We have compiled a list of top skills one needs to be a successful data scientist:
Python Coding
R Programming
Hadoop Platform
SQL database and coding
Machine Learning and Artificial Intelligence
Apache Spark
Data Visualization
Unstructured data
Python Coding: Python is the language of choice for most when it comes to data science. There are many reasons for its popularity among the data scientists, some of which are - its versatile nature which allows Python to be used for many kinds of applications; simplicity is also a major factor, Python language is easy to read and write; most important of all is the thriving open source community that Python has worldwide which keeps adding to the features of this programming language.
R Programming: R programming is preferred by many in the data science field due to the number of tools it offers while programming. Being proficient in at least one of the many analytical tools it offers is important if data science is going to be your choice of career.
Hadoop Platform: Although not mandatory, this is an important skill to have for a career in data science. According to a study done by CrowdFlower on 3490 LinkedIn data science jobs, Hadoop is the second most important skill to become a data scientist.
SQL database and coding: Learning SQL database is an important task to do for any data scientist enthusiast. MySQL offers quick commands that save time while performing operations on the database while also decreasing the level of technical expertise required to manage it.
Machine Learning and Artificial Intelligence: Machine learning is becoming the next hot prospect in the tech industry and its applications are endless. It is a field of data science as all Machine learning algorithms are applied to data. If you want to become a successful data scientist, then proficiency in these skills is necessary. A data science enthusiast should have good command over the following:
Reinforcement Learning
Neural Network
Adversarial learning
Decision trees
Machine Learning algorithms
Logistic regression etc.
Apache Spark: Apache Spark is a big data computation tool and is also one of the most used data sharing technologies around the globe. Data scientists prefer Spark over Hadoop due to its speed. Apache Spark is faster because it makes caches of the computations inside system memory while Hadoop uses the disk for read/write operations. Easy to use and high-speed computations are what makes Apache Spark stand apart. The tool is used to make the algorithms run faster. It significantly helps in the division of data processing of large chunks as well as in the case of complex and unstructured data sets. Apache Spark prevents any loss of data.
Data Visualization: A data scientist is just given a large chunk of data and tasked with analyzing it. To make relations between different data points, it is imperative that a data scientist has skills to use visualization tools such as d3.js, Tableau, ggplot, and matplotlib. When data scientists create results from the data, these tools help to put these results in a visual format for everyone to understand it better. One of the most important aspects of data visualization is that it significantly helps the organization in a way that brings them closer to the customer’s experience and needs by working directly with the data. Data scientists can gain insights from a particular data and use that result to act on a new outcome.
Unstructured data: Data given to data scientists is largely unstructured, so it is essential that a data scientist is aware of the necessary skills required to manipulate unstructured data as well. Unstructured data generally means content without any labels and unorganized into database values. For example, videos, social media posts, audio samples, customer reviews, blog posts, etc.
