Today, growing a business is all about collecting, storing and processing data effectively. It is the new fuel in today’s highly-competitive business landscape. With data, companies can vie with others, deliver better decision-making and improved customer service. To process the data efficiently, the role of a data scientist in an organization becomes vital, enabling business leaders to understand what they bring to the table, as well as their expectations from it.
In recent times, the demand for data science jobs has emerged at a neck break pace and is expected to grow as the amount of data is relentlessly rising day by day. The work of a data scientist begins once the data is generated. Their roles typically revolve around assessing data for actionable insights, identifying the data analytics problems to offer greatest opportunities to the company, determining the correct datasets and variables, gleaning large sets of structured and unstructured data from various sources, cleaning and authenticating the data to ensure accuracy, completeness, and consistency.
Data scientists are also responsible for developing and deploying models and algorithms to mine the stores of big data, analysing the data to spot patterns and trends, interpreting the data to explore solutions and opportunities, and communicating to find stakeholders using data visualization.
Why Data Science Matters?
In the latest digital environment, more and more businesses are turning to big data and unlocking its power which is increasing the value of a data scientist who knows how to extract and make actionable insights out of gigabytes of data.
Currently, there is a huge amount of data and it is continuously growing. Thus, realizing the value of data processing and analysis, data scientists come into the spotlight. They are likely to be the trusted advisor and strategic partner to an organization’s upper management by ensuring that the staff maximizes their analytical capabilities. Data scientists are able to communicate and demonstrate the value of the institution’s data to enable improved decision-making processes across the entire organization by scaling, tracking, and recording performance metrics and other information.
Moreover, they evaluate and explore the organization’s data, then suggest and prescribe certain actions to help enhance the institution’s performance, superior engagement with customers, and ultimately bolster profitability. Their job requires them to identify more opportunities for businesses, and continuously improve the value that is derived from the organization’s data.
Getting Started with Data Science
The nature of data science is a hybrid of diverse disciplines. It is a composition of different subject areas like math, statistics, database management, data visualization, programming/software engineering, domain knowledge, and much more.
So, to build a career as a data scientist, it requires knowledge of math or statistics. Knowing programming languages like Python, R, Java and others can be helpful. Aspirants must have an understanding of MS-Excel as it has a lot of functions that can come handy while processing data. Having knowledge of Probability along with Statistics is also constructive when it comes to being a data scientist as there is a need to predict, create a hypothesis, and study past data.
Making career in data science also requires candidates to hold a degree in mathematics, statistics, computer science, management information systems, or marketing; have substantial work experience in any of these areas; have an interest in data collection and analysis; enjoy individualized work and problem solving; good communication skills both verbally and visually; have courage to take on new challenges.
Becoming a data science expert requires candidates to be familiar with a toolset to work with data in various environments. The toolset contains a combination of SQL, command line, coding and cloud tool. Moreover, Python is perfect for general programming purposes as it already comes with a range of libraries, including visualization, machine learning, among others.
Data Science Courses and Certifications
With the rise in demand of data scientists, several institutes and tech companies are offering data science courses and certifications to aspirants.
Here are some of the best data science certifications and courses.
IBM Data Science Certification (Coursera)
This professional certificate from IBM is designed for anyone interested in building skills and experience to pursue a career in Data Science or Machine Learning. It involves 9 courses providing candidates with the latest job-ready skills and techniques covering a wide range of data science topics including open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning.
Data Science Certification from Harvard University (edX)
This certification program will provide key data science essentials, including R and machine learning using real-world case studies. Consisting of 9 courses, this immersive program from Harvard is among the best rated online masters programs available on leading e-learning platform edX. The courses in this certification program including R Basics, Visualization, Probability, Inference and Modeling, Productivity Tools, Wrangling, Linear Regression, Machine Learning followed up with a Capstone project.
Data Science Online (Berkeley ExecEd)
This program is generally for individual contributors and mid-level to senior managers coming from either the private or public sectors seeking a truly rigorous, hands-on experience with modern data analysis methods. It has an eight-module program that will support learners to cover every essential topic of data science. This online course will teach about basic mathematical and statistical concepts like mean, standard deviation, graphs, histograms, and logarithmic functions, as well as advanced concepts like forecasting machine learning, advanced regression models, building effective data science teams, and more.
