Top 6 Data Science Courses [May 2023]

Jump Into Data Science in Python is an intensive three-week cohort-based course conducted by Yale faculty, Dr. Elena Grewal and Sarah McGowan. It aims to help students transition from spreadsheet tools, such as Excel, to comfortably coding in Python. This 'flipped class' entails students watching self-paced, tailor-made videos and engaging in twice-weekly, hands-on practice sessions.

Ideal for individuals with familiarity in handling datasets and a solid understanding of high school math, the course covers Python basics, data loading and cleaning, data visualization, basic statistics, and problem-solving strategies for roadblocks.

Students learn through Python notebooks, the tool of choice for data scientists. The course is based on a highly-rated class taught by the instructors at the Yale School of Environment.

A former student shares, "Sarah and Elena do a great job of explaining the course content and making sure you understand it. Covered the most important topics in using Python for data science work, and gave great examples and tutorials."

Hands-on 3 Data Projects in 5 Weeks is a cohort-based course that equips learners with in-demand data and programming skills through hands-on projects.

Led by experienced data science trainer Jesús López, the course offers clear explanations and progressively challenging exercises to enhance Python proficiency.

Over the five weeks, students will complete three data projects for their professional portfolios, using datasets of their interest. They will also learn to leverage ChatGPT as a coding assistant, implement efficient coding practices, and make data-driven decisions with any dataset.

The course includes guided in-session practice with Jesús, ensuring personalized support and direction. It covers topics like data visualization, overfitting in machine learning, web scraping, APIs, Streamlit, survival analysis, algorithmic trading, and deep learning.

A testimonial from a previous student reflects the course's efficacy: "It exceeded my expectations, and I learned Python with confidence!" The course is ideal for anyone looking to gain a competitive edge in the job search for programming roles.

Advance Your Data Science Career with Proven Strategies is a two-week cohort-based course designed to equip data scientists with essential skills for advancing their careers. This course is hosted by Daliana Liu, an ex-Amazon senior data scientist with over seven years of experience in the field.

The course content is rooted in Liu's own career experiences and focuses on essential topics such as persuasive communication, project management, leadership, and career development.

Students will learn how to effectively communicate their projects, build influence within their teams, handle high-impact projects, and navigate the path to promotions. The course also offers guidance on how to deal with ambiguity, negotiate deadlines, and understand whether the quality of work meets expectations.

Additionally, it will assist participants in determining their own career paths, distinguishing between manager and individual contributor roles, and identifying their unique edge in the industry. The course includes four live sessions, personalized Q&As, and a three-month office hour service following the course.

A student testimonial states: "Daliana's course gave me the confidence to navigate complex projects and stakeholder interactions effectively. The lessons I learned have directly contributed to my promotion within my team."

Intro to R for UX Researchers is a concise, cohort-based course designed to expand your data analysis toolkit. Hosted by Alex Leavitt, a seasoned social scientist with experience at Meta, PlayStation, Disney, and Microsoft Research, this three-day course focuses on statistical analysis, visualization, and storytelling techniques for UX Research using R programming.

The curriculum, which spans 10 hours over three days and an optional orientation day, is accessible to anyone, regardless of their research experience level. As part of the User Research Fundamentals curriculum, the course covers both qualitative and quantitative UX Research methods. Participants learn the basics of R, how to apply R to UX Research, and key skills in data analysis and visualization.

The course has received positive testimonials, with one student, Dina Dajani, lauding the interactive and practical approach which allowed her to write code for real-time projects, greatly enhancing its relevance and long-term benefits.

Another student, Saeideh Bakhshi, highly recommends the course for those seeking to crunch, analyze, and visualize complex data. The course is taught in an inclusive and supportive environment, ensuring students feel comfortable as they learn new skills. It aims to provide students with an essential foundation in data analysis and the ability to create compelling narratives from data.

The Data Storytelling Bootcamp is a two-week interactive course that focuses on turning data into compelling narratives that inspire action. Guided by Evelina Judeikytė, an experienced information designer, the course aims to equip participants with the tools to structure, design, and present data insights effectively.

The course covers crafting a compelling message and narrative arc, understanding the intricacies of visual perception and information hierarchy, developing strategies for engaging presentations, and mastering a design process inspired by UX design and journalism best practices. The course is hands-on, involving interactive workshops and peer feedback sessions.

As a student, Pierre Auguste, Founder and CEO of Vinotracker, shares, "Evelina has a very didactic and efficient data storytelling approach. She coaches with a lot of simplicity, kindness and a real concern for adoption. We co-constructed my first data story and produced a beautiful result: she's very inspiring to work with!" This course is ideal for anyone interested in enhancing their data presentation skills and learning how to convey data-driven insights in an engaging and impactful way.

The Fundamentals of Data Analysis Using Pandas is a four-day interactive course taught by Matt Harrison, a seasoned trainer and author with over 20 years of experience in Python and data science.

This course is designed to help participants master patterns in the Pandas library to write clean, optimized code and prepare for machine learning applications. Students will learn to load and prepare data, clean numeric, categorical, and date data, and create effective exploratory data analysis (EDA) and visualizations. The course also covers how to organize code and notebooks for easy collaboration and sharing. Feedback on individual work is provided, and students are encouraged to learn from the solutions of their peers.

A student, Norman B, a Reliability Engineer, shares, "I liked the format and the structure of the examples made a lot of sense. I think in our engineering community sometimes struggles with the unrealistic example data we must work with that it’s hard to relate back to our jobs." This course is excellent for those looking to enhance their data analysis skills using the powerful Pandas library.