For a couple of months now, I have come across a series of conversations amidst data science beginners, and what I often hear them talk about is there are a lot of technical concepts in data science, and they find it difficult to understand everything at once.
YES!!,Technical concepts in data science are difficult to understand all at once. I remember how my journey in data science started.
I was all over the place looking for a way to understand these concepts.
Considering that you are reading this blog post, I assume you are a beginner who wishes to get started on the path of becoming a data scientist.
If that is the case, I must warn you that there is no quick way to study data science.
Many technical concepts in data science can be hard to understand, even for advanced practitioners.
In this article, we have compiled a list of technical concepts that every data scientists beginner should be acquainted with.
Programming is a very technical concept in data science. As a beginner, this concept would sound so difficult to you. Though it might be difficult, it is also very essential in the study of data science.
Programming is used to automate tasks and build complex tools. Data scientists use programming to analyze data, build models, and communicate results.
Programming languages like Python and R to wrangle, analyze, and visualize data in your day-to-day work as a data scientist.
The most popular programming languages in data science are Python, SQL, R and many more.
Data scientists use programming to analyze data, build models, and communicate results. So programming is essential to data science. And it’s also why we’re launching a new course in the Data Science specialization, Programming for Data Science with Python.
This concept is a statistical expression that defines all possible values and likelihoods that a random variable can take within a given range. Probability distribution helps us to understand the behaviour and properties of random variables.
In simple terms, the probability distribution is used to determine the probability of occurrence of an event over multiple experiments or trials.
Probability distributions are widely used in data science, machine learning and artificial intelligence.
Types of Probability Distributions every data science professional should know.
Machine learning is a data science technique that allows computers to use existing data to forecast future behaviours, outcomes, and trends. While machine learning shares many characteristics with artificial intelligence.
Machine learning algorithms build a mathematical model based on sample data, known as “training data,” to make predictions or decisions without being explicitly programmed to perform the task.
Types Of Machine Learning
An algorithm is a well-defined computational procedure that takes some value, or set of values, as input and produces some value, or set of values, as output. An algorithm is thus a sequence of computational steps that transform the input into the output.
An algorithm can be expressed in any language, from natural languages like English or French to programming languages like Python or Java. Algorithms are often also described using pseudocode: a combination of natural and programming languages.
An algorithm is very efficient and fast if it takes less time to execute and consumes less memory space. The time and space complexity of a program depends on the algorithm used in the program.
Some types of algorithms are listed below;
Probability is the mathematics of randomly occurring events. It describes the likelihood that an event will occur under a given set of circumstances.
Probability is expressed as a number between 0 and 1, where 0 indicates impossibility and 1 indicates certainty. The higher the probability of an event, the more likely it is that the event will occur.
A simple example is the tossing of a fair coin: since the two outcomes are equally probable, the probability of either heads or tails is 1/2 (which could also be written as 0.5 or 50%).
Data scientists use probability in statistical analysis to determine how likely something is to happen given a set of conditions. They use it to make predictions based on data collected from previous observations or experiments.
Statistics is an essential component of data science. It is a mathematical branch concerned with the collecting, analysis, interpretation, and presentation of vast volumes of data.
Statistics covers a broad area and includes many sub-disciplines. These include descriptive statistics, probability theory and inference, regression analysis and hypothesis testing among others.
Statistics is used in Data Science for the following reasons:
To find out the relationship between different variables.
To measure how important a feature is so that you can decide whether to keep it or not.
Statistical methods help to identify trends, patterns and relationships from data.
Statistics help in modelling relationships and making predictions based on these models. For example, Logistic Regression is a statistical model which is used for classification problems like spam detection.
In conclusion, there are a lot of technical concepts in data science every data scientist must understand, this article only covered a few of some of the concepts in data science.