Advanced Data Analytics using Knime and Excel

knime

1. Doing Data Science with Excel and Knime

2. MAKING LIFE EASIER WITH EXCEL The benefit of using excel in a data science : Offers a fast and easy way to get up close and personal with your data. To browse every data point in your dataset Well-compartmentalized Advanced functionality Simplify your data cleanup and analysis tasks

3. Main features Filters : Filters are useful for sorting out all records that are irrelevant to the analysis at hand . » Conditional formatting : By using conditional formatting, you can easily detect outliers and trends in your tabular datasets. » Charts : Charts have long been used to visually detect outliers and trends in data, so charting is an integral part of almost all data science analysis

4. Filtering in Excel Data filtering is the process of examining a dataset to exclude, rearrange, or apportion data according to certain criteria . For example, data filtering may involve finding out the total number of sales per quarter and excluding records from last month.

5. Filter a range of data Select any cell within the range. Select Data > Filter. Select the column header arrow . Select Text Filters or Number Filters, and then select a comparison, like Between. Enter the filter criteria and select OK.

6. Conditional formatting Conditional formatting makes it easy to highlight certain values or make particular cells easy to identify. This changes the appearance of a cell range based on a condition (or criteria). You can use conditional formatting to highlight cells that contain values which meet a certain condition.

7. Excel charting Charts are visual representations of data used to make it more understandable. Commonly used charts are: Pie chart Column chart Line chart Different charts are used for different types of data.

8. Excel charting

9. Reformatting and summarizing with pivot tables A Pivot Table is a powerful tool to calculate, summarize, and analyze data that lets you see comparisons, patterns, and trends in your data.  It is  an interactive way to quickly summarize large amounts of data. It is used to analyze numerical data in detail, and answer unanticipated questions about your data. It is especially designed for: Querying large amounts of data in many user-friendly ways.

10. pivot table

11. Automating Excel tasks with macros An Excel macro is a recorded sequence of Excel commands and actions that you can play back as many times as you want.  Macros can be used to automate just about any sequence of tasks in Excel, from something as simple as entering your company’s name and address into a spreadsheet to something as complex as creating a custom report. If you can do it in Excel, you can probably automate it with a macro.

12. Using KNIME for Advanced Data Analytics KNIME ( K o n stanz I nformation M in e r), is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept . The free and open-source KNIME Analytics Platform ensures the ETL process is powerful, scalable, repeatable, and reusable.

13. Using KNIME for Advanced Data Analytics Beginners and advanced users alike can use KNIME predictive analytics to » Upsell and cross-sell : To enable you to increase sales. » Churn reduction : Mine customer data and identify which customers you’re most likely to lose and why. » Sentiment and network analysis : Analyze the sentiment of people. » Energy usage prediction and auditing : Perform time series analyses.

14. Using KNIME for Advanced Data Analytics KNIME Software covers all kinds of data analytics functionality. For example classification, regression , dimension reduction, or clustering, using advanced algorithms including deep learning, tree-based methods, and logistic regression . This powerful and versatile open source platform offers a visual interface and a wide range of built-in algorithms to unlock the full potential of your data.  KNIME is a game-changer for anyone working with data .

15. Applying Domain Expertise to Solve Real-World Problems Using Data Science

16. Data Science for Driving Growth in E-Commerce

17. The work of data scientists in e-commerce involves: Data analysis: Simple statistical and mathematical inference. Segmentation analysis gets rather complicated when trying to make sense of e-commerce data . Data wrangling : Data wrangling involves using processes and procedures to clean and convert data from one format and structure to another s Data visualization design : Expect to use a lot of line charts, bar charts, scatter charts, and map-based data visualizations. Data visualizations should be simple and to the point.

18. The work of data scientists in e-commerce involves… Communication: After you make sense of the data, you have to communicate its meaning in clear, direct, and concise ways that decision makers can easily understand. Custom development work: In some cases, you may need to design custom scripts for automated custom data analysis and visualization

19. Optimizing e-commerce business systems by: Acquisition : Your brand acquires new users in the form of website visitors Activation : Acquired users activate, either through email subscription, RSS subscription, or social followings. Retention : Activated users take some sort of action — such as accepting an offer or responding to a call to action within your email marketing. Referral : Retained users refer new users to your brand’s acquisition layer. Revenue : Users make revenue-generating purchase