Learn About Data Science Careers (With Skills And Duties)
By Indeed Editorial Team
Published 25 October 2021
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Data science is the process of extracting useful information from large amounts of data to provide valuable insights. Since data scientists can help businesses make strategic decisions for scalability, they are high in demand. Learning about different data science careers can help you decide the role that would be the right fit for you. In this article, we discuss different careers in data science, explore the skills and responsibilities and find out the average salaries offered.
Typical roles in data science careers
Data science careers exist across numerous industries. Several industries rely on data science applications ranging from targeted marketing to detecting financial fraud. You can find multiple full-time and freelance job opportunities in the industry. Some roles in a data science career include:
Data scientist: They analyse, process, model the data and then interpret the results to create feasible plans for businesses to increase sales and revenue.
Senior data scientist: This is a more strategic role. These professionals supervise the data scientists, provide guidance on model building and help structure an end-to-end workflow.
Business intelligence analyst: A BI analyst studies market trends and business developments to get a clear view of a company's situation. They analyse business strategies and assess financial data to determine areas for improvement.
Data mining engineer: Using statistical software to analyse the data, these professionals identify correlations, patterns and outliers in the data using which they advise companies about risk reduction and income improvement.
Data architect: Data architects develop plans for data management systems that maintain, organise and protect data sources. They work in close collaboration with system developers and designers.
Responsibilities in a data science career
Across industries, your daily tasks may be similar. You collect data, organise it in an understandable format, analyse it and construct the right models that help you provide valuable information to business stakeholders. These daily tasks can involve:
Data acquisition refers to the process of collecting, filtering and cleaning data before placing it into a data warehouse or other storage solution. This is an organised system that allows for easy retrieval of knowledge and information. Data integration consists of three stages: extraction, processing and loading (or ETL).
ETL becomes easier with tools like Informatica PowerCenter, SQL Server Integration Services (SSIS), Talend Studio, DataStage and Fivetran.
As a data scientist, you spend most of your time on this important step. Data preparation involves spending time transforming disorganised dirty data into a scalable, useable and logical format. Data preparation includes some small subtasks, such as:
Data cleaning: Remove missing data and null values that may cause the model to fail. As missing values can result in incorrect findings, deleting them helps improve decision-making.
Data transformation: Normalize raw data into required outputs, using appropriate techniques such as normalisation.
Handling outlier data: When the data points are outside the usual range (anomalies), finding these outliers require extensive exploratory data analysis. Finding these outliers is vital to detect fraudulent data.
Data integration: This process combines data from diverse sources, such as files and databases, into a single unified view.
Data reduction: This step involves reducing the number of records or attributes, while ensuring that this does not affect outcomes. Reducing data contributes to minimising redundancies, reducing costs and storage requirements.
Data mining is a process used by businesses to transform raw data into valuable information. In the process of mining data, you may discover patterns and correlations between data. This process allows you to identify trends and developments and assist companies in making decisions.
Standard tools for data mining include Oracle Data Mining, Python-based tools like RapidMiner and H2O and open-source solutions like Weka.
Model building involves developing data sets for training, testing and production. After training the model and testing it to evaluate its accuracy on real-time data, it can be used to make predictions. These models can be used to perform several tasks such as prediction, classification, clustering, recommendations to help businesses make effective decisions.
Skills required in data science
A data scientist's skill set comprises technical and programming expertise along with a clear understanding of mathematical concepts. To develop these skills, you can start with the following:
Database tools: Understanding databases is instrumental when you store and analyse data. Get familiar with tools like Oracle Database, MySQL and Microsoft SQL.
Statistical concepts: Statistics deals with methods to collect and analyse data. Understanding this helps you interpret data and present it.
Probability: This mathematical concept helps you understand and predict the likelihood of a scenario or event.
Mathematical analysis: You use mathematical techniques like series, differentiation, integration and analytic functions to understand limits and related concepts.
Analytics tools: Learn to use software tools like Python, R and SAS so you can easily run analytics on data.
Data wrangling: When cleaning, manipulating and organising data, you can work on tools like Flume, Scoop, Python and R.
Machine learning concepts: ML helps you create prediction algorithms using data, which analyses current and historical data and projecting this learning on a generated model generated to predict likely outcomes.
Big data tools: Mastering tools like Hadoop and Apache Spark help you deal with large amounts of complex data. This is because these work significantly better than traditional data processing tools.
Data visualisation: This technique is used to provide a graphical representation of information and data. It helps gain valuable insights. Data visualisation is an art-like skill that you develop through hands-on experience.
Applications of data science
Data science offers solutions in sectors like healthcare, finance, e-commerce, security, semiconductors, gaming. Here are some applications of data science:
Data science techniques are used to classify information for intrusive section systems. The system then generates an alarm on the detection of extraneous elements that do not match the classification rule. This process detects security loopholes, attacks and anomalies. Detection of outliers using data science techniques helps detect fraudulent transactions.
Within healthcare, data science helps build advanced equipment used in diagnostics and treatment. Applications include:
Medical image analysis: Detecting tumours and artery stenosis is possible with machine learning. Support Vector Machines (SVM) is an algorithm used for this purpose.
Genetics and genomics: The study of DNA's impact on health by identifying the relationships between a patient's genetic makeup, diseases and responses to drugs.
Drug development: Data science has created ways to speed up the drug discovery process. Algorithms can predict the effects of different versions of a drug under development, bypassing the longer route of clinical testing.
Personalising user experiences
The retail and e-commerce sector collects and tracks customer details, transactions and product sales. This helps them identify customer purchase behaviour and product preferences. This improves the product recommendation system,
Advancing image recognition technology
There has been significant improvement in image recognition technology. The image recognition APIs in photo apps organise and group images automatically based on models trained on large amounts of data. Fashion and furniture retailers have enabled image searches in their digital shopping experience wherein people can search for similar products using a sample photo.
Helping airline businesses
Data science has helped many airlines improve various areas:
predicting flight delays
making data-driven decisions on aircraft purchases
design successful customer loyalty programs through personalisation
improve operational planning and execution.
Designing better games
The gaming industry is reliant on advanced machine learning algorithms. For instance, it automatically adjusts the game to a certain difficulty level based on the gamer's ability. Popular augmented reality (AR) games have also used data science to choose convenient locations for outdoor AR experiences.
Making speech recognition possible
Speech recognition is a common feature on smartphones, smart TV remotes and entertainment systems in cars. You can use voice commands to instruct systems like your car's navigation or search for movies by speaking their titles into your TV remote. Other applications include text-to-speech, which translates your spoken content into its written version.
How much do data scientists get paid?
Data science has some of the most lucrative options in the market. Your salary depends on your position, the company you work for and your geographical location. The average salary of data scientists is ₹8,50,138 per year.
Salary figures reflect data listed on Indeed Salaries at time of writing. Salaries may vary depending on the hiring organisation and a candidate's experience, academic background and location.
Please note that none of the companies mentioned in this article are affiliated with Indeed.
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