Top 10 skills you need to become a data scientist
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1. Programing & Softwares
Programming is one of the most important and essential skills required for a data scientist. You should know about some languages like C, C++, python, java, R, and SQL. These are the most favoured ones among beginners and professionals. In the field of data science, python is the most used language. After python, R is also a very liked language in the data science field.
Right now more than 75% of people are using python for data science work
And 20% TO 25% of people prefer R language for data science work
2 . Data Visualization
Data Visualization basically is a graphical representation of the finding from the data under consideration. You need to realize that good visualization effectively communicates and leads to the exploration to the conclusion. So data visualization is one of the most essential skills to become a data scientist because it helps you to better property things visually and helps establish a real-world Value from raw data. A few visualization tools to master are google charts, data wrapper, kibana, waterfall charts, etc.
3 . MACHINE LEARNING
For a data scientist, machine learning is a very important skill that you must have. When you work with a company that manages and operates a large amount of data where data is used for making all the decisions for the company’s growth. It may be a case that the demanded skills are machine learning. Machine learning for data science covers algorithms and different types of learning techniques like k- nearest neighbors, decision trees, random forests, Regression Models. And last but not least packages like Pytorch, tensor flow, and Keras also find their usability in machine learning for data science.
4 . DATA WRANGLING
Data wrangling is another skill that a data scientist must have. It is also called data munging. The process of mapping and converting data from a single raw data form in a different format is known as data wrangling. The data to be investigated is challenging to work and it will be chaotic. From data wrangling, you can provide a very accurate representation of actionable data in the hands of business and data analysis in a very short time.
A data scientist spends more than 65% of their time only on data management.
5 . Cloud computing
Cloud computing and data science coincide, typically because cloud computing gives a path to data scientists to use platforms like, AWS, Azure, Google cloud that provides an approach to databases, frameworks, programming languages, and operational tools.
You will be taken on cloud computing to perform tasks like data Acquisition, Sanitizing data testing predictive models and to perform data mining, etc
6 . Data Analysis
Data analysis focuses on processing and performing statistical analysis on existing datasets. Data analysis is the step where you understand all about the data and take its feel, this is usually the step where you learn a lot about the data. Data analysis is typically done in Xcel SQL and is the most important task of an analytics professional whereas in machine learning data analysis is a step in the whole process. In data science, you have to analyze two types of data 1. Structured data 2. Unstructured data.
A STUDY SAYS ABOUT UNSTRUCTURED DATA, 91% COMING FROM TEXT 33% FROM IMAGES 15% FROM VIDEO 10% FROM AUDIO AND 20% FROM OTHER SOURCES.
7 . Big Data
If you are a data scientist you need to manage structured and unstructured data because of the acceleration of the internet, social media networks fluctuate. There has been an unexpected jump in the rate of data we are creating. We can say Big data is everywhere now and there is a crucial requirement for assembling and saving data. As a data scientist, you need to interact with big data and learn how to manage and analyze that data.
Big data analysis has turned out to be an important factor as it helps in enhancing business decision making and giving the most significant edge over the contenders. You have to know about some framework or tools like Hadoop, apache storm, flink, and spark.
8 . DEEP LEARNING
It is an advanced form of machine learning. Deep learning is best suited for specific problem domains like image recognition and NLP. These days every organization is making deep learning models as it possesses the ability to solve the restrictions of conventional machine learning approaches
Deep learning is a high growth vertical in the field of artificial intelligence thanks to advancements in data storage capability and computational advancement. Libraries like TensorFlow, Keras, and PyTorch are a must if you want to build your career in the field of deep learning
9 . Advanced MS Excel
We can say Excel is the fundamental platform for advanced data analytics and comes in useful to run some quick analysis in python and R. You can do anything you want whenever you want and save as many variants as you prefer simply because data manipulation is relatively a lot easier & efficient with excel
CONCLUSION
Now you can see these are the most common skills you required to become a data scientist. Having these skills will help you to make a successful data scientist career. The demand for a data scientist is increasing day by day and it is expected that the requirement for the data scientist will increase in the future. Now, this is an excellent time to develop a career in the data science field. PIMS offers up-skilling opportunities in the data science domain for fresh graduates. Check out our PIMS DATA SCIENCE COURSE and make your career in data science
Big data analysis has turned out to be an important factor as it helps in enhancing business decision making and giving the most significant edge over the contenders. You have to know about some framework or tools like Hadoop, apache storm, flink, and spark.
