If you are a part of the technology sector and witness to the changes happening across the world due to disruptive technologies, you would instantly agree that gaining digital skills is the need of the hour. With growing digital transformation, many manual jobs are about to become obsolete and new job roles will evolve in the future, particularly those which require specialized digital skills. One such career field which has become the talk of the town is data science.
When you read any survey related to top-paying jobs, most in-demand job roles, or best emerging jobs, you will definitely come across the term ‘data scientist.’
For those who aren’t already aware of data science, here is a brief about what it refers to. Data Science is a field related to gathering, managing, analyzing, and visualizing data to uncover hidden trends and patterns so as to draw meaningful insights from them. With the help of these insights, companies are able to make data-driven decisions and develop better products.
Data science is a challenging field and due to the high demand for data-related roles, professionals are eager to enter this domain and explore their skills. Many of them begin with a data science program and gain the skills required to become a seasoned data scientist. This article lets you understand why learning statistics is important for a data science role.
Why Learn Statistics?
When you think of starting a career in data science, people would first suggest you gain expertise in core math concepts like linear algebra, calculus, probability, and statistics. From the above information, it is clear that making predictions and finding different correlations and trends in data is a crucial step in data science.
Today we deal with a massive amount of data and analyzing it becomes easier for data scientists when they use machine learning.
Statistics basically refers to the science of gathering and analyzing numerical data in huge quantities. To aid the decision-making process, it is statistics that help in attaining information about the data and unveil hidden patterns in them by performing mathematical computations on data.
In other words, it offers tools and methods to identify the structure and gain deeper insights from data. Probability and statistics are also the basic foundation of various predictive algorithms of machine learning.
Using statistical methods, data scientists are able to build various models like regression, classification, cross-validation, hypothesis, testing, dimension reduction, time series analysis, and more. Through statistical analysis, professionals scrutinize every data sample in a set of items from which samples are drawn (here sample is a subset of the population – a term used to refer to the place or source from where the data has to be collected).
In statistical analysis, the data scientists first understand the nature of data that is to be analyzed. Next, they figure out the relation of the data with the underlying population and create a model to summarize their understanding. The model is then validated and if approved, predictive analytics is employed to run scenarios that will assist future actions.
This, and many other complex analyses, can be performed by using sophisticated statistical analysis tools like RStudio, IBM SPSS Statistics, MATLAB, Minitab 18, and Stata.
How to Learn Statistics?
Now that you know why statistics forms the essence of data science, you should know how to begin learning statistics. Any good tutorial or course related to statistics should cover the following topics:
• Core statistics concepts like descriptive statistics, distributions, hypothesis testing, regression, and classification. Such concepts are usually implemented for experimental design, regression modeling, data transformation, and so on.
• Statistical features like bias, percentiles, variance, mean, median, etc. that are used while exploring a dataset.
• Central Limit Theorem, a powerful theorem related to sampling distribution, used widely when assessing problems and world situations.
• Bayesian thinking, an integral concept used in various machine learning models. It uses probability to model the sampling process and quantify uncertainty before gathering data.
• Dimensionality reduction, used when we want to reduce the number of dimensions the dataset has. In data science, this concept is used to reduce the number of feature variables.
Though the list is not exhaustive, these concepts should be taught in-depth when any learner wishes to master statistics for data science.
As evident from the above points, mastering statistics isn’t easy for a beginner. If you are serious about a career in data science, then we would definitely recommend you to take up an online course to learn statistics.
If you refer to other resources, you might end up getting redundant information. There are individual courses on statistics related to data science as well as specialization or master’s programs in data science that cover an introductory course on statistics. Depending on your needs, you can take either of them and gain a strong foundation of statistical concepts.
There is no better time than now to step into the world of data science. You may have heard about how lucrative a career in data science is, but one of the facts we can’t ignore is the supply of data scientists is low compared to the demand. Companies are having many open positions for data-related roles, but there aren’t enough skilled professionals that meet their expectations.
So, if you are willing to land a data scientist job, ensure that you have the right skills to grab the attention of hiring managers. If you fail to demonstrate them, you may be disappointed with the response of employers. You can take the first step by learning the basic statistical concepts, probability, calculus, and any programming language (though R and Python are preferred for data science).
Later, you can move on to advanced concepts of data science and prove yourself worthy of becoming a data scientist.