You want to make a career transition into data science. How can you find the right courses to get started?

Assessing your current skills and setting clear learning goals will guide your data science education journey. Utilize online tests, quizzes, or self-assessments to gauge proficiency in key areas. Reviewing your resume and portfolio can reveal skills demonstrated in previous projects. This self-awareness ensures you focus on areas needing improvement and tailor your learning path to align with your goals.

Embarking on a career transition into data science is a thrilling endeavor. To choose the right courses, start by assessing your skills & defining your specialization; like machine learning or data analysis. Explore courses on trusted platforms & check for a well-rounded curriculum covering programming, statistics, and visualization. Opt for hands-on projects to apply your knowledge practically. Then join data science communities online for insights and recommendations. Network on LinkedIn for mentorship and industry trends. Maintain a continuous learning mindset with courses that encourage ongoing skill development. This thoughtful approach,sets the foundation for a successful journey into data science.

To transition into data science, I recommend starting with a solid foundation in statistics. From my experience, enrolling in online courses on platforms like Coursera and Udemy (there are many more platforms. I opted for these initially.) provided a comprehensive understanding of key concepts. Actively engaging in practical projects within these courses honed my skills. Transitioning to Kaggle allowed me to tackle real-world problems, fostering a problem-solving mindset. Collaborating with researchers and joining a postdoctoral research group specializing in data science projects further enriched my experience. Continuous learning remains crucial, and I stay updated with industry advancements to navigate the evolving field of data science.

Consider the following in your decision: 1. What are the areas of data science you are interested in, such as ML, data analysis, data visualization, etc. If you are yet unsure, you can watch YouTube videos and read blogs in such areas and get to know which part interests you more. 2. Seek recommendations from professionals in the industry. LinkedIn is a good platform to search, follow, connect, and chat with such people. 3. Research online platforms check out the courses available and read the reviews. E.gs of platforms to check are Coursera, Udemy, Edx, DataCamp, Udacity, etc. 4. Join online communities, social media groups, LinkedIn communities dedicated to data science. Examples are Kaggle, analytics Vidhya, and plotly community forum.

To prep for a data science course:Self-Assess: Evaluate your skills in key areas: math, statistics, programming, data analysis, visualization, machine learning, and communication.Set Goals: Identify weaknesses and define specific learning objectives.Plan: Use online quizzes for assessment, select relevant courses, and engage with data science communities for guidance.Act and Reflect: Implement your learning plan, practice consistently, and adjust based on progress.This structured approach ensures targeted learning and efficient skill development in data science.

I always recommend starting online. With the likes of Dataquest or Datacamnp and the career tracks they offer, you can easily find out if this is really for you or not. With the career tracks on those platforms as well, they take you from ground zero to knowing at least the basics to get into an internship, a bootcamp or a junior role. This is especially good if you do not have a Statistics, Mathematics or Computer Science background.

For math interested people a university course like MIT OCW/MOOCs in machine learning is useful. From there a detour into deep learning using Andrew Ng/Jeremy Howard’s fastai courses prepares you for real world problems After attaining a certain base level of proficiency, you need to understand how to approach a machine learning problem so books around feature engineering and associated concerns or what is now dubbed as a data centric view of AI becomes helpful. The next level is understanding how machine learning operations/continual learning works in an enterprise. Lastly understanding the business domain and able to frame problems and solutions whether they involve machine learning/statistics/heuristics becomes the final frontier.

Choosing a suitable learning path is crucial for a successful data science education. Consider factors such as course difficulty, format, duration, content, and quality. Select a course that aligns with your skill level, challenges you, fits your learning style and schedule, covers relevant topics for your goals, and is well-designed and up-to-date. Reviews, ratings, and feedback from previous students provide valuable insights into the course's effectiveness and benefits. Tailor your learning path to your goals and interests for a rewarding educational experience.

While modern data science increasingly involves specialized domain knowledge, a strong foundation in core skills like statistics, programming, and machine learning remains vital for all data scientists. When choosing courses, it's wise to consider both expanding your core skills and delving deeper into your current field. For example, an educator might benefit from specializing in educational data science, but could also gain valuable insights by exploring related fields like social science analytics.

J'ai intuitivement porté mon choix sur R studio et je me suis lancé et j'avoue que j'étais épaté par les possibilités infinies qu'offre ce programme...je le recommande pour ceux qui sont intéressés à la science des données..du traitement des données à la visualisation..il combine les statistiques la cartographie et c'est un bon outil pour les publications sur les données...

To find the right data science courses first we should check our skill level and goals. To compare different courses we should compare content, instructor expertise and experience, students reviews. We should also check for hands on projects and practical application. We should start with beginner friendly course and then progress slowly. For data science aspirants Machine learning specialization and Deep Learning Specialization by Andrew Ng are my personal favourites.

Comparing and evaluating different data science courses is essential for a well-informed decision. Utilize online platforms, directories, search engines, social media, forums, or blogs to find and filter courses based on relevant criteria. Assess courses based on learning outcomes, syllabus, structure, assessment methods, and credentials. Ensure the course aligns with your goals, provides valuable feedback, and offers recognized credentials that enhance your resume. This thorough evaluation process enhances the likelihood of a rewarding and effective learning experience.

Too much time invested in this area to compare and evaluate courses would only yield to overthinking, indecisiveness, and a change in mindset. The main parameters to observe while choosing a course are: - Course Content - The date of publishing and date when the latest changes were made in the course content - The course rating and reviews, either on the EdTech platform itself or on legit social forums (Take a maximum of 3 best, 3 worst, and 3 average reviews to gain a rough overview of the course) - Price of the course to suit your budget - Duration of the course to suit your need - Credibility of the certification offered by the course - Existence of practical hands-on problems and challenges at various stages of the course

In my personal experience, I found that seeking to match your existing skills and knowledge and targeting the course that will help you elevate to the next level is key. The reality is that this is a constantly evolving field and hence even experienced data scientists will need to keep on learning to keep up to date with the state-f-the-art. Nevertheless, target what you wish to achieve (realistic expectations) given the time, duration, complexity of content and try to ensure that the course content entails plenty of examples that you will need to implement - learning by doing is a great way to learn.

You can achieve PhD-level proficiency in data science on any budget, from $0 to any high amount you can imagine. It really depends on your preferred learning style and the budget you can afford. There are many free online courses from top institutions like Ivy League universities, which are ideal for autodidacts. Alternatively, you can enroll in programs at these institutions for a higher cost. The bottom line is that you don't need to spend a lot to get educated. If financial resources are limited, it's better to focus on improving your self-learning skills.

I would always suggest to build a strong foundation first instead chasing the latest hyped trends. If you want to start & learn from the basics then I would recommend to go through Andrew Ng's Machine Learning Course. This will give you an essence of what algorithms at the core actually work like and boost your confidence when you are implementing or even talking about it.

Courses hardly go to such detail into what actually is practically needed in the industry. Skip courses. Read and practice using “The Deep Learning Architect’s Handbook” instead.

?Fundamental Concepts: a. Basic Statistics b. SQL for data exploration c. Data Visualisation techniques d. ETL vs ELT e. ML model basics (supervised learning, unsupervised learning, ANNs & GAi) ??Tools to know: - Python - PowerBi/Tableau - Weights&Biases - Alteryx ????These have some really good #beginner friendly courses (free): - IBM Cognitive Classes - Great Learning - CFI (esg and analysis basics) ????♀?These have resources for a little more advanced concepts: - Analytics Vidhya - LinkedIn Learning - W3 Schools - Tutorialspoint ??My two cents, get the gist of basic concepts first, and then move to technology/tools. Happy #Learning! ?

Doing lots of exercise and assignment from class or courses. Start doing a simple project and implement in a simple to advance for simple application which gives more importance and interesting to learn like an researching

Consistency and discipline is key. Make sure that your choice is final as a solid commitment. Go for it like your life depends on it: make plans, create habits, allow 150% of yourself to be spent here. You can of course get help, seek solutions online, seek assistance from generative chatbots, but ultimately, you are the ultimate decider on what you'll finally get. Try to construct your own environment of learning, balance the help, pointers, and clues, so the learning can get going and you're prevented from the feeling of getting stuck.

If someone wants to do a career transition , there are so many free paid boot camps are there to learn the data science concepts. According to me the best platform is YouTube & ChatGpt to learn the data science concepts. These are the most important portions in data science : 1) Python, numpy, pandas, oops concept 2) Statics, probability, liner algebra, differentiation concepts. 3) Machine Learning Algorithms 4) Deep learning 5) SQL 6) Do 4-5 projects 7) Deployment knowledge (AWS) Anyone can learn these things from YouTube and cover whole syllabus. If you need hands on experience and need proper help support you should join any offline boot camps.

Continuous learning is the key to sustain in this is field. This field is continuously changing, explore new things, stay updated and you are in. Keep hunting new topics

Continuous learning and improvement are crucial in the ever-evolving field of data science. Embrace a mindset of lifelong learning, stay curious, and remain open to exploring new concepts and technologies. Engage in hands-on projects, participate in online communities, and seek out opportunities to apply your knowledge in real-world scenarios. Stay updated on industry trends, attend webinars, and consider joining professional organizations. Regularly reflect on your progress, identify areas for improvement, and set new learning goals. By maintaining a proactive and adaptive approach, you'll not only enhance your existing skills but also stay well-prepared for the dynamic landscape of data science.

Simple way is working on day basis with simple changes in code and libraries to understand in a better way for different patterns. Best way to practice and teach

In the world of data science you can never know everything there is to know. Continuous learning is not an option but a lifestyle. It is also a field with some very immersive learning resources online. So always save some time of your day where you can learn as much as possible about some topic of data science.

The best advice I got was, in order to learn data science, DO data science. Courses are great, but most learning happens when you put your skills to practice on actual data.

-Before diving into specific courses, make sure you have a basic understanding of mathematics (especially statistics), programming, and databases. Familiarity with languages like Python or R is often essential. -Clarify your career goals within data science. Identify whether you're more interested in machine learning, data analysis, data engineering, or a combination. This will help you choose the right courses you need. -Many platforms offer specialization tracks or certifications in data science. These comprehensive programs cover a range of topics and often include hands-on projects. Look for ones that are well-recognized in the industry.

Any Data science related work would require us to have a good understanding of languages like Python, SQL. These two are the mostly used ones and knowing the basics of it would be very much useful. Sites like Datacamp would help us to Fastrack our journey towards learning these languages. And there are sites like Kaggle, Datalemur that can be used to test our knowledge by working on data related questions. Most important of all is to have more hands on experience rather than theoretical knowledge.

Data science is a combination of arts and science. A successful data scientist possess a strong statistical and mathematical knowledge, programing, and business acumen skills. However, it is a journey to earn 360 degrees mastery in this space. Ensure that you take every challenge as an opportunity to learn and consistently go beyond your comfort zone. Also, equally focus on your soft skills to develop empathy, story telling skills, and active listening ability. Remember, it is about people, process, and technology. Data science is vastly viewed as technical solutions for business problems to generate actionable insights, but you cannot develop it by overlooking people and process of the business you will be working for. Happy transition!

The very first step is to understand the basics of statistics and coding and then comes the next step of exploratory analysis with the help of these basics. Once you understand your data completely is when you can proceed to building models. There’s no hard and fast rule of which models to use so it is best to practice as much as possible on different datasets and preferably real world datasets because they are the most challenging and are the best learning experiences. Also taking to experts in the field can help you learn from their experiences and help decide your trajectory. And always make sure that you are clear on the basics of every step in the process.