5 Best Intro Courses in Statistics and Data Science

Learning statistics and data science can open the doors to a truly rewarding career. With the right level of education, you’ll have access to millions of job openings across all the top companies on Earth. Getting started is easy, too. You just have to take the best intro courses in statistics and data science to see if this is the right path for you. Here are five courses to consider as you look toward this promising career path.

Practical Statistics

In the ‘Practical Statistics’ class, the coursework will teach you how to draw accurate conclusions from data sets using key statistical techniques. To excel in this class, you’ll need to have a good understanding of SQL and Python, preferably while using NumPy or pandas libraries.

Throughout the course, you will study statistics and data science concepts for 35 hours, starting with Simpson’s Paradox. Additional modules will teach you about Bayes Rule, hypothesis testing, how to perform A/B tests, and so much more.

Data Science Specialization

John Hopkins University created its ‘Data Science Specialization’ collection to help budding professionals explore this career path in full. This collection has ten interconnected courses that introduce all the key skills you will need in this field, such as:

  • R programming
  • Machine learning
  • Data analysis
  • Debugging
  • GitHub

The courses begin with ‘The Data Scientist’s Toolbox,’ which goes over all the tools, data, and questions you will use to complete your day-to-day job duties. After that, it’s time to finish the collection of courses with the ‘Data Science Capstone’ where you’ll create a useable data product that shows off your skills.

Statistics for Data Science and Business Analysis

Statistics for Data Science and Business Analysis’ helps you build the initial skills needed to become a successful data scientist. You will start with the fundamentals of statistics before moving on to how to plot data, estimate confidence intervals, and complete hypothesis testing.

The course introduces each concept with well-written articles, on-demand videos, and downloadable resources. By the end of the course, you’ll know how to work with many different types of data and make excellent data-driven decisions.

Data Science: Statistics and Machine Learning Specialization

For an in-depth exploration of data scientist skills, sign up to complete the ‘Data Science: Statistics and Machine Learning Specialization’ collection. In this five-course series, you will get to learn about performing regression analysis, using data to draw accurate conclusions, and building prediction functions.

The courses begin with ‘Statistical Interference,’ which teaches you how to analyze data and form reasonable conclusions. Course three dives into ‘Practical Machine Learning,’ while the fourth course is about ‘Developing Data Products.’ You’ll get to show off what you learned in the fifth course by completing a capstone project.

MicroMasters Program in Statistics and Data Science

The ‘MicroMasters Program in Statistics and Data Science’ introduces all the key statistics, data science, and machine learning concepts you’ll need in your career. This course is more than just a quick exploration of the subject. You’ll spend 10 to 14 hours a week for a little over a year completing the program.

Your educational journey will begin with a look at probability before moving on to the fundamentals of statistics. Next, you will have a chance to learn machine learning with Python using linear models and deep learning. A capstone exam completes those studies, allowing you to dive into either data analysis in social science or statistics modeling and computation in applications.

Upon completing these courses, you should have a good idea of whether you want to pursue a career in statistics and data science. If you’d like to move forward, review the job listings that interest you, and then get to work on ensuring you have the right level of education for that

Leave a Reply

Your email address will not be published. Required fields are marked *