What is Data Science and how can Technology Aspirants Earn Big?

Data science is an interdisciplinary field of work that incorporates skills and specializations across domains including AI, machine learning, and beyond. Data science has been playing a critical role in revealing key insights that have proved to be beneficial for business groups worldwide. As the economies move toward a more digitized arena of business operations; the amount of data thus generated accounts for an unsurmountable pool. This is beyond human competence to make sense of. Hence, the role of data science has gained strength by leaps and bounds over the decades.

This whopping expansion of the data science market scale is an indication to guide big business and global recruitment opportunities for the deserving. This is where becoming a skilled data science professional with sheer capabilities in handling and designing quality machine learning models; and managing data science projects. Making a thriving career in data science is a reality with the right and globally trusted names in the credentialing arena. Let us understand the world of opportunity at the data science threshold; how it empowers businesses worldwide, and how to make it a brilliant career choice for all aspiring technology graduates and professionals.

Data Science Foundations:

Being an incredibly diverse field of work; and with high interrelation across domains; data science is based on core pillars that form the basis of all its operations. Let us investigate them in depth.

• Domain Expertise

  Domain means an area of specialization that empowers a data science professional to determine business questions and find relevant data analysis methods, gain insights, and offer the best possible decisions.

• Mathematics and Statistics

  These two are the foundational stones on which the entire data science trajectory stands. These help in processing data using machine learning algorithms and help find averages, medians, and other related calculations.

• Computer Science

  Programming language giants such as Python and R are critical to a data scientist’s role. Proficiency in computing languages assists in enhancing the overall productivity of the data science team.

• Insights Sharing

  Effective written and verbal communications are necessary to present data-driven insights in an easily comprehensible manner.

How Data Science Works?

Understanding data science workflow is an insightful revelation of exploratory data analysis, predictive model development, creating captivating data visualizations, and beyond. Data scientists work as per the below-mentioned procedure to attain the final business goal.

STEP 1 - PROBLEM IDENTIFICATION

Having a clear vision of the goals and a complete understanding of the problem at hand delivers better results and makes it easier to carry out further tasks with greater finesse and smooth collaboration.

STEP 2 - DATA COLLECTION

Collecting relevant data from all possible sources to perform the next steps in the workflow is of utmost importance. Any kind of misleading information can hamper the quality and credibility of the decision-making process.

STEP 3 - DATA ANALYSIS

To get familiar with the collected data, exploratory data analysis (EDA) is performed to monitor the patterns and correlations formed within the collected data pool.

STEP 4 - MODEL BUILD-UP

Building a machine learning algorithm or model utilizes the data thus prepared in the prior steps. The type of model selected depends on the business problem at hand.

STEP 5 - MODEL OPTIMIZATION

Improve the model’s prediction accuracy by optimizing with targeted iterations. It will depend upon the business problem that you are facing right now.

STEP 6 - MODEL EVALUATION

Performing the final step involves gauging the overall performance of the model by evaluating the scale of the model to perform larger operations on full deployment.

Explain a Data Science Job.

A data science job is the critical role that seasoned data scientists play to bring out the best-suited data-driven insights for business good. These could come in diverse roles and career opportunities on offer. Let us understand them below.

Top 5 Career Choices in Data Science:

• Data Scientist- Performs data analysis, develops ML models, and interprets results (annual average salary- USD 116K and above- Glassdoor)
• Data Architect- Designs overall data structure, and defines models, schemas, and data governance policies (annual average salary- USD 180K- Glassdoor)
• Data Engineer- Designs and builds data pipelines to collect, transform, and store large datasets (annual average salary- USD 132K- Glassdoor)
• Business Intelligence Analyst- Gathers requisite business stakeholders, designing, and developing BI reports (annual average salary- USD 135K- Glassdoor)
• Machine Learning Engineer- Builds and deploys ML models at scale, optimizes model performance, and integrates (annual average salary- USD 165K- Glassdoor)

What are the Skills Required to Become a Qualified Data Scientist?

Foundational concepts of data manipulation, data cleaning, EDA, programming languages, mathematics, and statistics form the basis of a data science role. Adding to these, data visualization, team collaboration, communication, and hands-on expertise in diverse capstone data science projects are a must-have to begin a hugely satisfying career in data science.

Are Data Science Courses and Certifications Useful?

If you wish to build a lasting career in data science; mastering core data science skills and expertise at popular data science projects is easy access to a data science certification. Making your career a priority; you are sure to scale big with these credentials gracing your portfolio and yield a massive return on your upskilling investment in the long run. Make the big move now!