Importance of data scientist in 2019

The demand for data science skills has grown significantly in recent years as companies look to glean useful information from the voluminous amounts of structured, unstructured and semistructured data that a large enterprise produces and collects - collectively referred to as big data.

The data scientist role is an offshoot of the statistician role that includes the use of advanced analytics technologies, including machine learning and predictive modeling, to provide insights beyond statistical analysis. A data scientist is a professional responsible for collecting, analyzing and interpreting large amounts of data to identify ways to help a business improve operations and gain a competitive edge over rivals.

Why there is a need for the data scientist in 2019?

Data scientists typically work in teams to mine big data for information that can be used to predict customer behavior and identify business risks and opportunities. Basic responsibilities include gathering and analyzing data and using various types of analytics and reporting tools to detect patterns, trends, and relationships in data sets.

A data scientist uses large amounts of data to develop hypotheses, make inferences and hone in on the customer, business and market trends. The data scientist must be able to communicate how to use analytics data to drive business decisions that may include changing course, improving a process or product, or creating new services or products. With the latter, the data scientist is involved in the development process. In the case of software, for example, the data scientist's role involves using data analytics to prescribe new features.

Data scientists also set best practices for data collection, use of analytics technology and data interpretation.

The mix of personality traits, experience and analytics skills required for the data scientist role is considered difficult to find, and, thus, the demand for qualified data scientists has exceeded supply in recent years. These professionals are tasked with developing statistical learning models for data analysis and must have experience using statistical tools, as well as the ability to create and assess complex predictive models.

What are the skills required for a Data Scientist?

The skills required for data scientists are - intellectual curiosity combined with skepticism and intuition, along with creativity. Interpersonal skills are also a critical part and many employers want their data scientists to be data storytellers who know how to present data insights to people at all levels of an organization. The leadership skills are also required to steer data-driven decision-making processes in an organization.

For data scientists, the education requirement is a bachelor degree in statistics, data science, computer science or mathematics.

The skills required for the job include data mining, machine learning and the ability to integrate structured and unstructured data. Experience with statistical research techniques, such as modeling, clustering, and segmentation, is also often necessary. There is also requires a knowledge of a number of big data platforms and tools including Hadoop, Pig, Hive, Spark and MapReduce; and programming languages that include Structured Query Language (SQL), Python, Scala, and Perl, as well as statistical computing languages such as R.

Programming knowledge for data science

Python

Python is the most common coding language typically required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists. Because of its versatility, you can use Python for almost all the steps involved in data science processes. It can take various formats of data and you can easily import SQL tables into your code. It allows you to create datasets and you can literally find any type of dataset you need on Google.

R language

R is specifically designed for data science needs. You can use R to solve any problem you encounter in data science. In fact, 43 percent of data scientists are using R to solve statistical problems. However, R has a steep learning curve. It is difficult to learn especially if you already mastered a programming language.

Hadoop

As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly convey data to various points on a system. That's not all. You can use Hadoop for data exploration, data filtration, data sampling, and summarization. It is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial.

SQL Database/Coding

SQL (structured query language) is a programming language that can help you to carry out operations like add, delete and extract data from a database. It can also help you to carry out analytical functions and transform database structures. You need to be proficient in SQL as a data scientist. This is because SQL is specifically designed to help you access, communicate and work on data. It gives you insights when you use it to query a database. It has concise commands that can help you to save time and lessen the amount of programming you need to perform difficult queries.

Apache

Apache Spark is becoming the most popular big data technology worldwide. It is a big data computation framework just like Hadoop. The only difference is that Spark is faster than Hadoop. This is because Hadoop reads and writes to disk, which makes it slower, but Spark caches its computations in memory. Apache Spark is specifically designed for data science to help run its complicated algorithm faster. It helps in disseminating data processing when you are dealing with a big sea of data thereby, saving time.

AI and Machine learning

This includes neural networks, reinforcement learning, adversarial learning, etc. If you want to stand out from other data scientists, you need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression etc. These skills will help you to solve different data science problems that are based on predictions of major organizational outcomes. Data science needs the application of skills in different areas of machine learning.

Data Visualization

As a data scientist, you must be able to visualize data with the aid of data visualization tools such as ggplot, d3.js and Matplotlib, and Tableau. These tools will help you to convert complex results from your projects to a format that will be easy to comprehend. Data visualization gives organizations the opportunity to work with data directly. They can quickly grasp insights that will help them to act on new business opportunities and stay ahead of competitions.

Unstructured data

Unstructured data are undefined content that does not fit into database tables. Most people referred to unstructured data as 'dark analytics" because of its complexity. Working with unstructured data helps you to unravel insights that can be useful for decision making. As a data scientist, you must have the ability to understand and manipulate unstructured data from different platforms.

Platforms for a data scientist

It’s become a universal truth that modern businesses are awash with data. More and more organizations are opening up their doors to big data and unlocking its power—increasing the value of a data scientist who knows how to tease actionable insights out of gigabytes of data.

Government

Big data helps governments form decisions, support constituents and monitor overall satisfaction. As the finance sector, security and compliance are a paramount concern for data scientists.

Science

Scientists have always handled data, but now with technology, they can better collect, share and analyze data from experiments. Data scientists can help with this process.

Healthcare

Electronic medical records are now the standard for healthcare facilities, which requires a dedication to big data, security, and compliance. Here, data scientists can help improve health services and uncover trends that might go unnoticed otherwise.

Business

Businesses need data scientists to make sense of the information. Data analysis of business data can inform decisions around efficiency, inventory, production errors, customer loyalty and more.

E-commerce

Websites collect more than purchase data, data scientists help e-commerce businesses improve customer service, find trends and develop services or products.

Finance

In the finance industry, data on accounts, credit and debit transactions and similar financial data are vital to a functioning business. But for data scientists in this field, security, and compliance, including fraud detection, are also major concerns.

Telecommunications

All electronics collect data, and all that data needs to be stored, managed, maintained and analyzed. Data scientists help companies squash bugs, improve products and keep customers happy by delivering the features they want.  

Social networking

Social networking data helps inform targeted advertising, improve customer satisfaction, establish trends in location data and enhance features and services. Ongoing data analysis of posts, tweets, blogs and other social media can help businesses constantly improve their services.

Other

You’ll find jobs in other niche areas, like politics, utilities, smart appliances and more.

 

It is becoming clear by the day that there is enormous value in data processing and analysis - and that is where a data scientist steps into the spotlight.

So, interested in a career in Big Data?

Simply learn a wide range of courses in data science. And you’ll be set up to succeed with instructor-led training from industry experts, as well as hands-on experience, practice tests, and high-quality eLearning contents.

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