Data Analysis

 

1 Sort: Sort by Color, Reverse List, Randomize List.

2 Filter: Number and Text Filters, Date Filters, Advanced Filter, Data Form, Remove Duplicates, Outlining Data.

3 Conditonal Formatting: Manage Rules, Data Bars, Color Scales, Icon Sets, New Rule, Find Duplicates, Shade Alternate Rows, Compare Two Lists, Conflicting Rules, Checklist.

4 Charts: Column Chart, Line Chart, Pie Chart, Bar Chart, Area Chart, Scatter Chart, Data Series, Axes, Chart Sheet, Trendline, Error Bars, Sparklines, Combination Chart, Gauge Chart, Thermometer Chart, Gantt Chart, Pareto Chart.

5 Pivot Tables: Group Pivot Table Items, Multi-level Pivot Table, Frequency Distribution, Pivot Chart, Slicers, Update Pivot Table, Calculated Field/Item.

6 Tables: Structured References, Table Styles.

7 What-If Analysis: Data Tables, Quadratic Equation.

8 Solver: Transportation Problem, Assignment Problem, Capital Investment, Shortest Path Problem, Maximum Flow Problem, Sensitivity Analysis.

9 Analysis ToolPak: Histogram, Descriptive Statistics, Anova, F-Test, t-Test, Moving Average, Exponential Smoothing, Correlation, Regression.

Data management comprises all the disciplines related to managing data as a valuable resource.

The main methods of representations and processing of charts

In Excel you can represent numbers in a chart. A chart is a graphical representation of numbers. On the Insert tab, you can choose from a variety of chart types, including column, line, pie, bar, area, scatter and more. The basic procedure for creating a chart is the same no matter what type of chart you choose. As you change your data, your chart will automatically update.

You select a chart type by choosing an option from the Insert tab's Chart group. After you choose a chart type, such as column, line, or bar, you choose a chart sub-type. For example, after you choose Column Chart, you can choose to have your chart represented as a two-dimensional chart, a three-dimensional chart, a cylinder chart, a cone chart, or a pyramid chart. There are further sub-types within each of these categories. As you roll your mouse pointer over each option, Excel supplies a brief description of each chart sub-type

The process of evaluating data using analytical and logical reasoning to examine each component of the data provided. This form of analysis is just one of the many steps that must be completed when conducting a research experiment. Data from various sources is gathered, reviewed, and then analyzed to form some sort of finding or conclusion. There are a variety of specific data analysis method, some of which include data mining, text analytics, business intelligence, and data visualizations.

Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.

Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.