Data Science has been under the limelight for quite some time, and it is here to stay. In simple words, Data Science is an advanced field of study that leverages a combination of mathematical, statistical, and scientific techniques, processes, algorithms, and tools to obtain meaningful information from both structured and unstructured data.
Since Data Science is all about analyzing data and extracting insights from within, Statistics plays a significant role in Data Science. Statistics is a discipline that primarily deals in collecting, analyzing, interpreting, and presenting data in ways that can be understood by all.
In the real-world scenario, Statistics is used across industries to process complex challenges and to aid Data Science experts to find valuable patterns in large datasets. Essentially, Data Science professionals employ different statistical methods to perform mathematical computations on data to make sense of the raw data.
Statistics for Data Science
Statistics is a highly useful tool for Data Science, especially when it comes to data analysis. Statistical methods take a targeted approach to data, thereby allowing Data Science experts to draw concrete conclusions on the data at hand rather than merely guessing. Statistics enables you to understand the data structure and prepare the data for further analysis via Data Science techniques.
Here are four fundamental statistical concepts that are crucial in Data Science:
1. Statistical Features
Statistical features are pivotal in exploring a large dataset that includes concepts like bias, variance, mean, median, etc. These are the basic features that you can easily implement within a code.
2. Probability Distributions
In Data Science, probability refers to the chance that an event might occur or not. It is generally quantified within 0 to 1, wherein 0 means the event will not occur, and 1 means the event will occur. Thus, a probability distribution is a statistical function that represents all the possibilities between 0 to 1 in a particular dataset.
3. Dimensionality Reduction
Dimensionality Reduction refers to the technique of reducing the number of random variables (features) in a given experiment by extracting a set of principal variables. The process is divided into feature selection and feature extraction. While the feature selection process produces a smaller subset of the original set of features, feature extraction reduces the number of dimensions, that is, the data present in a high dimensional space is fit into a lower dimension space.
4. Oversampling and Undersampling
Oversampling and undersampling are statistical techniques used for data classification. Often, the data at hand is mostly tipped over on one side, thereby making the model imperfectly balanced. For instance, a dataset having two classes may contain 100 samples for class 1, whereas 500 samples for class 2.
If this isn’t balanced, it throws off the model’s ability to make accurate predictions. In undersampling, you only consider a portion (equal to the samples of the minority class) of data derived from the majority class. However, in oversampling, you need to create copies of the minority class to match the number of majority class samples.
Types of Statistical Analysis
Statistical analysis is mostly concerned about gathering data from disparate sources, exploring and analyzing it, and visualizing the findings through appropriate data visualization methods. It is a vital tool for businesses since it allows them to uncover and predict the future market and consumer trends. There are two types of statistical analysis:
As the name suggests, descriptive statistics refers to the process of summarizing the data using visualization tools like charts, tables, and graphs. It does not draw any conclusion on the population (a set of variables in a dataset from which samples are drawn). Descriptive statistics aims to summarize the data in ways that make it easier to present and understand raw data.
Unlike descriptive statistics that primarily focuses on summarizing and presenting data, inference statistics enables you to experiment with hypotheses and draw concrete conclusions. In this approach, you will examine the complete dataset and apply the results to the group as a whole.
Learn Statistics for Data Science: The upGrad advantage
If you aspire to build a career in Data Science, you must have a strong foundation in Statistics. The best part is that you can master the fundamentals of Statistics right from the comfort of your home with upGrad’s Statistics for Data Science course. This is a free course offered by upGrad under its upStart-Priceless Learning program.
It is exclusively designed to empower individuals who wish to enter the world of Data Science, either as a beginner or as a career move. In this Statistics for Data Science free course, you will learn basic and advanced statistical concepts and use them to solve real-world challenges.
As is true of all upGrad offerings, you will be trained by top mentors and industry leaders. Apart from receiving one-on-one mentorship, you will also get a chance to participate in live interaction sessions and access industry-specific content and learning resources. On course completion, you will obtain a certificate of completion from upGrad.
upGrad’s Statistics for Data Science free course is a five-week program is divided into three parts:
1. Inferential Statistics
In this module, you’ll learn the basics of probability along with different methods of distribution and sampling. You will also learn how to describe sample data and make inferences on the population.
2. Hypothesis Testing
This module will teach you how to use hypothesis testing concepts on the sample data to test if the population data’s estimations are valid. Besides, you will also learn how to leverage different statistical tools for industry demonstration.
The third module focuses on teaching candidates how to apply your theoretical knowledge (gained in the first two modules) for the QA testing of a pharma company’s painkiller meds.
Taking an online course to learn Statistics for Data Science is an excellent option for aspirants who already have education or professional engagements. Online courses offer the flexibility to learn and progress according to your convenience and schedule.
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