Harvard Business Review called the Data Scientist’s job the sexiest job in the 21st century. Why is it that so? What is Data Science and what does a Data Scientist do?
Data Science is been a buzzword for quite some time now. The word ‘Science’ in the word Data Science means using scientific methods to turn data into values. Data Science can be explained as a blend of mathematics, statistics, tools, algorithms, machine learning techniques, and business acumen, all of which help us find out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions or to give solutions to the business problem in the form of data products or product recommendations. Data Scientists use various methods to analyze massive amounts of data to extract knowledge.
Which are some of the domains where Data Science is used?
E-Commerce: To identify consumers, recommend products, or analyze reviews, this technology is used. On social media websites we have seen many advertisements popping up based on our interests and searches is a classical use of Data Science technology.
- Healthcare: Medical image analysis, drug discovery, virtual assistants for doctors and bioinformatics are some key areas in healthcare where data science is used.
- Manufacturing: Predicting potential problems, monitoring systems, automating manufacturing units, and anomaly detection all use Data Science technology.
- Finance: Segmenting customers, risk analysis and strategic decision-making are the key areas in the Finance domain Data Science is useful.
- Transport: Car monitoring systems, self-driving cars, and enhancing the safety of passengers are all examples of how Data Science can be useful in the transport industry.
- Banking: Fraud detection, modeling possible credit risk for institutions and companies, and finding out the customer, and a customer’s lifetime value are areas that come under the banking domain using this technology.
To do all these, Data Scientists along with the project team involved need to extract knowledge and use various methods to analyze massive amounts of data. Here is a brief on the role and responsibilities of a Data Scientist and the skill set required to become one!
Data Scientist’s Role and Data Science Project Lifecycle:
Understanding the business problem: Data Scientists should ask relevant questions to understand the business problem clearly.
- Data Acquisition: Gather the right set of data from various sources like web services, logs, databases, and online repositories.
- Data Preparation: Once the data is gathered, then data preparation needs to be done. This includes data cleaning and data transformation. Data cleaning refers to the correction and removal of data with inconsistent data types, misspelled attributes, missing and duplicate values, etc. which makes it a time-consuming process. Data Transformation includes modification of data using pre-defined mapping rules.
- Exploratory Data Analysis: This step is done to understand what they can do with the data. With this step, the data scientist defines and refines the selection of what they call “Feature Variables” that will be used in the ‘Model’ development. This is the most crucial step in a Data Science project life cycle.
- Data Modelling: There are different machine learning techniques a data scientist applies to the data to identify the model that fits the business requirement. Then training and subsequent testing are done on the model to identify the best-performing model. There are different scripting languages like Python, R, and SAS which can be used for modeling the data.
- Visualization: This step involves the depiction of the insights into the most effective ways for the business to understand and resolve the problems. For visualization, different tools like ‘Tableau’, ‘Power BI’ and ‘QlikView’ can be used.
- Deployment and Maintenance: The finalized model after the business’s acceptance needs to be deployed into production and maintained for future activities. The real-time dashboards and reports will be built on top of these models and will be consumed by the business.
A Data Scientist must be proficient in any of the programming languages like Python, R, SQL, Java, etc. For statistics, mathematics, algorithms, modeling, and data visualization, Data Scientists usually use pre-existing packages and libraries. Data Scientists should also know how to access and query many of the top RDBMS, No SQL, and New SQL database management systems.
Data Scientist’s role is often confused with other similar roles like Data Analysts or Data Engineers. Data Analysts share many of the similar skills and responsibilities as a Data Scientist like processing and cleaning data, accessing and querying different data sources, summarizing data etc. However, the key difference is that Data Analysts typically are not computer programmers nor responsible for statistical modeling or machine learning. On the other hand, Data Engineers can be thought of as a type of data architect, less concerned with statistics, analytics, and modeling than their data scientists/analyst counterparts. Data Engineers are more concerned with data architecture, computing, data storage infrastructure or data flows.
The data Scientist job is an extremely important and high-demanding role that can have a significant impact on a business’s ability to achieve its financial, operational, or strategic goals. There is a huge demand in the market for skilled Data Scientists and thus there are many online certification courses also available on this topic that revolve around the concepts of Machine Learning, Data Analysis, Python programming for Machine Learning, etc. Udemy, Coursera, and Simplilearn are some of the online learning platforms where you can find many courses available in this technology. If you want to have an amazing, well-paying job in technology, if you like data and you are curious and not afraid of challenges, then Data Science is a job for you! Go ahead, explore, and learn!