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Due to constant development in the field of big data, the demand for data scientists at the enterprise-level has increased. It is used to refine the process of product development, increase customer retention, or find new business opportunities, organizations depend on the data scientists skills to grow, sustain, and stay ahead of the fellow competition. The data scientist career is a booming field in the corporate sector. To become a data scientist, you need to possess both technical and non-technical data scientist skills. In this article, we are going to see in detail the list of skills that a data scientist must have to excel in thier career.

Data Visualization

Data visualization is the key part of the data life-cycle. Quality prior experience and hands-on knowledge is needed on various visualization tools. Some of the visualization tools are kibana, Google charts, tableau, and data wrapper.  A skilled data visualization expert knows better about ways to build a story out of the visualizations. To begin, you must be well versed in the histogram, bar charts, pie charts, and then gradually move to thermometer charts, waterfall charts, etc.

Big Data

Nowadays, Bigdata is almost everywhere and there is an urgent need to collect and preserve the generated data. Due to the rise of the internet and IoT, there has been a sudden rise in the rate of data we are generating. Big Data Analytics has become essential as it helps in improving business, decision makings, and help to stay ahead of your fellow competitors.

Programming Knowledge

The programming language is the best way to connect and communicate with the machine. If you want to become a data scientist, you need to be comfortable with at least one programming language. Python, R, or Julia are to name a few and each language has its advantage and disadvantage. Python is the object-oriented programming language having multiple data science libraries along with rapid prototyping. On the other, R language is best for statistical analysis and data visualization.

Data Manipulation And Analysis

Data manipulation is the step in which you clean the data gathered and transform it into a format that can be easily analyzed in the upcoming stages. Normally, data manipulation takes up a lot of time but it supports you to make better data-driven decisions. 
Data analysis is the step where you know and understand everything about data. Generally, data analysis is done in SQL, Excel, Python, and is an important step in machine learning.

Machine Learning

A data scientist should have machine learning as a core skill.  The skill is used to build predictive models. You can begin the machining process by learning the logistic regression model and linear model. Then you can move ahead to advanced models like CatBoost, XGBoost, Random Forest, and more.

Communication Skills

Communication is the key in the data scientist role. The data scientist work role involves communicating technical terms with a non-technical term, such as sales and marketing departments. As a data scientist, you need to communicate by using data storytelling. With the help of storytelling, You can properly communicate your findings to your employers.

Fundamentals Of Data Science

You need to start learning the fundamental concepts of data science to begin your career. It is essential to understand the basic concepts of machine learning, data science, and artificial intelligence. You have to understand topics such as deep learning, business analytics, data engineering, tools, terminologies, supervised and unsupervised learning.

Statistics And Probability

To build sentences, you need to be familiar with grammar to build the perfect sentences similarly statistics is an important concept before you can produce high-quality models. You should clear with concepts such as mean, median, linear regression, mode, variance, and standard deviation.

Software Engineering

It is important to learn the basic concepts of software engineering subjects such as data types, compilers, software development projects, and time-space complexity. If you write clean and error-less code, then it will help you in the long run and help you collaborate with your team.

Model Deployment

Model deployment in the machine learning life cycle is an important skill that a data scientist must be clear. It is very crucial to know the basics of model deployment and why it is important.

Conclusion

Hope this article will help you understand the skills you must have to become a data scientist. If you need to excel in your data scientist career, then learn from the institute, work on live projects or start work as a data scientist-intern. To refine the product development, boost customer retention, or mine through data to search for new business opportunities, brands are increasingly relying on the data science skills to grow, stay updated, and grow.