Introduction to Data science and understanding the basics

Introduction to Data science and understanding the basics

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  Module 1
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  Business Analytics • Businessanalytics is the process of transforming data into insights to improve business decisions. • Data management, data visualization, predictive modeling, data mining, forecasting simulation, and optimization are some of the tools used to create insights from data. Business analytics involves a combination of the following • identifying new patterns and relationships with data mining; • using quantitative and statistical analysis to design business models; • conducting multi-variable testing based on findings; • forecasting future business needs, performance, and industry trends with predictive modeling; and • communicating your findings in easy-to-digest reports to colleagues, management, and customers.
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  To be moreprecise… • shaping and evaluating future company decisions based on the performance of past initiatives or market trends; • examining individual departments’ performance within an organization and influencing their growth efforts; • monitoring employees’ performance and productivity; • determining current and future staffing needs and the market skills needed to perform these roles effectively; • assessing and predicting how well potential investments will perform; • identifying demand for a particular product or service based on market trends and consumer behavior; • scheduling release dates for new products and media;
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  • evaluating productsales by location, and using that information to meet future customer demands; • creating optimal logistics routes for shipping and delivering merchandise; • making product recommendations based on customers’ past search habits; • gathering data from vehicles and equipment to improve future performance; and • identifying potential growth opportunities for a business, and how these scenarios could play out.
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  Different Types ofData • There are different types of data in Statistics, that are collected, analyzed, interpreted and presented. • The data are the individual pieces of factual information recorded, and it is used for the purpose of the analysis process. • The two processes of data analysis are interpretation and presentation. • Data classification and data handling are important processes as it involves a multitude of tags and labels to define the data, its integrity and confidentiality.
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  • What areTypes of Data in Statistics? • The data is classified into majorly four categories: • Nominal data • Ordinal data • Discrete data • Continuous data
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  Visualization • Data visualizationis the representation of information and data using charts, graphs, maps, and other visual tools. These visualizations allow us to easily understand any patterns, trends, or outliers in a data set. • Data visualization also presents data to the general public or specific audiences without technical knowledge in an accessible manner. For example, the health agency in a government might provide a map of vaccinated regions. • The purpose of data visualization is to help drive informed decision-making and to add colorful meaning to an otherwise bland database.
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  • Benefits ofdata visualization • Data visualization can be used in many contexts in nearly every field, like public policy, finance, marketing, retail, education, sports, history, and more. Here are the benefits of data visualization: • Storytelling: People are drawn to colors and patterns in clothing, arts and culture, architecture, and more. Data is no different—colors and patterns allow us to visualize the story within the data. • Accessibility: Information is shared in an accessible, easy-to-understand manner for a variety of audiences. • Visualize relationships: It’s easier to spot the relationships and patterns within a data set when the information is presented in a graph or chart. • Exploration: More accessible data means more opportunities to explore, collaborate, and inform actionable decisions.
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  • Tools forvisualizing data • There are plenty of data visualization tools out there to suit your needs. Before committing to one, consider researching whether you need an open- source site or could simply create a graph using Excel or Google Charts. The following are common data visualization tools that could suit your needs. • Tableau • Google Charts • Dundas BI • Power BI • JupyteR • Infogram • ChartBlocks • D3.js • FusionCharts • Grafana
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  • Types ofdata visualization • Visualizing data can be as simple as a bar graph or scatter plot but becomes powerful when analyzing, for example, the median age of the United States Congress vis-a-vis the median age of Americans. Here are some common types of data visualizations: • Table: A table is data displayed in rows and columns, which can be easily created in a Word document or Excel spreadsheet. • Chart or graph: Information is presented in tabular form with data displayed along an x and y axis, usually with bars, points, or lines, to represent data in comparison. An infographic is a special type of chart that combines visuals and words to illustrate the data. • Gantt chart: A Gantt chart is a bar chart that portrays a timeline and tasks specifically used in project management. • Pie chart: A pie chart divides data into percentages featured in “slices” of a pie, all adding up to 100%. • Histograms: A histogram is a graph used to represent the frequency distribution of a few data points of one variable. Histograms often classify data into various “bins” or “range groups” and count how many data points belong to each of those bins. • Geospatial visualization: Data is depicted in map form with shapes and colors that illustrate the relationship between specific locations, such as a choropleth or heat map. • Dashboard: Data and visualizations are displayed, usually for business purposes, to help analysts understand and present data.