Introduction to Data Science

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2. Introduction to Data Science

3. What is Data Science? As per Harvard Business Review Data scientists is considered as the sexiest job of the 21st Century. Data Science is an interdisciplinary field making use of scientific methods, processes, algorithms and systems for extracting knowledge and insights from structured and unstructured data, and applies knowledge and actionable insight from data across a broad range of application domains. Considering Data science training to carve a career in the field can be rewarding.

4. Data Science Definition Data science is the practice of mining large data sets of raw data, structured and unstructured for identifying patterns and extract actionable insight from it. It is an interdisciplinary field and the foundation of data science includes statistics, inference, computer science, predictive analytics, machine learning algorithm development, and new technologies for gaining insights from big data. Data science life cycle includes acquiring data, extracting and entering it in the system. Next stage includes maintenance, including data warehousing, data cleaning, data processing, data staging, and data architecture.

5. Stages of Data Science Lifecycle • Data science has five stages in the lifecycle: • Capture: Data acquisition, data entry, signal reception, data extraction • Maintain: Data warehousing, data cleansing, data staging, data processing, data architecture • Process: Data mining, clustering/classification, data modeling, data summarization • Communicate: Data reporting, data visualization, business intelligence, decision making • Analyze: Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis • Data Science training; is the right platform for learning various segments and aspects of lifecycle before venturing into the field.

6. Why Businesses need Data Science? The amount of data created every day has resulted in need for professionals to tackle and make sense of it. There is a huge mine of unstructured and semi-structure data coming from various sources and the traditional business intelligence tools are just not sufficient to make sense of it. Hence, Data science offers advanced tools for working on large volumes of data coming from various types of sources such as financial logs, marketing forms, sensors, instruments, text files, and multimedia files. Therefore, it is wise to consider data science bootcamps in case you wish to diversify in the field.

7. Application of Data Science • Anomaly detection (fraud, disease, crime, etc.) • Automation and decision-making (background checks, credit worthiness, etc.) • Classifications (in an email server, this could mean classifying emails as “important” or “junk”) • Forecasting (sales, revenue and customer retention) • Pattern detection (weather patterns, financial market patterns, etc.) • Recognition (facial, voice, text, etc.) • Recommendations (based on learned preferences, recommendation engines can refer you to movies, restaurants and books you may like)

9. Why consider a career in Data Science? Data science is considered a great career path and one of the in-demand career paths. There is not even a single industry that cannot benefit from data science. Apart from high-demand there are high salaries to expect in the career. As per Glassdoor, data scientist makes an average of $116,100 per year. SynergisticIT, Is the best data science bootcamp to consider in case you wish to be part of a high paying career.

10. Job Roles in Data Science • Here are few roles to consider in Data Science: • Data Analyst • Data Engineers • Database Administrator • Machine Learning Engineer • Data Scientist • Data Architect • Statistician • Business Analyst • Data and Analytics Manager