How to be a Data Scientist

Data science

Dubbed as the 21st century sexiest job, data scientist is one the most sought after roles in the world. Typically, most industries are dealing with digitized data in apps or an online presence. Hence, the demand for professionals who can sort, analyze, interpret, and find insights from data is on the rise. But, how can you venture into this booming career? Read on to learn how to be a data scientist and the essential skills needed. 

Education
A substantial educational background is required to acquire the right depth of knowledge. In fact, at least 88% of data scientists have a master’s degree while 46% have PhDs. The most common fields of study are statistics, mathematics, computer science, social science, physical science, astrophysics, and engineering. A degree in these courses offers you the necessary skills to process large chunks of data. 

R programming
R is open source software that allows you to analyze large data sets easily. It entails linear and non-linear clustering and modeling. It also ensures that you perform predictive and statistical analysis on real-time data and create visuals to relay the information to the business. Even so, R has a steep learning curve that requires you to source internet resources to ensure you are adequately adept with the programming.

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Python coding
Due to its versatility, python is the most common object-oriented programming language. It allows you to create datasets by incorporating different formats of data that you can use to import SQL tables easily. It is also an excellent data and visualization tool with a set of libraries that are specific to machine learning including scikit-learn and Panda. 

Hadoop platform
Ranked as the second most crucial skill for a data scientist, a strong understanding of Hadoop is a strong selling point. The software framework can store and process large data volumes across different clusters of computing devices. It is scalable, flexible and allows you to identify trends and project outcomes for better informed decisions. Besides, you can use Hadoop for data exploration, filtration, sampling, and summarization.

Apache spark
Similar to Hadoop, apache is framework software that caches its computations in memory. It can run complicated algorithms and hence you can use one or a cluster of machines to disseminate and process data. While it can prevent loss of data, its strength lies in the speed and its ability to compute seas of data easily.

SQL database
Structured query language or SQL is a programming coding that can help you add, delete, and extract data from a database. It also has concise commands to capture and break down data, edit indexes and tables to enhance accuracy. This will save you time and reduce the amount of programming you require to perform difficult queries.

 


Machine learning
Machine learning is a set of algorithms such as SciPy and NumPy that you can use to make projections based on the known information. You should acquire techniques including supervised machine learning, survival analysis, decision trees, outlier detection, computer vision, reinforcement and adversarial learning. The skills will enable you to publish datasets, build models, and increase personalizing the customer experience in sectors like retail.

Data visualization
Generally, people will understand pictures like graphs and charts more than raw data. With the aid of data visualization tools like d3.js, ggplot, Tableau, and matplotlib, data scientists can convert complex information into an easy-to-understand format. Hence, it is crucial to learn the principles behind visually encoding and communicating data.

Wrangling unstructured data
Data scientists sometimes work with content that doesn’t fit into the typical streamlined databases. That can entail blog posts, videos, social media, customer reviews, audios, and heavy texts lumped together. Therefore, learning the “dark analytics” (as pros call it), will help you manipulate unstructured date in different platforms and unravel useful insights. 

Domain expertise
As a data scientist you should have a solid understanding of the company you are working for and the industry at large. It is essential to discern which issues are crucial for the business to solve and what better ways it can leverage its data. Besides, a deep knowledge of the industry enables you to offer recommendations that are feasible to any constraints imposed on the business. 

Communication skills
Most businesses aren’t interested in what you analyzed but how it can impact their profits. Hence, a data scientist should be able to clearly and fluently interpret your findings to the non-technical team like sales or marketing department. You also need to use storytelling techniques to explain the technical terms in a layman’s language.

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Learning The Skills to land your dream job
With demand skyrocketing, you need to learn the skills required to be an adept data scientist. Starweaver is an excellent online resource where you can earn these proficiencies. We offer interactive tools as well as the best, most compelling data science content. Besides, we provide up to date tutorials that are supported by leading instructor-gurus and programmers.