Data science is a science that combines various algorithms, tools, and machine learning practices to discover meaningful patterns from structured and unstructured data. Structured data follows a fixed format, can be classified into preset categories e.g. telephone directories. Unstructured data, which exists in a lot more abundance than structured data, does not follow any preset formats and presents great variance in the forms that it can take e.g. tweets, emails, and images. Unstructured data also consists of the most relevant information that can impact decisions that need to account for user behavior.
The aim of data science is to extract valuable and powerful insights from the amalgamated structured and unstructured data using modelling techniques to optimize decisions that are not only limited to business but also increasingly include government, healthcare, etc.
What is machine learning and artificial intelligence?
You may have heard these fancy words being thrown around like no other and always wondered what they were actually about. Artificial intelligence is the use of intelligent machines to replicate human decisions, only with much greater levels of processing power and memory than human beings. Machine learning is how artificial intelligence achieves its aim. ML uses algorithms that ‘learn’ from the data that is fed to them. They identify recurring patterns and develop the ability to identify them in the future.
What educational path is required to become a data scientist?
A degree in Computer Science or Mathematics is ideal for one to pursue a career in data science.
What do data scientists do?
Data scientists typically extract, visualize and interpret data. For a list of career roles related to data science jump here
Video Discussion on Career Prospects of Data Science by an Expert
My Career Dreams organized a Facebook Live session with Khuram Rahat who has been associated with the data industry for over 3 decades in organizations like Teradata & Telenor. You may find the recorded session below.My Career Dreams also offers paid one to one online career counselling session and you may request one pertaining to data science here.
What do employers and organizations look for in a data scientist?
Employers like to see how driven a data scientist is for the role that he is being offered. Because of the exploratory nature of data science, it is important for a data scientist to be passionate about playing with large amounts of data to discover any patterns that it may reveal.
A data scientist must have the right aptitude for the field. Two significant aspects in this regard are:
Problem Solving It is imperative for a data scientist to be a problem solver i.e. understanding what techniques and methods they must apply to different data sets in various situations to extract useful insights.
Ability to Visualize Patterns Data scientists must be able to visualize patterns in data and represent them effectively.
Reason for joining
Applicants for an opening in data science must be inclined towards gaining experience and working on large datasets of data to discover new patterns rather than material gains alone.
Skills needed to succeed as a Data Scientist
Technical skills needed for data science
There is a rich variety of languages that are used by data scientists. These include Python, R, MATLAB, Hadoop, and SQL to name a few. Of these Python is the most versatile and adaptable language recommended by data scientists. However, all of these languages have their pros and cons and differ in their applications. It is recommended that you first decide which area of data science you are interested in and then conduct research into which language is the most relevant for that area. For example, MATLAB is often used in Engineering and Applied Maths applications. With so many resources available online, the type of tool a data scientist uses becomes irrelevant if they have the right ability to manipulate the data and extract results
Soft skills needed for data science
Anybody can learn a language or software but to truly outshine your competitors, you will need to embody the soft skills that are present in every data science professional.
The first important skill is creativity fueled by curiosity. Just because it had numbers, you thought it would be boring? An immense amount of creativity is needed to look closely at the data, to ask questions, to find innovative solutions, and to identify patterns that nobody else can.
The next skill which follows from this is business acumen. In order to use your creativity to the right end, you will need to have a deep understanding of how business works, the factors that need to be considered when making important decisions, and the implications they may have.
Since you will have to apply your data learnings to business decisions, you must have a business mindset too. If you end up on the more technical side of data science and find that business people rely on you to make their decisions, then strong communication should be a part of your portfolio. Not everybody would have your technical background, so simplifying your analysis to provide an understanding to them will have to be a critical part of your job.
Are data scientists paid well?
Data science as a career offers well paid jobs but that should not be the main reason for join the field. If an individual is passionate about the opportunity to work with large amounts of varied data and playing with new types of data sets from time to time, a lucrative career will come through.
What should a data scientist look for before joining an organization?
It is important to see the relevance of a data scientist in an organization. If the organization is keen on making data driven decisions in their business, a data scientist will have a lot of exciting new data sets to work on
Profile of the department
The data science function should be aligned with the business department of an organization rather than the IT team since they are the ones who will see the value of the data in making decisions and will be acting up on them
Profile of the head of department of the business division
The main aspects to consider in the person you will be reporting to include:
Their attitude towards your role
Outlook towards data analytics
Decision making orientation
Belief in value of data
Appetite for experimentation in organization
It is important to see how open the organization is towards trying new things and their willingness to invest in data
Some Real World Examples of Data Science
The first application of data science is present in the very mobile phone that you are using to read this! That’s right, the famous Netflix algorithm that suggests TV shows that are ‘recommended for you’ uses machine learning to learn more about your preferences based on factors like what shows you currently watch, the time that you watch shows on, the ratings you give to shows, and even the behavior you exhibit while browsing and scrolling through the Netflix catalog!
If you have ever taken a flight during the holiday season or used Careem in rush hour, you most likely would have had to interact with an ML algorithm. Ticket prices and fares are determined using complex machine learning algorithms that learn from your past behavior as well as demand data for the time you are traveling.
How does data science affect me in my daily life?
So you think that all of this techy information won’t make a difference for you? Well, you are surely in for a surprise. Ever been interrupted by a random ad before you could watch your favorite bit of standup comedy on Youtube? Ever came across an ad for a special discount on your favorite sneaker brand? Call them annoying, or call them useful, digital advertisements don’t target you out of the blue. Platforms like Youtube and Facebook show you ads that they think are actually relevant to you, based on your activity on social media and web browsers. The ethics of tracking your social media activity and using it to show targeted advertisements have been debated a lot on, but there is no denying that you can’t stay away from it.
Have you ever tried to order your favorite book from Amazon, or your dream pair of sneakers from Adidas? Were you so excited that you could barely wait to get them and so you paid a lot more for faster shipping? Well you stepped right into the wonder of data science in the field of logistics. Shipping companies like FedEx and DHL employ powerful systems to make delivery decisions on various available routes, modes of transport, and time, to make sure you get to incur the least shipping cost for the fastest time of delivery.
Data Science Related Jobs
Business Intelligence Developer
BI Developers enable businesses to make better business decisions based on past data that has already been collected. They help businesses to use data from internal and external sources to answer strategic and tactical business questions. The job involves developing IT solutions to tackle problems within the company.
Data scientists primarily collect clean and organize data for businesses. Their job involves analyzing complex loads of data to identify meaningful patterns to benefit the company. Cleaning data involves converting unstructured data into more structured forms that can be used to power business decisions. This job requires both a comprehensive statistical background as well as business knowledge.
The job of a data analyst is more geared towards the business. Using the patterns identified by data scientists, data analysts are responsible for deriving business decisions from these patterns. These strategic decisions could include pricing, placement, product development, and promotion, to name only a few.
Data engineers develop the technological infrastructure that enables data scientists to collect the data they need. This infrastructure includes cloud architecture, processing systems. The job of a data scientist depends on data engineers laying the foundations for the data scientists to work with.
The world of data science is beaming with possibilities. No matter who you are or what you do, you benefit from this revolutionary discipline every living moment! Now you know why data science is the future, and why you might want to become a part of this future!