Here's a list of top skills that you must have to be a successful Data Scientist:
1. 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.
2. 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 provides is essential if data science is your career choice.
3. Hadoop Platform: Although not mandatory, this is an essential skill for a career in data science. According to a study by CrowdFlower on 3490 LinkedIn data science jobs, Hadoop is the second most important skill to become a data scientist.
4. SQL Database and Coding: Learning SQL database is an essential task for any data scientist enthusiast. MySQL offers quick commands that save time while performing operations on the database while decreasing the level of technical expertise required to manage it.
5. 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.
6. Apache Spark: Apache Spark is a big data computation tool and 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 reading/write operations. Easy to use and high-speed computations are what make Apache Spark stand apart. The tool is used to make the algorithms run faster. It significantly helps in the division of large chunks' data processing and in the case of complex and unstructured data sets. Apache Spark prevents any loss of data.
7. 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, a data scientist must have skills in using visualization tools such as d3.js, Tableau, ggplot, and matplotlib. When data scientists create results from the data, these tools help put them in a visual format for everyone to understand 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.
8. Unstructured Data: Data given to data scientists is mainly unstructured, so a data scientist must also be aware of the necessary skills required to manipulate unstructured data. 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.