Data-Analytics-Week-1

Data-Analytics-Week-1

Chapter 1: The Data Analyst

What is Data analytics?

Data analytics is basically the science and techniques of analysing raw data to make conclusions about information. Data analytics has many processes and techniques that have already been automated into mechanical processes and algorithms that work over raw data for human consumption. Data analytics can be used by a wide number of entites such as businesses, to increase the overall performance therefore maximizing their project

Analysts working with data move through a series of different steps as they seek to gain business value from their organization's data. Analysts move through a process as they acquire new data, clean and manipulate that data, analyze it, create visualizations, and then report and communicate their results to business leaders. These processes are:

1. Data Acquisition
2. Cleaning and Manipulation
3. Analysis
4. Visualization
5. Reporting and Communication

Roles of a data anaylst

1. Data collection a preparation
2. Data Analysis
3. Data Visualization and Storytelling
4. Decision Support - Making recommendations based on data-driven insights to help guide business decisions, providing context around the data, including potential implications and future trends.
5. Collaboration and Communication:
6. Continuous Learning and Adaptation:

Analytical Techniques Analysts use a variety of techniques to draw conclusions from the data at their disposal. To help you understand the purpose of different types of analysis, we often group these techniques into categories based on the purpose of the analysis and/or the nature of the tool. Let's take a look at the major categories of analytics techniques.

Descriptive Analytics Predictive Analytics Prescriptive Analytics

Machine Learning, Artificial Intelligence, and Deep Learning Algorithms are used in machine learning to extract knowledge from your datasets that you may utilize to inform future decisions. This is true irrespective of the particular field in which you operate, as machine learning finds application in a wide range of domains.

Analytics Tools Software helps analysts work through each one of the phases of the analytics process. These tools automate much of the heavy lifting of data analysis, improving the analyst's ability to acquire, clean, manipulate, visualize, and analyze data. They also provide invaluable assistance in reporting and communicating results.

Data analysis in Microsoft Excel Data analysis in RStudio

Chapter 2: Understanding Data

Data structure is an important factor in understanding how data works.

Data types is also important as it defines what catagory data goes into and also helps with data analysis.

Person Data

Let's explore some of the most common data types that give structured data its structure.

Character: The character data type limits data entries to allow only allow valid characters.Depending on your needs, multiple data types are available that can enforce character limits.

Alphanumeric: The most widely used data type for storing character data. Data element contains both numbers and letters. For example To accurately represent a given street address, both the house number and the street name are required.

Each database software has its unique method of implementing character data types to handle the nuances related to character data. The most significant difference has to do with how much data a particular data element can contain.

Unstructured Data Types

While much of the data we use to record transactions is highly structured, most of the world's data is unstructured. Unstructured data is any type of data that does not fit neatly into the tabular model.

Categories of data

Quantitative vs. Qualitative Data Quantitative data consists of numeric values. Qualitative data consists of frequent text values.

Discrete vs. Continuous Data Numeric data comes in two different forms: discrete and continuous. Instead of counting, when you measure things like height and weight, you are collecting continuous data. While whole numbers represent discrete data, continuous data typically need a decimal point. Another way to think about it is that discrete data is useful when you have things you want to count Instead of counting, when you measure things like height and weight, you are collecting continuous data.

Categorical Data In addition to quantitative, numeric data, there is categorical data. Text data with a known, finite number of categories is categorical.

Dimensional Data Dimensional modeling is an approach to arranging data to facilitate analysis. Dimensional modeling organizes data into fact tables and dimension tables. Fact tables store measurement data that is of interest to a business. A table holding appointment data would be called a fact table. Dimensions are tables that contain data about the fact. For appointment data, the veterinarian's office manager may want to understand who was at an appointment and if any procedures were performed.

Common Data Structures

In order to facilitate analysis, data needs to be stored in a consistent, organized manner. When considering structured data, several concepts and standards inform how to organize data.

Structured Data Tabular data is structured data, with values stored in a consistent, defined manner, organized into columns and rows.

Unstructured Data Unstructured data is qualitative, describing the characteristics of an event or an object. Images, phrases, audio or video recordings, and descriptive text are all examples of unstructured data.

Semi-Structured Data Semi-structured data is data that has structure and that is not tabular. Email is a well-known example of semi-structured data. Every email message has structural components, including recipient, sender, subject, date, and time. However, the body of an email is unstructured text, while attachments could be anything type of file.

Text Files Text files are one of the most commonly used data file formats. As the name implies, they consist of plain text and are limited in scope to alphanumeric data. One of the reasons text files are so widely adopted is their ability to be opened regardless of platform or operating system without needing a proprietary piece of software. Whether you are using a Microsoft Windows desktop, an Apple MacBook, or a Linux server, you can easily open a text file. Text files are also commonly referred to as flat files.

A unique character known as a delimiter facilitates transmitting structured data via a text file. The delimiter is the character that separates individual fields. A delimiter can be any character. Over the years, the comma and tab grew into a widely accepted standard. Various software packages support reading and writing delimited files using the comma and the tab. In addition, many coding languages have libraries that make it easy to write comma- or tab-delimited files. When a file is comma-delimited, it is known as a comma-separated values (CSV) file. Similarly, when a file is tab-delimited, it is called a tab-separated values (TSV) file.

JavaScript Object Notation JavaScript Object Notation (JSON) is an open standard file format, designed to add structure to a text file without incurring significant overhead. One of its design principles is that JSON is easily readable by people and easily parsed by modern programming languages. Languages such as Python, R, and Go have libraries containing functions that facilitate reading and writing JSON files.

Consider Figure 2.29, which illustrates data about the first three pets from Table 2.1, formatted as JSON. As a person, it is easy to see that the information corresponding to an individual pet is within curly braces, with name-value pairs corresponding to the data elements and values.

Extensible Markup Language (XML) Extensible Markup Language (XML) is a markup language that facilitates structuring data in a text file. While conceptually similar to JSON, XML incurs more overhead because it makes extensive use of tags. Tags describe a data element and enclose each value for each data element. While these tags help readability, they add a significant amount of overhead.

HyperText Markup Language (HTML) HyperText Markup Language (HTML) is a markup language for documents designed to be displayed in a web browser. HTML pages serve as the foundation for how people interact with the World Wide Web. Similar to XML, HTML is a tag-based language. Figure 2.33 illustrates the creation of a table in HTML containing the data for a single pet.

Module 2: Data Preparation and Exploration

Chapter 3: Databases and Data Acquisition The vast majority of transactional systems generate structured data, and we need technology that will help us store all of that data. Exploring Databases

There are many different database options to choose from when an organization needs to store data. While many database products exist, they belong in one of two categories:

Relational Nonrelational

Database use cases

Online Transactional Processing OLTP systems handle the transactions we encounter every day. Example transactions include booking a flight reservation, ordering something online, or executing a stock trade. While the number of transactions a system handles on a given day can be very high, individual transactions process small amounts of data. OLTP systems balance the ability to write and read data efficiently.

Normalization

Normalization is a process for structuring a database in a way that minimizes duplication of data.

First normal form (1NF) is when every row in a table is unique and every column contains a unique value. Consider Figure 3.15, where there are separate rows for Giacomo and Eleonora. While Giacomo and Eleonora have the same home address, appending Eleonora's name in the Person_Name column to Giacomo's in the first row would violate 1NF. However, since each row is unique, Figure 3.15 is in 1NF.

Second normal form (2NF) starts where 1NF leaves off. In addition to each row being unique, 2NF applies an additional rule stating that all nonprimary key values must depend on the entire primary key. To get to 2NF, the table from Figure 3.15 evolves into the tables in Figure 3.16. Note that the Person_Address table has a composite primary key composed of Person_ID and Addr_ID. The values of both Addr_Code and Addr_Desc are associated with the composite primary key.

Suppose Eleonora starts working from home and updates the Addr_Desc for her row with the value “Work.” That causes data corruption, as the Addr_Code corresponds to the Addr_Desc. Addr_Desc depends on Addr_Code, and with that change, “H” would mean both “Home” and “Work”.

Third normal form (3NF) builds upon 2NF by adding a rule stating all columns must depend on only the primary key. Evolving Figure 3.16 into 3NF results in Figure 3.17. If Eleonora starts working from home, the only value that needs to change is Addr_Code in the Person_Address table. The description for each type of address is in the Addr_Code table. Similarly, the description for each state code is in the State_Code table. The result is that there is little redundancy in the data's storage location and that keys enforce the relationships between tables. Databases in 3NF are said to be highly normalized.

Online Analytical Processing OLAP systems focus on the ability of organizations to analyze data. While OLAP and OLTP databases can both use relational database technology, their structures are fundamentally different. OLTP databases need to balance transactional read and write performance, resulting in a highly normalized design. Typically, OLTP databases are in 3NF.

On the other hand, databases that power OLAP systems have a denormalized design. Instead of having data distributed across multiple tables, denormalization results in wider tables than those found in an OLTP database. It is more efficient for analytical queries to read large amounts of data for a single table instead of incurring the cost of joining multiple tables together.

Data Acquisition Concepts

To perform analytics, you need data. Data can come from internal systems you operate, or you can obtain it from third-party sources. Regardless of where data originates, you need to get data before analyzing it to derive additional value. In this section, we explore methods for integrating data from systems you own. We also explore strategies for collecting data from external systems.

Data from transactional systems flow into data warehouses and data marts for analysis. Recall that OLTP and OLAP databases have different internal structures. You need to retrieve, reshape, and insert data to move data between operational and analytical environments. You can use a variety of methods to transfer data efficiently and effectively.

One approach is known as extract, transform, and load (ETL). As the name implies, this method consists of three phases:

Extract: In the first phase, you extract data from the source system and place it in a staging area. The goal of the extract phase is to move data from a relational database into a flat file as quickly as possible. Transform: The second phase transforms the data. The goal is to reformat the data from its transactional structure to the data warehouse's analytical design. Load: The purpose of the load phase is to ensure data gets into the analytical system as quickly as possible. Extract, load, and transform (ELT) is a variant of ETL. With ELT, data is extracted from a source database and loaded directly into the data warehouse. Once the extract and load phases are complete, the transformation phase gets underway. One key difference between ETL and ELT is the technical component performing the transformation. With ETL, the data transformation takes place external to a relational database, using a programming language like Python. ELT uses SQL and the power of a relational database to reformat the data.

ETL Vendors

Whether you choose ETL or ELT for loading your data warehouse, you don't have to write transformations by hand. Many products support both ETL and ELT. Before you pick one, carefully evaluate the available free and paid options to determine the one that best fits your needs and system architecture goals.

An initial load occurs the first time data is put into a data warehouse. After that initial load, each additional load is a delta load, also known as an incremental load. A delta load only moves changes between systems. Figure 3.23 illustrates a data warehouse that launches in January and uses a monthly delta load cycle. The initial load happens right before the data warehouse becomes available for use. All of the transactional data from January becomes the delta load that initiates on the first of February. Figure 3.23 illustrates a monthly delta-load approach that continues throughout the year.

long your batch window is, think carefully about moving current data into the data warehouse without losing history.

Data Collection Methods Augmenting data from your transactional systems with external data is an excellent way to improve the analytical capabilities of your organization. For example, suppose you operate a national motorcycle rental fleet and want to determine if you need to rebalance your fleet across your existing locations. You also want to evaluate whether it is profitable to expand to a new geographic region, as well as predict the best time and place to add motorcycles to your fleet.

Application Programming Interfaces (APIs)

1. Web Services
2. Web Scraping
3. Human-in-the-Loop
4. Surveys
5. Survey Tools
6. Observation
7. Sampling

Working with Data

Data Manipulation When manipulating data, one of four possible actions occurs:

Create new data. Read existing data. Update existing data. Delete existing data. The acronym CRUD (Create, Read, Update, Delete) is a handy way to remember these four operations.

SQL uses verbs to identify the type of activity a specific statement performs. For each CRUD activity, there is a corresponding DML verb, as Table 3.8 illustrates. These verbs are known as keywords or words that are part of the SQL language itself.

SQL Example:

Select Animal_Name, Breed_Name from Animal

The previous query returns the same results as this query:

SELECT Animal_Name, Breed_Name FROM Animal

SQL can also span multiple lines. For example, rewriting the previous query as follows will return identical results:

SELECT Animal_Name, Breed_Name

FROM Animal

Sorting;

SELECT Animal_Name, Breed_Name

FROM Animal

WHERE Animal_Type = 'Dog'

AND Weight> 60

ORDER BY Date_of_Birth ASC

If you want to return the youngest dog first, you change the ORDER BY clause as follows:

SELECT Animal_Name, Breed_Name

FROM Animal

WHERE Animal_Type = 'Dog'

AND Weight> 60

ORDER BY Date_of_Birth DESC

The ASC keyword at the end of the ORDER BY clause sorts in ascending order whereas using DESC with ORDER BY sorts in descending order. If you are sorting on multiple columns, you can use both ascending and descending as appropriate. Both the ASC and DESC keywords work across various data types, including date, alphanumeric, and numeric.

Exam Prep:

1. Describe the characteristics of OLTP and OLAP systems. 

The two main categories of relational databases are transactional (OLTP) and analytical (OLAP). Transactional systems use highly normalized schema design, which allows for database reads and writes to perform well. Analytical systems are denormalized and commonly have a star or snowflake schema. Remember that a star design simplifies queries by having a main fact table surrounded by dimensions. A snowflake design is more normalized than a star. While this approach reduces storage requirements, the queries are more complex than in a star schema.

2. Describe approaches for handling dimensionality. 

It is crucial to keep track of how data changes over time to perform historical analysis. Although an effective date approach is valid, the SQL queries to retrieve a value at a specific point in time are complex. A table design that adds start date and end date columns allows for more straightforward queries. Enhancing the design with a current flag column makes analytical queries even easier to write.

3. Understand integration and how to populate a data warehouse. 

The more data an organization has, the more impactful the analysis it can conduct. The extract, transform, and load (ETL) process copies data from transactional to analytical databases. Suppose an organization wants to use the power of a relational database to reformat the data for analytical purposes. In that case, the order changes to extract, load, and transform. Regardless of the approach, remember that a delta load migrates only changed data.

4. Differentiate between data collection methods. 

Data can come from a variety of sources. An organization may scrape websites or use publicly available databases to augment its data. While web scraping may be the only way to retrieve data, it is better if a published application programming interface exists. An API is more reliable since its structure makes for a consistent interface. If you want to capture the voice of the customer, a survey is a sound approach. Collecting data through observation is a great way to validate business processes and collect quantitative data.

5. Describe how to manipulate data and optimize queries. 

Analytical databases store massive amounts of data. Manipulating the entire dataset for analysis is frequently infeasible. To efficiently analyze data, understand that SQL has the power to filter, sort, and aggregate data. When focusing on a particular subject, creating a subset is an ideal approach. Although it is possible to create permanent tables to house subsets, using a temporary table as part of a query is viable for ad hoc analysis. When an analytical query performs poorly, use its execution plan to understand the root cause. It is wise to work with a database administrator to understand the execution plan and ensure that indexes exist where they are needed.

Chapter 4: Data Quality

Duplicate data occurs when data representing the same transaction is accidentally duplicated within a system.

While duplicate data typically comes from accidental data entry, redundant data happens when the same data elements exist in multiple places within a system.

ETL stands for Extract, Transform, Load. It's a process used in data warehousing and analytics to move data from its source to a destination where it can be analyzed. Here's what each step involves:

Extract: This step involves pulling data from various sources such as databases, applications, files, or APIs. The data can be structured (like from a relational database) or unstructured (like from log files or social media feeds).

Transform: Once the data is extracted, it often needs to be cleaned, filtered, and transformed into a format that's suitable for analysis. This may involve tasks like data cleansing, joining multiple data sources together, aggregating data, or converting data types.

Load: Finally, the transformed data is loaded into a target database or data warehouse where it can be queried and analyzed by business intelligence tools, reporting tools, or data analysts.

The ETL process is crucial for ensuring that data is accurate, consistent, and accessible for analysis. It helps organizations make better decisions by providing timely and reliable insights from their data.

Invalid Data Invalid data are values outside the valid range for a given attribute. An invalid value violates a business rule instead of having an incorrect data type. As such, you have to understand the context of a system to determine whether or not a value is invalid.

Nonparametric Data Nonparametric data is data collected from categorical variables.

Data Outliers A data outlier is a value that differs significantly from other observations in a dataset.

Programming languages, including SQL, Python, and R, all have data type validation functions. Use these functions to validate the data type for each column in a data file before attempting a database load. It's in your best interest to detect and remediate data type issues as early as possible to ensure data is ready for analysis.

Data Quality Management

Circumstances:

Data Acquisition Data Transformation and Conversion Data Manipulation Final Product Preparation

Automated Validation Many data sources feed analytics environments. While some of these data sources are other computer systems, others depend directly on people. Whenever people interact with systems, it's possible to introduce data-related errors. Whether source data is machine- or human-generated, one way to prevent data entry mistakes from adversely impacting data quality is to automate data validation checks.

Data Quality Dimensions It is essential to consider multiple attributes of data when considering its quality. Six dimensions to take into account when assessing data quality are accuracy, completeness, consistency, timeliness, uniqueness, and validity. Understanding these dimensions and how they are related will help you improve data quality. Let's take a closer look at each of these dimensions.

Data Accuracy Data Completeness Data Consistency Data Timeliness Data Uniqueness Data Validity

Understanding Data Quality: Data quality is foundational for effective analysis. Analysts need to be aware of various challenges such as data duplication, redundancy, and inconsistencies. Identifying outliers is crucial as they can skew statistical analysis results.

Data Preparation Techniques: Analysts employ various manipulation techniques to prepare data for analysis. This includes merging or blending data sources, restructuring data, recoding variables, and reducing data dimensions to facilitate analysis.

Normalization: When dealing with disparate measurements across multiple columns, normalization is essential to ensure fair comparisons and avoid exaggerating the impact of certain attributes.

Recognizing Quality Errors: Analysts must be vigilant in recognizing where quality errors can occur throughout the analytical process, from data acquisition to visualization. Automating quality improvements can streamline data preparation processes.

Dimensions of Data Quality: Data quality can be assessed across dimensions such as accuracy, completeness, consistency, uniqueness, and validity. Defining business objectives in terms of these dimensions helps ensure that the right data is available for decision-making.

Continuous Quality Assessment: Continuous assessment of data quality is vital. This involves setting expectations, conducting audits, and employing advanced statistical techniques to monitor and improve data quality over time.

ELABORATION:

Understanding Data Quality:

Data Duplication and Redundancy: Organizations integrating multiple systems often encounter duplicate or redundant data. Identifying and eliminating such duplications is essential to ensure data integrity and accuracy. Inconsistencies: Data inconsistencies can arise from missing values, values outside expected ranges, or data not matching the intended data type. Analysts need to identify and rectify these inconsistencies to ensure data reliability. Outliers: Outliers are data points that significantly deviate from the rest of the dataset. They can skew statistical analysis results and lead to inaccurate conclusions. Analysts must identify and understand outliers to appropriately handle them in their analysis.

Data Preparation Techniques:

Data Manipulation: Analysts use various techniques to manipulate data, including merging or blending multiple data sources, restructuring data to fit analytical needs, and recoding variables to standardize formats. Dimension Reduction: With the abundance of data available, analysts may need to reduce the dimensionality of datasets by selecting relevant features or reducing the number of rows or columns to improve computational efficiency and focus on relevant information.

Normalization:

Fair Comparisons: Normalization ensures fair comparisons across different attributes by scaling or standardizing data to remove biases introduced by differences in measurement scales or units. Avoiding Exaggeration: By normalizing data, analysts prevent certain attributes with larger numeric values from dominating the analysis, ensuring that all attributes contribute proportionately to the results.

Recognizing Quality Errors:

Vigilance: Analysts need to be vigilant in recognizing potential quality errors at every stage of the analytical process, including data acquisition, transformation, conversion, manipulation, and visualization. Automation: Automating quality checks and improvements can streamline processes and reduce the likelihood of human error, enhancing overall data quality and efficiency.

Dimensions of Data Quality:

Accuracy: The degree to which data reflects the true values or states of the entities being described. Completeness: The extent to which all required data is present within the dataset. Consistency: The absence of contradictions or discrepancies in the data. Uniqueness: Ensuring that each data record is unique and not duplicated within the dataset. Validity: The conformity of data to predefined rules or constraints.

Continuous Quality Assessment:

Setting Expectations: Establishing clear quality expectations based on business objectives helps guide the assessment process. Audits and Monitoring: Regular audits and monitoring procedures help identify and address quality issues proactively. Advanced Techniques: Employing advanced statistical techniques such as anomaly detection or predictive modeling can enhance quality assessment capabilities and inform targeted quality improvement efforts. By paying close attention to these aspects and implementing appropriate strategies, analysts can ensure that the data they work with is of high quality, thereby enhancing the reliability and effectiveness of their analytical endeavors.

Data Analysis Week-2

Chapter 5: Data Analysis and Statistics

Measures of Frequency

Mean, Mediam and Mode I done in high school so I know those concepts and how to work with it.

Inferential Statistics

Inferential stats is basically a branch of statistics that focuses mainly using sample data to drawing conclusion about overall populations.

LMS NOTE(For example, suppose you are trying to quantify the weight of college students in the United States. With more than 20 million college students, getting a complete census is not feasible. By combining balanced, representative sample data with inferential statistical techniques, you can confidently make assertions about the broader population.)

Confidence Intervals:

A confidence interval basically describes the POSSIBLITY THAT A SAMPLE STATISTIC contains the true population parameter in a range of values around the mean. Upper and Lower bound values.

Hypothesis Testing

Data Analysts need to build convincing arguments using data. They use hypothesis testing to accomplish this. A hypothesis test consists of two statements, only one of which can be true. It uses statistical analysis of observable data to determine which of the two statements is most likely to be true.

Example: Suppose you are a pricing analyst for an airline. One question you might want to explore is whether or not people over 75 inches tall are willing to pay more for additional legroom on roundtrip flights between Chicago and San Francisco. By conducting a hypothesis test on a sample of passengers, the analyst can make an inference about the population as a whole.

Analysis Tech

When encountering data for the first time its always good to perform a EDA which is an exploratory data analysis.

Chapter 6: Data Analytics Tools

1. Spreadsheets
   • The spreadsheet is the most powerful and most common and widely used tool in the data analysis world. Spreadsheets provide a intuitive and easy way to store our data in row's and columnn's.
   • Spreadsheets are basically productivity software packages used to allow us to create document which put and organize our data based on datatype in a spreadsheet, in rows and columns.
   • Spreadsheets provide users with a flexibility to store their data even if values are mixed(datatypes).
   • Literally anyone can use spreadsheets because they are easily readable.
2. Programming Languages
   • R
     1. R is a stats programming that was created in 1990 and was niche but two decades later it gain popularity and is used among by many statisticians, data scientists, and business analysts around the world.
     2. It is a free and open source software. R also continues to grow in popularity because of its adoption by the creators of machine learning methods
     3. Almost any new machine learning technique created today quickly becomes available to R users in a redistributable package, offered as open-source code on the Comprehensive R Archive Network (CRAN), a worldwide repository of popular R code.
   • Python
     1. Python is an general purpose programming language so it can do everything from data analysis to coding a video game.
     2. While python is flexible it is harder to use than R since you need somewhat of a coding background to make use of it fully.
     3. Python also has specialized libraries that focus on the needs of analysts and data scientists. In particular, the Python Data Analysis Library (pandas) provides a set of tools for structuring and analyzing data.
   • Structure Query Language(SQL)
     1. The language of databases.
     2. SQL is divided into two major sublanguages Data Definition Language (DDL), which focuses on defining the structure of the database itself, and Data Manipulation Language (DML) which works with the data inside the database.
     3. The Data Definition Language (DDL) is used mainly by developers and administrators.
     4. Examples of DDL functions: CREATE, ALTER, DROP.
     5. Examples of DML functions: SELECT, INSERT, UPDATE, DELETE.
3. Statistics Packages
   • IBM SPSS
     • One of the most popular statistical software packages and the oldest. Released in 1968, this packages is still used by many statisticians.
   • Stata
     • Stata is yet another older package which still continues to receive updates today.
     • Stata is less widely used than the more popular SAS and SPSS tools.
   • Minitab
     • Minitab shares most of the same features as SPSS, SAS, and Stata but fits into the same category as Stata - an older tool that is not widely used today.
4. Machine Learning
   • IBM SPSS Modeler
     • IBM's SPSS Modeler is one popular tool for building graphical machine learning models.
   • RapidMiner
     • RapidMiner is another graphical machine learning tool that works in a manner similar to IBM SPSS Modeler. It offers access to hundreds of different algorithms that may be placed in a visually designed machine-learning workflow.
5. Analytics Suites
   • These tools allow analysts to ingest and clean data, perform exploratory statistical analysis, visualize their data, produce models, make predictions, and communicate and report their results.
     • IBM Cognos -Cognos Connection is a web-based portal that offers access to other elements of the Cognos suite.
       • Query Studio provides access to data querying and basic reporting tools.
       • Report Studio offers advanced report design tools for complex reporting needs.
       • Analysis Studio enables advanced modeling and analytics on large datasets. -Event Studio provides real-time data monitoring and alerting, allowing business leaders to be immediately notified when certain events take place and/or provide automated responses to those events.
       • Metric Studio offers the ability to create scorecards for business leaders to quickly analyze key metrics from across the organization.
       • Cognos Viewer allows stakeholders to easily interact with data and analyses prepared using Cognos.
     • Power BI
       • Power BI is Microsoft's analytics suite built on the company's popular SQL Server database platform.
       • Power BI Desktop is a Windows application for data analysts, allowing them to interact with data and publish reports for others.
       • The Power BI service is Microsoft's software-as-a-service (SaaS) offering that hosts Power BI capabilities in the cloud for customers to access.
       • Mobile apps for Power BI provide users of iOS, Android, and Windows devices with access to Power BI capabilities.
       • Power BI Report Builder allows developers to create paginated reports that are designed for printing, email, and other distribution methods.
       • Power BI Report Server offers organizations the ability to host their own Power BI environment on internal servers for stakeholders to access.

Chapter 7: Data Visualization with Reports and Dashboards

Understanding the business requirement

Historical reporting

Real-time reporting