Data Science

Prerequisites of Data Science

Today data science has become very important for every company. The role of data science in each company for improving their business is increasing day by day. In order to find out unseen patterns, extract meaningful information, and make perfect business decisions modern tools, as well as techniques, are used on the massive amount of data that is collected from different sources. In total, this is what data science deals with. To build predictive models data science uses machine learning algorithms.

If you want to start a career in data science, it is necessary to acquire a piece of very good knowledge in the course material data science, strong communication skills are required its only because then only the useful insights and the conclusions which are invented can be shared and discussed with the higher authorities as well as to the teammates. When working with real-time projects very good practical experience is also acquired.

So in this module let us discuss all the prerequisites that are required in order to learn data science. As we all data science is a technique that can be applied in any domain. Let's get into some of the prerequisites that needed to be known so that each and everyone can easily make a transition mainly toward data science.

Prerequisites of data science

Prerequisites of data science

Technical Data Science Prerequisites and Non-Technical Data Science Prerequisites are the main two categories of data science prerequisites.

Before starting to learn data science  we should know some technical  concepts  and let us look what they are:

  1. Machine learning 

    Machine learning is known as the back bone of data science . In order to make quality predictions and estimations each and every data scientist should have a deep knowledge in machine learning. This will help the machines to take proper and right decisions mainly in real time with out the help of human beings intervention. The machine learning is the main branch of artificial intelligence and it is completely based on idea where the system will be able to learn from data, pattern identification and decisions are made with minimal human intervention.

  2. Modelling

    Mathematical models are used to support data science. Quick calculations and predictions are made with the help of mathematical models all these are done on the data which is obtained from different sources. Modelling is mainly used to identify the most appropriate algorithms which is more suitable for problem solving and it also guide with how to train the models.

    The understanding of the problem, extracting the useful data, data cleaning ,Exploratory data analysis, features selection, incorporating the machine algorithms, testing the models and finally deploying the model are the various steps that are involved in data science modelling.

  3. Statistics  

    Statistics are known as the core of data science. In order to get meaningful insights from data first thing is to understand the data very well. To understand , to interpret and to evaluate the data in a detail manner statistics is the best tool.(link can given to basics of statistics)

    Mainly there are two types of statistics and they are descriptive statistics and inferential statistics. Descriptive statistics are again divided into measure of central tendency and measure of variability. Then measure of central tendency consists of mean, mode and median. The measure of variability consists of range, variance and dispersion. Data can be generated from different sources and these generated data are Collected and stored then it is Measured after that Analysing is done and finally it is visualised . All these are done successfully using statistical models and graphs.

  4. Programming 

    In order to execute a successful project completely based on data science high level programming is required. There are many programming language in that most common languages are python and R. Among these two languages python is the most common language because it is very easy to learn as well as it supports multiple libraries mainly for data science. Apache Hadoop, Tableau are the main programming tools in data science.

  5. Databases

    How a data science works, how we should manage a database, and how we will extract the useful insights from data all these things should be known by a data scientist. Database plays very important role in each and every data science project its because we are obtaining data from different sources and initially it is stored in a database and data is retrieved from the database. A database is nothing but it is a structured set of data which will be there in the computer memory or it is stored in the cloud. There are various ways as well as methods in order to access the data. A data scientist should design, create and interact with the database which is there in the computer memory or cloud based on which project we are working. To handle structured data a data scientists needs SQL and the structured data is there in the relational database.

  6. Be good at mathematics

    In the life cycle of data science project each and every modules consists of selecting the features, creating models, modelling every where mathematics is highly involved . Great knowledge in maths are required for each and every data scientist. Mathematical study is very important for a data scientist to reach somewhere in the data science career its all because to perform machine learning algorithms, to extract useful insights from data and for analysing the model for all these things mathematics is required. Statistics, probability, linear algebra and calculus are the main kind of maths used in data science.

  7. Data visualization 

    One of the important prerequisite for data science is data visualization. Representing the data with the help of graphs, pie charts, maps etc is known as data visualization.

    For better data visualisation there are multiple components such as data component, geometric component, mapping component, label component, scale component and ethical component. Data visualization is known as the subset of data science . The very effective data visualization techniques are scatter chart, bar charts, box plot, pair plot, kde charts, histogram, hexbin plots, line charts, heat maps, pie charts, area plot etc.