Data science career guide

Introduction

In this article, we will be discussing data science as a career.

Data science continues to be a hot topic for the large community that is focusing on collecting data in order to extract meaningful insights and aid business growth. A lot of data is an asset to any organization if it is used efficiently. With the advent of the age of Big Data, traditional Business Intelligence tools have started to fall short in processing the massive pool of unstructured data. Hence, Data Science came in with more advanced tools to work on large volumes of data from multiple sources,i.e., financial logs, multimedia files, marketing forms, sensors and instruments, text files, and more.

What is data science?

**Data science** is a broad field of study aimed at maintaining data sets and deriving meaning out of them. The unique combination of statistics, mathematics, and business domain knowledge offered by data science has the potential to help organizations in finding new ways to increase their profits, get into new markets, and launch effective campaigns for their product.

Applications of data science

Given the importance of data in today’s world, companies are utilizing insights from data to make strategic business decisions, build applications, and provide solutions. As a result, Data Science has many applications and use-cases. A few of them are:

Weather forecasting

Data Science has numerous applications in predictive analytics. In the specific case of weather forecasting, data is collected from satellites, ships, and aircraft to build models that can forecast weather and predict natural calamities with great precision. This helps to take significant measures at the right time and avoid maximum possible damage.

Healthcare

From medical image analysis, cancer detection and care, genoming, drug discovery, disease prediction and prevention, and monitoring patient health are just a few of the applications of Data Science in healthcare. The medicine and healthcare industry are heavily utilizing Data Science to improve patient's lifestyles and predict diseases at an early stage.

Cyber security

Big data comes with big threats, which makes it mandatory for all to leverage data science in order to mitigate cybersecurity risks. For example, IBM has utilized data science to introduce security-related products.

Talent acquisition

Talent Acquisition now uses data science to make informed decisions. Big data helps to identify red flags during the hiring process and, as a result, the cost of acquiring new employees and training them goes down. According to the research, 69% of talent acquisition professionals still make use of age-old operational methods like spreadsheets and other ad-hoc tools to maintain databases; hence, the vast scope of data science will greatly improve the process.

Fraud and risk detection

Some of the major applications of data science are in the Finance and Insurance sectors. These sectors utilize big data and data science techniques to identify patterns of fraudulent transactions, predict the next fraud in progress, and notify both the bank and customers to save responsive measures later on. Anti-money laundering incidents are on the rise, but with analytics, non-compliance fines can be easily implemented, and reputation loss risks mitigated.

Customer sentiment analysis

Customer sentiment analysis has been around for a long time. Social media is the most readily and easily available tool for analysts to perform customer sentiment analysis. These analysts use language processing to identify words that identify customer attitude towards the brand. This feedback helps businesses improve their products.

Recommendation engines

Recommendation engines are quickly becoming popular in the retail and e-commerce industry. Retailers leverage these engines to drive customers to buy more products. Although traditional models have drawn intuitions away from using browsing history, purchase history, and basic demographic factors, with data science, a large volume of data can train models better and more effectively to show precise recommendations.

Inventory management

Inventory management is crucial and hectic for business owners. Powerful machine learning algorithms can analyze data between the elements, supply the data in great detail, and predict correlations among purchases. The analyst then uses this data to come up with a strategy to increase sales, confirm timely delivery, and manage the inventory stock.

Self-driving cars

Intelligent cars are a classic example of Data Science. An intelligent vehicle collects data in real-time from its surroundings through different sensorslike radars, cameras, and lasers to create a visual (map) of its surroundings. Based on this data and advanced Machine Learning algorithms, it takes crucial driving decisions like turning, stopping, speeding, etc.

Now, let us look at the skills needed to become a data scientist.

Skills needed to be a data scientist

Data Science is a multidisciplinary subject, but there is a misconception that one needs to have a Ph.D. in science or mathematics to become a data science professional. However, anyone with a normal educational background and an intellectual curiosity towards data science can become a data scientist. We have enlisted a few skills that can be really helpful before jumping into this field.

Mathematical expertise

Without any doubt, both `classical statistics` and `Bayesian statistics` are critical to Data Science. However, other concepts are also important such as `quantitative techniques` and specifically `linear algebra`, which is a central system for many inferential techniques and machine learning algorithms.

Strong business acumen

Data Scientists extract useful information that is critical to a business and are also responsible for sharing this knowledge with major stakeholders. They are a valuable asset to the company because they have complete statistics for each department. Hence, strong business skills can really speed up the work.

Problem-solving and risk analysis skills

Data scientists need to maintain exceptional `problem-solving skills` and master the art of calculating the risks associated with specific business models.

Technology skills

Data Scientists often come across complex algorithms and sophisticated tools. They are expected to be comfortable using one or a set of languages from `Python`, `R`, `SAS`, `SQL`, and sometimes `Java`, `Scala`, `Julia`, and others. Data Scientists should also be able to navigate their way through technical challenges that might arise and avoid any bottlenecks or roadblocks that might occur due to a lack of technical soundness.

Now that you are familiar with the tools and technologies, we will look at some of the top roles in this career option.

Top roles in data science & analytics

For quite a long time, data science has been the center of attraction in the corporate world because they are very few skilled experts. Companies are always looking for analytics professionals who have the right skills to put an organization’s data to work. Here are a few types of roles one can target in this domain:

The data scientist

A data scientist can handle crude data using the latest technologies and techniques, extract the necessary details, and disseminate the acquired knowledge to their colleagues in an informative way.

The data analyst

Languages like `R`, `Python`, and `SQL` are a part of the data analysts’ toolbox. Much like the data scientist role, a broad skill-set is also required for the data analyst role, which combines technical and analytical knowledge with ingenuity.

The data architect

Data is being collected in almost every organization. Industries like banking and FMCG require data architects to integrate, centralize, protect, and maintain their data sources.

The business analyst

A business analyst performs the role of the middle person between the business folks and the techies. Uber, Dell, and Oracle are a few prominent companies looking for a business analyst.

The statistician

The job of a statistician is regularly overlooked or replaced by fancier-sounding job titles. This is a bit of a pity, given that statisticians, with their solid foundations in statistical theories and methodologies, can be seen as the pioneers of the data science field.