Data science is linked to numerous other modern buzzwords such as big data and machine learning, but data science itself is built from numerous domains, where you can get your expertise. These domains include the following: * Statistics *...
Data science is linked to numerous other modern buzzwords such as big data and machine learning, but data science itself is built from numerous domains, where you can get your expertise. These domains include the following:
Visualizing the types of data Visualizing and communicating data is incredibly important, especially with young companies that are making data-driven decisions for the first time, or companies where data scientists are viewed as people who help others make data-driven decisions. When it comes to communicating, this means describing your findings, or the way techniques work to audiences, both technical and non-technical. Different types of data have different ways of representation. When we talk about the categorical values, the ideal representation visuals would be these:
Frequency distribution tables A bar chart would visually represent the values stored in the frequency distribution tables. Each bar would represent one categorical value. A bar chart is also a baseline for a Pareto diagram, which includes the relative and cumulative frequency for the categorical values:
Bar chart representing the relative and cumulative frequency for the categorical values If we'll add the cumulative frequency to the bar chart, we will have a Pareto diagram of the same data: Pareto diagram representing the relative and cumulative frequency for the categorical values Another very useful type of visualization for categorical data is the pie chart. Pie charts display the percentage of the total for each categorical value. In statistics, this is called the relative frequency. The relative frequency is the percentage of the total frequency of each category. This type of visual is commonly used for market-share
*Statistics * A good understanding of statistics is vital for a data scientist. You should be familiar with statistical tests, distributions, maximum likelihood estimators, and so on. This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren't) a valid approach. Statistics is important for all types of companies, especially data-driven companies where stakeholders depend on your help to make decisions and design and evaluate experiments.
Machine learning A very important part of data science is machine learning. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. ** Choosing the right algorithm** When choosing the algorithm for machine learning, you have to consider numerous factors to properly choose the right algorithm for the task. It should not only be based on the predicted output: category, value, cluster, and so on, but also on numerous other factors, such as these:
Big data Big data is another modern buzzword that you can find around the data management and analytics platforms. The big does not have to mean that the data volume is extremely large, although it usually is. learn more Data science online course SQL Server and big data Let's face reality. SQL Server is not a big-data system. However, there's a feature on the SQL Server that allows us to interact with other big-data systems, which are deployed in the enterprise. This is huge! This allows us to use the traditional relational data on the SQL Server and combine it with the results from the big-data systems directly or even run the queries towards the big-data systems from the SQL Server. The answer to this problem is a technology called PolyBase:
Learn the essentials of statistics in this complete course. This course introduces the various methods used to collect, organize, summarize, interpret and reach conclusions about data. An emphasis is placed on demonstrating that statistics is more than mathematical calculations. By using examples gathered from real life, students learn to use statistical methods as analytical tools to develop generalizations and meaningful conclusions in their field of study.
Welcome to this course on Data Science For Beginners With Python. What is Data Science? In this Python Data Science Tutorials provides an Introduction to Data Science with Python - Exporting data to file, Aggregate Statistics, Describing.
When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them