Data Analytics for Beginners: Start with 3 Learning Paths

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Data Analytics is a complete application-oriented subject which is the amalgamation of the few areas like Statistics, Mathematics, Database Management System, Computer Science as well as domain-related skills. As a Data Scientist, one need to be having some knowledge about all the areas with expert knowledge in couple of areas. One of the major difference of Data Analytics from IT is that as a Data Scientist someone needs to understand the domain which may not be the case in IT. The Data Analytics relates to providing useful insights from the vast business data which the organization can use for generating benefits by taking informed decision. Another important aspect where the Analytics or Data Science is different from IT is that more experience in Analytics gives more versatility to work on more types of business problems which may not be a case in IT where after a certain period of time (for example, 15 years of experience), the technical skill may not be what is desired and someone needs to take up more of Project-management oriented role.

Analytics is providing more opportunities these days taking over the baton from the IT which was booming in the first few years of the last decade.

The Data Science field is attracting professionals from diverse sectors combining both IT and Non-IT as the growth opportunities in some previously booming areas have become very much limited over time due to rapid transformations in the business scenarios. The paradigm shift in the prevalent technologies in the VUCA (Volatile, Uncertain, Complex and Ambiguous) business world called for the advent of new technologies and skill-sets for sustaining in the new era. It requires for many professionals skilled in legacy IT technologies to be shifted to or be armed with new generation skills like Analytics and Data Science. The question comes how someone with legacy skills or novices from Non-IT sectors can be quickly armed with Data Science skills or Analytics skills. This article is supposed to throw some lights on that part. 

As has been discussed earlier, Data Science is an amalgamation of various areas which include Statistics, Mathematics, Database Management System, Computer Science as well as domain-related skills. With the passage of time, someone working in the field will be getting more and more knowledge, however, what is the basic minimum skills required to jump-start in the field. There is no direct answer to this and it is very subjective too. However, some ideas have been given here which could help the budding Analytics enthusiasts in deciding the priorities to venture through a career-path in analytics field and simultaneously shaping their career goals.

Level 1

Excel – Excel is the old workhorse satisfying the 70% data analysis needs for more than one and half decades by now. Few of the Excel functions like Goal Seek, Pivot Table etc. have hugely benefitted the business for getting useful insights from the large mass of data. Being armed with all useful Excel functions is compulsory to kick-start an Analytics career. 

Descriptive Statistics – Any body thinking of starting a career in the field should be acquainted with the basic statistical measures which include measures of central tendency and measures of dispersion. Being conversant with this step will pave the way for getting further statistical knowledge in the next level.

Getting knowledge in these two areas will pave the way for moving to the next level. 

Level 2

Inferential Statistics – Inferential Statistics serves as the door for the Analysts to start working in the real-life business problems in the time to come. The learners should be familiar with the knowledge of Probability, Probability Distribution, Sampling Theory, Testing of Hypothesis, Correlation and Linear Regression to be somewhat familiar with the field. 

Basic R or Python Programming – R or Python Programming has become an integral part of the Data Science by now. Any Data Science enthusiasts should be familiar with at least one of the programming language to be set foot in the Analytics world. For someone who is more interested to go for statistics-based problem solving, R is more preferable for them, the statistical functions library becoming richer there, however, those desiring to perform high-end operations in Machine Learning and Artificial Intelligence should choose Python as it is more powerful in this aspect. 

Getting knowledge in these two areas will pave the way for moving to the next level. 

Level 3

Exploratory Data Analysis – From here, the budding analysts will start working with the real-life business data and will get ideas on how to utilize learned statistical and programming knowledge in solving real-life problems. The analysts will learn basic exploratory data analysis skills which include the missing data imputation, outlier detection and removal, data transformation and dimensionality reduction. The foundation in this topic will pave the way for next level of problem-solving to get deep insights from the dataset. 

Machine Learning Skills – Knowing Machine Learning will pave the way for the beginner to start using sophisticated skills like predictive analytics for taking optimum business decisions. Knowledge of statistical techniques like Logistic Regression and classification techniques will pave the way for high-end analytics works which will provide the learner a solid foundation in the Analytics domain.