A beginner’s guide for getting started with data science

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Machine learning is also one of the cornerstones of the course on data science. Machine learning is considered to be the operating arm of artificial intelligence.

1- Introduction

Data science is an interdisciplinary field that makes use of various methodologies and approaches to derive insights from unstructured data sets. The tools, technologies, methodologies and algorithms that data science makes use of accounts for its application in various domains. Data science is not a uni-dimensional field but involves the knowledge of various disciplines like big data, data structures, statistics, business intelligence and advanced algorithms. The data science courses in India include all these subjects along with some elective subjects that give the learners the freedom to choose the subject that aligns with their interest.

2- Constituents of the syllabus of data science

The constituents of the syllabus of data science can be divided into various sector specific yet integrated areas of study. In order to get started with data science, it is important to kick start your learning prospects with books like introduction to data science.

  • The syllabus is supplemented by mathematics and statistics with the aim of giving professionals quantitative and scientific skills to work on different projects. Machine learning and Big Data Analytics are at the heart of the syllabus of data science. There is hardly any data science project that does not make use of Big Data Analytics and machine learning.
  • One of the practical components of the syllabus of data science is the programming language of python. This allows you to develop basic skills for training different models with the help of machine learning techniques.
  • Data structures and data modeling are also vital components of the syllabus of data science.
  • In addition to this, other constituents of the syllabus of data science include statistics, optimization techniques, cloud computing, fog computing, edge computing, data visualization and even matrix computation.
  • Artificial intelligence is one of the cornerstones of the course on data science. Needless to mention, it is one of the most important components of the course on data science.
  • Exploratory data analytics, data mining and data visualization are also a part and parcel of the course on data science.

 

3- Courses at the beginner’s level

The courses at the beginner’s level are relatively simpler and include exploratory data analytics with the aim of developing familiarity with basic techniques like data collection, data mining, data cleansing, data processing, data warehousing and data visualization. The courses at the beginners level include the development of skills like storytelling, communication and presentation.

In addition to the above-mentioned components, there are three basic prerequisites that are important parts of a data science course at all three levels, that is, the beginners level, the intermediate level and the professional level. These three components include big data analytics, Artificial Intelligence and machine learning. Let us take a close look at these three components in much more detail.

3.1- Big data analytics

Big data analytics is the art of uncovering information in large data sets and discovering patterns and correlation among them. The most important benefit of Big Data Analytics is that it helps businesses in quantitative decision making. Under the umbrella of Big Data Analytics, various technologies like predictive modeling and statistical algorithms are used to derive meaningful insights out of large data sets.

Problems of various businesses are solved with the help of analytics and various strategies are employed to derive answers to core problems challenging business growth. This is a simple example of business intelligence. With the help of business intelligence, it becomes possible to optimize the performance and operation of different business organisations. Big Data Analytics is important because it helps in data driven decision making and also helps in drafting effective marketing strategies and enhancing income earning prospects of a company. It also boosts the operational efficiency of a business and provides it an edge over its competitors.

Big Data Analytics helps data professionals to collect data from various sources including cloud and mobile applications. Some of the rich sources of data include social media content, mobile phone records and data which is captured with the help of sensors in the environment of IoT. After data is collected, it is processed and stored in a proper structured format. The third stage involves the improvement of the quality of data by fixing any inconsistencies in it. The collected data is analysed with the help of different types of analytics softwares and methodologies like machine learning, deep learning and text mining. Finally, the insights derived from data are portrayed in a simplified format with the help of powerful data visualisation tools.

3.2- Artificial intelligence

Artificial intelligence is the replication of human intelligence in machines and this is achieved by training machines with the help of large data sets so that they can optimize their capability to achieve a particular goal. Artificial intelligence is one of the most important courses related to data science because it gives practical orientation to the theoretical learning of students. Artificial intelligence in itself is a vast field that involves the paradigm of learning, reasoning and perception. The powerful application of artificial intelligence gives us an insight into the domains where it can prove to be a prime game changer.

It is important to take a look at the four types of AI that form a part and parcel of different AI courses.

Reactive artificial intelligence

The first type of artificial intelligence is called reactive artificial intelligence. This type of artificial intelligence uses a set of algorithms and inputs to optimize the existing strategies to execute a particular task. For instance, it helps in optimizing the strategy to win a game of chess.

Limited memory artificial intelligence

The second type of artificial intelligence is called limited memory artificial intelligence. This type of artificial intelligence is named so due to the limited capacity of memory or temporary memory that is available to it. However, it helps in learning with help of previous experience and adapting to novel situations. An example of this is the capability of self-driving vehicles to adapt to unique situations in heavy traffic.

Theory of mind AI

The third type of artificial intelligence is called theory of mind AI. As the name indicates, it is an adaptive type of artificial intelligence which has a great capability to retain previous experience. Consequently, this is the type of artificial intelligence which is used for training various types of chatbots. Theory of mind AI is the type of AI which helps machines to pass the Turing test so that there is very little difference between the communication aspects of man and the machine.

Self-aware artificial intelligence

The fourth important type of artificial intelligence is called self-aware artificial intelligence. It is one of the most superior types of artificial intelligence which allows the machine to believe in its own existence. Although artificial intelligence has not reached this level of consciousness, it is believed that self-aware AI is the final level of machine intelligence that is depicted in science fiction. The research in this domain is still going on and is considered to be the future of AI Technology.

3.3- Machine learning

Machine learning is also one of the cornerstones of the course on data science. Machine learning is considered to be the operating arm of artificial intelligence. It can be classified into supervised learning, unsupervised learning and semi-supervised learning. The advanced aspects of machine learning help in training of different types of machines with the help of advanced algorithms. Some of the most important algorithms of machine learning include linear regression, logistic regression, classification, clustering, support vector machine and decision trees. In addition to this, deep learning is also a type of machine learning that helps in deriving insights and identifying patterns in text and images with the help of artificial neural networks.

4- The bottom line

In one word, the course on data science is one of the most holistic courses that gives interdisciplinary knowledge to professionals.

 

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