Data Science is a need of the current competitive digital world. There is a big list of tasks and innovations that are all possible only with data science. It is used in healthcare, sports, banking, finance, and government sectors in order to maintain and store every small piece of information. Data science is becoming more and more important as it is revolutionizing almost every industry. It helps to enhance their processes to save money, effort, and time. Data science helps businesses reduce wastage by analyzing different marketing channels’ performance and focusing on those offering the best and highest returns on investment (ROI). Data science converts raw data into valuable and useful information. You can learn and start your career in Data Science by checking out ProjectPro Data Science Projects.
Data Science is considered one of the most popular emerging domains and most in-demand career options. LinkedIn says that this domain dominates its emerging job position with around 37% hiring growth over the past three years. On the other hand, IDC says that global data will grow to 175 zettabytes by 2025. Here comes the main role of data science that enables companies to efficiently understand gigantic data from multiple sources and derive meaningful insights to make effective data-driven decisions.
Therefore, there is a high demand for skilled data scientists to maintain data issues. It increases the value of certifications such as Data Science courses in Hyderabad, Bangalore, etc. These certification courses can help candidates gain expertise in this domain.
This article is all about learning data science and becoming a master in it.
What is Data Science?
Data science is a technical process that involves scientific methods, statistics, specialized programming, advanced analytics, AI, and maths to identify and explain the business insights hidden in data. It extracts actionable insights from ever-changing and large volumes of data collected and created by organizations. Data science involves preparing data for analysis and processing, implementing advanced data analysis, and showing the results to reveal trends/patterns and enable stakeholders to make informed conclusions.
Here Data scientists are the professionals who master the full spectrum of the data science life cycle and maintain a level of flexibility and understanding to maximize returns at each step of the data science process to identify useful intelligence for their organizations. Data science life cycle involves steps: Maintain, Process, Analyze, Communicate, and Capture.
A data scientist has to perform several big and small tasks to achieve the goals, such as data mining, classification, clustering, data modeling, data summarization, confirmatory/exploratory, predictive analysis, text mining, regression, qualitative analysis, data reporting, data visualization, business intelligence, decision-making, data extraction, signal reception, data entry, data acquisition, data warehousing, data cleansing, data staging, data processing, data architecture, etc. So being a data scientist is a challenging job role that can provide you with an attractive salary package and industry recognition.
How to Start Learning Data Science?
If someone asks ‘How can I become a data scientist?’ many people will say that it is not easy without knowing machine learning, linear algebra, programming, visualization, statistics, distributed learning, etc. But learning data science is not that tough. Follow the below-mentioned steps and find your path to gain expertise as a data scientist.
- Prepare Your Base- Very first step to learning data science is to make your base strong. It requires good knowledge of programming language and the ability to work with data in that language. It also requires mathematical fluency to become really good at data science, so you only need a fundamental understanding of mathematics to get started.
- Learn Python and R- Now it’s time to get a sound knowledge of Python and R programming language. R is more popular in academia and Python is popular in industries. These languages support the data science workflow.
- Learn Data Analysis, Visualization, and Manipulation- If you want to learn data science it is also essential to learn data analysis, manipulation, and visualization using Panda Library. It provides a high-performance data structure (Data Frame) that is suitable for tabular data with different columns that are similar to an SQL table or an excel spreadsheet. It involves tools and techniques for writing and reading data, filtering data, handling missing data, cleansing messy data, combining datasets, visualizing data, etc. It helps increase your efficiency with data.
- Learn Machine Learning- Learning data science is incomplete without learning machine learning and building machine learning models. It helps predict the future or automatically extract insights from data. For machine learning in python, it is necessary to learn how to use the scikit-learn library.
- Deploying Machine Learning Model- Along with machine learning and deep learning, you must also learn to deploy ML models. It is the process of making machine learning models available to end-users for use. There are several services for deploying ML models such as PythonEverywhere, Flask, Microsoft Azure, MLOps, Heroku, Google cloud, etc.
- Real-World Testing- The validation and testing of the machine learning model after deployment are also essential to check its accuracy and effectiveness.
- Learn Data Collection- Learning data collection is also an important key in the field of data science that includes knowledge of various tools for data from local systems, CSV files, and scraping data from different websites through the Python library.
- Soft Skills- Besides technical skills, soft skills like good communication, analytical thinking, teamwork, business understanding, task management plays a vital role to become a successful data scientist. So try to achieve these skills and gain expertise in the field of data.
- Get Motivated To Learn and Practice Always- Always try to get surrounded by the things or people that can motivate you to practice what you have learned. You can update your knowledge with personal data science projects, online courses, Kaggle competitions, reading books and blogs, taking part in meetings/conferences, joining professional clubs, etc.
So It is just a start, and the journey has only begun. Data science is just an ever-evolving field where you can learn so much and explore your skills.
