Starting Your Career in Data Science

Introduction

The ever-evolving nature of technology has transformed the mode of operation of every industry. In the fast-paced technical era, data science has emerged as a buzzworthy career opportunity.

The data science job has been termed by Harvard as the “Sexiest Job of the 21st Century. Tech-giants with the likes of Google, Amazon, and Facebook have successfully implemented data science to elevate their business.

Why has this field captivated our imagination?

From archaeologists to scientists or statisticians, the discovery of trends in data and deriving meaning out of historical facts has been everlasting. The expansion of industries and a shift towards social media for promotional activities, marketing, and personal usage has generated an enormous amount of data in recent years.

The primary question is what do we do with such a large number of data and what is the significance of these data? Is there any real-world implementation? Such questions have carved the way into forming this distinct field known as data science.

The core of data science comprises several educational backgrounds, namely mathematics, computer science, and statistics. The definitions have evolved over the years.

An ideal definition of data science can be termed as the study of structured and unstructured data to extract meaningful information for data-driven decisions with the use of statistics, scientific tools, and machine learning techniques that help organizations in business growth.

Growing Popularity of Data Science

Data science is considered to be an asset to any enterprise. According to a recent report by Towards Data Science shows that the daily generation of data stands at 2.5 quintillion bytes. The current projections on data generation show an expected rise of up to 133 zettabytes by 2025.

In recent years, the data has been generated in the form of financial data, healthcare data, multimedia content, sensor, and intelligent system logs to name a few. It is of utmost importance to handle such large volumes of data more effectively.

A staggering 256 percent increase in the number of data science jobs have been listed since 2013, suggests the growing demand of these professionals.

As a multidisciplinary field, data science is computationally equipped to work around an immense volume of data. The organizations are now offered a competitive advantage in the market with a thorough analysis of data to introduce suitable changes in advance that can contribute towards achieving efficiency in the overall functioning of an enterprise.

This has been instrumental in making organizations accept data science as essential in their business, thus creating a huge demand for data scientists across the world.

Is Data Science Career Future Proof?

A stagnant career becomes tedious, usually indicating a need for a change in the job. However, the labor market is not as forgiving and certain professionals struggle and try to bring in drastic changes to remain relevant.

The world of employment has created several data science jobs with abundant opportunities to choose from as per the specialization. People looking to enter this field, need not worry as the demand for data scientists is not going to slow down over the next decade.

A possible change that is likely to emerge within a data science profession is the evolution of specialized roles that will introduce specific job titles in this domain. Aspirants should start to specialize in the respective area of interest. Some of the lucrative data science career roles are:

Data Scientist

Data scientists are the brains behind making data meaningful. The key responsibility of a data scientist is to identify important information by solving complex problems from the available data.

The term “scientist” is associated with the fact that a data scientist often uses scientific tools for analyzing data such that an organization can plan the future course of action depending on the results of the analysis.

Data Analyst

Although data scientists and data analysts have been misinterpreted by individuals that do not have the same background, it is important to highlight the difference between them.

The data analyst role existed in the pre-data science era. Typically, a data analyst is responsible for data collection, processing of the data, and performing statistical techniques on large datasets. Business insights and trends are uncovered during the data cleaning procedure.

Data analysts have proven to be effective in finding information out of the gathered data and providing organizations with valuable analysis for their business. A data analytics career may lead to becoming a key member of an organization’s think-tank for crucial decision-making processes.

Data engineer

A data engineer is an important member of an organization. Rightfully considered as the backbone of an organization, a data engineer is in charge of building data pipelines, managing large databases, validating raw data for errors, and ensuring the correct flow of data within the organization.

Ideally, such engineers are backed by the experience of working with multiple programming languages and tools and are considered to have in-depth knowledge and expertise to help in the growth of an organization.

Business Intelligence Expert

A business intelligence (BI) expert or “BI analyst” as commonly known, is responsible for analyzing the data for an organization’s operational efficiency with the idea of generating more profits.

A business intelligence analyst is involved with the technical aspects of the business as opposed to the analytical role of a data scientist. A know-how of the popular machines and a strong industrial background to understand the changing trends of the industry and bridge the business and IT requisites for an overall improvement of an organization.

Machine Learning engineer

A machine learning engineer is an expert with machine learning and deep learning techniques.

The key responsibilities include the development and deployment of computational models. These experts do not require to have a business understanding or work with predictive models and mathematics and statistics part of a data scientist job.

Some of the job titles that are interlinked with data science are being discussed in the following section.

Database Administrator

Data scientists and their role in analyzing data have been covered immensely. A crucial point of source ensures the provision of data that is possible with the help of database administrators.

Such experts monitor the centralization of data and make it available to the point of contact, i.e. data scientists and analysts for carrying out their responsibilities efficiently.

A database administrator is also responsible for the development and security of the database. To have a career as a database administrator, one must acquire knowledge of how AWS and the cloud platform operates and a background in data warehousing.

Predictive Analyst

Due to the competitive nature of the employment market, some aspirants have not progressed as expected. For those, there are possibilities of extending your career similarly to the data scientists. Predictive analysts solely focus on the predictive outcome by examining a large dataset with a statistical approach.

The reports from these experts are important in determining further analysis such that the organizational goals are on the right track. These analysts are skilled in technologies such as big data to discover ongoing trends that are helpful from a business perspective.

Technical Skills required for a Data Scientist

Some prerequisites help in establishing a successful career as a data scientist.

• The knowledge to work around mathematical models.
• Statistics.
• Proficiency in coding languages such as Python programming.
• Must be able to handle large databases.
• The knowledge of analytical and data visualization tools, i.e. SAS, HADOOP, R, Tableau, SPARK, etc.
• To be able to climb the corporate ladder in a data science domain, it is extremely important to understand how machine learning works and various algorithms associated with it. The knowledge of supervised and unsupervised learning is a must.
• The know-how of high performing algorithms such as regression, decision trees, support vector machine, clustering, and Naïve Bayes and the implementation of these algorithms according to different problem statements will work wonders for a data scientist. 

Data Science Skills and Usage

The yearly salaries of data scientists across the globe are shown in the illustration below.

Source

LinkedIn voted data scientists to be the most promising career in 2019 due to growth factors in the profession and the range of average salary is $130,000 which was significantly higher than most jobs.

However, it is important to note that the salary varies as per the experience, location, skills, and companies. As per the Bureau of Labor Statistics, this profession is still evolving and it is likely to see a growth of 15 % by 2029 that is much faster than average jobs.

Data Science Interview questions

We have discussed the roles and skills required to be a successful data scientist, however, the trickiest part is facing the interviews. The questions are often arising out of the experience of an interviewer and it is noteworthy that the companies want the very best of them.

To get started, some of the important interview questions are explored in the following section.

1. What is the difference between supervised and unsupervised learning?

Supervised learning requires labeled data for training and it follows a feedback mechanism procedure. Some of the examples of popular supervised learning algorithms are decision trees, support vector machines, and logistic regression.

On the other hand, unsupervised learning uses unlabeled data for training without any feedback mechanism. Examples of unsupervised learning algorithms include k-means clustering, apriori, and hierarchical clustering.

2. What is selection bias?

An error that is related to the selection of non-random population samples. It causes inaccurate analysis as the sample obtained is not representative of the intended research. There are several types of selection bias namely sampling bias, time-interval bias, data-related bias, and attrition.

3. What is a bias-variance trade-off?

Bias is an error that occurs due to the oversimplification of the machine learning algorithm. During a training process, such models make simplified assumptions and may lead to underfitting.

Variance is an error that occurs due to a complex algorithm in which the trained model learns noise from the training data set thereby causing poor performance on the test data. These models may lead to overfitting. The goal of a machine learning algorithm should be to have low bias and low variance to achieve a good and reliable performance of a model.

4. What is a confusion matrix?

The confusion matrix is a 2×2 table that comprises the output provided by the binary classifier. Typically, there are 4 outputs such as true positive, false negative, false positive, and true negative. Several measures in the form of accuracy, precision, recall, sensitivity, and specificity can be derived from a confusion matrix.

5.Why is a ROC curve plotted?

The ROC curve is essential for the graphical representation of the trade-off between the true positive rate and the false positive rate at different thresholds.

6. Explain the support vector machine algorithm.

Support vector machine (SVM) is one of the most reliable algorithms that fall under the category of supervised learning. It can be used for regression as well as classification problems.

7. Define p-value?

To perform a hypothesis test with the use of statistics, a p-value acts as the evidence against a null hypothesis. The smaller the p-value, the greater is the significance of the evidence for rejecting the null hypothesis. A p-value is a number between 0 and 1.

8. What is a Decision tree algorithm?

Decision tree algorithms are categorized under supervised machine learning algorithms. Its implementation is mainly for regression and classification problems. The purpose of a decision tree algorithm is to be able to break down the data set into subsets. A decision tree comprises nodes and leaf nodes and capable of handling categorical and numerical data.

9. Explain about ensemble learning.

Ensemble learning allows the combining of individual models that can be put together to improve the stability and predictive capacity of the model. There are mainly two types of ensemble learning techniques.

• Bagging

The bagging method helps to implement the individual models on a smaller sample population that allows achieving nearer predictions.

• Boosting

Boosting is an iterative method that is used to adjust the weight of an observation based on the previous classification. Boosting is useful for reducing the bias error rates to build an efficient predictive model.

10. What is a Random Forest?

Random forest is a popular machine learning method used for various types of regression-based problems as well as classification tasks. It can be implemented for treating missing values and outliers.

A random forest can be considered as a type of ensemble learning method in which weaker models are combined to develop a stronger model. In this method, multiple trees are used as opposed to a single tree and each tree provides a classification. The forest performs the task of choosing the most votes from all the results of the trees. For a regression problem, the average of the classification results of each tree is being used

Recent trends in Data Science

With an overview of how a data science profession may progress, it is important to identify the current trends in data science that will help aspirants to stay updated. Below is a list of recent trends to watch out for in the field of data science.

Graph Analytics

Graph analytics is a powerful tool that is used to determine the relationship between complicated data points in a graph. This tool can be used to represent the data in a visual format and providing maximum insights. Some of the usages of graph analytics include clustering, shortest path, page rank, etc.

Python-the preferred programming language

Python has become the most preferred language for any task in the field of data science and artificial intelligence. The differentiating factor from other programming language is the ease of usage and the support for libraries that are integrated within it.

Python can work with large datasets and complex computational problems in lesser time with a lesser amount of codes. The most popular libraries of python are TensorFlow for handling machine learning workload, sci-kit-learn for training machine learning models, PyTorch for supporting computer vision, and natural language processing tasks, Keras for mathematical computation.

Infusion of Augmented Analytics in the data science world

Augmented analytics gains better insights into the data by the elimination of errors that may introduce bias in a decision making process. This process aims to deliver a better business intelligence process with the infusion of technology such as artificial intelligence into the mix that introduces automation in certain aspects of a data science process.

Conversational AI

As the world is introduced to newer technologies such as Alexa, conversational AI has become an emerging area of research. Conversational AI produces tons of data over its interaction with clients or from personal speech-based AI technologies.

The smart systems and automotive driving assistance systems are incorporated with such technologies; thus several key aspects can be derived from these data sources. To find the meaning behind the data generated for business growth and customer satisfaction, data scientists are pooled in by these companies offering such services.

Cloud-based Analytics

To provide uninterrupted services without the hassles of storage, businesses have provided cloud solutions. The cloud server helps in gaining faster access to the data anywhere in the world. It allows for on-demand computing for data scientists to perform their tasks effectively with limitless processing power and storage capacity being available.

In-memory Computing

In-memory computing has gradually garnered steam and is among the emerging trends. The aim is to vastly improve how data processing is performed. The database is stored within the memory as opposed to traditional methods of a physical hard drive and relational databases.

A real-time computation is possible with in-memory computing to make instant decisions and reporting, thus making it cost-effective and availability of real-time results.

Blockchain Analytics

Blockchain is identified with a collection of data that is managed by a cluster of computers. The chain connects each of these blocks with the help of cryptography. Blockchain is essential for the maintenance and validation of records whereas data science works on extracting the information from such data.

Data science and blockchain share a similarity in terms of processing data. The combination of these technologies is being explored in industries that generate large volumes of data. The industries such as transportation and freight may benefit tremendously from its implementation.

Automate Machine Learning

Auto ML is the process of providing automation in the application of machine learning to real-world data. This process manages the complete data pipeline starting from handling the raw dataset to the point of deployment of the machine learning model.

Auto ML may prove beneficial for individuals lacking in programming skills. It offers data scientists the to perform end-to-end machine learning solutions in a more simplified and faster manner.

It is interesting to note that some of the Auto ML models have been found to have outperformed models designed from scratch by an expert. However, it is suggested to hone your skills despite the provisions of automation in the creation of models.

Data Fabric

Data Fabric is a relatively new trend that has joined the lexicon of the data science world. In simple terms, data fabric is an environment that consists of architecture and set of data services that helps in the efficient management of data for an organization.

It provides dynamic functionalities such as hybrid cloud data services for better data visibility, data access and control, data protection and security and incorporating machine learning capabilities. The ultimate goal is to maximize value for an organization by providing a holistic approach to deal with the increasing complexity of data.

Conclusion

Data is the “new oil” that is ruling our lives up to the foreseeable future. Organizations that have implemented data science in their mode of operations have surpassed their competition. Data science jobs are booming for all the right reasons and proving to be a valuable asset for various industries.

There are countless opportunities in the field and the job roles have only grown in recent years. Gaining the right qualification under the belt and practical hands-on experience of the technologies guarantee to accelerate careers in a short time.

With the rapid pace of advancement and innovations, data science will continue to contribute and make a greater impact in the world. For those debating and doubtful about this profession, the data generation has been behemoth and unlikely to come to a standstill for decades to come making this profession future proof.