You want to get ahead in data science. What are the best training courses for you?

Understanding where you are and where you want to go is crucial in any learning journey, especially in a rapidly evolving field like data science. To set clear and actionable goals we need to Reflect on your existing knowledge and skills. Identify gaps in your understanding and areas you want to improve. Determine if you want to learn the basics, specialize in a tool/technique, or become an expert. Set specific, measurable, achievable, relevant, and time-bound (SMART) goals. Beginners: Look for comprehensive introductory courses. Intermediate/Advanced: Focus on specialized topics and advanced techniques.

To advance in data science, you should consider courses that cover a broad spectrum of topics including statistics, machine learning, data visualization, programming, and domain-specific knowledge. 1. Coursera: Machine Learning by Andrew Ng 2. Kaggle: Competitions and Kernels 3. DataCamp: Interactive data science courses 4. Books: "Python for Data Analysis" by Wes McKinney 5. YouTube: 3Blue1Brown and StatQuest with Josh Starmer Remember to work on practical projects alongside these courses for a well-rounded skill set.

To advance in data science, define your goals and assess your current level. Beginners should start with comprehensive introductory courses covering data analysis, visualization, statistics, and programming. Look for courses like "Data Science Fundamentals." Intermediate or advanced learners should focus on specialized topics such as machine learning or deep learning. Platforms like Coursera, Udacity, and edX offer suitable courses for all levels. Consider hands-on projects for practical skills.

Research course options Once you've defined your goals and level, research available courses that align with your objectives. Look for reputable online platforms like Coursera, edX, Udemy, or DataCamp, which offer a wide range of data science courses taught by industry experts and academics. Read reviews, course descriptions, and syllabi to ensure the content matches your needs and interests. Consider factors like course duration, format (e.g., self-paced or instructor-led), and prerequisites. Additionally, explore free resources such as YouTube tutorials, blog posts, and open-access textbooks to supplement your learning.

It's very important to first - 1. Define your goals - Clear, Concise and Quantifiable 2. Assess your current skill level 3. Identify hard, soft and people skills required to reach goal 4. Identify the reasonable time period required to learn identified skills 5. Once you have the plan ready - there are lot of reliable resources: i. Online Platforms- Coursera, DataCamp, Udemy(Lot of good content to learn people skills and soft skills), edX (Check out CS50 course to learn basics of python programming) ii. Content Creators- Youtube channels - Krish Naik, CampusX, zedstatistics and many more iii. Books- Designing Machine Learning System (Chip Huyen) Understanding Deep Learning (Simon Prince) iv. DS related Groups, Communities on LinkedIn

Let's talk numbers! Coursera and edX offer a plethora of free courses, perfect for those on a tight budget. If you're willing to invest, their paid certifications often come with added benefits like graded assignments and certificates of completion. Don't forget to explore platforms like Udemy, which frequently run promotions on data science courses. Time-wise, consider the course duration and your availability. Shorter, intensive courses may suit busy professionals, while longer programs offer comprehensive learning experiences.

Consider specialized certifications If you're aiming to advance your career or specialize in a particular area of data science, consider pursuing recognized certifications. Organizations like IBM, Google, Microsoft, and AWS offer certification programs in various data science domains, including machine learning, big data, and data engineering. These certifications not only validate your skills but also provide structured learning paths and resources to help you prepare for exams. Keep in mind that certification programs often come with associated costs and time commitments, so weigh the benefits against your budget and availability accordingly.

Budget is not a problem is today's world where most online courses are available for free/very low costs. Coursera and EDX have a wide range of courses for Data science covering a wide range of topics and areas. Coursera offers courses for free; you just need to fill a financial aid for every course you want to pursue. It is also one of those platforms where techies from Google, IBM, MSFT, deliver courses. EdX on the other side offers courses at 90% scholarships, in partnerships with top universities. It also contains courses that can fetch you credits for a master's degree if you want to plan one.

Consider your budget and time availability when selecting data science courses. Some are free or low-cost but require self-discipline. Others are expensive but offer guidance and certification. Short, intensive courses may lack depth, while long, comprehensive ones may not fit your schedule. Find the balance between budget, time, and learning objectives.

Whenever I want to look for courses, I just type the name of the topic (say, matrix factorisation) and then add a “site:.edu” to the Google search. This gives me a list of university websites that cover these topics. Many of them have video courses, or at least notes, and plenty of references to academic papers and authoritative textbooks. That allows me to declutter my first page of Google and get to learning the first principles without fuss.

Consider reputable platforms When selecting training courses, opt for reputable platforms that offer high-quality content and resources. Platforms like Coursera, edX, Udemy, and DataCamp are popular choices for data science training due to their extensive course offerings, experienced instructors, interactive learning environments, and flexibility. These platforms often provide a mix of lecture videos, quizzes, assignments, and projects to cater to different learning preferences. Additionally, they may offer community forums, mentorship opportunities, and completion certificates to enhance your learning experience and showcase your achievements.

Are you a visual learner who thrives on interactive content, or do you prefer in-depth lectures and readings? Platforms like DataCamp and Khan Academy provide interactive, hands-on learning experiences tailored to different learning styles. If you prefer a more traditional classroom approach, universities and colleges offer online data science programs with structured curricula and expert guidance.

Consider your preferred mode and style of learning when choosing data science courses. Determine if you prefer online or offline, synchronous or asynchronous learning. Decide if you prefer lectures or projects, individual or collaborative learning. Tailor your search accordingly. For instance, if you prefer online and asynchronous learning, opt for MOOCs or video tutorials. If you prefer offline and synchronous learning, consider bootcamps or workshops. Choose courses that align with your preferred learning style and personality for optimal engagement and effectiveness.

In data science learning, the mode and style of training are crucial. Some people thrive in interactive, hands-on environments, while others prefer self-paced online courses. It’s essential to identify your learning style and choose a course that aligns with it. This ensures you stay engaged and absorb the material effectively, ultimately leading to a more fruitful learning experience.

Asynchronous courses allow you to learn at your own pace. I prefer written materials (books, notes) that concisely cover what’s to be learnt, and then providing plenty of exercises to practice. One has to get to practicing soon instead of just spending a lot of time reading/watching videos. Or else, instead of practicing data science, we just end by “practicing to read.”

I prioritize self-learning and have discovered invaluable resources that have guided me on my DS journey. Krish Naik (my top recommendation), Statquest, Simplilearn, and Intuitive Machine Learning are my favorite YouTube channels for learning about a wide range of topics, each offering unique insights. In addition to video content, O'Reilly's textbooks are my go-to sources due to their clear and easy-to-understand explanations. Their textbooks "Essential Math for DS" and "Hands-on ML with Scikit-Learn, Keras, & TensorFlow" are my favorites. Additionally, I follow some industry professionals on LinkedIn (check my following) for their visual summaries and cheat sheets on ML concepts. All these resources are freely available online.

To find reliable data science courses, research and compare various sources and resources. Check the credentials, reviews, and ratings of instructors, organizations, and platforms. Evaluate the curriculum, content, and outcomes of courses. Consider the availability, accessibility, and support offered. Look for sources providing high-quality, up-to-date, and engaging courses aligned with your goals, level, budget, time, mode, and learning style. Platforms like Coursera, Udacity, and edX are reputable sources offering a wide range of data science courses. Additionally, consider community forums, professional networks, and recommendations from peers for trusted resources.

Stay updated with the latest trends and advancements in data science by following reputable sources like Towards Data Science, KDnuggets, and Data Science Central. Joining online communities such as Reddit's r/datascience and LinkedIn's data science groups can also provide valuable insights and networking opportunities.

Data Science is science first and foremost. That means that it entails deep understanding of the scientific method as well as language - mathematics. Coding is just the means. Stitching pieces of code everyone can find online is the easy part, and it by no means is data science. Make sure you understand in depth concepts, math, algorithms. That will serve you best. Good luck!

I may be biased, but if you want the find quality data science courses on the internet- deeplearning.ai is by far one of the best sites. It is run by Dr. Andrew Ng, cofounder of Coursera and considered by many the father or machine learning education. The courses are all relevant, practical and they are free!

Things have been given many different names today, just like Data Science. But one must still focus on getting the basics of statistics in place. The start might be a bit slow, but it snowballs into an enormous repository of knowledge soon. If you have your basics right, you don’t have to “learn from scratch” when something new comes along. You can just give it a cursory read and “you’ll know it.”

Many courses available today are ineffective and do little to prepare students for the real-world corporate data rich environment, often merely replicating each other’s academically oriented curricula. I recommend focusing on gaining real experience, enhancing data engineering skills, and building projects." courses out there are useless, don't do much to prepare for real world corparate and just copy each others academic oriented classwork. I would focus on real time experience, learn some business/communication skills, improve data engineering skills and build interesting projects. .