A Beginners Guide to a Data Science Career

Much has been seen about the rise of data science as a driver that is considerably advancing over the past years. It is not too extreme to say that due to the rapid growth, the need for skilled professionals in data science has increased tenfold.

Data is transforming almost everything. As a result, there is an emergence of data scientists at industries and big corporate organizations worldwide, one of the major driver toward launching a data science career. Data science has evolved as a field that follows approaches from fields like statistics, predictive analytics, data mining, and data analysis. If this catches your interest, data science has a deep connection with computer science; as stated by Forbes – “Data Science is the story of      the coupling of the mature discipline of statistics  with a very young one – computer science.”

Let’s get started: Data Science

We’re immersing in a world drowning in data. For every message, we click “send” its data that we’re generating. Imagine the number of messages that are being sent across the globe within split seconds. Buried in all these data are multiple questions whose answers are yet to be found. The reason why we need data science.

A simple explanation of the term data science – as defined by Hal Varian, Google’s Chief Economist, 2009, he says it is the ability to take data, understand it, process it, and extract value from it, visualize it, and further communicate it – the most important skill.

Data science is the hot new gig in the technology world.

Why data science is gaining popularity?

Data is the key to making every organization thrive in business. It is one of the most important features that is helping business leaders make insightful business decisions. Data science still being the buzzword in the present time, thus earning a career as a data science professional is perhaps the best career option in the present time.

The application of data science has transformed almost every industry, from banking to healthcare and manufacturing, data science is seen to play a vital role.

  • Personalized healthcare and recommendations
  • Stamping out tax frauds
  • Prediction of incarceration rates
  • Identifying and predicting a disease
  • Optimization of shipping routes in real-time
  • Obtaining value from soccer roster
  • Price optimizations in Uber cars
  • Medical image analysis in healthcare
  • Identifying the potential customer base

Data science is a wide field thus its applications are diverse and enormous. Industries need data more than ever; else businesses won’t be able to move forward.

What does data science comprise of?

The components of data science highlight important elements ensuring that the data is here not just to change but improve our world.

  • Statistics – statistics play a crucial role in data science. As a data scientist, extensive knowledge in statistics should be at their fingertips. This helps in gathering and analyzing numerical data collected from multiple sources.
  • Data visualization – assessing and understanding data can be difficult for someone without knowledge in data science. Therefore, with the help of data visualization tools, such data can be converted into visual presentations like graphs and charts making it easier for a layman to decipher.
  • Machine learning – it is a field of study that gives computers the ability to learn through experiences, without the need for human intervention.
  • Deep learning – Deep learning, a subset of machine learning uses complex multi-layered neural networks to learn what comes naturally to humans: they learn by example.

Can you name the tools used in data science?

You’re probably excited about learning the tools majorly used in data science. Well, we’ve got it covered for you. However, if you wish to get hands-on experience using these tools, perhaps you might need to get yourself enrolled in one of the best data science certifications.

  • Hadoop – an open-source distributed framework used for managing data processing and helps in data storage. You’re likely to come across using this tool while building a machine learning project from scratch.
  • Hive – it is a data warehouse that is built right on top of Hadoop. This tool generally provides an SQL-like interface used to process structured data in Hadoop.
  • MapReduce – the MapReduce works in two phases the Map and Reduce, while Map helps to deal with the mapping of data, Reduce shuffles, and reduces the data. Being a significant part of Hadoop, this framework or programming model is used to process large amounts of data.
  • Spark R – with the help of Hadoop, processing input using Spark R can be tricky and cannot function in a distributed ecosystem. This is where Spark R is used, it offers simpler ways of using R and Apache Spark and helps in distributing data frames.

Explain the complete process in data science?

You will find majorly five processes in the data science workflow, they are:

  • Data collection

In this process, data is gathered from multiple sources (internal and external). The process is followed to provide actionable insights or solutions to complex problems. Such data can be gathered from sources like census datasets, logs from web servers, APIs, or social media, etc.

  • Data preparation

This helps in cleaning data showing inconsistencies like incorrect data, missing value, and blank columns. Before it moves for the modeling, these data need to be explored, processes, and conditioned.

  • Modeling

Techniques used as to how the relationship needs to be drawn between input variables are planned in this process. It is done using statistical formulae and data visualization tools – SQL analysis services, R, and other tools that will help in model planning.

  • Building the model

Techniques such as clustering, association, and classification will be applied to train the datasets. When the model is all set and prepared, it will be tested against the testing dataset.

  • Deployment

The deployment is when the final model needs to be delivered with the report, codes, and documents. If the model gets through the test, it can be utilized as a real-time production environment.

  • Results

The results are finally communicated to the stakeholders thus making decisions whether the results are right or wrong.

Most of the jobs related to the data science career include job roles like data analysts, data scientists, statisticians, data engineers, data administrators, and business analysts.

Here’s what you need to do to grab a job as a data scientist:

Education: Bachelor’s degree in computer science, social science, mathematics and statistics, and physics. The most common fields include mathematics and statistics followed by engineering and computer science.

Mathematics and statistics: The individual should have a solid background in these subjects.

Programming skills: Basic programming skills is a must, else it can get difficult for you to learn R and Python.

Machine learning skills: Build practical knowledge and hands-on experience in machine learning. Make use of online repositories like Kaggle and take on real-world challenges.

Keep up with the latest trend: It is crucial to stay updated with the ongoing trends in the industry. In the present time, organizations are seeking to hire professionals skilled in AI, machine learning, robotics, and data analytics.

Build a portfolio: Make sure you have included the projects you’ve worked with, make sure not to fill your portfolio with theory. Recruiters are interested in your practical skills and not theoretical knowledge.

Endnote

Data science is not going away soon. With swaths of data generated daily, job opportunities in this field will skyrocket.

Now it the right time to boost your career in data science

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