Become a Data Scientist in 2024 | Data Science Roadmap 2024

2023 has been a pivotal year for AI and yes it is difficult to keep track of all the developments make sense out of them and still pursue your dream to become a data scientist in fact we have got a lot of queries about what should be the road map to become a data scientist in 2024 which tools to learn what skills to focus on hence for all of you who want to make it big in 2024 here is our road map to become a data scien first we'll give you a framework which is basically this equation look to become a data scientist you need essentially three things right set of tools diverse set of techniques and a skill to design impactful Solutions now these skills have a multiplicative effect for example if You Learn Python which is a tool you can very well try uh techniques like exploratory data analysis or Eda and uh in this learning road map to become a data scientist in 2024 we are going to give you you a stepbystep framework on what tools and techniques you must learn along with how to build the highly sorta design skill one major change in this year's road map is that it is squeezed into 9 months last year's road map was uh 12 months those of you who know uh but there is much more added to what you need to learn this year given the rapid advancements happening around Ai and you have to cover that ground fast hence this 9mth road map is prepared assuming you shall study for a minimum 15 to 20 hours a week on this note let's begin in the first 3 months your goal would be to learn the foundational data analytics skills like programming basic statistics Eda software engineering skills and finally the basics of cloud computing with the knowledge of these skills you can start applying for data analyst roles right after the first 3 months now here's what you need to learn during the course of the 3 months first first of all begin with a tool that is a programming language like python or R python of course is a great choice it has a wide range of applications it is very easy to learn and extremely versatile thereafter you also should learn a domain specific language uh which is SQL SQL is used for task like quering a database managing manipulating data stored in relational databases as well next in line is uh software engineering skills here focus on uh tools like git and GitHub these are web-based platforms that allow you to store and manage code online in addition you need to learn Linux commands as it will help you in navigating processing and managing data efficiently next one on our list is cloud computing here learn any one of the following platforms be it AWS gcp or Azure choose a platform that aligns with your future goals and uh get an idea of it uh get on with the basic functions like how to set up a machine running uh Jupiter notebooks how to optimize for Storage security on the platform Etc next up focus on techniques like basic statistics that are useful for machine learning this includes descriptive statistics probability hypothesis testing and regression analysis the next technique to focus on is Eda Eda or exploratory data analysis is the process of visually and statistically summarizing interpreting and understanding the main character istic of a data set it includes techniques like univariate bivariate analysis so you must learn how to perform Eda with the help of python uh for that you must know the relevant python libraries like pandas mclip cbone and Etc and you know what with AI tools like CH GPT and its code interpreter Eda has become so much easier now all you need is to provide your data set to chat GPT and start asking questions like check for missing values how to impute those missing values with with mean or median and lastly you may also check for outliers then comes design over here strengthen your problem formulation skills for uh this question and Define how a problem may be answered all throughout this uh quarter 1 focus on strengthening your analytical skills by practicing problems around logical reasoning data interpretation and basic mathematics additionally focus on enhancing your PPT skills during this first quarter all right by the end of quarter 1 you will have a solid foundation for machine learning and as I said at the beginning at this point you may start applying for data analyst roles yes you will need to create a resume a cover letter and a LinkedIn account now and with the help of chat GPD you may do that in a span of minutes another good exercise during quarter 1 is to keep up with the developments in the generative AI space so that's another thing you should watch out for in the second quarter you should focus on essential mathematics for machine learning and further Advanced ml topics like uh deep learning covering both NLP as well as computer vision thereafter end to-end projects covering model deployment as well and uh at the end of this second quarter you may start participating in data science competitions that are organized on kaggle as well as data hack platforms along with applying for entry-level data science jobs as well now let's double click on what you need to learn in the second quarter we have focused on the tools in the last quarter here we'll focus on the techniques the first technique is essential mathematics that is needed for machine learning this includes understanding of Concepts like linear algebra gradient descent followed by learning various supervised and unsupervised machine learning algorithms and finally various model evaluation Matrix like accuracy precision and Recall now to learn how to solve real world problems with machine learning you must practice tons of machine learning projects and for that you may pick up from these projects that we have sorted for you find links to these projects in the description part below with a handful of projects under your belt you may now move on to more advanced machine learning techniques like Ensemble learning followed by fundamentals of deep learning like basics of neural networks popular deep learning Frameworks and transfer learning once you cover all of these you will get deeper into deep learning focus on natural language processing and computer vision followed by end to endend projects under these particular domains all right then comes mlops so over here essentially what you are learning are the tools and what you decide to do with them are the techniques such as model deployment in our first quarter you have already covered Cloud platforms next in Cloud platform Comes mlops This is basically taking ml models to production scale for mlops you may focus on these particular tools make your yourself familiar with the containerization and the app building Frameworks like steam lid and gradio you have covered the mlops platforms like asor AWS or gcp now focus on necessary techniques for managing end to end machine learning Project Life Cycle which is build train deploy and maintain finally you have learned all the skills you need next you must do end to-end projects along with documenting them on GitHub this way you are solving real world problems just like a data scientist would do in quarter 2 we will focus on communication skills to improve your communication skills practice storytelling by writing blogs or making YouTube videos for that matter additionally focus on structured thinking you may improve this by practicing guest teates reading case studies and uh practicing mind mapping as well you must also learn additional uh techniques like model implementation skill which includes AB testing this is a methodology for uh comparing two versions of of a model against each other to determine which one performs better and finally monitoring of these models uh is also something that is to be covered by the end of quarter 2 you shall have an endtoend understanding of building basic as well as advanced machine learning models and deploying them as well now you are eligible to apply for entry-level data science jobs all right now we have come to the final quarter of this learning road map and uh there's one and only one objective over here to land a full-time job in the domain of data science here's all what you need to learn within this third quarter if you are a working professional who is upskilling to become a data scientist you may want to build some domain expertise in your current area of work let's say you are a data analyst at a insurance company with the tools and techniques you have uh learned in the past 6 months you may now want to uh use them to develop algorithms that are necessary for solving problems within the insurance business this is a key technique to implement whatever you have learned so far and uh if you are a fresher who is just starting out in that case you may do exploration by trying out a diverse range of data science problems available on uh platforms like kaggle or data hack in terms of learning you may focus on Advanced topics around AI like generative models that entails large language models and different Fusion models trust me guys knowledge of these will make you industry ready and by the way guys at this point you may want to choose a track either natural language processing AKA NLP or computer vision if you choose the generative model for NLP track here's what you need to learn for getting started with llms uh you may want to understand what llms are different types of large language models that are available also learn about Foundation models then comes prompt engineering with within this you should Master prompt engineering and its various techniques and tricks then comes rag which is retrieval augmented generation under this learn how to build rag applications using llama index or Lang chain thereafter comes fine-tuning large language models at this level you should be able to fine-tune llms on domain specific data sets using PFT which is parameter efficient fine-tuning if you choose the generating model for computer vision drag here's what you need to learn first of all to get started with the stable diffusion models focus on fundamentals like what is diffusion model different types of diffusion models also learn about stable diffusion models then comes prompt engineering under this master different prompting techniques for getting optimal results from a text to image model like mid Journey or di 3 uh thereafter comes fine-tuning OFA stable diffusion model within this you should be able to fine tune a stable diffusion model on a domain specific data set again using PFT thereafter personalizing in stable diffusion models here you need to learn controlling stable diffusion models like dream booth and instruct Pi tops in terms of design skill in this quarter focus on design thinking it is a nonlinear iterative process that is used to understand end users challenge assumptions redefine problems and create innovative solutions this process involves uh basically five phases empathize Define ideate prototype and test this is definitely something you must learn as a data scientist after quarter 3 be it a fresher or a working professional you will be able to apply for full-fledged data science roles for this update your resume start preparing for interviews we have a whole bunch of videos that will guide you on how to go about getting your uh dream job at this point additionally using the knowledge of uh natural language processing and computer vision in the Genera VI domain you may also start exploring ideas of building your own AI applications so this is your complete 9month learning path to become a data scientist in 2024 as analytics Vidya we have helped more than 400,000 data science aspirants achieve their dreams through our industry Focus career road maps and if you are looking for a stepbystep guide uh to become a data scientist without leaving your job you can enroll in our black Bel plus program as part of this full stack data science program you get a personalized learning road map cated just for you along with 50 plus handsone industry projects one-on-one mentorship dedicated interview preparation and placement support and yes you may also join us on our analytics with Community platform where you get uh data science and generative AI Community groups tailored to your interest opportunities to learn alongside your peers and above all you get free access to live webinars and AMA sessions from industry experts again so that's all for today for more such informative data science content subscribe to our Channel and hit that Bell icon to get notified whenever our new video is live see you in the next video till then Happy learning and bye

Channel: Analytics Vidhya

Views: 5,517

Keywords: data science roadmap, data science roadmap 2024, become a data scientist, become a data scientist in 2024, become a data scientist in 6 months, become a data analyst in 3 months, data scientist roadmap, how to become a data scientist, how to become a data analyst, ai data scientist, ai data science, generative ai for data science

Id: TjzRS-oyUtY

Length: 13min 19sec (799 seconds)

Published: Tue Dec 12 2023