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Skills to Master to be a Successful Data Scientist

Data science is a field where statistics, artificial intelligence, scientific methods, algorithms, and processes are used to build insights and extract value. Data science unifies these different datasets to provide a deeper understanding of certain phenomena. Professionals who practice data science are called data scientists.

Data scientists are responsible for collecting and analyzing data. A data scientist must be able to find patterns and trends to further understand a product or customers. Several experiments, testing, and other methods may be done to ensure a data-driven analysis and insights.

Importance of Data Science

Data science has proven importance across different industries. In this field, large data is provided to provide a big-picture analysis for a problem, market trend, product, and customers. Data provides businesses and organizations the power to make informed decisions.

Different industries have been making use of big data and analysis to improve their services and products. In Medicine, data has been used to improve the care provided by hospitals. For public transportation, London has been using statistical data to manage unexpected circumstances and map customer journeys. Data science also plays a huge part in the automotive industry as data science is the main technology that enables them to overcome challenges. These are just some of the industries that utilize data science.

Data science has been given importance and is becoming one of the rapidly growing careers across different countries. In Toronto, Data Scientists have an average base pay of CA$88,077 per year.

Now that we have discussed the things you need to know about a data scientist, let’s get onto the skills you need to be a successful one:

6 Key Skills for a Data Scientist


Statistics is very important for a data scientist. Data-driven companies where stakeholders depend on data for decision-making heavily rely on data scientists.


With programming, you are able to manipulate data and apply algorithms to come up with insights. Data scientists do not need to be experts in the matter but should be comfortable enough working with it. Python and R are the most used programming languages by data scientists.

Data Visualization

Visualization is an essential of data science. It is the gateway to create more meaningful information. This is also the part where data scientists tell the story based on the data they have gathered. Pie charts, histograms, heat maps, etc. can be used for your visualizations. The common tools used in this are Tableau and Power BI.

Machine Learning

Through machine learning, you are able to build predictive models. This is helpful for organizations that manage huge amounts of data. Machine learning includes K-nearest neighbors, Random Forests, Regression Models, and so on.

Data Wrangling

Data wrangling is where data scientists clean and unify a mess and complex sets of data. This is the part where you have to understand how to deal with the imperfections in data and prepare it for further analysis.

Software Engineering

It is always a good asset to have a software engineering background. Writing clean and good-quality codes can help you in collaborating with other team members.

Towards Data Science is a great resource you can read further on about data science. Read more to further enrich your knowledge in the field.

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