Career Change To Data Science

How Long Does It Take To Become A Data Scientist

You can learn the skills needed to become a Data Scientist in as little as 12 weeks, which is why it has become increasingly common for neophyte Data Scientists to attend data science bootcamps, which allow for more hands-on learning and targeted skills development.

The general consensus, however, is that given the complexity and seniority of the role, it may take years of experience before you can become a good Data Scientist.

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Understand And Exercise The Job Role Of A Data Scientist

To be able to become a successful data scientist, you need to have a concise and clear knowledge of the differences between the profile of a data analyst and a data scientist. As a Data Scientist, you will have to bring a completely novel approach and perspective to understanding data. A data analyst might only be responsible for describing the trends and correlations while as a data scientist you will have to uncover new business questions and build machine learning models to answer those questions based on data.

Data Scientists are

• Involved in building ETL pipelines
• Data Mining using Application Programming Interfaces
• Responsible for data cleaning using data science programming languages like Python or R.
• Build predictive models using machine learning algorithms such as gradient boosting, linear regression, logistic regression, decision trees, Random Forest, and more.
• Evaluate models to validate the accuracy of analyses.
• Test and improve the accuracy of already built ML models.
• Build visualizations to narrate the story of an advanced analysis result.
• Build various automation tools and techniques like libraries to ease the day-to-day tasks for the data science team.

Core Technical Skills You Need To Transition Into Data Science

There are certain key skills you need to learn in order to succeed in data science. While these skills can vary depending on your role and your project, there are certain skills that are usually applicable across domains.

Ive taken this handy table drawn up in this article to explore the various technical skills you should be aware of:

ProgrammingBig Data/Data EngineeringBusiness IntelligenceMachine LearningAdvanced Machine Learning Domain Knowledge

Again, just want to reiterate that you wont need to know ALL of these. It will depend on your role. But certain points like domain knowledge and programming will translate to most data science roles.

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Is Data Science A Growing Field

Yes, the data science field is one of the fastest-growing in technology, with more than 2.7 million new jobs in data forecast to be created.

This growth also looks set to continue when you factor in the increased importance of data skills. According to the 2020 Digital Skills Survey, 89 percent of professionals believe that improved data skills will improve success at their organization, and 78 percent believe that AI is the technology that will have the greatest impact in the coming years.

89% of professionals believe that better data skills will improve success at their organization.

Why Did You Change

I kept having the feeling that I wanted to contribute more through my career, and I couldnt see a path forward at this company.

I also didnt want to be my boss. Not that I could be my boss, since it was a privately owned company .

I longed to actually make a change, and not just report the facts.

I didnt want to stay in the same engineering role, but I loved problem solving. I thought I could have more of an impact, and a more senior role, if I got my PhD. I wanted to be at the table making decisions, not doing the grunt work to report facts.

Additionally, the pay at this job was pretty low. As an engineer, I was making 30% less than other engineers in different cities. This firm was located in a trendy place where everyone wants to live. So, the firm took advantage of this fact and actually paid less than in other big cities. But, the cost of living was just as high. It was not ideal.

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Develop The Right Data Skills

If you do not have any work experience in data, you can still become a Data Scientist, but you will have to develop the right background to work toward a data science career.

Data Scientist is a high-level position before you reach that degree of specialization, youll want to develop a broad base of knowledge in an associated field. That could be mathematics, engineering, statistics, data analysis, programming, or IT some Data Scientists have even started out in finance and baseball scouting.

Data Scientist Related Skills

• Information technology

But whatever field you begin with, it should include the fundamentals: Python, SQL, and Excel. These skills will be essential to working with and organizing raw data. It doesnt hurt to be familiar with Tableau as well, a tool youll use often to create visualizations.

Keep an eye out for opportunities to help you start thinking like a Data Scientist the more this background lets you work with data, the more it will help you with the next step.

What Is Data Science

Data science is a much broader scientific discipline, of which data analytics is a single aspect. Data scientists generally work with large, unstructured datasets. While a data analyst tends to focus on drawing conclusions from existing data, a data scientist tends to focus on how to collect that data, and even which data to collect in the first place. They need a far deeper level of insight into data than is required of a data analyst.

If this feels a bit vague, you can think of data science as being like the construction industry. Its purpose is to create data structures that can be used for specific purposes. Just as it takes many different skills to plan, design, and construct a brand new building, it takes many skills to plan, design, and construct these data structures.

Broadly, we can divide data science into the following categories, each with specific skill sets and tools associated with it:

As you can see, data science is really an umbrella term for a wide range of different disciplines. Another increasingly popular domain is data engineeringyou can read about . So, if youre thinking about a move from data analytics, consider which aspect of data science most interests you. This will help as you formulate a career plan. One things for certainwhichever path you choose, youll have plenty to get your teeth into!

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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.

Speak To Data Science Professionals

There are many data science professionals in the workplace and asking them questions about the field can help you learn some tips and skills that you can use. For example, a data scientist who has 10 years of experience in the field might tell you how the field has changed in the last decade, where the field might grow and the programming languages you can learn to get an edge over other potential candidates. Talking to professionals can also help you understand what hiring managers are searching for in candidates and what positions there may be in specific industries.

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Dabble With Machine Learning Algorithms

Using existing tools is one thing. However, data scientists often have to create solutions from scratch. Machine learning algorithms are a common example, and are often used in data science. Dabble with algorithms like or random forest to get a feel for how they work. Read around the topic and youll learn which ML algorithms work best for different data types, and which tasks they can be used to solve.

Data Science Career Prospects Are High Because Its A New Industry With Many Career Opportunities

Data science jobs are still new to the industry and majority of them did not exist five years ago. The modern big data industry is different to older professions such as Law, Accounting, Actuarial Science or Finance. These professions have established standards, institutions and strict career processes.

The big data industry on the other hand, is constantly evolving based on the needs of businesses and their data centric project requirements. The new projects and divisions require multi-skilled individuals from diverse industry backgrounds who have been trained with latest big data technology.

In just the last two years, the big data industry has rapidly adopted new technologies and innovative techniques. This means new data science graduates are equipped with the latest skills and practical experience to help businesses utilise their data. Many businesses are making teams redundant due to automation while on the other side, employing large teams of data professionals to build new smart technologies. This trend is increasing year-on-year as businesses are learning how to utilise their data for their operations, products and strategy.

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There Is A Clear Career Path And Career Track On How To Change Careers To A Data Scientist

The first step to start a career in big data is training to be a data scientist. The Institute of Data has a range of programs specifically designed to help accelerate your learning and job placement in the big data industry.

To fast track your data science career path and excel within the industry you will require:

• Education & Certification you need to become educated in data science and analytics processes through an industry recognised course
• Industry Networking you need to meet and build industry contacts
• Work Experience you need to gain at least 2 years of practical industry experience working as a
• Data Scientist Job once trained, certified, and experienced, you will possess the successful makings of a Data Scientist and will be earning a six-figure salary . You will have a sought-after skill set making you a valuable asset to any business you work for.

I Already Have Experience In It Why Should I Upskill To Data Science And Analytics

Lets take a quick survey do you want to:

• Become more valuable to your company and aid in resolving current business problems?
• Contribute to making your company more successful?
• Create greater career prospects and opportunities for employment?
• Double your current salary?

If you answered yes to the above, then its clear why you should upskill to data science and analytics if you already have an IT background.

Its easy to see why theres such a demand for skilled data scientists worldwide given the benefits they deliver to organisations. Back in 2012 the Harvard Business Review dubbed data science to be The sexiest job of the 21st century. Their claim still holds true today, with the industry experiencing exponential growth since.

Back in 2018 on Indeeds best jobs in Australia, data scientists ranked at number six with 378% growth over the past 3 years and an average base salary of $113,223. Upskilling to data science may be the best investment you ever make.

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Advice From Springboard Experts On Career Transition

No one is going to push you to reach your data science destination. Be proactive in finding lucrative data science opportunities and volunteer yourself whenever a data science task comes up within your team or there are similar open data science job opportunities in your organization. Acquiring and learning all the data science skills might take longer than expected because even the best data scientists in the world still have lots to learn. Do not worry about having limitations with your skillset as you can just better with each day. All you need is a determination to reach a particular skill level.

The career transition from data analyst to a data scientist should be accompanied with a well-crafted transition plan. We suggest you do complete research on what the data scientist job role entails and then do a self-assessment of your existing analytic skills. Identify the experiences and skills gap that you need to fill while making the transition. Having identified the skills gap, brand yourself as a data scientist through the roles and responsibilities taken on by a data scientist with the goal of transforming into a successful enterprise data scientist.

Since youre hereThinking about a career in data science? Enroll in our Data Science Bootcamp, and well get you hired in 6 months. If youre just getting started, take a peek at our foundational Data Science Course, and dont forget to peep our student reviews. The datas on our side.

About Sakshi Gupta

How To Make A Career Transition From Data Analyst To Data Scientist

The career transition from data analyst to data scientist is not a linear progression where you begin your career as a data analyst and work your way up to a data scientist. You need to follow a series of practical steps and resources to climb up the success ladder as a data scientist. Here is the 3-step approach to making a successful career transition from data analyst to data scientist

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Are You Happy With The Change

Yes, I’m very happy in my role as a data scientist.

I get to work on new and challenging problems constantly. I also get to be surrounded by smart PhDs who also love tackling problems.

Because this role interfaces with so many different groups, I kind of feel like a consultant within the organisation. Its really fun to put my thinking hat on and solve problems for different groups.

With my coaching, I love learning about each client’s aspirations and helping them craft their winning applications. I strive to demystify the process so more people are able to navigate graduate school successfully. It truly is a wonderful experience and I wouldn’t be able to do this had I not made my career change.

I am far happier having an impact now, rather than waiting twenty years to possibly have an impact in academia.

Fields That Transition Well To Data Science

It may go without saying, but well say it anyway: careers related to data are often a good way to transition to data science. Other fields that transition well include:

• Business and finance
• Software engineer
• New computer science, math or physics graduates

It helps data science career changers to have some background and skills in creative problem solving, coding/programming and statistics and probability.

That doesnt mean, of course, that you shouldnt pursue a data science career if you come from a completely different background. Check out books, videos and tutorials. Reading as much as you can about data science, taking online courses and tackling data-related projects can certainly add to your experience and help make for an easier transition to a data science career. There are also prerequisite courses designed to help prepare students for a data science degree program.

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If I Were To Be Absolutely Honest Fear Was A Big Factor

The career path in communications was seemingly more straightforward, but the relentless march of technology had unsettled me.

I had always thought as a writer, Id not be affected. Even as all the talk aboutAI replacing jobs in Singapore was ongoing, there were always comforting examples of machinesepically failing at producing even passable writing.

Then I read about the rise ofrobot writers and Natural Language Processing, which has made vast improvements to and Siri. Given how thoroughly the media industry has already been disrupted by technology, picking up digital skills suddenly sounded crucial.

One thing Ive learnt is when change looks impossible, it will come drastically and abruptly when it eventually happens.

Case in point: For years, people had trouble getting a taxi at certain times and in certain areas of Singapore. Countless tweaks were made to the taxi system to no avail. Then ride-hailing apps stormed onto the scene and hailing a taxi became quaint overnight.

Like most salarymen, I was faced with two choices.

Option 1: Deepen and ride on my existing credentials for as long as possible and hope disruption doesnt hit me, or

Option 2: bite the bullet and upskill.

If I were just a decade older and a tad closer to retirement, say, 45 Option 1 might have been a tempting choice. But I chose the latter as I was still only 34 and had my whole working life ahead of me.

Proven Steps For Career Transition From Data Analyst To Data Scientist

In this article

• Advice from Springboard Experts on Career Transition

How to make a career transition from Data Analyst to Data Scientist ? This is one of the most common questions our admission counselors come across. Our career counselors come across several talented data analysts who are keen to make a career transition to become a data scientist but are not sure where and how to begin.

I am a data analyst but keen on further advancing my career as a data scientist. I am bored of interrogating data to produce reports and recommendations, it does not seem exciting to me anymore. I am inclined to know more about a data scientist career path where I can use my skills to build robust machine learning models to solve business problems at scale. I am very excited about becoming a data scientist. How can I make the career transition from Data Analyst to Data Scientist?.

Are you thinking the same? Have you reached a crossroad in your professional life where you feel the need to upgrade your analytical skills or take up an exciting job role like that of a data scientist? Everybody has such moments in life. While a few get the right guidance and opportunity to accomplish it, others fail miserably. If you are one of those aspiring professionals looking to make a career transition from data analyst to a data scientist, this blog will be the best companion for you along your transition career path.

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