Data Science

I decided to start learning Data Science because solving problems interest me.  I'd like to have a work from home position where what I do uniquely helps the company.  I have an analytical personality and I am good at analyzing details.The training platform is helpful because there is theoretical learning.  Like it explains the new topics and then backs it up with code for you to do the work of what you just learned.  The projects give you a chance to work on real world business topics that you might actually do in the field.  The tutors are helpful when you can get one.  I don't like that its challenging to casually get explaination for things you may not understand.   Code reviewers are tough, sometimes they give a hard time.  Community managers are as helpful as can be.  The other classmates are golden, and a great support system.
I am learning new skills and concepts I never even thought of, and I think I will be able actually do the skills needed. I just am really hopeful I will be able to get a great job in this field.  I chose TripleTen because of the gaurnatee  for the job.  The program is really challenging and it takes a lot of dedication.  Just have to trust that the team here is training us well and we will acquire good paying work in our field. 

I started this data science journey to transition into an exciting new field from engineering. I compared several bootcamps prior to selecting TripleTen and it was the best fit for me due to the flexible study schedule, breadth of the curriculum, continuous support from the community managers & tutors, and affordability.I liked TripleTen's interactive platform with real-world tasks in each sprint, which makes it easy to see the value in the material, and how it can be applied to solving data science problems. The sprint projects are also real-world tasks, optimized to focus on material from the sprint and put the knowledge to practice.  TripleTen's comprehensive data science program has helped me learn Python, SQL, data analysis, data visualization, Git, and machine learning models, both supervised and unsupervised for classification and regression tasks. Even though support is readily available from tutors during office hours and quick-to-respond community managers, I encourage you to challenge yourself and not ask for help until you've really tried as this will make the solution more rewarding and memorable.

               I chose to try to enter the data science field a few years after graduating with my B.S. in physics. I chose not to enter into a technical field after graduating, because I was tired of struggling to understand processes that seemed insurmountably difficult, such as learning to code and using these languages to analyze experimental data. After a few years of working in other fields (and coincidentally meeting other data scientists in my personal life), I decided to check out the current state of the coding world, and landed on data science as the most logical blend of my interests and experience. I began to brush up on the coding language Python using old videos I had saved from school, and eventually using Codecademy. These initial steps showed me that coding could be, at worst, frustrating but possible to learn, and at best, fun. I learned a good bit through these platforms, but I had trouble pushing myself to explore new ground, because I had nobody to provide validation for my work, and solutions were readily available. I applied to a lot of jobs, and even made it through most of an interview process, but ultimately kept falling short.
               Enter TripleTen (formerly known as Practicum) – I liked the idea of frequently working on highly independent projects that would be reviewed by working professionals, completing externships with companies to gain more real-world experience, and taking advantage of their career services program upon graduation. I think the information is presented well, and contains topics that one who is learning independently might not stumble upon – basic software engineering, building applications, advanced math, like some linear algebra and calculus, and more. I took many math classes on linear algebra in my undergrad, but TripleTen’s presentation of the topic and its integration with data science has allowed me to much more fully understand its real-world applications. 
               I work full-time, spending about 44 hours a week away from home for work. I have hobbies, and responsibilities at home, but no dependents, and personally I am able to handle the workload pretty well. I do not track my time precisely, but 15-20 hours spent per week on the program is probably accurate. Community learning events are offered, like workshops and co-working, though I have mostly focused on moving through the curriculum itself. The code reviewers leave valuable feedback, and you can tell they invest time into reviewing your work. The same person will review your projects once you reach a certain point in the program, which helps build familiarity. I haven’t yet started the career support or acceleration programs, but I am looking forward to it, as it seems like a very valuable offering. The program also offers generous scholarships for recent grads, or at least when I enrolled. 
               I have not started any externships, or other extra-curricular projects, yet, because I only recently reached the point in the program where I am able to participate, and none are available right now, but I am excited to start as soon as possible. It would be nice if they were available more often. They are also available throughout the 6 months of career acceleration post-curriculum. Overall, I would recommend this program to both beginners and to people with some experience. I have not really started my search for a new job, so I cannot speak to my final outcome, but I feel optimistic. Recruiters for not-explicitly-data-science related jobs have seemed impressed when I tell them about this program, so that’s something tangible that supports my optimism! 
               I always heard that learning to code follows a hilly curve where at first it seems easy, then hard, then you feel like a master, then it feels like it is all impossible, and so on. I have found some truth in that. In college, I never made it past the first hump. Now that I have spent a while learning it all, I feel like I have passed that initial hump, plus another hump or two! It is simply a collection of skills that needs learned and practiced. TripleTen offers a robust environment in which to accomplish this, at a great monetary value, especially with any discounts/scholarships that are available.

I have been in Practicum's program for 4 months now.  The Data Science program I am in is a 9 month program.  The First part of the program was extremely tough.  I am coming from a no coding background; non-the-less I was expecting a challenge; as it's not easy to attain these type of skills.  I like the way the platform is set up; although it did have a few tech issues which set me behind on some things.  The lessons are just bit size enough; yet sometimes can feel overwhelming.  There have been changes since I have been in the program; and at first I could get help with tutororing fast and easy-- now it has been more challenging to get a tutor when I need help.  That is something I don't like.  For the most part I feel the program goes in order for the things we should learn; but there were a couple projects so far that I felt would have been easier or made more sense if the order of learning was better arranged.  I like that Practicum has a comaradarie of classmates.  There is a community where we can share, video and spend time with our other classmates online and not for nothing this is one of my favorite things about Practicum; is the support that I get from other classmates-- all in different parts of the program which is helpful.  The community managers are pretty helpful at being liasons and helping fix any issues.  I have witnessed a graduate get a job in the field which is a positive notion.  This program is really challenging and I hear from others in the industry it will set graduates up nicely to get a position in the work field.  I will also say; some of the reviewers who check the projects are unnecessarily challenging-- and it feels almost they are holding up the process on purpose.  That is something else I don't like.  This program states you don't need prior experience; but I recommend it, even if it's just watching youtube to gain some knowledge.  To come in and keep up with knowing nothing from scratch can be discouraging. The workshops are not as helpful as some tutorials on YouTube.  But the learning process is more to the point than to try to learn everything on your own.  Learning Data Science has also helped in overall parts of my life on a personal level.  Overall I like Practicum and I think it was a good choice for me because of the community in the program I am now connected with.  I do feel I am learning the trade. 

In my time in Practicum's Data Science bootcamp I have developed a good understanding of the Python pandas library and how to use it to manipulate and look into data files. The learning pace is quick, and to stay on track you must spend a good amount of time per week studying and reviewing the coursework. I am still attending the bootcamp while working a full-time job, which is certainly challenging, but if you have the desire and motivation to learn data science then you can make it happen. If you have any questions or problems with the coursework or the projects, there are tutors and instructors that are available every day throughout the week that will happily assist you. There are also learning sessions every Saturday where instructors will go in-depth into the course material and show some different examples of how to use what you've learned throughout the bootcamp. Practicum makes all the resources you need to do well available, it's up to you to use them to your advantage and help develop your learning and knowledge of the subjects. My community manager checks in with me often and makes sure that I can access what I need for learning easily and readily, and regularly offers reminders about opportunities for communication with tutors and instructors. Practicum's cost for the Data Science bootcamp was very reasonable, especially after working out a 30% discount with the recruiter. The job acceptance statistic was very attractive too, one of the major reasons why I chose Practicum for my Data Science training. 

Decided to start learning data science after graduating college with a psychology degree and wanted to combine my psych research skills with data science.  The training platforms and projects provide an adequate challenge for intermediates. 

My interest in data science started after my early introduction while studying for my engineering degree. After the completion of my degree, I decided to break into the data space and did a bit of self-learning using multiple resources available online. I came to the quick realization that I need a structure to guide my learning and also curate what I really need to know, without trying to understand everything that there is out and getting into a rabbit hole. The bootcamp covers all skills you will need and libraries for the data science projects; from data gathering, data cleaning, data exploration, model building and training and finally model validation, all of this in the context of business value. This was a structure that practicum provided. The additional service of code review by industry data scientist was also an important part that validated my skill set and morale boost. Each module was well explained and expanded, and I saw the value in this having tried to study using other resources. However, the specialty modules (nlp, computer vision and deep learning) in my opinion are introductory, understandably so and for students with an interest in these field; additional study will be required to build on the basics taught in the boot camp. The community on slack were very professional, quick to respond to any learning concerns and questions about the projects and other personal projects.  Overall, practicum has been valuable in my journey to become a data scientist

I decided to start my data science career after graduating in Engineering. Back then, I had average coding skills, although completely new to Python, and some decent Math background. I looked forward to a course that would be suitable to my level and that would help me start working in this career. After carefully searching online and reading through reviews of courses in this same Course Report website, I ended up picking Practicum, and now that I am about to finish this course, I am very satisfied and happy with my choice!Practicum came as a cost-efficient option that would also give me the choice of learning at my own pace if needed. I had a demo of the first Python sprint before entering the course, which is a complete basic course and is a great way to test the platform before enrolling. I was surprised at how the topics were explained in a concise and efficient way, while making sure I was understanding the practical aspect of what I was learning. After this great initial experience the difficulty increases with each sprint, and although I had previous knowledge in some chapters, there were still challenging new topics to learn. The explanations are mainly text and questions to answer and they feel very dynamic, with graphs, images and step-by-step coding practices. Support is always a click away, while questions about the learning material can be asked to tutors available on the Slack channels. The whole staff and environment is very welcoming and I sincerely admire the teaching methods of tutors to prepare for jobs later on.At the end of each sprint, there is a project that helps in grasping what was learned and adds to the list of projects developed by the student, and these are really the best part of the bootcamp. That's when students start communicating on the Slack channels to ask questions about the challenging parts of the project. Projects are developed in the practicum platform app and there are also weekly webinars aimed to help students, and they mimic real-world tasks. There are also weekly webinars to expand on specific topics about Data Science, and that's a nice way to get to meet other students.Practicum also provides a Career Prep course, but before I finished the bootcamp I started working as a researcher in an university, so I haven't started it yet. The Python skills I learned were more than enough to start working as a Python developer, and I noticed that even some of my colleagues did not know some resources that I had learned. I generally feel confident and motivated to keep growing my Data Science career. I still intend to attend the Career Prep course and then I will be able to complete this review.

I started the Data Science program at Practicum about 6 months ago (it is a 9-month program). I can say that 6 months later, I feel that I have learned a lot! I started the program because I wanted to make a change in my career path that I felt would challenge me in ways that I have not been challenged before and to acquire a job that best suits my lifestyle as a mom of 2. My background is in psychology and academic tutoring, so it was a pretty significant shift going from that to data science. That being said, I have always been very passionate about data and statistics, which led me on the path to discover the relatively new world of data science. Being a mom of 2, I knew that taking the formal education route again (I already had a Bachelors degree) was not going to be a viable option time-wise or financially, so I started looking for online programs that offered a fast but concentrated training without breaking the bank. I researched several other online programs before I discovered Practicum, but ultimately I chose practicum because it was cost-effective, it's expertise and curriculum was rated highly, and because they guarantee job placement in your field within 6 months of graduation. Six months into the program, I could say that this program definitely provides a solid foundation of training in data science. The learning platform provides a flexible way to learn the material, and each cohort has tutors that we can access regularly with any questions. I will say the only con is that although the curriculum is challenging it can sometimes be unclear how to solve certain assigned tasks within the chapter. Nevertheless, the tutors are usually available to provide extra explanations as to how to go about with the solutions. When I first started the program, I found that delegating about 15-20 hours a week studying was sufficient to move along at a steady pace. However, as the topics become more advanced I find myself dedicating about 30 hours or so a week completing material and projects. So, although the program is online, you should still expect to dedicate several hours toward it a week if you intend on staying on track. I am nearing graduation in a few months, which I am very excited about! I feel as though I went from knowing nothing about data science to being able to understand theoretical and practical applications of data science. In this way, I feel that Practicum is preparing me for a career in data science. I look forward to being able to apply everything this program has top in my job in the future! 

Taking this Data Science bootcamp was undoubtedly the best choice to make in my career path. As a Finance and Accounting professional, I wanted to take my data analysis skills to the next level and had previously tried to learn Python for Data Science autonomously, but was lacking the right structure to progressively gain sufficient knowledge so as to be able to complete an end-to-end project, being fully aware of the underlying mathematical foundations each ML model is based on. 
At first, I wasn't really sure whether I would be able to commit 20 hours a week on top of my 40 hours workload, but fortunately, decided to challenge myself to do so. The whole Practicum team, from the Community Managers to the tutors, are always willing and happy to help with anything that may pop up, making the programme fit your own schedule and prioritizing the goal of building up a solid understanding of each sprint content over forcing students to meet strict deadlines. I would definitely recommend this course to anyone aiming to break into the industry on the right footing.

I was looking for a place which would allow me both working at a full-time job and boosting my Data Science career, which I have transferred to after being a Business Analyst for 10+ years. Although, back then, my professional life started with wealth of data analysis, I had been focusing more on entrepreneurship, operations, and regulation throughout my career; thus, at the very beginning, as a Data Science professional I faced plenty of challenges: given how fast the overall industry of Data has been developing for the past 10-15 years, I figured out that I had enough gaps to bridge and that I needed some guidance. I was introduced to Practicum by my ex-colleagues and decided to check it out. I realized eventually that both the Data Analyst and the Data Science courses are packed with state-of-the-art content and overall approach is sprint-based, so I decided to sign up for both of them and make the DS course just after graduating from the DA course. The Practicum community managers navigated me through all the organizational questions, so I could just continue enjoying seamless learning experience, so now I am three months left until my graduation from the Practicum Data Science course.Now, straight to the point. Beyond the outstanding UI, you can literally feel how much effort was invested in the course content development. With Practicum, you get a very clear roadmap: all the sprints are well structured as collections of short lessons, where the next one builds upon the previous one, while every lessons provides practical exercises, both as part of the theoretical part and when you finish going through the theory. By the way, there are several lessons devoted to developing your soft skills, which impressed me so much: those pieces of wisdom are absolutely invaluable!Projects are at the center of the learning process; they are the best way to gain valuable hands-on experience, since a thorough code review gently points out the ways you can make your code better; on top of that, tutors encourage you to keep trying additional techniques/alternatives and provide references to upgrade your skills even more. The satisfaction from having a project accepted is comparable only to that from being able to apply the freshly learned skills straightaway to my work.One important component is the support: 24/7 technical support, tutors answering your questions on Slack, weekly webinars, community managers who are constantly checking in with you to hear whether you are experiencing any issues - you receive support from everywhere, even grader pop up messages are insightful and funny! All in all it makes this journey so comfortable, even that the course is quite a commitment and you have to be ready for late nights and busy weekends)

I’m not new to Yandex Practicum. I tried their Interface Design program a few years ago, but although I didn’t finish it (it wasn’t my thing after all), I really liked the program itself, so I knew that I would come back.

How did I find myself in DataScience?
I changed jobs a year ago, and my new duties included working with spread sheets. I found working with data and formulas exciting, so I started looking for ways to implement this passion. I had heard of data science before, and it sounded too foggy and abstract to me. But at some point (after some research and a few videos on YouTube), I realised that it’s not that complicated, and, more importantly, it is exactly what I would like to do. So I started the DS training program, bearing in mind the thought that it will be just for fun. I said to myself that it would be a good way to spend my free time, a hobby with the potential to become something bigger.

How is it going?
I am in it for six months now, and I cam already say that there is no way back. The further I get — the more interesting it becomes. At the moment I am far more comfortable with Python and Pandas than with my old friends Excel and Google Sheets. I also really like how Yandex has built its learning environment. It feels like I am back at university, but now it is online, and it is also better. After completing the curriculum I am already aiming to take their course on mathematics (for now this one is only available in Russia), because I always wanted to be proficient in this domain. I didn’t see the way I could to this before, but now I believe that it is completely possible.

What’s so special about Practicum?
I feel like they did a really good job in gathering all the best learning tools and cutting off all the unnecessary elements of classic education. In our study process we have:

The very well written learning text. It is story-like theoretical material that comes in chunks. These chunks are split by interactive illustrations or graphs and short tasks in which you implement what you just learned. This way, you rarely get bored or unfocused, even with tough topics such as statistics. The platform also has a great coding environment, so you can work on your local machine, or completely in the web—whatever you choose.
Code review is the second one of the best things about this course. Each sprint ends with the course project; each project represents a possible business case (some of which I already encountered in my current job). My code is reviewed by real professionals of the industry. In their comments, they often leave very insightful advice, share tricks, and provide additional materials. Now I love when my work gets commented on.
Workshops and tutors. In addition to the main material, each week or two there is a workshop. It is like a web seminar, during which one of the tutors shows you how he approaches one of the cases on the topic. This happens online, so you can ask questions right during the process, but each workshop gets recorded so you can watch it later. I personally prefer to watch it later, following and repeating each step on my machine. You can also watch several records on the same topic, but with different tutors. Each of them has his or her own techniques and tricks, so it is also helpful.
Communications via Slack is a key part of the university-like experience. I can always ask questions or help others in public channels or in private conversations. I also get to share my thoughts, feelings, and achievements with other students from different cohorts. The community manager from my cohort writes to me from time to time, asking how I’m doing, whether I need help, and so on.
Phew. That’s a lot. In the end, I want to say that I wish I had had a school and university experience like this earlier, but I am grateful that I have it now. Of course, there are some places where Practicum could be improved, but i find them to be minor and not worth mentioning, and I know that the team is working on it. I also participated in a new design and features test for the platform. For that I was given with Amazon gift card, which I spend on buying books on machine learning and statistics.You can reach me via LinkedIn or Telegram and ask questions if you want: search for Wanomir✌️. And no, I wasn't paid for this 🙃

An online bootcamp that prepares it's students for careers in data science.The bootcamp is fully online with what I think is great UI.  There are always experts on hand via slack that will help out as soon as possible.  There are also zoom workshop days on the weekends where one of the experts will break down the material learned in the week in an open forum setting. There are also channel managers that are always helping run the organization to its fullest potential. The bootcamp is broken into sprints which are further broken down into modules. Each sprint is meticulously created to have you learn skills and then build upon those skills repeatedly. The modules further break down those sprints into a more digestible format, each one with mini quizzes at the end so you can implement what you just learned. There are projects at the end of each sprint which you can display on your Github to create your portfolio. So far this year I have 6 projects in my Github!Practicum also provides an extensive post-bootcamp career course. This includes but isnt limited to interview prep, resume formation, LinkedIn formatting and portfolio building. Also if you dont find a job in I believe 3 months after you graduate they will provide you with a full refund.I have about 5 more months in the bootcamp and love it. I would recommend Practicum to any and everyone.

I am currently a professional in the biotech industry and was looking to make a career shift into a position that would provide more freedom with employment and hopefully would allow me to gain the opportunity to work from home. I specifically chose data science as I felt that it could most closely be applied to my field. I was always interested in learning how to program but I had zero experience before I started the Practicum Data Science Program. The program is very well structured and is well paced for a programming novice such as myself. The program uses Slack as a community to interact with your colleagues and tutors. I must admit the response time from the tutors were much better in the first half of the program as opposed to the second half. If I ever got stuck during the first half I could post a question and it would usually be answered within an hour. During the second half it sometimes takes until the next day to get an answer. This can sometimes be quite frustrating if you can't figure out the problem in your code and cannot progress any further until you get the help you are looking for. But that can be expected as you are supposed to be able to troubleshoot more on your own as the course progresses.My favorite aspect of this program is the flexibility that it provides. I have fallen behind schedule a few times since starting the course due to work constraints. However, I have never had an issue discussing the matter with my course manager and then moving to a new cohort. All the staff has been very supportive. I suggested this course to my co-worker as I was getting started and he has joined as well and has also greatly enjoyed the course. I highly recommend this course to any working professional that needs a flexible program to be able to learn on their own schedule.

Sprints make the program more organized so that you can pace yourselfThe managing community team is always there to help you with whatever you needIf you need help with something and can't figure it out from office hours you can always have 1 on 1 help

Multiple different courses in a 9 month program. Each course gives you 2-3 weeks to complete it and includes lessons along with a project to help improve your skills. 

I started my journey to become a data scientist with no experience in coding. I tried using online sources but was quickly overwhelmed by the bulk of material and things I needed to know. More than anything, I was looking for a structured program. A kind of road map to becoming a data scientist. I decided to start the course on Practicum and I am glad I did. The program is very structured and there is a definite road map that you can see. The lessons are quite understandable too.Even with no prior experience in coding, I was able to understand different coding concepts and practice them on the website. And even times when I felt lost, all I had to do was ask questions and the tutors would give solutions to whatever problems I was facing. The support is really great.Also, with this program, you don't have to go through long installations of different software just to get you up and running. All tasks can be coded and solved on the platform and so no need to worry about getting a new PC due to heavy software requirements.I'm currently training and using machine learning models and algorithms on different real life projects. That's a huge step up from not knowing how to code when I first started.In all, it's a great course, real value for your money.

Although I have a background in statistics through my studies in public health, I always felt really unprepared to enter into a data-related career and counted myself out of so many jobs and opportunities because of it. But even after going through just half of the Practicum Data Scientist curriculum, I have grown not only in my hard skills but also in my confidence to pursue a career as a data scientist. The support system is excellent and all the tutors and instructors are incredibly knowledgeable. I'd highly recommend Practicum to anyone who is looking to make a career shift into data science!

This is a great program of those unfamiliar with the field. If you are new to the field, like myself, you might find yourself spending more time on each 2 week sprint that what they say, but it is time well spent. The curriculum is great. There is solid organization, incremental learning, and you will produce tangible projects that display your learning. The slack forum allows conversations between students and the tutors. Tutors will respond within 24 hours as a rule. Sometimes you will run into technical cliches where the right solution is offered, but the automated grader will not accept it, but that is quickly resolved by reaching out to the tutors who will report it to the technical side.While I am just starting with the career accelerator portion of the program (optional), I believe that Practicum will help anyone determined to find a good job by helping with resume prep, interview skills, and soft skills. Indeed, during the 9 months the program integrates soft skills with the technical skills so one should feel well prepared for finding an entry level job at graduation.Initially, the program will guide you every step of the way through the learning materials. As things progress, your tutors will emphasize that you need to start investigating the documentation to find the answers. I believe this mirrors the real world, where your employers will expect you to research things before you ask for help.Overall, a great program that has given me skills and knowledge. I am very grateful for the opportunity to learn from practicum.

I started the data science (DS) Bootcamp with Practicum by Yandex during the first week of October 2021. I choose to attend a DS Bootcamp because I want to prepare myself well for a career in DS. The cohort and community manager, Ana, is always very responsive and supportive of any questions during the learning process. The instructors and tutors are very knowledgeable, and they actively answer any questions that I have within 24 hours and usually within only a few hours. The interaction on the Slack channels is very smooth, responsive, and engaging. If some tough questions are hard to explain by typing in Slack, tutors will happily hold 1-1 Zoom sessions with any questions. I found the personalized support very motivating and beneficial.
The DS Bootcamp has 17 modules/sprints on essential data science topics: basic python, data preprocessing, exploratory data analysis (EDA), statistical data analysis, integrated project 1, data collection and storage (SQL), introduction to machine learning, supervised learning, machine learning in business, integrated project 2, linear algebra, numerical methods, time series, machine learning for texts, computer vision, unsupervised learning, and the final project. Each module/sprint has chapters to study and interactive exercises and lessons. At the end of each module/sprint, an assigned sprint project requires applying the knowledge and skills in that module and some previous modules to solve problems. It is a cumulative learning process. Modules and projects in later modules require the skillful application of skills learned from previous modules. That also means that as one progresses, the project feels harder. So if you start off feeling, "Wow, these are too easy for me!" Then wait for a while, you will realize, "Wow, these are not that easy for me!" I like to be challenged and grow, so the progression of the complexity of the modules/sprints feels excellent!
It is a flexible learning process. There are deadlines for each module and project. A certain level or amount of extension is possible if one falls behind. If you want to finish faster, that is possible too. As long as you can complete the modules' lessons and projects and pass the project code reviews, you can continue and move forward. Speaking of project reviews, the reviewers are sharp at picking out mistakes. The perk is that they also offer more efficient code or approach to solve problems if my code is too bulky or output looks too "ugly." The community manager will coordinate the process and facilitate your needs, whether you are going fast or slow.
Now, 16 weeks into the program, I am at module 11. I spend a few hours learning and flexing my DS muscles each day. I am feeling stronger. This journey with many like-minded friends, peers, and data scientists is super enjoyable. I like the journey more and more. My heartfelt appreciation goes to everyone I come across on this journey. Thank you.