Career Change Into Data Analytics


Who Are The Instructors And How Are They Selected

Data Analytics Career – Is It Right For You?

All of our highly qualified Data Analytics instructors are Business Intelligence experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain part of our faculty.

Plan Your Learning Roadmap

The other important aspect you need to plan is your learning roadmap, especially if youre moving into a technical field like data science and you are new.

Almost certainly, you need to pick up new skills Python, R, and SQL being the more common programming languages. It is also essential for you to learn other relevant topics such as statistics, machine learning, and deep learning.

There are plenty of online learning platforms Coursera, Udemy, Datacamp, Dataquest, Codecademy, Leetcode etc and you cant possibly do everything. So, you really need to plan and prioritise which courses to invest your time in.

You should also plan for and set aside time to do personal side projects or Kaggle competitions. This is to give your potential recruiters some level of assurance that although youre switching from a different career, youre not totally inexperienced in data science.

It is not my intention to prescribe a roadmap for learning data science in this post, but here are some nice resources you can check out:

Besides finances and learning journey, there may be other factors you should consider. It is not possible for me list them out here and after all, its likely that you would have factors unique to your own circumstances.

The bottom line is, make sure you plan and prepare yourself well.

Planning is bringing the future into the present so that you can do something about it now. Alan Lakein

You Have Better Writing Communication And Leadership Skills

Technical skills, like programming knowledge or math, are vital for data analysts. However, the most underrated data analytics skills are soft skills . Carrying out the technical aspects of the role is important but means little if you cant effectively communicate your findings.

From cover letters and applications to presentations and emails, older data analysts tend to have much better writing, communication, and leadership skills, fine-tuned after years of experience. These are the power skills you can only develop over time and set your application apart from the rest of the pile. Plus, your ability to integrate these skills directly into any hybrid role makes you a greater asset.

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How To Write Your Resume And Cover Letter For A Career Transition

Your cover letter and resume can make or break your chances of securing an interview. Indeed provides some helpful tips in its Career Change Cover Letter Guide. These include:

  • Highlight successes from previous positions. You want to show your future employer how you can add value to the company.
  • Include skills that can transfer to your new position. Remember to include both hard and soft skills.
  • Incorporate research in your cover letter. Play up your strengths and experience. Tailor your cover letter to the company and position.
  • Explain why you want to change careers. Here is your chance to tell your potential new employer why youre looking to change fields or industries.

The Balance Careers offers these additional tips:

  • Illustrate your transferable skills. If possible, use anecdotes and examples from your work to show how your skills would translate to this new position.
  • Express passion for the company. Theres nothing wrong with showing some excitement about a new position and company in your cover letter.

Why Do You Need To Have A Good Understanding Of Machine Learning Techniques

Digitalization and the American workforce

As a data scientist, you must be able to understand the fundamentals of machine learning techniques such as regression, decision trees, ensemble learning models, etc. but you must also be able to explain them well.

Some of the questions asked in interviews go as follows:

  • What is regression analysis?
  • Can you write the equation for a simple linear regression model?
  • Great! Can you show the regression chart in the choice of your language?
  • Let us understand the type of questions. The first question specifically targets your conceptual knowledge, whereas the second question goes deeper and tests your deeper understanding. The third question tests your concepts along with the programming skills.

    Through these 3 questions, the interviewer was able to understand your core knowledge, programming skills, and communication skills.

    Storytelling is one of the most crucial skills of a data scientist and for that, you must have a good hold of your machine learning concepts, communication skills, and structured thinking.

    Heres one of the most popular tutorials to get you started:

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    Skills Required For A Successful Career In Data Science

    Apart from the statistical know-how and programming skills, the most important thing is an individuals love for data. Here is our quick checklist of data science skills –

    • Programming Skills – You need to know a programming language like Python or R.
    • Database Querying Language – SQL

    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!

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    Moving To Data Analytics From A Different Career

    Can you switch things up professionally and move from accounting or risk management to data analytics? The first question you should be asking is, do you really want to? Have you done the research to fully understand what it will be like to work in data analytics? Or are you just interested in the trendy sounding AI and machine learning job titles?

    Why Data Analytics?

    Most data analysts must put in some hard graft before theyre handling the cutting edge technology youre fantasising about. So think very carefully, and take the time to consider what part of a data analysts role interests you, rather than the job title itself. This will help guide you in the right direction as you reroute your career path.

    Understand what a data analyst does: they use technical tools to parse through large quantities of raw information in order to generate meaningful insights. Essentially, anything data-related will fall under their remit, whether its removing corrupted data or preparing reports for the business.

    Good on Paper

    Data analysts require strong critical thinking and communication skills. So, at the very least if you can demonstrate the ability to think analytically about data, look beyond the numbers to the patterns beyond, and clearly and effectively convey your findings, youll be in with a good chance.

    The Portfolio

    Get Technical

    In the Know

    Looking Ahead

    Identifying The Right Companies

    How To Get Started In Data Analytics (Free Short Course)

    If youre exploring a career in data science, its important to know which companies are hiring. Do your research and consult with industry professionals. Reach out to colleagues and connections on LinkedIn. See which companies are making headlines and great strides in the data science industry.

    According to Glassdoor, the following top companies are hiring for data scientist jobs:

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    If Youre Not Ready For A Project

    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:

    What Is The Application Procedure For This Professional Certificate Program In Ai And Machine Learning

    This AI and Machine Learning Course has a three-step application process:

  • All interested applicants must apply online for the AI and Machine Learning Course using the application form.
  • Candidates will be shortlisted by an admissions panel based on their application.
  • The shortlisted candidates will be offered admission, which they must accept by paying the AI and Machine Learning Certification program cost.
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    What Is Data Science And The Spectrum Of Data Science

    Youve read the headlines, surveyed the web to understand what data science is, looked at a few courses and articles, and are feeling confident about your choice.

    Its a great decision!

    But before you dive into the granular details of what you need to cover to make your own data science career transition, you should first spend some time understand what data science actually is. And even more importantly, what is the spectrum of data science, and where you would potentially fit in.

    Im not going to bore you with long lines of definition so heres a short explanation:

    Data Science is an amalgamation of Statistics, Computer Science, and specific domain knowledge.

    Statistics and computer sciences are the generic fundamentals that can be perfected by studying and a little bit of practice. It is the domain knowledge that takes time, research, and effort to gain.

    You dont need to master each vertical but having a decent grip on all will help you in the long run.

    Data Science is quite a big field in itself. It starts with simple data reporting activities to advanced predictive modeling using Artificial Intelligence. As you can observe by looking at the Data science spectrum below, the higher the complexity the higher its business value:

    As you can see here, there is a LOT of value and a lot of roles under the data science umbrella!

    Are You Good At Logical Thinking How Good Are Your Numerical Ability And Problem

    My experience as a Data Scientist

    In the current scenario, getting your first break in data science can be difficult. Around 30% of analytics companies evaluate candidates on their prowess at solving puzzles. It implies that you are logical, creative, and good with numbers.

    The ability to bring a unique perspective into solving business problems can provide you a huge advantage over other candidates. Such abilities can only be developed with regular practice and consistent efforts.

    And trust us having a logical thinking mindset with solid numerical and problem-solving abilities is absolutely key to achieve success in the data science spectrum. This isnt a routine job! Youll be relying on your soft skills a lot more than you might expect.

    Let me give you a quick example to illustrate this. Lets say you are sitting with your team and your clients and youve just been given a business problem. As a data scientist, youll need to perform quick and dirty back of the envelope calculations to see if some things should be analyzed first or can be removed. As long as they are 80% right, go ahead.

    For example, if charge-offs have doubled within a month, it is unlikely to be driven by unemployment . Similarly, if the Credit limit increase only impacts 2% of your portfolio, the charge offs from this program need to increase by 50x, if portfolio risk has doubled, which is unlikely unless you have given free Credit without looking at the population.

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    A Polished Portfolio Is King

    In data analytics, a portfolio does a lot of the heavy lifting in showing off your skills. Its a necessity for all data analysts as it demonstrates your competencies in practice. A traditional resume is still important, of course, since this allows potential employers to see your work experience and skill sets at a glance. So make sure its up to date, too.

    If youre new to data analytics, creating a portfolio of work might seem like jumping the gun, but you can include pet projectsthey dont have to be client-facing. To get started, we recommend looking at some examples of the best data analytics portfolios on the web and checking out our guide to building a data analytics portfolio from scratch.

    First What Are The Different Roles In Data Science

    Before you actually understand what tools or technical skills you need to transition into data science, you should be aware of which role you want to apply for. Thats right contrary to popular belief, data scientist isnt the only role in this field!

    One of the things we have observed about these opportunities is the indistinguishable description of job roles. Even though the majority of recruiters use the right description for various data science job roles, the candidate might not be able to make that differentiation. Therefore, this confusion between the job role and job description might lead the aspirant to apply for the wrong jobs and missing out on appropriate opportunities.

    Even in such a flourishing industry, there is confusion with respect to job roles. A loose understanding of job roles may cost data science transitioners their dream job. And this is precisely the driving force behind writing this section.

    So, here are 8 data science roles that are widely acknowledged in the industry:

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    Get Your First Data Analytics Job In 4 Steps

    By General Assembly

    Making a career change can be scary, especially if self-doubt of Im not good enough starts creeping in. However, there is no point in staying in a job or career that no longer brings you joy or fulfills you professionally.

    If youre reconsidering your career, youre not alone over the last two years, over 50% of employed Americans have considered a total career revamp. Chances are, you know a relative or friend who is going through a similar career dilemma right now.

    If youre considering making a bold move to data analytics, weve got you covered. Understand if a career in Data Analytics is right for you in four easy steps.

    What Is The Potential Time Required To Become A Hands

    How I’d Learn Data Analytics in 2023 (If I Had to Start Over)

    If youre looking for a specific timeline thats not quite how this works. The answer is that the time varies from person to person. Weve already understood that your previous work experience might play a part in your data science transition journey, right?

    Well, that also plays a part in deciding how long it might take you to become a data scientist.

    A lot of organizations have conducted polls around this and each poll has a different answer! One poll conducted last year by a respected organization concluded that it takes 5 years for a beginner to transition into data science. Another poll by a different pollster concluded it takes 3 years.

    You can see why this varies from person to person right?

    • Your previous work experience
    • How much time you can dedicate to studying the various aspects of data science
    • How good you are with logical thinking and number crunching
    • If youve learned to program before

    These are just the core questions thatll help you come to a potential timeline. We at Analytics Vidhya put together a comprehensive roadmap at the start of every year, called a learning path, that takes you on a step-by-step journey to become a data scientist. Thats a one-year plan and assumes you can dedicate a significant amount of time to each topic .

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    How To Find Data Scientist Certification In Chicago

    There are many Data Science training in Chicago. You will also find various offline and online certification courses. According to the amount of time you invest in learning, you can choose whether you want part-time, full-time, or self-paced learning certification courses in Chicago. Many of the universities, as well as trusted ATOs, provide Data Science training in Chicago. You can create a list of all such training institutes, and compare these on the basis of average customer reviews, feedback, and other insights.

    How To Learn Data Science Skills

    Theres no sugar-coating it: The process from data analytics to data science is gradual and often imprecise. This can be challenging but also be rewarding, as it means you can carve your own career path. The first step is to take charge of your personal development. Pursuing your interests will help you build the foundational skills you need, while allowing you to decide which areas of data science most interest you. While the transition wont happen overnight, the good news is that you can start right away.

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