The growing consumption of technology and internet-based services is leading to the generation and accumulation of enormous data every day. Companies need to analyse this unstructured data to understand customer behaviour, extract actionable insights, create and execute relevant marketing plans and make objective decisions to build and maintain brand loyalty.
As new technologies are bound to emerge and evolve and dealing with data would become more complex, the demand for skilled professionals for data management, organisation and analysis would be higher than ever. So, if you are also planning to build your career as a data scientist, here are a few important things to know.
Data could be anything from personal details such as name, email-id, contact number or address, that a customer uses to register on different online platforms, to their activities such as adding products in the cart, using a search engine, checking the weather, reading news, ordering food, paying bills, downloading music, etc.
These unique or recurring activities are saved in the form of data based on which customers receive emails, relevant product ads, deals and discounts messages, push notifications, etc. Management and analysis of this data are critical for organisations to grow their customer base and keep their regular and potential customers updated about their activities like new product launches, schemes and promos.
Data scientists closely work as interns, employees or freelancers with different departments in public or private organisations. They obtain, manage, process and clean the data, apply techniques like statistical modelling, machine learning and artificial intelligence, measure the data and present the final results to the higher authorities in their respective organisations.
As companies have different short and long term goals such as reducing production expenses, hiring more employees, increase traffic on the website or app, creating a new product, branch out business in a new location and increase their sales, data analysis helps them determine how they can achieve these goals.
Data scientists communicate with different departments on a regular basis and understand their requirements and goals and conduct industry research to solve business problems. They extract structured data from their databases through SQL and collect the unstructured data available online through surveys, web scraping and APIs.
They deploy complex analytical measures to clean the data, remove irrelevant information, find the crucial missing data and prepare data for final use. Based on this final data, companies understand the industry trends and modify or improve their existing strategies or procedures to increase efficiency, credibility and brand value.
Data science encompasses multiple concepts like data mining, machine learning, data analytics, deep learning and artificial intelligence. Studying and analysing data is a complex process that requires an understanding of new-age technologies. Individuals willing to make a career in data science need to hone multiple soft and domain skills.
You must have strong mathematical and statistical reasoning, along with the working knowledge of at least one programming language like Python. You should have a good idea of working on data extraction, data loading, data transformation, data exploration and data wrangling.
Knowledge of computer science, data storytelling, machine learning, statistical analysis, business intuition, critical and analytical thinking, inquisitiveness and interpersonal skills are a few more essential skills that data scientists must possess.
Individuals with relevant skills and experience can work in different entry-level to senior roles such as technical specialists, research analysts, machine learning engineers, data analysts, data engineers and data science generalists.
Enrolling in an affordable, accessible, advanced and up-to-date online data science training is the best way to study for beginners from technical or non-technical backgrounds. These short-termed online training modules feature basic level lessons on Python, statistics, predictive modelling and machine learning so that even beginners can explore the field.
After enrolling, you get an overview of data science, understand its different applications and get insights into how data science disrupts industries. While learning Python, you get skilled in reading CSV files and understand variables, operators, functions, dictionaries and data structures.
In statistics, you learn data distribution, probability, types of testing and understand inferential statistics and descriptive statistics. In the machine learning module, you get to explore different predictive models and their stages, data extraction and exploration, univariate and bivariate analysis, model building and linear regression.
Courtesy: Internshala Trainings e-learning platform to learn new-age skills from Internshala.