Globally, companies have realized the importance of data in running their businesses effectively as it enables them to make informed decisions around growth, optimization, and performance and further interpret the data accurately to reduce uncertainty in decision-making. With this, the demand for data scientists has drastically grown in every sector. Today, Data Science has become the core that supports enterprises for analytics, mining or extraction, NLP, ML, AI, etc.
The report by Michael Page India’s The Humans of Data Science indicated that Data Science will create roughly 11.5 million jobs by 2026. LinkedIn’s Emerging Jobs Report also ranked Data Science as the fastest-growing segment, which has witnessed a growth of over 650% since 2012 and is slated to grow to 230.80 billion dollars by 2026.
KnowledgeHut, a leading technology courses provider, recommends the following top 5 in-demand skills to get future-ready:
Statistics and Probability
To perform tasks and execute to get the desired output, Data Scientist is expected to have a strong command on statistics and probability. Before creating high-quality models, it is required to understand the concept of descriptive statistics including mean, median, mode, variance and standard deviation. With respect to probability distributions, there are hypothesis testing and confidence intervals that one needs to focus on.
Python and R Programming
Python and R Programming are the most common coding language required in Data Science roles for organizing unstructured data sets and generating adequate outcomes that are desired by most companies irrespective of their domain. To manipulate the data and apply sets of algorithms as and when required, Data scientists should possess these languages. They do offer certain frameworks and packages that help in gathering numeric and statistical data.
Data Extraction, Transformation and Loading
With numerous data sources available such as MySQL, MongoDB, Google Analytics, etc, Data Scientists must know how to extract data from such sources and then convert it into a suitable format or structure for querying and analysis. Once this process is done, they must load the data into the Data Warehouse, which will be used to analyze it. Data Science is a suitable career choice for candidates with an ETL (Extract, Transform, and Load) background.
Data Exploration and Data Wrangling
The initial phase in the data analysis process is exploratory data analysis (EDA) to figure and make sense of the data, and to modify the resources to get desired answers to problems. This process is done by observing patterns, trends, outliers, and unexpected outcomes, among others. Data Wrangling, on the other hand, is a lengthy and time-consuming process however it will help in making better data-driven judgments. Some of the common data wrangling techniques are missing value imputation, outlier treatment, correcting data types, scaling, and transformation.
Machine Learning and Artificial Intelligence
Candidates with comprehensive knowledge of Machine Learning and Artificial Intelligence surely stand out as proficient professionals in the world of Data Science. With a clear understanding of machine learning and AI concepts, an individual can work on different algorithms and data-driven models, and can simultaneously handle large data sets such as cleaning data by removing redundancies.
What’s Next
Aligning with the rapid demand for Data Scientists at the enterprise level across all industry levels, it is imperative that aspiring candidates focus on acquiring the above in-demand skills to be successful Data Scientists.
There are vast career progression opportunities for Data Scientists across domains including data analytics, big data, data mining and data science. In-demand roles that require specialization include Data Engineer, AI Engineer, and Business Analyst.
More and more specialized Data Science skills will continue to emerge with newer technologies fast-evolving. Data Scientists will continue to be one of the most in-demand roles over the next decade.
