Introduction to Data Analysis Presentation PDF

Introduction to Data Analysis 1  Agenda What is Data Analysis Types Of Data Analytics Data Analysis VS Data Science Data Analysis as a Role Fields of Data Analysis Steps When Approaching New Project Sk...

Introduction to Data Analysis 1  Agenda What is Data Analysis Types Of Data Analytics Data Analysis VS Data Science Data Analysis as a Role Fields of Data Analysis Steps When Approaching New Project Skills Needed for Data Analyst Introduction to Power BI 2 What is Data Analysis? 3 Introduction: Data analytics, also known as data analysis, is a crucial component of modern business operations. It involves examining datasets to uncover useful information that can be used to make informed decisions. This process is used across industries to optimize performance, improve decision-making, and gain a competitive edge. Data Definition: Data is a collection of facts, such as numbers, words, measurements, observations, or descriptions of things. Types of Data: Qualitative Data: Quantitative Data: Descriptive information (e.g., Numerical information (e.g., statistics, colors, names, labels) measurements) 4  Applications of Data Analysis 1. Faster and better business decision 2. Predict future sales and purchases 3. Analyze the effectiveness of marketing campaigns 5  In short … Based on some historical data, Data analyst should be the analyst knew very important information about a specific the decision maker product so that could increase sales 6  Companies collect vast amounts of data, but in its raw form. When data is in this raw form, it doesn't have any meaning or uses This report can tell a story and it’s easily understood. 7  Data Analysis The process of examining, cleaning, transforming, and modeling data to extract useful information, identify patterns, and support decision-making. It typically involves working with structured data to generate insights through descriptive and diagnostic analysis. VS Data Science A multidisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves advanced techniques like machine learning, predictive modeling, and data engineering to solve complex problems and drive strategic decision-making. 8 Roles in Data 9  Data Analyst Data Scientist Primarily focuses on Focuses on advanced data analyzing existing data to modeling and developing generate actionable insights. algorithms to solve complex Produces reports, problems. dashboards, and Data gathering Develops predictive models visualizations that help Data cleaning and algorithms that provide business stakeholders make Data analysis insights into "what will happen" informed decisions. or "what actions to take.“ The focus is more on "what Creates automated systems happened" and "why it that make use of data models happened." for continuous improvement and optimization. 10  Fields of Data Analysis Data analysis is found in all fields Sales Marketing Medical Business Operations Pharmaceutical Real Estate 11  Skills Needed for Data Analyst Technical Skills Soft Skills & Mentality 12  Soft Skills & Mentality Communication Skills Teamwork Presentation Skills Problem Solving Decision Making Mentality and ways of thinking 13 Technical Skills 14  What do we need from the Data? 1 - What happened ? 2 - Why did it happen? 3 - What will happen ? 4 - What should we do ? 15 Types of Data Analytics 16 Types of Data Analysis Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Cognitive Analytics 1 7 Types of Data Analysis Descriptive Analytics: Descriptive analytics helps answer questions about what has happened, based on historical data. 1 8  Types of Data Analysis Diagnostic Analytics: Diagnostic analytics helps answer questions about why things happened. It further investigates the findings of the Descriptive analytics to: 1) Identify Anomalies 2) Collect data related to those anomalies 3) Apply statistical techniques to discover relationships and trends that explain these anomalies 1 9  Types of Data Analysis Predictive Analytics: Answers questions about what will happen in the future. Prescriptive Analytics: Answer questions about what actions should be taken to achieve a goal or target. Cognitive Analytics: Helps you to learn what might happen if circumstances change, and how you might handle these situations. 2 0 Data Analysis as a Role 21 1. Domain Knowledge Domain knowledge is the most important You can never solve a problem that you don’t understand So, you must understand the business that you are solving. Ex: We have a problem with marketing: We should understand the product need and the client to whom we are delivering the product so we can ask for data and produce a solution. 22 2. Gather Relevant Data Getting the data needed After understanding the business problem we’re trying to solve, we then start getting the data that can support the analysis of ❖ How the problem happened ? ❖ How long it may last ? ❖ How to Solve the problem ? 23 3. Define data quality and implement data preprocessing We cannot perform accurate analysis with poor-quality data So, we must define data quality standards and clean the data accordingly For example, we need to: ❖ Remove duplicate entries ❖ Handle missing values ❖ Standardize data formats This helps in producing reliable and valid results from the analysis. 24 4. Report the results using data visualization tools We cannot convey insights effectively without proper visualization So, we must use data visualization tools to report the results For example, we can use: ❖ Charts and graphs ❖ Dashboards ❖ Interactive visualizations This helps stakeholders understand the analysis and make informed decisions 25 Now let’s assume that a data analyst is approaching a new project.. What are the steps needed? 26 In short … Process of Data Analytics contains 4 steps: 1. Data Collection 2. Data Cleaning 3. Data Analysis And Data Interpretation 4. Data Visualization 27 1. Define the questions you need to answer 28 2. Collect the data 29 Where do we get the data? The data can be collected through multiple resources 1. Databases 2. Websites 3. Or even manually Whatever the way, there must be some data that is related to my problem. 30 3. Clean the data 31 What to clean? The data might have some problems like 1. Anomalies 2. Duplicates 3. Missing data etc. 32 4. Analyze the data 33 Reporting This step is about putting your final results in a nice looking and easy to understand dashboard or dynamic report. Here you give some answers to the questions 34  What is the Power BI ? is a technology-driven business intelligence tool provided by Microsoft for analyzing and visualizing raw data to present actionable information. It combines business analytics, data visualization, and best practices that help an organization make data-driven decisions. 35  Advantages of Power BI User-friendly interface: Power BI has an interface allowing users to visualize and analyze data easily. Data integration: Power BI allows users to easily integrate data from various sources, including Excel, SQL Server, and cloud-based sources like Azure and Salesforce. Interactive dashboards: Users can create customized dashboards and reports to display data in a way that is useful and interactive to them. Real-time data: Power BI supports real-time data processing, allowing users to view up-to-date data in their dashboards and reports. Web publishing: Power BI allows users to share their dashboards and reports with others, making it easy to collaborate on data analysis projects. 36  Why named Power ? 01 03 POWER Query Editor Data Modelling 02 DAX 04 Publish online in power bi (Data Analysis Expressions) Service 37 Any Questions ? 38 39