Switch Career To Data Science


Do You Collectively Decide What Data Science Problems To Work On Or How Do You Decide That

How to switch career to data science from non computer science background

As I mentioned, the data science problems we focus on are very close to society so we focus on few business domains which we call – HEARTS

H – Healthcare

T – Transportation

S – Smart city

These are the primary focus areas. At the same time horizontally we cut across with respect to the technology with natural language processing , computer vision, speech technology to some extent IoT and robotics as well which we like to call embedded intelligence where we try to embed the machine learning or deep algorithm itself in the hardware side as well. Then we have collaborated with multiple governments including the Government of India, State Governments of Telangana, and West Bengal. We are also working with multiple academia such as IIIT Hyderabad, IIT Kharagpur, ISB Hyderabad.

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When you started off there was really nothing defined called data science yet it was analytics broadly. What has changed from then to now when it is suddenly cool data science is very much in demand? How have data science job roles changed?

So the biggest transformation in your experience has been this scale-up of processing power, hardware, data science, and machine learning libraries and interfaces and APIs?

Access Solved End-to-EndData Science and Machine Learning Projects

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Going Freelance As A Data Scientist

Although most people who study data science are looking for full-time employment with an established company or startup, its worth remembering that data science skills afford you the opportunity to work as a freelancer.

Its not uncommon that companies have data science work, but not enough of it to justify hiring a full-time data scientist. Its also not uncommon for companies with a new interest in data science to hire a data science consultant and work through a few freelance projects before committing to permanent data science hires. And of course, even companies with established data science teams may need extra help from time to time. These are all potential clients for a freelance data scientist or data science consultant.

Advantages of Freelancing

You can make more money. Depending on the client and the project, a data scientist with a full suite of skills (like someone whos gone through most of our Data Scientist path can charge rates of $100 to $200 per hour or even more. Often, youll be able to make more while working fewer hours per week than you might as a salaried employee.

You decide what you do. Early in your freelance career, you may not have a lot of choice in what projects you take. But once youve established yourself as a reliable and skilled freelancer, youre likely to find you have the freedom to pick and choose the projects or companies you work with.

Downsides of Freelancing

Tips for Data Science Freelancing

How To Switch Your Career To Data Science

Undeniably, Data science has become one of the hottest industries over the past few years from now. Being dominant in almost every sector, data science is powering up businesses and helping them in making business decisions and thats what makes it special and demand is rising like a storm in the market for such professionals. In fact, people with no such background have also taken their way toward data science and by going through different processes many have made a career transition.

Data Science is the study of data using tools and technologies to build predictive models and derive meaningful information. Career transition helps you in getting a Handsome Salary and alongside expanding your knowledge in various sectors. This is something called A Good Call. Now, the question arises, if youre already working in some domain then How to switch your career in Data Science?and to make your way smooth and provide you in-depth details, we have drafted this article that will guide you through all the way so that you can start your new path towards data science.

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I Switched Careers At 34 And Became A Data Analyst Heres How

DISCLAIMER: This article is sponsored by SkillsFuture Singapore, the government agency leading the SkillsFuture movement. This writer took data courses funded by SkillsFuture before committing to a more expensive course down the line.

About a year ago, I packed up my belongings, returned the company key card, and said my farewells to colleagues. I had spent seven years as a journalist. Two in corporate communications. My next destination was something quite different a data science bootcamp.

I had resigned from jobs before, but this was the first time I was quitting without another position already lined up.

Many would question the need for such a radical change. After all, why leave a stable career to pursue something that I had absolutely no experience in?

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What Is The Structure Of The Data Science Learning Path

Data Science can be understood as the incorporation of different parental disciplines like software engineering, data analytics, data engineering, predictive analytics, machine learning and so on. The learning path should include all of these and a lot more to ensure that you emerge as a skilled Data Scientist. Below is a list to briefly summarize the structure of the Learning Path.

Are You Stagnant In Your Current Role

Ah the good old professional growth ceiling. Most of us in our professional lives have felt at some point that we are at a crossroads in our careers. Weve taken a certain job as far as we could and theres no real learning or growth possible anymore.

That is a classic story of stagnancy hitting your career. And then you scamper around looking for new jobs that will fulfill the immense potential you have.

This is as good a reason as any to transition to data science. It is THE field to get into right now and if you can put in the disciplined effort to make the transition, youll find it a very fulfilling career move. Stagnancy is not something people complain about in the data science space!

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Answer By Ayush Biyani

I see two other answers where they advise you to switch to analytics if you like it. I would like to point out a few realities to you so that you know what they all assumed you have. I assume you have some experience under your belt in finance.

  • Analytics job is not your other routine job where people have a feeling I can manage it somehow . You are either in it fully or not there. There is no middle way out. so dont flatter yourself by saying Mai manage kar lunga
  • Analytics requires respect and knowledge both for the following entities: Maths in general, statistics, number crunching, programming
  • Analytics is supposed to make somebody take some decision that brings some profits or cuts some loss so it requires a lot of brutal business focus. Trust me this is not as easy as it sounds. I have been seeing it for 7 yrs now.
  • Analytics tasks require structured thinking. I have seen that this is where most of the people who are new to its struggle and then give up. Ask yourself can I think structurally boring manner for the rest of life. Is it me? Only if the answer is yes should you consider shifting.
  • Since you are new you have to swallow your Area head ego in most of the scenarios and be willing to learn and at times unlearn the way things work. Ready for it? ready to work with a fresher better suited at these tasks without an MBA and 5 years younger than you.
  • This one is most important- Do you really consider yourself a keen and a fast learner? Yes? Sure?
  • Learn Some New Programming Languages

    3 ways you can switch to a data science career from non technical background

    Most data analysts get by with a solid understanding of Python. Data scientists usually add the programming language R to their arsenal, too. Check out someintroductory tutorials for R, or advance your Python skills by building applications in your spare time. Whatever you do, challenge yourselfyoull learn best by experimenting and making mistakes. Aim to upskill in other technical areas as well, for instance by playing around with distributed computing or statistical tools.

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    What Is A Data Analyst

    A data analyst

    • collects data from various sources
    • organizes it effective to identify underlying relationships
    • transforms it into a form for easy analysis
    • performs statistical analysis,
    • and visualizes it using various graphs, charts, and other visualizations to derive meaningful insights helpful for making profitable business decisions.

    Aspect#: Bridge The Knowledge Gap By Acquiring The Missing Data Science Skills

    For the remaining 1/3rd part of the equation, you need to

    • Learn about backend Data management and database architecture and design.
    • Get involved in Data ETL methods to build continuous data pipelines.
    • Analytic SQL such as SQL for aggregation, analysis, and modeling
    • Big Data, Hadoop
    • Learn programming in R and Python
    • Data Science concepts such as Data Manipulation, Data Visualization, Statistical Analysis, and Machine Learning .
    • ML techniques: K-NN, SVM, Naive Bayes and Clustering.
    • Computer science concepts like performance complexity and implications of computer architecture like I/O and memory tuning.
    • Mathematical and Statistical concepts Algebra, Calculus, Probability, Statistics, Regression
    • Business level end-to-end know-how

    A shift from C programming to Python helped Shweta develop insights and interest towards datasets that inspired her to indulge in ML and DS courses. She further did a Masters in AI and at present works on ML and NLP. Shweta says for non-technical professionals, domain knowledge is an added advantage, as DS is a multidisciplinary field.

    Get To Know Other Data Science Students

    Brandon Beidel

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    Do You Have Experience In Data

    We regularly encounter talented business intelligence professionals looking to land their first data science role. They are often frustrated by the perceived lack of opportunities for them. A lot of them feel that their role is repetitive, or they just need to perform whatever has been asked of them.

    They actually miss the fact that they are closer to data science opportunities than any other professional out there!

    Why transition into data science in easier for a Business Intelligence professional:

    Business Intelligence professionals hold a massive advantage over almost anyone trying to transition into data science because of the following reasons:

    • BI professionals already have access to data scientists in various projects
    • Existing know-how of how to manage and handle data
    • BI professionals have the business context and they work closely with businesses
    • They have experience with basic data exploration steps as very often business asks for these in addition to the reports they use.

    In other words, these folks work in the first half of a data science project. Thats already more industry experience than most aspiring data scientists!

    Develop Your Math And Model Building Skills

    How to switch your career into Data science

    As a data analyst, you will be extracting, munging, and visualizing data to aid business decisions. Though mathematical logic is involved in the analysis part, its not heavy math. Data analysts are usually inclined towards the minimal requirement of mathematics while data science requires a strong mathematical foundation.

    As a data scientist, you will have to write algorithms from scratch requiring an in-depth understanding of linear algebra and calculus so advanced math is an absolute necessity to understand how a machine learning algorithm works and behaves. Having a strong mathematical base helps understand the nature of the machine learning model and how it can be tweaked to improve its accuracy. Even if you are using the predefined libraries, it is essential to understand the calculations that are being performed behind the scenes before you can actually apply them to the actual business problem.

    Apart from making maths your friend, you will need to enhance your model building skills by working with your peers and other data scientists to solve challenging business problems. This will help you explore your model building skills and evaluate them.

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    What Is The Pay Scale Of Data Scientists Across The World

    With the growing demand for data scientists but a scarcity of talent, companies across the world are paying high salaries to skilled professionals. A Data Science certification further increases your earning potential. Here are the data scientists average annual salaries across some top countries :

  • India – Rs.7,08,012
  • London – £50,585
  • Why Do You Need To Perform Profile Building Activities Like Blogging Speaking At Meetups And Participating In Data Science Competitions

    Let us say that you are interested in cricket, you learn and practice cricket daily but how will you grow yourself? It wont happen by practicing in nets daily! You must be recognized and get noticed for your talent by participating in a competition and getting in touch with potential trainers. Similarly, you must be recognized by potential recruiters and enthusiasts to grow yourself. Let us see how:

  • Participate in competitions Data Science competitions are a sure shot way to improve your performance as a data scientist. Although it may take you a while to get adjusted, it will help you in the long run. You can go on the DataHack platform and pick a problem statement of your choice and get started. Recruiters love the candidates who have built their knowledge through practical applications.
  • Start writing articles If you have a knack for data science and a passion for writing then what is a better way to express yourself than writing articles? Article writing helps you learn all the hard technical concepts and turn them into easy-to-grasp topics. Article writing is another great way to help you catch the eyes of potential recruiters.
  • Speak at meetups Data science is a growing field and we have an ever-growing community, Many important data scientists, researchers, thought leaders attend them. These platforms are a great platform to grow yourself as a data science professional.
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    How To Become A Data Engineer

    With the right set of skills and knowledge, you can launch or advance a rewarding career in data engineering. Many data engineers have a bachelorâs degree in computer science or a related field. By earning a degree, you can build a foundation of knowledge youâll need in this quickly-evolving field. Consider a masterâs degree for the opportunity to advance your career and unlock potentially higher-paying positions.

    Besides earning a degree, there are several other steps you can take to set yourself up for success.

    professional certificate

    Information Engineering, Google Cloud, Bigquery, Tensorflow, Cloud Computing, Google Cloud Platform

    Now Do You Want To See What You’re Actually Capable Of

    How to switch your career to Data Science.

    I recently created a FREE Python + Data Science + Machine Learning mini-course for professionals just like you.

    It only takes around 3 hours to get through – but I want you to start seeing for yourself that you can do this.

    I want you to see that this isnt some unachievable goal and instead see that it is right there for you if you want it.

    I want you to start a lifelong journey in one of the most exciting fields that there is, one that will future-proof your career.

    I want you to expand what you thought you were capable of – and most importantly I want you to have some fun along the way, and to do some things that youll be excited to tell you friends or family about!

    Over a couple of hours, you will…

    • Learn about the Python programming language
    • Install Python and investigate the basics of coding in it
    • Use a Python package called Pandas to explore and analyse a dataset – one of the fundamental day-in day-out tasks that a Data Scientist or Analyst does

    Then…you will move onto the first exciting project.

    You will write around 7 or 8 lines of code which will loop through some clever logic over and over again until it finds all of thePrime numbers that exist under 1 million. And, this is so cool – because it will do this in somewhere around half a second!

    This first project will give you a glimpse into some more of the interesting and amazing logic that you can use within Python.

    From there youll start your journey into Machine Learning – where you will:

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    Take A Structured Course

    While its great to explore different tools and skills, its a good idea to cement what youve learned through a structured data science course. While theres no substitute for working on real projects, theres no harm in getting an online qualification, either. Itll look good on your resumé and will show any potential employers that youre serious about moving into the field.

    Nlp Or Natural Language Processing

    NLP is regarded as the hottest field of the industry. Businesses trip over each other to get themselves the best NLP talent. Hence, there was never a better time to engage with NLP. There is a Natural Language Processing frameworks that you will be introduced to in this section. From BERT to RoBERTa , you will learn to work with some of the state-of-the-art frameworks.

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