According to Wikipedia, data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.

Companies spend a lot on data science in order for them to have the information with which they can make smart decision, it is therefore important for them to hire the best in the field. And according to Glassdoor’s 50 best Jobs in America for 2019, data scientist was ranked the best for the 4^th year in a row.

In no particular order, we look at the skills needed to become a successful data scientist

1. Programming:The knowledge of some programming languages will be needed in manipulating data and utilizing certain algorithm so as to come up with a meaningful insight. These programming languages include Python, R, Perl, or C/C++, Java and database querying language like SQL.
2. Statistics and Probability: Statistics depend largely on probability theory and as well, probability relies on the statistical theory to help data scientists predict or analyze trends from data and to also to understand more about the data.
3. Data Wrangling: The data collected are usually in raw or complex format and needs to be organized before any meaningful analysis can be done. It is therefore imperative for data scientists to know how to work with unstructured data.
4. Data Management: The essence of data management is to help companies in optimizing the use of data so that they can make decision that will benefit the company. So a data scientist should be able to define, retrieve and manage data effectively, securely and cost-effectively.
5. Data Visualization: In a simple term, data visualization is the presentation of data in a graphical or pictorial form. Some important tools needed for this include Google Analytics, MS Excel, Fusion Charts, SAS Tableau, PowerBI.
6. Machine learning: Machine Learning itself is a subset of data science, and for companies that deal with huge amount of data, ML is always in demand. The Machine Leanring methods needed for such task are Random Forests, Regression Models, Naive Bayes, TensorFlow etc.
7. Cloud computing: Cloud computing is often needed in data science to access the resources that are needed to manage and process data. For instance, cloud computing allows the data scientists to use cloud platforms such as Google cloud, AWS and Azure which provides access to programming languages, databases, operation tools etc.

As a beginner who is not sure of where to start, you should not feel overwhelmed by the skills mentioned above, you can start with one of the skills and be determined to continually improve on your skill sets.