With digital disruption, exponential amount of data became available to the business world and beyond. This has manifested into exponential demand for data scientists across industries and in the government. The data explosion has further happened due to internet accessibility through smart phones which has even enabled interior rural places to also use internet through their phones. Hence, no doubt that data science training programs are really being considered seriously not only by individuals but also by the organisations. The reason for this is ever higher demand for data scientists and related fields.
Although there is a huge demand for data scientists and related jobs, the supply of data scientists has not kept pace. Recognising this issue and in order to meet the demand, many organisations are now investing in talent development through data science training programs within organisations. Even individual professionals recognising the need to close the knowledge gap, also join independent data science training programs.
But does one choose the right data science training program?
In order to understand it, we will first understand what is data science? Followed by what could be the key learning paths and hence, which data science training course is for you.
Data science is a wide term and generally it connotes finding hidden patterns from huge data through use of various tools, algorithms and techniques. It goes beyond just discovering – it’s also about proactively making decisions through predictive and prescriptive analysis of historical data. This means that data science is not only about presenting data in an understandable format like in the case of descriptive analysis but historical data can also be used to predict an event as well as be prescriptive in nature like a doctor’s prescription based upon the symptoms of an issue. The techniques and tools applied will differ and will be more advanced in the case of predictive and prescriptive analytics. Hence, one thing that needs to be seriously considered before opting for a data science training program whether by the individual or the organisation is the end objective.
Another aspect of data today is its structured and unstructured nature. Most of the data today is unstructured owing to the digital influx however, we still get some structured data particularly from an organisation’s perspective. Any of the above descriptive, predictive or prescriptive analytics can be applied on both the structured and unstructured data.
Furthermore, data science is a really broad term than just limited to machine learning. It includes data integration, architecture, visualisation, business intelligence, decision-making, predictive analytics and more. Hence, it is important to understand before starting a data science training program that it is beyond machine learning.
The key skills to consider while planning or choosing a data science training program should even include industry-relevant domain understanding besides the current in-trend technical knowledge that is most in demand. While on the technical side, the most important in-demand language for advanced data-science is python few organisations also focus upon other technical programmes such as R as well. Many organisations deal with extremely humungous data where understanding of Hadoop etc. becomes essential. It may even be that you as an individual or at an organisational level do not require really advanced data science skills – in which case the right language, analytical tools or software – tableau, excel etc. – maybe included in your data science training programs.
However, gaining programming knowledge is also not enough. Another important aspect is gaining requisite statistical and mathematical knowledge. This is usually needed at an advanced level. Further, it is also important to understand or have some idea about the domain in which one would like to work in the future. Another area as a data scientist you will need to be aware of is related to data governance. Data is being generated across every domain so it becomes critical to follow compliance. While using it. Data science can be applied in varied functions and industries. Some of the interesting data science applications are in industries related to entertainment, sports, media and more.
Whether as an individual or as an organisation, you are planning to choose the right data science training, the pointers mentioned may form a great starting point. This will help in creating more business value at your place of work. At an organisational level, a well-designed data science training program can also help you attract great talent. However, this will also depend upon the data science training path you are going to offer through the programs within the organisation. Individuals will need to choose the one which suits their future aspirations the best.
The learning paths can be multiple based on the opportunities. One can become either an analytics consultant, data science consultant, business intelligence analyst, a data scientist, a data analyst, machine learning specialist, data science specialist, statistical analyst or a business analyst. As mentioned earlier, one can also look at multiple domain and industries to work in. Depending upon the industry one chooses, there could be further requirement of training both from domain and technical perspective. The thing to note is does your industry churn a lot of data? Is there a need to enrol in a more advanced data science training program to manage this humungous data? What about data protection laws? Is this something applicable in your industry?
For instance, if you are a data scientist in a bank which is a multi-national then besides dealing with a lot of sensitive information you are also managing humungous volume of customer data. Here the need could be to analyse and secure a huge amount of data. Data science training programs that are more aligned to AI engineering, data science full stack including hadoop and tools like Kafka, HBase, Hive, and Cassandra etc. maybe more useful. Similary media, entertainment and OTT platforms etc. are also producing a lot of consumer data. This is due to high consumption of online content. In such industries predictive analytics plays a huge role in determining what content will be most profitable as per the consumer trends. On the other end are FMCG brands where analytics proves quite useful again for product and consumer segmentation. This helps in brands to design their promotional campaigns. However, in industries like luxury that thrives on personalised offerings and interactions, data science application may have a specific role and advanced knowledge may not be required.
Hence at an organisational level – before deciding a data science training program it is necessary to evaluate what value the training will contribute towards the business. For individual professionals too, this is extremely critical; assessing the skill-set that will enable them to perform their roles in a particular industry successfully is what matters.
In spite of being a relatively new field, data science is growing and evolving really fast. It is really important to take an approach that will give businesses and professionals a competitive advantage. It is thus necessary to choose a data science training program that is best suited for the business objectives at a company level and fulfils the future career perspective for an individual professional.
