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Big Data has taken the world by storm, and it seems to only be gaining momentum as the next big thing. There is a huge demand in India (and the rest of the world) for people with expertise in data science, an interdisciplinary field that combines statistics, computer science, data visualization, and other disciplines to extract insights from data sets too large or complex to manage through traditional methods like linear regression and other related methods. Data science has become one of the most sought-after skills by employers in India. If you want to pursue a career or how to get into data science and how to become a data scientist, it’s not enough to just know your programming languages and algorithms; you need to also learn how to manage large amounts of data and develop valuable insights from it.
1) Must Have Courses
Computer Science, Statistics, Mathematics, and Economics. Especially computer science and statistics courses. These will give you both mathematical rigor and hands-on experience with real data sets. If possible, take some data science and machine learning courses as well (in my opinion there is no better way than doing things for yourself). In general, take anything that seems even remotely relevant to your interests. The best predictor of whether you’ll like being a data scientist is having done something closely related before. And we all have areas where we enjoy working outside our domain knowledge – if only we did more of it!
2) Must-Do Projects
Once you have your degree, get a job, and learn as much as you can about data science — now it’s time to kick off your career. Build up work experience by taking freelance or contract work with startups and smaller companies, where you’ll be able to help out with their projects on an ongoing basis. You can also start small by developing side projects of your own, like web apps or mobile apps that help solve problems related to data science. Don’t let any opportunity go by without putting yourself out there! And remember, for any project or job – always ask for feedback at all stages of your work; it will only help you get better and make sure that what you are doing is relevant. Ask people you know for referrals, too: if they know someone who might need someone with your skillset, put them in touch! Expand Your Network.
3) Get exposure through MOOCs
Many major tech firms host MOOCs (massive open online courses) on platforms like Entri, but you don’t need one of those degrees to get your foot in the door. Find a course that teaches tools you can use at work, like R or Python, and take it. You’ll learn while building professional skills and contacts (and some new ones).
4) Learn from Mentors and Seniors
Identify people who are currently doing what you want to do or have done it previously. Meet them, make friends with them and ask them questions. Most will be flattered that you have chosen them as mentors and excited about helping you along your journey. Asking an expert several question is better than asking just one question because they will be able to see if there are any holes in your knowledge base (or anything else that they may deem problematic) and can help fill those gaps so that you can then move forward with confidence. Also, by learning from experts, it saves time as they’ve already taken many of the lessons learned on their journey. In this way, you can also build a network for future employment opportunities. Get to know data scientists: The more data scientists you get to know and interact with on various platforms like LinkedIn, blogs etc., the more avenues of learning will open up for you.
5) Learn from Peers
Look for online groups, meetups and community events where you can discuss data science with professionals. There are many such venues to tap into; you can also try searching your city’s name along with data science or big data. LinkedIn is another valuable resource that can be used for networking as well as starting conversations with industry leaders through messages and connections. Those connections can even help get you an interview at one of their organizations—which brings us to our next point… – Practice reading code: Understanding how the technology works will not only make you more employable but also allow you to work better with other programmers on data science projects. One way to do this is by reading blog posts and tutorials that show how someone else solved a problem (or attempted to solve it).
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6) Attend Workshops, Conferences, and Meetups
To become a data scientist, there is no better way than attending workshops, conferences and meetups. The reason is simple. You will get to learn many techniques and best practices from leading professionals in their fields. Also, you will be able to meet like-minded people who share your interests and can become lifelong friends as well as mentors. These events also help you get your name out there on social media platforms by making connections with other participants or uploading valuable content on your own accounts. Attend these gatherings regularly and be an active participant if you want to make it big in the field of data science. In fact, one of the most important things that experts recommend doing to make it in this industry is networking with people so that they can be useful sources for information and advice. So do not miss any opportunity that presents itself to attend a conference or workshop. Moreover, this should ideally be done as often as possible so that you are up-to-date with all the latest developments happening in this ever changing field.
7) Build a Community Around you
Creating strong professional relationships can help you figure out what skills and qualities you need for success. For example, if you talk to enough senior data scientists, you’ll hear about how important it is to have advanced knowledge of SQL, database architecture and analytical tools such as R or Python. On LinkedIn, it’s smart to join groups related to your career interests (in professional communities) so that you can ask questions about their work experiences. If there aren’t any groups related directly to your field of interest, don’t be afraid to create one! It may take time at first but once it’s established there are many more people who will respond with advice and possible connections. You can also build community around you by joining meetups or conferences related to the field, giving talks on topics you’re passionate about, hosting webinars and asking others to present.
8) Follow the Best on Social Media
The best way to learn how to become a data scientist is by reading other professionals’ social media accounts. You should follow and take note of their techniques as well as their thought processes. Make sure you read and watch every day because that way you will be able to stay updated with all of the new methods being taught, such as R programming tutorials or SAS online training. Pay close attention when they write posts, blogs, tweets or even Facebook updates; these are some of your best learning tools. Here are ten ways you can make yourself into a data scientist -Develop technical skills: Learn about important languages like Python, SQL, Java and R. These are crucial for any data science career.
-Build personal skill sets: There are many personality traits that might help people succeed in this profession including creativity, problem solving skills, interpersonal skills and self-confidence.
-Network: It’s important to know who the leading experts are in this field so make sure you’re always connected on LinkedIn and Twitter with them as well as checking out their sites for knowledge related to the subject matter.
9) Use Tools like Python and R.
Having at least one programming language under your belt is important. Python and R are among some of the most commonly used languages, and they’re good ones to start with. You don’t have to become an expert—just knowing these basics will put you light-years ahead of most people who want to break into data science—but it is important that you not only understand what each language does but also how they work together. That said, there are loads of other languages out there, like Ruby or Scala, and more are being developed every day.
10) Be Passionate about what you do
It goes without saying that passion will help you keep going when times get tough. A data scientist must be curious, inquisitive and constantly striving to learn more. This is one of my top tips for aspiring data scientists, don’t become complacent with what you know. Always try and push yourself with regard to knowledge and skill sets. Only by doing so can you progress as an individual and further your career in data science. Just remember that no matter how good you are or think you are – there is always someone out there who knows more than you! I think it’s very important when embarking on a career as a data scientist that your passion is matched by your curiosity for learning new things & increasing your knowledge about areas which interest you most. Below are some other key points to consider when trying to make the transition from where you are now, to becoming a data scientist:
– Research different companies and determine if they have any positions available
– Check whether they offer internships or opportunities for people just starting their careers
– Speak with friends/colleagues who work at companies that employ data scientists – find out the pros/cons of their current roles and speak with them about ways in which their skills could be applied within the industry
– If possible take relevant courses at the university level.
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