Introduction To Data Science

What is data science?

Data science (DS) is a multidisciplinary field of study with the goal to address the challenges in big data. Data science is an area that manages, manipulates, extracts and interprets knowledge from the huge amount of data.

Theories and techniques from many fields and disciplines are used to investigate and analyze a large amount of data to help decision-makers in many industries such as science, engineering, economics, politics, finance, and education.

Need for a data scientist:

Data Scientist, the Sexiest job of the 21st century. Many companies struggle to manage their data and how to extract useful knowledge from their data. Companies use data to run and grow their everyday business. The fundamental needs of data science are to make a better decision which can help them to reach the top in their business.

Data science skill sets:

  •         Statistics and probability
  •         Database Storage and Management
  •         Data mining Techniques
  •         Machine learning and AI
  •         Programming  (R, SQL, Python)

Opportunities for Data Science:

With experts predicting that 40 zettabytes of data will be in existence by 2020, Data Science career opportunities will only shoot through the roof! Shortage of skilled professionals in a world which is increasingly turning to data for decision making has also led to the huge demand for Data Scientists in start-ups as well as well-established companies. A McKinsey Global Institute study states that by 2018, the US alone will face a shortage of about 190,000 professionals with deep analytical skills. With the Big Data wave showing no signs of slowing down, there’s a rush among global companies to hire Data Scientists to tame their business-critical Big Data. 

Here are some leading data science careers:

  •         Machine learning Engineer
  •         Data scientist
  •         Data Architect
  •         Data Warehouse Architect
  •         Business Intelligence Developer
  •         Data Mining Engineer
  •         Hadoop Engineer

Market Trend of data science:

  •         The field of Data Science is currently in transitional mode, and 2019 may well set the stage for advanced data technologies to take over routine business processes for higher efficiency and productivity while the human data scientists tackle more complex challenges.
  •         Increased business automation will not lead to the obsolescence of data scientists because smart tools will offer higher capabilities to human experts. The data scientist is becoming a “reinvented” scientist with sufficient free time in the future to explore complex business problems, while advanced technologies take over the routine processes.
  •         AI-enabled technologies will deliver truly personalized customer experience through interactive demos, live simulations, and visualization of custom solutions.
  •         Blockchain will go mainstream, encompassing diverse industry sectors from banking and finance to health insurance. Blockchain not only promises data protection and fraud detection but also smart contracts and automated governance in Data Management.

Data science usecases:

  •         Market Basket Analysis
  •         Price Optimization
  •         Inventory Management
  •         Sentiment Analysis
  •         Waranty analytics
  •         Location of new stores
  •         Fraud Detection

Concepts and definition of

1)   Artificial Intelligence

            Artificial Intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self-correction. Applications of AI include image recognition, speech recognition, and machine vision.

2)   Machine Learning- Deep Learning

            Deep learning is a subset of machine learning where it functions in a similar way but its capabilities are different. Deep learning is an artificial intelligence function that imitates the behavior of the human brain in processing data and creating patterns for use in decision making. Machine learning algorithm almost always requires structured data, whereas deep learning relies on layers of the Artificial Neural Network.

3)   Natural Language Processing (NLP)

            The field of study that focuses on the interactions between human language and computers is called Natural Language Processing (NLP).NLP techniques are very useful for sentiment analysis.

4)   Computer vision

            Computer vision is a field of computer science that works on enabling a computer to see identify and process an image in the same way that human vision does.