A Newbie’s Guide To A Career In Data Science

Have you been hearing about Data Science all around but wondering what does it take to make a career in Data Science?

You’ve landed at the right place. In this article, we’ll help you understand what do Data Scientist’s do and what skills are required to become a Data Scientist.

Data Science: What It Is and How It Emerged As A Career

Data science provides great salaries and opportunity to work on real world problems and offers exposure to ever-evolving and exciting world of machine learning.

The development of fast, compact computing and the accessibility of the internet have turned millions of logged on users into nationwide study subjects.

Everyday users’ preferences or decisions, whether pair of socks or a bank loan, contributes to a wealth of data that can provide insights. These insights are then converted to profitable gain for businesses in a plethora of industries.

And because it is a relatively emerging profession that is growing exponentially, the demand for scientists is greater than the supply, making the profession a lucrative one with great prospects.

Here we overview what a budding data scientist should understand about the field and the skills required in order to excel.

Types of data science roles

Data science is not a linear job. It is, instead, a broad field where you can specialize in a few areas depending on your capabilities and skill set.

For example, a scientist with a strong background in statistics may be drawn to the statistical methods regarding data, whereas some scientists may prefer to be at the helm, managing projects by overseeing a team of data scientists.

Being a new field, many of the following jobs may be merged into one at many organizations, particularly smaller ones.

However, here we explore the broad categories of data science and the skill sets generally grouped into them.

1. Data Scientists

Data scientists are software engineers with a strong hold on statistics.

Think of them as the cowboys of big data, working on huge disorganized data sets by organizing them into structured collections of information through which they can extract usable insights to help businesses.

They do this by applying their mathematical, statistical and programming expertise to sort through tons of data.

Next, they dig deeper: analyzing the data using the present information on the subject, their circumstantial understanding and a non-bias approach in the hope to gain the more accurate insights to solve business challenges.

Their role can be broken up into three parts:

  • First, they are managers running projects that begin with cleaning data sets and recognizing possible insights. They even build predictive models to extract these insights and use the results to help form solutions.
  • Second, they are mechanics. In order to come up with the best solution, and with their statistical and algorithmic knowledge as their power tools, a data scientist has to constantly tweak and tinker the predictive models that they have applied to the data set.
  • Third, they are intermediaries. They use their know-how in programming and algorithmic thinking to facilitate a holistic approach to data mining.

One instance of this can be, a data scientist who might use data to build a predictive model to estimate the number of employees that will quit in a certain month.

Through data engineering, they can then use the same model to extract a prediction from a sample data set.

2. Data Analysts and Business Analysts

These two roles are closely linked; however, as the names suggest, one leans towards studying the data whereas the other’s role concerns the said data’s impact on business.

If you are a data analyst, you are essentially sorting through datasets in a bid to discover their potential.

A business requires insights from data and data analysts explain to other relevant members of the organization what benefits and insights can be gained based on their analysis of the  data.

A data analyst’s role is not that of a data scientist, per se, but more of an entry-level position.

The role of business analysts is to envision the business benefits that the data can provide by, for example, using it to answer data-related questions .

For instance, “Should more funding go toward marketing or product development?”. By anticipating the possible outcomes based on insights given by the data analysts, business analysts help an organization make the right decision.

3. Data Engineers

Data engineers are the preliminary organizers for large amounts of data, the sous chefs to data scientists.

These software engineers handle database systems, they sift through and organize the data across numerous servers, clean up data sets and may even implement predictive models given to them by data scientists.

Data engineers convert large amounts of haphazard data into handy arrangements that can be analyzed with ease.

In addition to programming languages such as Python & R, data engineers benefit from Hadoop-based technologies such as Hive and Pig, for the processing and storage of large data sets.

Familiarity with database tech like MySQL and Cassandra – which relates to storage, altering, mining, and searching for information in a database – is also advantageous.

As with any field, areas of specialization may vary from one data engineer to another.

Where certain engineers are inclined toward configuring the technology managing data models, another’s role can revolve around data storage and its management.

These are some of the prominent data science roles that you can take after studying data science but the question of whether it is wholly a technical field or a non-technical one still remains.

So let’s check out what it’s all about.

Technical vs Non-technical

Data science may seem like a wholly technical field but because its implementation is with the aim of solving problems and benefit businesses, practical knowledge and awareness of industry and the workings of business is essential to make a good data scientist.

In a nutshell, technical proficiency is necessary, but so is domain knowledge.

Technical aspects:

On the technical side, having strong statistical understanding and being able to garner and present the perceived value from large, unstructured datasets through mining and processing of that data, is most important.

This requires proficiency in math, programming, and statistics.

Other tech-heavy requirements in the field are:

Programming languages: You need to know programming language in order to clean, sort and organize your data. Python & R is basically ubiquitous when it comes to coding in the field of data science. Other examples of programming languages are Weka, SAS etc

In addition to this, a data scientist must be skilled at handling unorganized and unstructured data from various sources (text, video, audio, HTML, numerical, etc).

Data could also be sourced from different areas or departments within an organization.

A data scientist would need to provide valuable insights keeping the big picture in mind.

For example, if a data scientist is working on a project to help an e-commerce website detect fake products, he may need to have a good understanding of social media to conduct a sentiment analysis of user reviews in various social media platforms and the brand that is selling the product itself.

Non-technical aspects

The non-technical aspects required to be a good data scientist can be a bit harder to pinpoint as they are intrinsically linked to knowledge gained through experience and practice.

1. Domain knowledge:

Any good data scientist will need to be well-versed in the nuances of business.

The ability to make sound judgements and take actions to propel your business in the right direction plays an essential role (more so in an area as technically-driven and crucial as this).

The bridge between technology and business has to be strong in order to address and solve problems and expedite growth.

2. Communication:

Insights within the data are known to you but alien to colleagues in other departments. Similarly, other teams may hold insights that will be very helpful in data science.

As a data scientist, the ability to successfully communicate your knowledge, insights and plans to your non-technologically-inclined colleagues is integral when working toward solutions to business challenges.

You can be a technical whiz but there will a ton of non-technical professionals involved in the implementation of processes emerging from your insights.

3. Open mind:

An open mind, which is the result of sheer experience in the field, is also one of the greatest non-technical skills required.

Having an open mind helps you sense patterns that one would otherwise gloss over. Almost intuitive, a good data scientist can almost feel the value within unstructured data sets.

But obviously, this comes with years of practice and trial and error.

Statistics, Mathematics and Calculus – How Much Do I Need To Know Of Each?

This depends on whether you are interested in theory or practicing.

While practitioners can mostly hone their skills through experience, theoreticians require impeccable knowledge in at least one of the three main bases: statistics, mathematics or calculus.

1. Maths

A solid foundation in mathematics is a necessary tool as machine learning concepts are often linked to linear algebra.

Even with the availability of simplified machine learning packages such as Weka and R-caret, sound mathematical knowledge of their functioning is required to gauge the workings of the algorithms in order to extract the best results.

Application-oriented roles may need less in-depth knowledge than research and development areas.

For starters, you can begin with Andrew Ng’s videos on linear algebra in machine learning, Khan Academy’s videos on linear algebra or, if you wish to go more in-depth, MIT’s Open Courseware videos on the subject.

2. Calculus

Calculus plays a large role in machine learning.

Calculating derivatives and gradients for optimization is crucial for many machine learning applications.

For example, gradient descent is used in the training process of several machine learning algorithms to update the parameters of the system in order to reduce error with every system update until it is ready to start making predictions.

For a beginner’s course on calculus, look no further than the wealth of information on Khan Academy.

3. Statistics

Knowledge of statistics and probability is imperative in taking small decisions such as tweaking a predictive model to actually plan out your team’s overall game plan for research and development.

Tons of industries use statistics to better their business prospects and its use in data science and machine learning is precisely for that purpose.

As a good data scientist, you will have to be comfortable with statistics.

Descriptive statistics and probability theory is needed in data analysis in order to make better decisions regarding business.

Probability distributions, statistical significance, hypothesis testing, and regression are all statistical concepts that are integral.

Other terms that you will come across are conditional probability, priors and posteriors, and maximum likelihood.

These are important concepts that are related to Bayesian thinking.

Bayesian thinking involves readjustment of principles as new data is added. It is at the heart of many machine learning models.

To get started with statistics in machine learning, try Elements of Statistical Learning or David Barber’s Bayesian Reasoning and Machine Learning (both freely available online).

Building Your Resume: Taking The First Step In Your Data Science Career 

Once you’re done understanding the basics and are ready to transition to a career in data science, it’s important to tweak your resume to one that appeals to people that are hiring in these fields.

Remember, it’s a new field, so you won’t be alone in showcasing unrelated projects that you’ve worked on.

What’s important is building a solid portfolio or a website repository. That’s the best way to display the value you can add.

A good portfolio should include:

1. Your best work: Quality over quantity is important here.

Show off your most admirable projects and don’t let these get lost in a bid to showcase every single thing you have done.

Showcasing your projects or past work can serve as a great portfolio to potential employers.

Remember, your work speaks volumes so in addition to a crisp, articulate resume, employers are keen to see the fruits of your experience first-hand.

2. Concise and well-made portfolio: The way you present your work is a reflection of your person. Keep things well-designed and approachable.

Don’t forget to display your contact information where it’s easily accessible. If you are making a website, don’t make it like a resume.

3. Design it as you would for a world-class company. You are selling yourself here so let people know about who you are, your interests, your work and how all these aspects converge to make you a great data scientist.

4. Showcase your own data: We’ll assume you’ve been living for at least 20 years at this point, so you’ve already got plenty of data about yourself.

Try to find ways to showcase this data, with what you’ve learned in data science.

Perhaps you could make an algorithm of academic achievements, insert data about yourself and let it predict how well you’re likely to perform for the job you’re applying to.

5. Impactful work: If there have been any projects that are innovative or successful in changing situations for the better, add it in.

This isn’t limited to technical output, it could even be a blog post highlighting important issues, thoughts on the data science field or a how-to guide to help amateur scientists.

6. Take part in competitions: For those who lack project experience, taking part in data science competitions is a good way to up your game and add assets to your portfolio.

There are platforms, such as Datakind and Datadriven, which let you undertake real corporate or social issues where you can use your skills to contribute solutions to these problems.

The field of data science is one of spectacular opportunity and promise.

At the same time, it’s still very much emerging and it can be difficult for a newbie to find his or her footing.

There’s no standard definition of what a data scientist does – it’s an amalgamation of technical know-how, business knowledge and mathematical skills; different proportions of these abilities defining the kind of work and roles you will be able to undertake.

Final Thoughts

To figure out which role you are inclined toward and where you will most comfortably fit in, it’s key to actually get the feel of data science on a practical level.

Yes, technical knowledge is important but building a portfolio of interesting projects using whichever resources (and there are tons available) can help you get a deeper understanding of data science, motivate you to explore more and lead to higher retention of the knowledge you grasp along the way.

Relate your projects to interesting areas that appeal to your audience. And soon, you will have a body of work to showcase should your dream opportunity come up. 

And if you want to go for an extensive learning journey in Data Science and Machine Learning, give us a shoutout right here.

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