How Did You Choose Your New Career
Ive always loved problem solving, and wrestling with new challenges. And I’m passionate about reducing pollution and preserving our resources.
Therefore, I started researching roles where people had leadership roles centered around sustainability and environmental resource management. I soon discovered that all the roles I liked required a PhD.
So, I decided to pursue my PhD in environmental engineering. I figured I could become a professor, or work in government research once I graduated.
About half-way through my PhD, I realised academia would not fit my lifestyle. I had a husband, and also a brand new baby. My husband had a career, and he did not want to bounce around the different academic institutions while I pursued tenure.
Therefore, I changed tack. As a graduate student I’d started learning different programming languages, and creating machine learning models to solve my research questions. I loved this blend of science and technical coding. I also began hearing about the hot field of ‘data science’, which totally blew me away. I had no idea this was a career option! And a highly paid one!
I decided this was the perfect way for me to use my analytical nature, and solve real-world problems for large organisations. I was all in.
After learning so much about the graduate study application and funding process, I also wanted to use this knowledge to help others achieve their goals, which led me to setting up my coaching services.
Gain The Soft Skills You Need To Transition Your Career Into Data Science
The nature of data science jobs requires them to not only be technically thoughtful but also possess the necessary soft skills to help them excel in their roles. Soft skills help data scientists to effectively communicate with the different teams they deal with on a daily basis, critically analyze situations, make decisions, and communicate their results and findings in a better way. Here are some of the most important soft skills you should work on in your transition journey:
- Communication skills
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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Python Vs R Vs Other Languages Which One Should You Learn
Raise your hands if youve ever asked this question or have answered it before. Im fairly certain all of you will have come across this eternal dilemma about choosing the perfect programming language to start your data science career.
Unfortunately, there is no so-called perfect language for data science. Each language has its own unique features and capabilities that make it work for certain data science professionals.
And the choice isnt limited to Python, R, and SAS! We are living in the midst of a golden period in programming languages as well see in this article.
Some languages may be suitable for fast prototyping while others may be good at the enterprise level. So lets clear the confusion once and for all and see which is the best language that suits your data science career goals.
Here are the most popular programming languages being used in the data science industry right now:
To understand what each language brings to the table and see their comparison, I highly recommend checking out these two excellent articles :
Tip : Networknetwork And Network
Your chances of scoring a job as a data scientist improves exponentially based on your network size.
In-person networking is the best way to expand your network in a meaningful way, however, its not always possible to make it out to networking events.
The second best scenario is LinkedIn.
Creating meaningful connections on LinkedIn is as simple as finding people in your industry, sending them a message and keeping up to date with their happenings.
The crucial piece people miss about LinkedIn opportunities is that they dont let others know they are open to opportunities.
I found great success by adding: Open to new opportunities on my LinkedIn title.
Further, Medium supports a great network of Data Scientists that, Im sure would be happy to connect. Read a cool article? Find the author on LinkedIn and chat with them about prospective opportunities!
I work as a Data Science consultant and come across many opportunities . Let me know who you are on and Id be happy to connect you to a company that is interested!
Find me here:
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I Switched Careers At 34 And Became A Data Analyst Heres How
This article is written by The Woke Salaryman, and 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 resignedfrom 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?
In Many Ways My Job Search Was Also Eye
On one hand, I experienced the infamous and harrowing whiteboard test where interviewers younger than myself pushed me to my limits .
On the other, I was absolutely taken aback by an encounter with someone in a senior tech role who was oblivious to the most basic of tech and data concepts .
It drove home the point that in todays environment of rapid technological change, no one no matter your seniority or designation is exempt from lifelong learning.
I may have achieved a small measure of success in my career switch so far, but if I rest on my laurels, I can easily be made obsolete by those who are hungrier and armed with knowledge I dont possess.
This may seem daunting, but I got my start in data analytics precisely because someone was willing to evaluate me based on whether I had the relevant skills and the capacity to learn, and not based on my seniority or a piece of paper I earned from university years ago.
And in this world where skills are king, anyone who is willing to learn has a fighting chance.
TWS: This is a future where people are paid more for their knowledge, skills and contribution, not how long theyve clung on to the corporate ladder.
And its both a terrifying and exciting time to be alive.
Stay woke, salaryman.
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Not Every Person Who Does Data Science Work Has The Title Of A Data Scientist
On the other side of the coin, I know plenty of data analysts and data engineers that got to work on data science projects, like prediction models, anomaly detection models, and recommendation systems.
And so, there are a couple of implications that you can get out of this:
How To Make A Career Switch To Data Science
Has your career reached a crossroad where youre considering a career switch? Are you looking to transition to Data Science? Tech is so fascinating in the current industry that transitioning to tech from multiple other backgrounds makes new trends!
And Data Science! Oh you got to be kidding, Data Science is the most in-demand career choice and you, me & everyone want to leap into a Data Science career.
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Moving Into Data Science: Lessons For Leaders
- Reverse engineer job listings. Find your dream job, identify the skills you lack to qualify for it, and then learn those things.
- Build on your existing knowledge. Your business domain skills or other tech knowledge can inform your new data science job.
- It may make sense to apply for jobs in smaller companies rather than large ones. The smaller firms are less stringent about job requirements, and you have a better chance of learning a wide range of data science skills.
This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.
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!
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How Experienced Professionals Can Start With Data Science
If youre a working professional holding an experience in different domains, it doesnt matter whether the experience is relevant or not, all it requires is you to have a list of required skills and technologies that you should work on.
1. Mathematics: You must be well versed in all of the sections that have been discussed above.
2. Programming Language: However, you can pick any language but it is recommended to pick Python so that you can get more exposure in the data science field.
3. Tools & Frameworks: Here are some of the popular frameworks and tools you would have already gained insight about Tableau, MS Excel, Power BI, Pycharm, SQL, etc. Here are the Top 10 Python Libraries for Data Science you must definitely have a look at.
4. Additional Curriculum: For best practice, you can enroll in the Data Science Live Course that will help you in enhancing the required skills and will provide the hands-on experience to learn all the required tools and techniques.
To learn in-depth knowledge of Python, you may consider these two courses:
These were the list of tools, frameworks, and languages that are required to work in the data science domain for different positions and hierarchies if youre a beginner. Next is the list of some soft skills that are also necessary to look after during this phase.
Required Soft Skills for Data Science:
- Critical Thinker
- Business-Centric Awareness
Anyway Back To The Main Story
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.
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Hear It From An Analyst Programmer
Now an Analyst Programmer at Ramco, Antoni Harrish shares his journey to inspire one and all. He thinks that if you drive your learning from the basics to the advanced level, then nothing can stop you. Upskilling in the right career path can help you make a successful transition into Data Science. So, are you ready to accelerate your career into Data Science?
We can go on and on with interesting motivating stories like this. What you really want to be looking at is that there is no bar for making any career. You can jump from a non-IT background to an IT background, all you need to do is get started!
While the field of data science remains a strong ground of opportunities equally for experienced professionals as well as beginners, it demands a great deal of hands-on experience with the right tools & technologies. Any interested person with any educational background and an analytical frame of mind can take up the Data Science course.
Another Successful Switch To Data Science
Shubham Nehete, another techie made his grand transition into Data Science with hard work & persistence. Practice is the real key to success in programming. Admitting the same, Shubham mentions that the live sessions and the recorded sessions helped him a lot in building his foundation in Data Science. He also felt that the mock drills assist and give a good glimpse of the interview in advance.
Top companies are hiring Data Analytics Professionals, even without any prior coding knowledge. Yes, when you enter into interview rounds, all that is getting analyzed is your knowledge and skills.
So, anyone and everyone stands a fair chance to enter into the field of Data Science, provided you gear up with the essential skills and know-how. Jumping with joy already?
This one would really make you get started with Data Science.
Panchal was from a non-coding background. He longed to become a Data Scientist and he did it! Though a novice to all the programming languages, he learned them all with constant support from the ZEN team and got placed with a lucrative package in Paripoorna Software Solution Service Pvt. Ltd.
When it comes to transitioning from a non-tech to a tech career, one needs to strive a lot and the journey is not at easy, we admit. But
If a 3 to 5 months effort and hard work can reap you benefits of a lifetime, then why not take a chance and make the big leap at once!!!
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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!