Do you have an analytical mindset? If you’re interested in decoding data, you can consider a career in data analytics. In 2012, Harvard Business Review described ‘Data Scientist’ as the “Sexiest Job of 21^st Century’. Till date, data analytics and data science jobs are considered as the hottest jobs. According to Burning Glass Technologies’ comprehensive marketplace study on data science and analytics skills, the job market of these jobs is growing exponentially.

Data Science- A Broader Term

Data Science is a broader term than data analytics and focused to deal with a large chunk of structured and unstructured data including data analysis, data preparation, and data cleansing. The process involves understanding of data to provide accurate insights for making key business decisions by asking unique questions that help in business innovation.

Essential Skills Required for a Data Scientist

Data scientists use various techniques to develop solutions for different business problems by applying predictive analytics, machine learning, sentiment analysis etc. These techniques are used to cleanse, process and understand data for extracting business insights.

Regardless of your skills or previous experience in the domain, you can always find a path for pursuing a data science career. Since there is a shortage of skilled data scientists, this domain offers an appealing career path or career transition for the working professionals.

Here are the 5 must-have skillsets for a data scientist as expected from the employers:

1. Programming Knowledge

Knowledge of programming languages such as Python, R, SQL are critical skills, while other languages like Spark, Scala, Java, C/C++ etc are good to have for data scientists. These languages are used to organize and analyze unstructured data. An in-depth understanding of different programming languages helps the data scientists to compare the advantages and disadvantages of each language while using it for data-driven decisions. Programming skills allow the implementation of statistical knowledge to practice and solve data science problems.

Python is the most commonly used programming language in data science because of its adaptability.

2. Statistics

Knowledge of statistics is a must for a data scientist as mathematics and statistics are the building blocks of ML algorithms. Different statistical principles, algorithms and functions are employed for data analysis. Statistical techniques such as distributions, mean, mode, median, variance, central limit theorem, hypothesis testing, kurtosis, skewness, linear algebra, calculus, etc., are used to explore data, identify relationships between two variables, uncover data anomalies, and predict future trends.

3. Machine Learning

Another important skill a data scientist must possess is the knowledge in machine learning algorithms. ML algorithms use historic data to build business models that can predict the output for the business processes. A data scientist should be familiar with both supervised and unsupervised machine learning algorithms such as basic regression, multi-regression analysis, regularized regression, logistic regression classifier, support vector machine classifier, K-nearest neighbor classifier, decision tree classifier, random forest classifier, and Kmeans clustering algorithm.

4. Data Visualization

Data visualization is the visual representation of data in the form of a chart, graph or any other visual formats. It helps to understand the trends and patterns of the data in various business processes. Data visualization converts a small and large amount of datasets in the form of visuals, which is used for interpreting data easily to recognize the areas of attention, predict sales, and understand consumer behavior.  Specialized tools like ggplot and Tableau are used to represent data in a visual format.

With data visualization tools, data scientists can tell impactful stories to the audience using data. With great storytelling skills, they can create an appropriate context based on the data generated and explain the insights effectively to the audience.

5. Cloud Deployment

Data Scientists must know the process of deploying ML models into the production environment. Model Deployment or deployment of the machine learning model is as critical as building an ML model. Data scientist with this skill have an upper hand in organizations. Kuberflow, Mlflow and TFX are some of the tools used to simplify the model deployment process. ML model deployment is the process of applying a model for making predictions using the datasets.

There are four types of cloud deployment models such as private clouds, public clouds, hybrid clouds, and community clouds. A data scientist must choose the right cloud deployment model based on organizational needs.

Conclusion

If you would like to become a successful data scientist, you have to acquire the necessary skills to deal with organizational challenges. As the data scientist has to evaluate data that helps to make informed business decisions, he/she also required to have a combination of good communication skills, knowledge of numbers, problem-solving skills, and understanding of business consequences.

Acquire the skills and knowledge for a data scientist with a PGDM/MBA in Business Analytics.