Skills Needed To Become A Data Scientist

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Today many organizations, including the Big Tech ones like Facebook, Google, Tesla, Amazon, Microsoft, and also others like Uber, Air Bnb etc, require some sort of analysis performed on their data. From recommendations that are personalized for every individual user to applications that have advanced image processing capabilities, the uses of data science are far-reaching.

With more data being generated every second today than we have generated since the beginning of human existence, it is important to come up with some sort of mechanism that helps us to convert this huge amount of data into meaningful information.

Data Science - Computing That Creates Art

Data science has been dubbed as the sexiest job of the 21st century. However, it isn’t a magic wand you can whip out on whim to wave at a dataset, hoping that the spells you muttered would make information jump right out at you. It isn’t a simple software that you can master in a matter of weeks either. Data science is a multidisciplinary field that consists of data mining, analytics, statistical analysis, machine learning, and data engineering amongst a host of other fields. It deals with the extraction, manipulation, and interpretation of data that has been collected from a variety of sources. Simply put, data science allows businesses to make informed decisions based on the data that they have collected.

To become a data scientist, one needs to have the right set of skills. Let’s take a look at some of them!

Top Skills Required To Become A Data Scientist

  • Analytical Skills One must be ready to solve problems by identifying various solutions. An inquisitive and analytical mind is a prerequisite to become a data scientist. It also requires one to have a desire to understand what the data tells them, thereby forming initial analysis and developing insights.
  • Team WorkBeing a team player can take you places. Data science is not a one-man job and requires the skills of different people to produce effective results. One might have to work together with other data scientists, data architects, data engineers, and data aggregation specialists.
  • Communication SkillsGood communication skills help you to go far ahead as a data scientist. One can clearly communicate ideas both in writing and verbally. Understanding the data science keywords can also help you to communicate efficiently.
  • Machine Learning and Artificial IntelligenceMachine learning helps to learn about the various models that can be used for the purpose of predicting and analyzing data. It helps to process huge amounts of data using different algorithms and models that are specifically designed for data processing. It can also help to automate various tasks so that it makes the job of a data scientist easier.
  • Mathematics Mathematical concepts help to build machine learning models that can perform data management tasks. Statistics, closely related to mathematics, is one of the most important subjects to learn when it comes to becoming a data scientist. Essentially, the subject helps to understand and process the data in order to attain different results. Statistics also helps to understand the multiple ways to solve problems and provides ways to select the best mathematical approach to follow for effectively managing data.

    Some of the important mathematical concepts to be familiar with include calculus, linear algebra, and probability.

  • Data ManagementData management skills include extracting data from various sources, transforming data to easily perform operations on it, and loading the data into a data warehouse. One must also know various big data processing frameworks so that they can train large quantities of data using various ML/AI models and techniques.
  • Programming LanguagesThis one is pretty straightforward. To become a data scientist, you need to know how to develop and test in different programming languages. Some of the most commonly used programming languages for data science include Python, R, SQL, C++, Java, Scala, MATLAB. Python is one of the most popular programming languages and is widely used in data science.
  • Software and ToolsIn order to help data scientists, there are many software and tools available. Some of these tools help you to manage large volumes of data by moving it onto the cloud, interpret data efficiently, and to extract information from data sets. The most popular software and tools to learn for a data scientist include SAS, Hive, TensorFlow, Apache Hadoop, and Apache Spark.
  • Data VisualizationData visualization helps to “see” what the data seems to be telling us. It helps us to understand patterns and data variations so that we can interpret our data better. Due to its ability to produce visuals in the form of graphs, it can be read by even those who are not trained in data science or have technical prowess. Some of the most popular data visualization tools include Tableau, Microsoft Excel and Power BI.
  • Microsoft ExcelUnsurprisingly, it is absolutely essential that we mention Microsoft Excel because of just how powerful the tool is. We can create visualizations, perform calculations, automatically generate graphs, and even automate the pages to acquire data from various sources!

These are some of the most important skills to have as a data scientist. Of course, pursuing a degree in data science can help you to become a well-rounded professional with the right set of skills. Take a look at our article that lists some of the best courses available to gain more insights about data science!

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