Mid Career Change To Data Science


Anyway Back To The Main Story

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

Ive talked to a lot of professionals recently – and while many are saying that theyre seeing the rapid growth of Data Science & Machine Learning – they dont know how to start their learning journey, or how to even get a better grasp of what this all actually means in practice.

They know a move in this direction would be hugely beneficial for their careers, because lets face it – the world is quickly becoming completely data-driven. If youre not keeping your eye on these trends then you genuinely run the risk of getting left behind or being overtaken by others who are looking for opportunities to explore these areas, or at least finding ways they can fuse them or add them into their current role.

What Projects Are Included In This Data Science Course In Houston

This Data Science training in Houston features more than 15 real-life, industry-based projects highlighting different domains. These projects help you master concepts of Data Science and Big Data. Here are a few of the projects:

Capstone Project:

Description: Youll go through dedicated mentor classes to generate a high-quality industry project where you solve a real-world problem by leveraging the skills and technologies that you learned throughout the program. The capstone project includes all the key points of data extraction, cleaning, and visualization, and how to build and tune models. You can also choose the domain/industry dataset you want to work on, based on whatever options are available.

After you successfully submit your project, you will earn a capstone certificate, showcasing your expanded learning and skills to potential employers.

Project 1: Products rating prediction for Amazon

Domain: E-commerce

Amazon, one of the leading US-based e-commerce companies, usually recommends products to customers that fall in a similar category that jibes with their activity and reviews. Amazon would like to boost this recommendation engine by increasing its capabilities, letting it predict ratings for non-rated products and adding them accordingly to the customers recommendations.

Project 2: Improving customer experience for Comcast

Domain: Telecom

Project 3: Attrition Analysis for IBM

Domain: Workforce Analytics

Domain: Retail

Domain: HealthCare

Domain: Insurance

Dont Dwell On Questions That Already Have Answers Here Are Three Things I Wish I Knew Before Starting My Career Transition To Data Science

You have reached a point in your career that it does not make sense to continue doing the same thing. Maybe you are bored, dont earn as much as you deserve or, like me, simply never liked your job. Amidst a career turmoil, you came across data science and noticed there is a massive opportunity by switching careers. Also, you have found several coding tutorials on YouTube by Data Scientists.

However, despite many experts online, maybe a few of them have been in a career transition to data science. Probably even fewer did such a change from a completely unrelated field in their late 30s. This suggests that what you have been watching/reading may not apply to your reality. That said, you should watch those videos with a pinch of salt. After all, you do not want to waste your valuable time. So, here are three things I wish someone in a similar career and life stage would have told me before making a career change to data science:

1- Choose Python and move on.

If you have done your homework, then you know there are basically two programming languages optimal for a career in data science: R and Python. Although R is used among statisticians and researchers, and it can be used for Data Science, Python is by far your best choice.

Dont dwell on which programming language you need to learn. This decision can save you valuable time, especially if you are in your 30s. So, choose Python and move on.

2- Dont fall for quick tutorials, prioritise a structured course.

focus on Python.

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

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:

Which Industries Use Data Science The Most

Why mid

Data science is finding applications in almost all the major industrial sectors like healthcare, banking and finance, retail, automotive, marketing, manufacturing, and government agencies as well. So, this Data Science training is beneficial if you are stepping into any of these sectors for your professional career.

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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.

Data Science Program For Professionals

Live virtual sessions by MIT Faculty

Live Weekly virtual sessions with the MIT faculty in Data Science & Machine Learning

Personalized Mentorship and Support

Weekly online mentorship from Data Science experts & a dedicated program manager provided by Great Learning, for academic and non-academic queries

Build your Data Science Portfolio

Work on 2 hands-on projects and a 3-week capstone project using Python

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Additional Life Challenges Can Add Stress

Making a career change later in life can be tough, especially when needing to hit those vital career progression benchmarks. First, theres the need to update your skill set, while juggling existing responsibilities, like a full-time job, or a family. Plus, younger people tend to have more flexibility, such as the ability to relocate for the right job opportunities. And pulling the odd, unplanned late-night is more challenging for those with families.

How to overcome this challenge: The solution is all about balance and planning. Carving out and protecting even small amounts of time can help focus your progress. You should also start open and honest conversations with your employer about your outside responsibilitiesthis will also allow them to support you as best they can. Fortunately, data analytics tends to be a flexible job with remote working becoming the norm, which can help overcome some of these problems.

% Online Masters In Data Science Self

Step By Step Transition Towards Data Science
  • Self-Paced Learning: Many people interested in data science will learn from online companies and bootcamps because of the flexibility, but do not earn degrees. Our self-paced format within each 7-week period allows students to complete their coursework at a pace that works for you, as you make progress toward a masters degree.
  • Expert Faculty Assistance: Eastern provides you with all of the resources needed to complete your education at your pace. But unlike most online resources, Easterns expert faculty will be available to assist with your individual questions and help you along the way.
  • Data Science for All: Our program is a fit for all types of learners. Learn to code from scratch, with any level of experience. If youve never coded before, or if youre an expert, Eastern’s self-paced design is perfect for whatever background you have.
  • Learn Industry-Standard Tools: Master Python, R, SQL, Tableau, and Qlik, the top coding languages necessary for your ideal data science career.

Jordan Miller, MS ’22

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The Career Outlook In Data Science Is Worth A Career Change Or Transition To Analytics

The career opportunities in data science are advancing quickly with IBM predicting a 28% increase in demand for data scientists. The ability to analyse, manage, find insight and predict data are skills that are valuable across all current industries, verticals and new innovative businesses.

The industry needs more data professionals and the talent is likely to come from a multitude of professions and backgrounds. Industries need professionals to upskill in this area because these professionals already have the domain expertise to apply data-driven technology and techniques. This means there is an opportunity to transition into a data science career even if you are not from a big data or analytics background.

Use More Of The Credits Youve Earned To Accelerate Your Career

Move your education forward without repeating courses. At Bellevue University, you can transfer your entire associate degree even your A.A.S. toward a bachelors degree.**

See how your credits transfer.

No repeating coursesreceive credit for all college courses you’ve taken from an accredited institution.**

No transfer fees or application feesavailable for community college students from partner schools

A faster path to graduationcomplete your bachelors in an accelerated cohort program

See how your credits transfer.

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Should A Ux Designer/researcher Become A Data Scientist Chris R Becker

This is an intriguing career transition! Ill be honest I hadnt considered a UX person wanting to transition into data science.

This answer by Chris R. Becker focuses on learning data science tools keeping UX experience in mind. He states some of the tools which UX designers are already using and how those tools could be used for data science purposes. He emphasizes more on working with a data science team to dive deeper into critical data science topics.

So I Chose A New Career That Built Digital Skills Upon My Existing Analogue Abilities

Response: Advice on Making a Mid

You might think Im just jumping on the data science bandwagon as its the hottest thing and is a growing industry. Thats only half true.

While my career thus far may seem far removed from data analytics , there are actually numerous similarities:

A professional storyteller makes complex issues easily understandable. I saw data as an additional way to explain the world: Reporters are using machine learning in new forms of journalism while corporate communications professionals are vying to showcase their companies data abilities.

Data Analyst
Data analysts gather data through multiple sources such as tracking user behaviour and public databases. Journalists gather data through multiple sources such as expert interviews and public records.
Data analysts turn unstructured, unwieldy data that is often incomplete into easily understandable structured data that paint a whole picture. Communications professionals simplify lengthy, inscrutable corporate jargon into a sharp pitch that captures the essence of a subject.
Tell coherent stories with data. Tell coherent stories with information.

Being aware of these was immensely helpful at job interviews when faced with the question of why should I hire someone with no experience?

PS: If youre curious what your career pathway could be, you can check out the Skills Framework for some clarity. The one specifically for people in the ICT sector here.

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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?

Answer By Arun Korupolu

There is no background required for you to become a data scientist in the long-run, its all about your interest and you asking whether you are interested to work with data and envisage yourself in a role where data and decision making are aligned.

I can suggest a high-level learning pathway, but individual learning needs with regards to time and effort might require appropriate tweaking in these steps.

Beginners ideally would need to start learning programming, in case they have no prior experience. You can follow this learning in three steps:

  • Learn Programming and become proficient in that language
  • Gain the knowledge in these subjects Intermediate Statistics & Probability, College Algebra, Linear Algebra, Machine Learning algorithms and methods
  • Work with independent projects. Try to implement your learning in a step by step fashion while solving the objectives of these projects

My suggestion is a high-level overview of what you can do to start, but you will find the best path once you begin learning by doing.

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How To Transition Your Career Into Data Science: Next Steps

Transitioning into data science will not be an easy journey, as it requires persistence, consistency, and patience. However, with good planning and hard work, you will be able to achieve your goal and land your first job in the field. Data science is constantly evolving, so it is important to develop learning habits that will help you advance in your career and gain new skills. Showing your skills and having a perfect resume and portfolio is also a critical step in your transition journey and your career advancement so make sure to dedicate time to enhance and update them regularly. If youre wondering where to start, the 365 Data Science Program is here for you. It offers self-paced courses led by renowned industry experts. Starting from the very basics all the way to advanced specialization, you will learn by doing with a myriad of practical exercises and real-world business cases. If you want to see how the training works, start with a selection of free lessons by signing up below.

Learn data science with industry experts

Youssef Hosni

Computer Vision Researcher / Data Scientist

Switching Career Paths From Accounting To Data Science Or Moving From Finance To Data Science Are Valid Career Options

50+ Successful Transition Stories To Data Science In 2 months- Ft Ineuron Team â¨ð¥ð¥ð¥ð¥ð¥ð¥ð¥ð¥ð¥ð¥

Moving from a career in finance or accounting to a career in data science is a strategic long-term career move that will pay dividends in the future. The skills and processes you are currently utilising and implementing on a daily basis are a great foundation to become a Data Analyst, Data Scientist, Segment Leader, AI Machine Learning Researcher/Practitioner, or a Business Analyst. Employers are interested in professionals with previous career backgrounds who have developed domain expertise and/or the softer skills of the workplace.

So, if you make the switch, you will be adding qualifications and skills to your existing career. You will utilise your previous skills in a data science job. You will have the advantage of becoming an enhanced data science professional with the aptitude to help businesses become data-driven. You will deliver real outcomes to businesses by using your full breadth of skills in accounting/finance and newly acquired data science skills.

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There Is A Disconnect Between What Is Being Taught For Data Science

So to make sure that my experiences were the same as others, I went out and I talked to hundreds of leaders, Hiring Managers, and Recruiters in the field. I asked about:

Skills, tools, techniques, attitudes, & education…

I asked them what it is that differentiates a good Data Scientist a great Data Scientist…

I asked what it is that differentiates a candidate that lands a role versus those who get the rejection letter over and over again.

I combined all of this insight, and created a Data Science programme called DATA SCIENCE INFINITY. But I’m not here to sell you that. I’m writing this article as I want to ensure that you feel empowered. I want you to feel like you have some direction.

Why Be A Data Scientist

Data is everywhere. We use it at work, at home, or when conducting online commerce, and more is generated every day. So, Data Scientists are consequently the highest ranked professionals in any analytics organization. Glassdoor ranks the career of Data Scientist second in the 50 Best Jobs for 2021. Theres a shortage of Data Scientists, so thats why its a great idea to take this data science course in Chicago. An expert Data Scientist understands the requirement and constraints of business problems, collects the right data and makes it usable to design the right analytical strategies, apply the most effective techniques or algorithms to come up with actionable insights for implementation.

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