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What skills do data scientists need

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Data scientists are very much in demand today, because companies across all verticals require experts who can deal with Big Data. Are you wondering what Big Data is? As the name indicates, it refers to the large varieties, volumes and velocities of data. Data scientists need to draw useful insights from this data, and help in critical, data-driven decisions for businesses. So, they need to have certain special skills to get hired by top companies.

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If you want to pursue a data science career and get better at it every day, here are the skills you need to equip yourself in:

Technical Skills

  • Statistical skills and computing

Statistics is one of the most-used skills in data science. Since it is a vast subject, you may not have the time or knowledge to master all of its concepts. However, to become a successful data scientist, you must have sound knowledge of topics like probability distribution, regression analysis, hypothesis testing, dimensionality reduction and the like.

  • Mathematical skills

Most data science courses require you to have studied math stream in your 12th or have math as one of your subjects in your bachelor’s degree. This is because data science uses several math concepts you should be aware of.  Though you don’t have to be a math expert, you should know enough about concepts like linear algebra, calculus, statistics, geometry, trigonometry, vector models and the like.

  • Programming knowledge

You need to have good knowledge of programming languages like Python and R to become a good data scientist. While knowledge of other languages like C, C++, JavaScript and the like is preferred, knowledge of Python and R is mandatory.  You should also know how querying languages like SQL work.

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  • ETL (Extract, Transform and Load)

The primary job of a data scientist is to extract the data properly from multiple sources of generation. These sources could be databases like MySQL, MongoDB, Google Analytics and more. This raw data, then, needs to be transformed into a proper format, and saved properly, as this forms the basis for all analytics & research work. For analysis purposes, you will need to access the Data Warehouse, where the transformed data will be loaded eventually.

  • Machine Learning

As a data scientist, you should be able to understand the logical reasoning behind certain patterns in the data, and implement the appropriate tools to understand these patterns, make accurate predictions, reduce human interventions, create algorithms for predictive and analytical models, and remove redundancies, especially in complex data sets. All these tasks are possible only if you have sound knowledge of the latest machine learning and artificial intelligence trends used in the field of data science. 

  • Deep Learning

This skill is directly related to your knowledge of programming languages. Once you have written the programs using Python or R languages, you can use specific data learning tools like Automatic Text Translation to help the computers understand your language. Your knowledge of deep learning and NLP (Natural Language Processing) will ensure that the computer understands natural languages easily, and gives accurate output, thereby saving a lot of time, effort and money in the bargain. In short, when you have this skill, you can make the computer mimic the brain of human beings!

  • Data Visualization

One of the primary responsibilities of a data scientist is to present data in an attractive and understandable visual format, so that the management can understand the critical factors easily. As an aspiring data scientist, you should immediately brush up your knowledge on data analysis tools like Advanced Excel and Data Visualization Tools like Power Bi & Tableau. Though there are many other tools in this category, these two are the most commonly used in data science.

  • Data Wrangling and Exploratory Data Analytics

Data scientists are involved in extensive data-wrangling exercises in their day-to-day job. This involves working on large volumes of data, removing duplicates, understanding relationships among complex variables, and presenting them in an easy-to-understand format. So, you should be an expert in tools like data wrangler, spreadsheets, OpenRefine, Tabula and GoogleData Prep, as all of these help you in cleaning the data and preparing them for further analysis.

  • Big Data

Big Data, as we already explained, refers to the billions of bytes of data that are produced every day across various industries. If you are an aspiring data scientist, you should equip yourself with all the skills that help you to deal with large volumes of data. You should know where to source the data from, scan through the data quickly to identify duplicates, store the processed data properly, present them in visual formats, and do all types of analysis to gain useful insights. Knowledge of Big Data tools like Hadoop, Spark, RapidMiner, Knime and, is a must for data scientists today.

Non-technical skills

  • Excellent Communication skills

As a data scientist, you will be required to communicate with your peers, superiors, clients and all other stakeholders regularly. To get inputs and feedback on the data, you need communication. To present the final data to the management and explain the insights that you so painstakingly prepared, you need communication.

  • Problem-Solving and Critical Thinking

In your job as a data scientist, you will come across many small and big challenges. You need to think smartly in these cases, and always focus on creating practical and cost-effective solutions for the problems. Making the right decisions at the right time (like the exact tool to use for analysis, sources to pick and choose for extracting data, choosing the right visual format for the final presentation of data, etc.) will make a huge impact on the decision-making of the management.

  • Intuitive skills and attention to detail

As you may have to deal with large volumes of data on a daily basis, you may find many chances of committing errors due to oversight. However, the margin of such kind of errors is very low for data scientists. The base of any strong analysis is data. So, to be a successful data scientist, you should have an eye for detail, and you should be able to trust your intuition to read between the lines when analyzing huge volumes of data.

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