursera: It gives an informed view of Data Science and why it is relevant and applied in different industries. The course helps get an idea of the landscape of the current scenario in Data Science.
Python is a very commonly used language in Data Science due to its simplicity and wide range of libraries availability that may be used in manipulating, analyzing, and visualizing data.
Recommended Free Course
Python for Everybody by University of Michigan: This is a course that deals with Python programming and also leads the student through the basics of data structures, networked application program interfaces, and databases, all very central in the science of data.
Statistics and probability are the bedrock on which the analysis of data stands. It helps one make sense of data, understand patterns, and even predict outcomes.
Recommended Free Course
Introduction to Probability and Data with R from Duke University on Coursera: This course will introduce you to the main considerations of probability and statistics around data distributions, sampling, and hypothesis testing are key concepts for any data science project.
Data wrangling is the process of cleaning up raw data and arranging it into a proper format that could be appropriate for doing data analysis. This data wrangling is a very important process, as it is believed that data in the real world is raw, messy, and sometimes unstructured in shape.
Recommended Course
Data Wrangling with MongoDB by MongoDB University: Learn how to clean and wrangle data with MongoDB, a highly relevant NoSQL database that's great for handling large and unstructured datasets.
Visualization of data is needed to translate and disseminate the information from complex data analysis. Simple graphs are created from complex data, making them easily understandable.
Recommended Free Course
Data Visualization with Python by IBM, Coursera: This course introduces you to some of the available data visualization libraries in Python, including Matplotlib and Seaborn, and how to create informative and attractive visualizations.
If one has to be enlightened about the backbone of all predictive analytics and data-driven decision-making, it is in machine learning algorithms and their various applications.
Recommended Free Course
Machine Learning by Stanford University: This is one of the most popular and thorough courses online, taught by instructor Andrew Ng. It is primarily about methods and models developed for data analytics.
SQL, Structured Query Language, assists a professional in managing and modifying relational databases. SQL skills are highly important in querying databases and extracting data.
Recommended Free Course
SQL for Data Science by University of California, Davis: Core SQL and how to apply it to data analysis by creating more complex queries to extract important insights from databases.
As volumes grow, the use of traditional processing tools is impossible. The role can be played only by Big Data technologies like Hadoop and Spark, which take care of heavy data processing.
Recommended Free Course
Introduction to PySpark by Edureka: This class is pooled to have an overview of big data technologies like Hadoop and Spark to process big data for further analytics.
Given the associated with touching conversant information, one should understand the ethical and privacy concerns that set up the proper field of uses of data.
Recommended Free Course
Data Science Ethics by University of Michigan on Coursera: The course will guide you through the stewardship issues related to data science.
Issues of data science, information privacy, security, and bias.
It allows you to leverage your skills and knowledge for use in real-world situations. This is fantastic because it shows an employer your potential and what you can do.
Recommended Free Course
Applied Data Science Capstone by IBM: This will be a project course that will let you work on an actual data science problem and use most of the data wrangling, analysis, and visualization machine packages.
Now that you got to know about all the crash data science courses for beginners. Here’s a list of other modules you can master under free data science courses as a beginner to learn the required auxiliary skills.
Without version control, collaborative working and code management are impossible. Git is the most widely followed software package for version control.
Recommended Free Course
Version Control with Git by Atlassian on Coursera: This course gives an individual the very basics of Git. It includes feature branching, merging, and how one can collaborate using Git.
One of the most powerful tools of analysis is Excel. It turns out to be useful in cases when the volume of data is small and changes are minor. It is applicable in several areas of the business environment.
Recommended Free Course
Excel Skills for Business by Macquarie University on Coursera: This course will take you from the very basics of functions within Excel to advanced data analysis techniques.
Cloud-based platforms, AWS, Azure, and Google Cloud provide services not limited only to scalable data storage, processing, and analytics. The domain of cloud computing grows exponentially in its importance for a data scientist.
Recommended Free Course
Google Cloud Big Data and Machine Learning Fundamentals by Google Cloud on Coursera: Within this course, students will get acquainted with the major components of services provided for big data and machine learning by Google Cloud.
1. Consistency
Study and practice every day. Set aside some fixed hours every day for learning and practicing new skills.
2. Hands-On Practice
Apply what you learn to real projects and exercises. This will help in cementing theoretical knowledge and improving memory retention.
3. Join a community of other data scientists
Tap support from others in the data science community through online forums, meetups, or hackathons.
4. Keep current
Data science is moving. Keep pace with new trends and new technology marketplaces by reading blogs, webinars, and research papers.
Starting a journey in the field of data science is exhilarating and challenging at the same time. Basic Python programming, statistics, and machine learning modules could easily be understood by any beginner with the help of freely available resources. Any aspirant willing to learn can master these modules and, with practice via projects, or data science internships, can develop skills in the dynamic and highly rewarding field.
Most of the preliminary data science courses have no prerequisites for enrollment. The topics might just seem more reachable to the candidates if they have a good understanding of basic mathematics, particularly statistics, and are conversant with basic programming constructs. Most of the classes provide preparatory classes that enable students to be ready for the topics.
Your free data course can take almost any duration, depending on how deep and extensive the study material is meant to be. With an entry-level course, you can be through in some weeks of working part-time. On the other hand, good and more comprehensive free data science courses often take a couple of months. That said, about the amount of time available to you to put aside for study each week.
While free courses on data science will give you a feel for the knowledge and the skills, it is usually from more practice that job readiness comes, which can be attained through projects, internships, or work experience. Bringing together portfolios with projects and showing relevant experience with real data are very important in showing your potential employer what you can do.
To get started in learning Data Science, you are mostly going to need the following:
A computer with an internet connection.
Common tools and software.
Most of the classes will use Python for data analysis and machine learning.
It is an interactive web application to write and run your code.
Some libraries we have used extensively are Panda, NumPy, MatplotLib, and Scikit-learn.
Not to forget SQL, database management tools, and questions.
Others may also introduce you to big data tools such as Hadoop, Spark, or cloud platforms like AWS, Google Cloud, or Azure.
Studying online sometimes makes it hard to keep motivated. Here are some tips to keep you on the right path:
Define your goals for the course and set out some milestones. Work on a schedule, allocate some hours weekly to study, and stick to the agenda. Get involved with the community, either online by participating in forums, study groups, or meetups of others in the sector/profession. Practice these learnings from project work or through competitions like Kaggle. Keep track by continuously checking in on what one has learned and how much closer that has brought them to the goal.
