Big Data for Pearson Btec Higher level 3.ppt

Big Data and Business Analytics

1. BD201: Big Data & Business Analytics Week 1

2. Learning Objectives Gain an understanding of practices and processes used by a junior data analyst Learn key analytical skills and tools to support data-driven decision-making Discover terms and concepts relevant to the field of data analysis Evaluate the role of analytics in the broader data ecosystem Conduct a self-assessment of your analytical thinking and problem-solving abilities

3. What is Data Analysis? Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves a range of techniques and tools to extract insights and knowledge from data. 1 Collect Gather relevant data from various sources 2 Prepare Clean, organize, and format the data 3 Analyze Use statistical methods and algorithms to uncover patterns and relationships 4 Interpret Draw meaningful conclusions and insights from the analysis 5 Communicate Present the findings in a clear and actionable way

4. The 5 V's of Big Data Volume: The massive and exponentially growing amounts of data being generated from various sources, including social media, IoT devices, and business transactions. Variety: The diverse types of data, including structured, unstructured, and semi-structured, that must be managed and analyzed effectively. Velocity: The high speed at which data is being created, processed, and stored, requiring real-time or near-real-time analysis and decision-making. Veracity: The reliability and trustworthiness of the data, which is critical for making informed decisions based on accurate information. Value: The potential insights and business value that can be extracted from the vast amounts of data, driving innovation and competitive advantage.

5. Roles of a Junior Data Analyst Data Collection: Gather and validate data from various sources, ensuring accuracy and completeness. Data Preparation: Clean, transform, and organize data to make it ready for analysis. Exploratory Data Analysis: Utilize statistical methods and visualizations to uncover insights and patterns in the data. Reporting and Presentation: Communicate findings effectively through reports, dashboards, and presentations. Problem-Solving: Collaborate with stakeholders to identify and address business challenges using data-driven approaches.

6. Key Analytical Skills Data Cleaning Removing errors and inconsistencies from datasets. Data Analysis Extracting insights and patterns from data. Data Visualization Skill in creating clear, compelling data visualizations to communicate findings effectively. Example: If you have a list of temperatures, data cleaning means making sure they're all in the same unit (e.g., Celsius), analysis might involve finding the average temperature, and visualization could be a graph showing temperature changes over time.

7. Cleaning Data for Accurate Analysis Data cleaning is a crucial step in the data analysis process. When working with a list of student test scores, it's important to review the data and correct any errors or inconsistencies. This ensures the analysis is based on accurate information, leading to reliable insights and informed decision-making. Incorrect Score Corrected Score 150 15 72 72 28 28 By addressing data errors, such as the score mistakenly entered as 150 instead of 15, the analysis will provide a more accurate representation of student performance. This allows for meaningful insights to be drawn and appropriate actions to be taken.

8. Visualizing Data with Graphs Bar Charts Bar charts compare categorical data, such as sales across different product categories. Scatter Plots Scatter plots show the relationship between two numeric variables, such as height and weight. Histograms Histograms display the distribution of a single variable, such as the ages of a population. By choosing the right visualization, you can clearly present complex information in an intuitive, easy-to-understand format. These graphs help identify trends, outliers, and patterns that may not be immediately obvious in raw data.

9. Exercise 1 Data Set You are given a set of test scores: 20, 18, 22, 25, and 150. Calculate the average score and create a bar chart to visualize the data. Label the Y-axis 'Score' and the X-axis 'Student'. Title the chart 'Test Scores'. Analyze the data, identifying any outliers and explaining their potential significance.

10. Exercise 1 : Solution The bar chart shows the test scores for a group of 5 students. The data reveals one outlier score of 150, which should be investigated to ensure data accuracy before calculating the average.

11. Common Data Analysis Tools Spreadsheets (e.g. Excel) for basic data organization, calculations, and visualizations SQL for querying and analyzing data stored in databases R programming for advanced statistical analysis and custom data modeling Tableau for creating professional-grade data visualizations and interactive dashboards

12. Exercise 2: Data Analysis Tools Tools: Excel SQL R Programming Tableau Descriptions: Data visualization tool Spreadsheets Programming language for data analysis Used for querying databases Match the tool with its description.

13. Exercise 2 : Solution Excel Spreadsheets SQL Used for querying databases R Programming Programming language for data analysis Tableau Data visualization tool

14. The Data Life Cycle 1 Data Collection Gather relevant data from various sources, ensuring accuracy, completeness, and timeliness. 2 Data Cleaning Identify and address data inconsistencies, errors, and missing values to prepare the data for analysis. 3 Data Analysis Explore the cleaned data using statistical techniques and algorithms to uncover meaningful insights and patterns. 4 Data Visualization Present the analyzed data in clear, intuitive visualizations that facilitate understanding and decision-making. 5 Interpretation Draw conclusions from the visualized data, translate insights into actionable recommendations, and communicate findings effectively.

15. Analytical Thinking Analytical thinking is the ability to break down complex problems, gather and evaluate relevant information, and arrive at logical conclusions. It is a crucial skill for data analysts to uncover meaningful insights from data.

16. Exercise 3: Analytical Riddle Riddle 1 If you have three apples and take away two, how many apples do you have? Riddle 2 What has keys but can't open locks?

17. Exercise 3: Solution Riddle 1 If you have three apples and take away two, how many apples do you have? Solution 1 You have two apples. (Because you took two, but they are still in your possession.) Riddle 2 What has keys but can't open locks? Solution 2 A computer keyboard.

18. Exercise 4: Data Life Cycle Arrange the stages of the Data Life Cycle in order. In your own words, describe why this process is important for data analysis. Interpretation Collection Analysis Cleaning Visualization

19. Exercise 4: Solution 1 Data Collection Gathering relevant, accurate data is crucial as it forms the foundation of analysis. Poor data collection leads to unreliable insights. 2 Data Cleaning Ensures that the data is free of errors, duplicates, or inconsistencies. Clean data improves the accuracy of analysis. 3 Data Analysis Identifying trends, patterns, and correlations within the data. Proper analysis uncovers actionable insights. 4 Data Visualization Presenting data in a visual format (charts, graphs) simplifies the understanding of complex trends and makes the insights more accessible. 5 Interpretation Drawing meaningful conclusions from the analysis. Accurate interpretation is key to making informed decisions.

20. Analytics in the Data Ecosystem The Importance of Data Analysis in Decision Making Data analysis is essential for data-driven decision making, enabling businesses to make informed choices based on real evidence. Example 1 : A retail store analyzes customer buying patterns to determine which products to stock more of. Example 2 : A company uses sales data to identify the top-selling products and optimize production accordingly.

21. Exercise 5: Connecting the Dots Describe a situation where data analysis could help make an important decision. Explain the steps involved in using data analysis for this scenario.

22. Exercise 5: Solution Imagine you are the manager of a small online bookstore, and you want to improve your sales strategy. You have access to data on customer preferences, browsing behavior, and purchase history. You believe that analyzing this data can help you make more informed decisions to boost sales. Steps: Data Collection: Gather information on customer preferences, browsing behavior, and purchase history from your online store's database. Data Cleaning: Review the data to ensure there are no duplicate entries, missing information, or errors in customer profiles. Data Analysis: Analyze the data to identify patterns. Look for trends in popular genres, authors, or specific book titles. Data Visualization: Create visual representations of the data. For example, generate charts or graphs to show which genres are most popular among customers.

23. Exercise 5: Solution Con. 5. Interpretation: Interpret the results of your analysis. For instance, if you find that mystery novels are highly favored, consider offering more promotions or creating targeted marketing campaigns for this genre. 6. Decision-Making: Based on your analysis, make informed decisions on which books to feature prominently on your website, which genres to highlight in promotions, and whether to offer personalized recommendations to customers. 7. Implementation : Put your decisions into action. Adjust the website layout, update promotional materials, and fine-tune your marketing strategy based on the insights gained from data analysis. 8. Monitoring and Evaluation: Continuously monitor the impact of your decisions on sales. Keep track of which strategies are most effective and be prepared to adjust your approach as needed.

24. The Versatile Role of Junior Data Analysts Marketing Junior data analysts can analyze customer data to identify trends, target specific demographics, and optimize marketing campaigns for maximum impact. Finance They can analyze financial data to identify investment opportunities, assess risk, and optimize portfolio performance. Healthcare They can analyze patient data to improve treatment plans, track disease outbreaks, and optimize healthcare resource allocation. Sports They can analyze player performance data to improve training strategies, scout opponents, and optimize team performance. Junior data analysts can work across diverse industries, from marketing and finance to healthcare and sports. Their data-driven insights help organizations make informed decisions that drive business success. By leveraging robust analytical skills and data visualization techniques, these entry-level professionals play a crucial role in unlocking the power of data. The versatility of junior data analysts stems from their ability to apply analytical skills to a wide range of data sets and domains. They can use their skills to solve complex problems, uncover hidden insights, and make data-driven recommendations that drive innovation and efficiency in various industries.

25. Thank You