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Introduction to Data Science : Skills Required

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  • Difficulty Level : Easy
  • Last Updated : 16 Jun, 2022

Data science is an interdisciplinary field of scientific methods, processes, algorithms, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. Big Data Analytics or Data Science is a very common term in the IT industry because everyone knows this is some fancy term that is gonna help us to deal with the huge amount of data we are generating these days. Let’s find out what the skills required are:datascience

  1. Math Skills: 
    • Multivariable Calculus & Linear Algebra: These two things are very important as they help us in understanding various machine learning algorithms which play an important role in Data Science.
    • Probability & Statistics: Understanding Statistics is very important as this is the branch of Data analysis. Probability theory is also important to statistics and it is mentioned as a prerequisite for learning machine learning.
  2. Programming Skills: 
    • Programming Knowledge: You need to have a good grasp of programming concepts such as Data structures and algorithms. Languages used are python, R, Java, and Scala. C++ is also used in some places where performance is extremely important.
    • Relational Databases: You need to know databases such as SQL or Oracle so that you can fetch the required data from them whenever needed.
    • Non Relational Databases: These are of many types but mostly used types are: i) Column: Cassandra, HBase ii) Document: MongoDB, CouchDB iii) Key-value: Redis, Dynamo
    • Distributed Computing: It is one of the most important skills to handle a large amount of data because we cannot process this much data on a single system. Tools which mainly used are Apache Hadoop and Spark. It has two main parts: HDFS i.e Hadoop Distributed File System which is used for storing data over a distributed file system. The other part is map-reduce by which we process data. We can write map-reduce in programs in java or python. There are many other tools also such as PIG, and HIVE.
    • Machine Learning: It is one of the most important parts of data science and the hot topic of research among researchers so every year new developments are made in this. You at least need to know common algorithms of supervised and unsupervised learning. There are many libraries available in python and R. List of Python Libraries: i) Basic Libraries: NumPy, SciPy, Pandas, Ipython, matpolib ii) Libraries for Machine Learning: sci-kit-learn, Theano, TensorFlow iii) Libraries for Data Mining & Natural Language Processing: Scrapy, NLTK, Pattern
  3. Domain Knowledge Mostly people ignore this thinking it’s not important but it is very very important. The whole purpose of data science is to extract useful insights from that data so that it can be beneficial to a company’s business. If you don’t understand the business side of your company like how your company’s business model works, and how you can make it better, then you are of no use to the company. You need to understand how to ask the right questions to the right person so that you can get the valuable information you need to extract the information you need. There are some visualization tools used on this business end such as Tableau which helps you display your useful results in a proper non-technical format such as graphs or pie charts which business people can understand.
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