Transition To Data Science Career

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If Youre Not Ready For A Project

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

If you feel like youre not ready to start your own project, here are some tangible next steps that you can use to guide your learnings:

  • Start with statistics. I think statistics is so important because most machine learning concepts and data science applications revolve around statistics. And if you dread statistics, data science probably isnt for you. Id check out Georgia Techs course called Statistical Methods, or Khan Academys video series.
  • Learn Python and SQL. If youre more of an R kind of guy, go for it. Ive personally never worked with R so I have no opinion on it. The better you are at Python and SQL, the easier your life will be when it comes to data collection, manipulation, and implementation. I would also be familiar with Python libraries like Pandas, NumPy, and Scikit-learn. I also recommend that you learn about binary trees, as it serves as the basis for many advanced machine learning algorithms like XGBoost.
  • Learn linear algebra fundamentals. Linear algebra becomes extremely important when you work with anything related to matrices. This is common in recommendation systems and deep learning applications. If these sound like things that youll want to learn about in the future, dont skip this step.
  • Learn data manipulation. This makes up at least 50% of a data scientists job. More specifically, learn more about feature engineering, exploratory data analysis, and data preparation.
  • Lastly, here are a couple of resources that may help you get started:

    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?

    Organization And Time Management Skills

    Changing careers is a project. It requires a strategic plan, a timeline, and specific milestones. Ask yourself these questions:

    • Why do I want to be a data scientist and what subjects am I passionate about?
    • Will I quit my job and take the time to learn the skills I need or will I make the transition in parallel with my current work?
    • What am I good at? What are my weaknesses?
    • How much time and money am I willing to spend on changing careers?
    • What are the new skills and qualifications that I need to acquire for the new career path?
    • What is my learning style?

    Once you answer these truthfully, you need a plan. In the end, you have to find what works for you. Here are some things you can do:

    • Print a weekly plan and stick it on your wall.
    • Organize your daily schedule on a Google calendar.
    • Identify what works and what needs to be readjusted in your plan. Maybe you underestimated how much time you needed for a course or the learning resource you chose is not quite as you imagined.
    • Dont be afraid to change your strategy. Go to online forums and search for a better resource. Or read some articles on time management and read on how to set SMART goals.
    • Keep checking tasks off your to-do list. This momentum will push you out of that initial frustration period of learning something new.

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    Building Up Technical Know

    Since organizations are dealing with millions of data points, they need technological solutions which can help them apply the algorithms at scale. Here, tools like R, Python etc., become extremely important. The analyst in this case would use tools like R to apply logistic regression to all the customers in the banks database so as to identify potential churners, Sharma fromUpGrad.

    Subramanian believes though there are hundreds of tools and packages that help you master various facets of analytics, you can get started with relatively few tools. A statistical programming language like R or Python and a database querying language like SQL are good enough to start with. One would be surprised at how much analytics you can get done with Excel alone, but the limitation is usually in the formats and characteristics of data you can work with in Excel.

    Do I Really Need To Learn Programming To Break Into Data Science

    5 Points to successfully transition to a Data Science Career

    Heres the thing there is no one size fits all approach to data science. Weve already seen the different roles available in this field earlier. The skillset required for each role varies on the requirement for that role plus the project or organization youre working for.

    I suspect a lot of you reading this will want to become a data scientist . So do you need to learn to program?

    Yes, you absolutely do. We already saw the different steps data scientists work on earlier. Youll need to lean on your programming skills at each stage from gathering the data from different sources all the way to deploying your model. This is a key skill you need to learn and master. There is no getting away from it.

    Now, what if youre more inclined towards a business analyst or a senior manager role in data science and analytics? Programming isnt a must for these roles but youll still need to at least become familiar with drag and drop tools like Tableau or Power BI. These are commonly used to map visualizations and extract useful insights for the business.

    Which brings me to the next question which programming language should you learn for data science?

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    Learn About The Requirements Of The Role

    Start by researching the role to ensure it will be a good fit. Assess your comfort level with developing the more technical skills required to move from business intelligence and reporting to designing machine learning methods, building machine learning models, and creating algorithms. You must be comfortable committing to a lifetime of learning because data scientists reskill to remain current as technology evolves.

    Undergraduate Experience At Vit

    VIT is one of the best places to study B. Tech biotech in India due to its international connections and international outlook. Overall, I would say that VIT provides the students with all the opportunities one can think of for a biotech undergraduate provided that the student is willing to learn and apply that knowledge.

    The curriculum is a bit rigorous, so it is not for the faintest of heart! All the semesters are filled with a lot of exams, quizzes, assignments, and projects.

    The campus is one of the finest in the country with 320+ acres of space filled with lush green open spaces, academic buildings, and on-campus halls of residence a.k.a. hostels. The quality of the food served in the mess is good, but the taste is not that great, so most of the hostellers prefer to eat outside campus twice or more per week. The management is inflexible with respect to discipline maintenance and takes strong action against anyone who does not abide by their laws.

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    Learn Data Science Fundamentals

    A data science course or bootcamp can be an ideal way to acquire or build on data science fundamentals. Expect to learn essentials like how to collect and store data, analyze and model data, and visualize and present data using every tool in the data science toolkit, including specialized applications like visualization programs Tableau and PowerBIamong others.

    Many job postings list advanced degrees as a requirement for Data Science positions. Sometimes, thats non-negotiable, but as demand outstrips supply the proof is increasingly in the pudding. That is, evidence of the requisite skills often outweighs mere credentialism.

    Whats most important to hiring managers is an ability to demonstrate mastery of the subject in some way, and its increasingly understood that this demonstration doesnt have to follow traditional channels.

    What Is A Data Science Course

    Non Tech to Data Scientist Career Transition | Data Science Course Review – Intellipaat

    In a Data Science course, you will learn about many concepts if you are a beginner or an intermediate. This is a training program of around six to twelve months, often taken by industry experts to help candidates build a strong foundation in the field. Besides the theoretical material, our Data Science course includes virtual labs, industry projects, interactive quizzes, and practice tests, giving you an enhanced learning experience.

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    Should You Transition To Data Science

    This might seem an odd question on this page. Arent you already sure you want to transition into data science?

    We want to present a different twist here. Data science, as you must already know, is not everybodys cup of tea. You need to have a certain skillset along with a lot of discipline to learn and carve out a career in this space.

    So here, were going to pen down answers to 5 key questions you should know the answer to BEFORE you take the giant data science-level career leap. These questions will help you understand what skills and experience you should have to ensure your data science career transition is a smooth one.

    Be Prepared To Fight Imposter Syndrome

    One common challenge experienced by many career movers is imposter syndrome. Its never easy having an established career and suddenly becoming a newbie. In this case, its all a matter of mindset: keep yourself motivated and excited about all the new things you are about to learn! Omdena has hosted a webinar on how to overcome imposter syndrome as a data scientist, which includes knowing the skills gap that you need to fill and identifying the skills you already learned from your previous career.

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    Heres How You Can Beef Up On Basic Data Analyst Skills:

  • Learn Hypothesis generation and analysis through plots, graphs, and reasoning
  • Get up to speed with statistical reasoning concepts such as causality and probability theory. To get started, check out this link
  • Data science is also about products, learn how to leverage data to figure out product features, enhancements
  • Data munging is the art of cleaning data. It is a time consuming job and entails dealing with missing data and changing schema. Before diving into large datasets, explore cleaning data
  • While Kaggle is hailed as a stepping stone for honing machine learning and data analysis skills, the competition features curated datasets that are anonymized and cleaned.
  • Understanding the business domain one wishes to work in. The data analysis problems are solved according to the business needs, hence domain understanding is a must
  • Data science is a long-learning process. Switching to data engineering and learning statistics on your own can be one learning path towards a deeper learning experience
  • Develop Your Math And Model Building Skills

    How does one transition careers from software engineer to data ...

    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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    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.
  • Why Data Science Career Transition Is A Positive Move

    Data is playing a pivotal role in formulating the best marketing strategies for businesses. It has a role to play in every aspect of life. Hence even companies have realized the significance of data analytics. This has led to a surge in demand for data science experts who can leverage their skill sets and comprehend the information available to the organization, thereby formulating strategies that can help their business grow by meeting the demands of the consumers.

    Find the growing proliferation and significance of data, and there is a global shortage in talent supply. We Are creating more data than ever, accumulating all the social media platforms and website information. Today we generate around 2.52.5 quintillion bytes of data every day. This has created a golden opportunity for every individual who wishes to make a career in data science.

    Enterprises are flooded with information, which is present in a haphazard manner. Big data, data analytics, internet of things are some key trends. The work of a data science professional is to filter out this data and extract useful information which can help a company formulate its strategies.

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    Tip : Find Your Own Data Science Project

    Find a project youre passionate about, whether it be a problem youd like to solve or a library youd like to learn turn this into a project that youll put onto your github as a portfolio piece.

    Finding a problem is best done through conversations. Engage with your community, your friends or Even strangers. Find out what bothers them, or talk to them about ideas youve always had.

    Hash out your idea, make it simple. Your project isnt going to change the world. The most important part here is to start on one.

    Once you find your idea youd like to build, tell a friend or make an open commitment to your community that youll be building it. Most importantly, highlight the features of your app and the time itll take for you to have it done .

    I relied on my commitment to my peer group to build this cannabis recommendation app that generates revenue today.

    Read about how I built it on Medium here: How I built it

    If youre looking for a project, I come across companies looking for pro-bono work all the time. Connect with me on and Ill find the best project for your goals!

    Did Your Finance Background Help Or Hurt You As You Applied For Jobs

    Career Transition Series – Episode 2 | Data Science Career Transition | Edureka Reviews

    With smaller companies, I felt my finance expertise came into the picture. Smaller companies dont have the kind of resources to train someone who doesnt have any experience in that field. For example, I applied to healthcare and automotive companies, which arent finance sectors, but they were close. And there were similarities between the two where the finance expertise was beneficial. But for the larger companies, like Facebook, theyre looking for data scientists and people with advanced skills.

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    How Difficult Is Data Science

    No two data science jobs are the same. Each one calls for specific competencies and comes with its own set of responsibilities. So, how hard is data science? For some, studying data science may be challenging at first. Data science is a multidisciplinary field that combines technical know-how with soft skills . Those joining the workforce may find that there is a fairly high barrier to entry, too.

    Across industries, the profession has unique complexities and comprises a great deal of problem-solving. And on top of that, the field is constantly evolving. But there are multiple opportunities to learn along the way, and with ample preparation, a career in data science is achievable. Data scientists help create practical solutions that advance an organizations goalsand they learn how to do that by obtaining a robust knowledge base and skill set. In addition, to a professional degree, data scientists can choose to pursue a variety of data science certifications and other professional development resources to grow their knowledge, while bolstering their credentials.

    Being intellectually curious, paying close attention to detail, acting as a proactive problem solver and being open to learning new skills on the job are a few personal traits that may help these professionals to be effective in the workplace, master core competencies and keep up with new developments in the field over the course of their careers.

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