Data science combines math and statistics, specialized programming, advanced analytic, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning.
The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century”. Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes.
The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. Typically, a data science project undergoes the following stages:
- Data ingestion: The lifecycle begins with the data collection–both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more.
- Data storage and data processing: Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data manage.
- Data analysis: Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalabilityment teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data waerhouse, data lake, or other repository.
- Communicate: Finally, insights are presented as reports and other data visualizations that make the insights—and their impact on business—easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.
paths to becoming a datan analyst
- Acquiring these skills is the first step to becoming a data analyst. Here are a few routes you can take to get them that are flexible enough to fit around your life.
- Bachelor’s degree: If you’re considering getting a degree to become a data analyst, focusing your coursework in statistics, maths, or computer science can give you a head start with potential employers. In 2020, the Indian government allowed fully online degrees for the first time. Now the country has over 500 approved online degree programmes available.
- Professional certificate: Entry-level professional certificate programmes usually require no previous experience in the field. They can teach you basic skills like SQL or statistics while allowing you to create projects for your portfolio and provide real-time feedback on your work.
- Self-study: If you want a path that doesn’t include formal training, you can independently learn the skills necessary for data analysis. You can build a portfolio to show prospective employers.