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TechieGen Career Guide
How To Become A Data Scientist
TechieGen’s Data Scientist career guide is intended to help you take the first steps toward a lucrative career in data science. The guide provides an in-depth overview of the data skills you should learn, the best data training options, career paths in data science, how to become a Data Scientist, and more.
15 Minute Read
Table Of Contents
What Is Data Science?
Data science is an interdisciplinary field focused on extracting meaningful information from large sets of data. To discover hidden patterns, Data Scientists use math, science, algorithms, and systems to identify opportunities for increased efficiency, productivity, and profitability.
In simpler terms, data science uses math and technology to find hidden patterns (and ways to be more productive and profitable) in raw data. To find those patterns, a Data Scientist spends a lot of time collecting, cleaning, modeling, and examining data, from numerous angles, some of which have not been looked at before.
Essentially, data science is about knowledge creation: it makes use of the most state-of-the-art techniques and tools the fields of computer science and statistics have to offer to turn a mess of data into knowledge that an organization can use to inform their business practices.
Among the most noteworthy techniques a Data Scientist uses are predictive causal analytics, prescriptive analytics, and machine learning. The first, predictive causal analytics, uses data to predict the likelihood of different possible outcomes of a future event. Prescriptive analytics goes a step further, suggesting a range of different actions based on those possibilities, with an eye toward optimizing outcomes.
Machine learning, unlike the two techniques just mentioned, is not the “what” but the “how” of data science: it’s the practice of using data-based algorithms that improve automatically based on past experiences – essentially learning to do their job better – to discover patterns and make predictions.
That said, in the real world, the practice of data science involves much more than simply using computers to crunch numbers. In fact, Data Scientists may be heavily involved in the decision-making process across departments, which means that, practically speaking, data science also involves collaborating with others, and especially knowing how to communicate important findings to other people.
What Does A Data Scientist Do?
The common perception that Data Scientists crunch numbers is not too far off the mark; they do work with large sets of data, deciding what data is needed, cleaning the data, building models of what the data can show, and organizing it to reveal latent information—and this effort is always directed toward some kind of goal.
every industry has its own types of data, and its own ways to leverage that data to help meet desired outcomes. In every case, though, data science serves as a way to help leadership make better, more informed decisions—whether that’s improving a product, understanding a new market, retaining customers, effectively deploying a labor force, or making better hires.
Data Scientists, therefore, use a combination of techniques and concepts, including:
Descriptive Analytics
Studies large sets of data to understand the way things are, including correlations and even causations that aren’t immediately obvious.
Predictive Causal Analytics
Draws inferences from data using a variety of statistical techniques—including data mining, predictive modeling, and machine learning—to predict the possibilities of a future event.
Prescriptive Analytics
Provides intelligence-based recommendations to produce a desired outcome or accelerate the results of a given application or business process.
Machine Learning
To put it simply, machine learning – or the process of a computer learning how to better perform a task as it gains more experience doing so – uses algorithms to make predictions and find patterns. Machine learning spans a wide array of ideas, tools, and techniques used by Data Scientists and other professionals, and it’s one of the most popular methods for processing big amounts of raw data.
It might be easiest to view machine learning as a part of data science. Machine learning frees Data Scientists from the tedious task of sifting through massive volumes of data by using complex algorithms and problem-solving methods including supervised and unsupervised learning, regression, classification, clustering, and neural networks.
Skills To Learn To Become A Data Scientist
1. Probability & Statistics
Data Science is about using capital processes, algorithms, or systems to extract knowledge, insights, and make informed decisions from data. In that case, making inferences, estimating, or predicting form an important part of Data Science.
Probability with the help of statistical methods helps make estimates for further analysis. Statistics is mostly dependent on the theory of probability. Putting it simply, both are intertwined.
What can you do with Probability and Statistics for Data Science?
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Explore and understand more about the data
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Identify the underlying relationships or dependencies that may exist between two variables
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Predict future trend or forecast a drift based on the previous data trends
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Determine patterns or motive of the data
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Uncover anomalies in data
Especially for data-driven companies where stakeholders depend on data for decision making and design/evaluation of data models, probability and statistics are integral to Data Science.
2. Programming, Packages and Softwares
Of course! Data Science essentially is about programming. Programming Skills for Data Science brings together all the fundamental skills needed to transform raw data into actionable insights. While there is no specific rule about the selection of programming language, Python and R are the most favored ones.
In no particular order, here’s a list of programming languages and some packages for Data Science to choose from:
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Python
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R
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SQL
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Java
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Julia
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Scala
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MATLAB
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TensorFlow (great for Data Science in Python)
3. Data Visualization
What can you do with Data Visualization for Data Science?
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Plot data for powerful insights (of course! 😀)
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Determine relationships between unknown variables
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Visualize areas that need attention or improvement
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Identify factors that influence customer behavior
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Understand which products to place where
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Display trends from news, connections, websites, social media
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Visualize volume of information
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Client reporting, employee performance, quarter sales mapping
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Devise marketing strategy targeted to user segments
Some of the popular Data Visualization tools include: Tableau
Building A Data Science Portfolio
An online portfolio is key for anyone working in the world of data science — because it’s the best way to show employers evidence of your skillset, be it your Python prowess or your knack for data modeling.
But knowing where to start can be tricky, and you don’t want your data science portfolio to just be… a data dump. Budding Data Scientists should be aiming for the opposite: A curated, well-rounded showcase of your best work that’s capable of catching an employer’s eye.
With that in mind, here are some tips on how to build a data science portfolio.
1. Don’t Include All Work in Your Portfolio
The first thing on your agenda needs to be conducting an inventory of all the data science work you’ve done to date. And it’s worth thinking outside the box — consider everything from an eye-catching data visualization produced for a big-name client, to a thesis project where you showed off some powerful Python coding skills. Figure out which projects make the cut for your data science portfolio. You want a few pieces that best showcase your range of skills and the whole data science process, from starting with a basic data set, to defining a problem, doing a cleanup, to building a model, and ultimately finding a solution.
2. Do Showcase Your Communication Skills
For data science jobs, employers will want to see your number-crunching and coding abilities, but that’s not the only thing they’re looking for. In a data science portfolio, you can show off your communication skills by coupling portfolio samples with an accompanying narrative, showing the work you did to find a solution to each problem. You could write a whole blog post around a piece of work you’ve done. It’s also worth including a bit about yourself — like your passions and past work experience — as part of the non-data elements of your portfolio.
3. Do Consider GitHub Instead of a Website
You can build a basic online portfolio to showcase your data science work. But why not use a platform where other Data Scientists are already gathering? GitHub — a popular software development platform — is used by millions of Developers and Data Scientists around the world, meaning your work will be hosted in a space frequented by potential future coworkers, mentors, and hiring managers.
Job Opportunities Of A Data Scientist
data science is a very good career with tremendous opportunities for advancement in the future. Already, demand is high, salaries are competitive, and the perks are numerous – which is why Data Scientist has been called "the most promising career" by LinkedIn and the "best job in America" by Glassdoor.
Back in 2009, Google Chief Economist Hal Varian told the McKinsey Quarterly that “the sexy job in the next 10 years will be statisticians.” As odd as it might seem to use the words “sexy” and “statisticians” in the same sentence, he was right. Ten years later, we’re inundated by data—2.5 quintillion bytes of data are generated every day, and much of that data is just waiting to be put to good use. According to researchers from MIT, “companies in the top third of their industry in the use of data-driven decision-making were, on average, five percent more productive and six percent more profitable than their competitors”—a significant margin when it hits the bottom line.
All that to say while things are relative (data science comes with its challenges, after all), Data Scientists can expect to be in a very comfortable position well into the future.
data science jobs are some of the fastest-growing, most in-demand in technology. Since 2012, Data Scientist roles have increased by 650 percent, and this rise shows no sign of stopping. The U.S. Bureau of Labor Statistics predicts that the demand for data science skills will increase by another 27.9 percent by 2026. And, according to a report from McKinsey, that spells a shortage of between 140,000 and 190,000 people with analytical skills—not to mention another 1.5 million managers and analysts who will be required to understand how data analysis drives decision-making.
Data Scientist salaries have also risen with demand; Data Scientists can typically expect to make six figures. Demand also translates into an ability to relocate far more easily—from city to city, and even internationally.
Programming Languages To Learn
One of the biggest challenges in working data science is the number of different languages and applications you’ll need to learn. Unlike some fields of tech, where it has been possible to focus on one or two platforms, the interdisciplinary nature of data science means you’ll need to learn at least a half-dozen languages—and use all of them in combination.
Python
A must-have, but one with a manageable learning curve. Python is the top programming language of choice for many Data Scientists, who appreciate its accessibility, ease of use, and versatility. BrainStation’s 2019 Digital Skills Survey found that Python was the most frequently used tool for Data Scientists overall.
R
Because it’s purpose-built for data analytics, R tends to be quite different from other platforms, giving it a reputation for being more difficult to learn than other analytics software. Even with ample experience using other data science tools, you may find R quite foreign at first. It’s worth the effort, however: it boasts nearly every statistical and data visualization application a Data Scientist might need, including neural networks, non-linear regression, advanced plotting and more.
SQL
Another must-have. Fortunately, SQL is relatively easy to pick up, quite readable, and intuitive. Because its commands are limited to queries, it usually takes only two or three weeks for beginners, and far less for experienced programmers. Once you have an understanding of SQL, you’ll be able to update, query, edit, manipulate, and extract information from structured sets of data, especially large databases.
Java
Although easier to learn than its forerunner, C++, Java is still a bit more challenging than Python, thanks to its lengthy syntax. Some experts suggest that it takes nearly a month to learn the basic concepts of Java, and another week or two to begin applying those ideas in a practical way. Java is a good tool for weaving data science production code directly into an existing database; the popular statistical analysis utility Hadoop runs on the Java Virtual Machine.
Scala
User-friendly and flexible, Scala is the ideal programming language when dealing with great volumes of data. Applications written on Scala can run anywhere that Java runs, making it useful for complex algorithms or large-scale machine learning. Scala does feature a steeper learning curve than some other programming languages, typically taking several weeks to get a handle on, but its massive user base is a testament to its usefulness.
Julia
A much newer programming language than the others on this list, Julia has quickly made an impression thanks to its lightning-fast performance, simplicity, and readability, especially for numerical analysis and computational science. That’s not to say you can learn it overnight; while it’s relatively easy to jump into and begin experimenting right away, expect it to take a few months to master Julia. But once you have, it’s a great tool for solving complex mathematical operations—one reason it’s a fixture in the financial industry.
MATLAB
A popular statistical analysis tool, this numerical computing language is useful for high-level mathematical needs like Fourier transforms, signal processing, image processing, and matrix algebra, contributing to its widespread use in academia and industry. If you have a strong mathematical background, you might learn MATLAB in as little as two weeks.
While you won’t likely use all these programs every day, you’ll want to at least be familiar with each of them and their capabilities.
Because of the often technical requirements for Data Science jobs, it can be more challenging to learn than other fields in technology. Getting a firm handle on such a wide variety of languages and applications does present a rather steep learning curve. Of course, this is one of the reasons for the current global shortage of data science professionals—and why they’re in such high demand.
Data Science Resume
Consider the data science skills and projects that are most relevant for the particular position you are applying for. Focus on showcasing these in your resume. Select projects that demonstrate your technical data skills, as well as how you helped solve a problem. Create a list with the specific skills, tools, and programming languages used for each project.
After you complete your planning, you can move on to drafting your data scientist resume. As you begin writing, there are a few best practices to keep in mind.
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Be concise: Data science resumes should be roughly two pages long. Employers may be reviewing hundreds of applications, so only include your most relevant data science skills and experiences. Professional resume formats and resume templates are great resources to help keep your important information below the page count.
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Use bullet lists: Bullet points keep your resume organized, easy to read and draw attention to key terms and attributes.
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Use action verbs: Choose simple, purposeful action verbs that highlight your accomplishments and explain your contributions to a team or project. Examples of action verbs include: constructed, solved, accelerated, reduced, and launched.
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Use numbers and key metrics instead of generic adjectives: Avoid adjectives like “strong” or “experienced.” These words lack specificity and substance. Instead, use concrete metrics and specific examples to showcase your achievements. Quantify your accomplishments so employers can clearly see the value you can bring to their team.
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Write specific, powerful accomplishment statements: These statements describe what you have achieved in your career. A general outline for data science accomplishments statements are: Action verb + task + result. For example, “Developed new forecasting models which increased company efficiency by 50 percent.”
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Don’t bury the lede: Emphasize your most important and relevant experiences at the top of each section or heading.
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Highlight past projects: Include relevant work and data science projects on your resume that display your skills and make you stand out. Project work is particularly useful if you do not have many years of experience.
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Simplify jargon: WWhile you should include relevant technical keywords, avoid overloading your resume with jargon. Some Hiring Managers may not have a technical background, so make sure they can still understand your accomplishments.
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Edit and proofread: Do a careful spell check and grammar check. Show employers that you are thorough and detail-oriented. A second pair of eyes is also useful, so ask a friend or peer to review your resume.
The purpose of a data science resume is to provide an overview of your experiences, skills, and accomplishments as a Data Scientist. The resume is your introduction and pitch to an employer. Resumes tell the story of your career in a brief and organized format. They highlight your relevant accomplishments and show the value you can bring as a Data Scientist. Ultimately, the resume can help you move forward in the job application process and secure an interview. In the interview stage, resumes also act as reference documents for the hiring team.
Data science resumes should include technical skills that are relevant to the position you are applying for. A good strategy is to first list all your data science skills, including any software and tools. Next, review the job description and highlight the skills that are required in the role. In your resume, list skills that match those in the description. You can also add a few additional skills that you think are related or relevant, or that will help you stand out. Some of the most important skills for Data Scientists include:
Technical Data Skills
Data analysis, Data wrangling, Data modeling, Statistics, Data visualization, Programming, Quantitative analysis, Machine learning, Machine learning models, Data mining, Debugging, Hypothesis testing, A/B tests, Regression
Data Tools and Languages
R, Python, C, C++, C#, HTML, Java, JavaScript, PHP, SAS, SQL, Scala, MATLAB, SQL, Server, NoSQL, Hadoop, OpenRefine, TensorFlow, Cloudera, Tableau, Microsoft Excel, Octave, Spark, PowerBI, Plotly, Bokeh, Matplotlib, Seaborn, Keras, Pytorch, AWS, Hive.
For entry-level data science jobs, it is particularly important to first do some planning and preparation before you begin writing a data science resume. This goes for Senior Data Scientists as well to ensure you include up-to-date examples that highlight recent projects.
Here are a few steps to follow before you start writing your resume and some sample resumes to help land that dream data science job.
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Research the company, the role, and relevant data skills
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Reference resume templates and samples to build a resume outline
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Add relevant education experience, work experience and data projects to the correct section of your resume
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Highlight experience with machine learning and data tools
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Craft concise bullet points using the action verb + task + result format for each experience, emphasizing data-driven successes
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Have a trusted peer proofread your Data Scientist resume for grammar and spelling to make sure your experience is professionally presented
As a Data Scientist, you are expected to work with big data and data sets, identify relevant data, and then make informed decisions and recommendations to solve business problems.
A Hiring Manager’s business problem is finding the right Data Scientist to fill an open position. If you approach writing a resume with the same approach as you would while analysing data, you will put yourself in a great position to create a stand out data science resume and cover letter.
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