Business Intelligence Layers Development Using Python

Explore transforming data frames with Python pandas by creating lists from frames, copying and combining data, filtering with query and between, inserting columns, and performing arithmetic operations on frame values.

Develop data filtering and slicing with between, format dates and strings, apply 14% VAT to orders over 2000 with positive profit, and export a CSV report.

Call remote functions across platforms to transform data remotely, validate accounts, and convert dictionaries to structured data frames for database storage using rest services and pandas.

Connect to the Oracle database using the Oracle Ezekial Developer client and test the connection. Run basic select queries on tables such as debit and department and apply where filters.

Learn to transform data remotely by building a Python-driven connection to a secure database server, using an engine and cursors to execute sql, commit transactions, and verify results.

Learn to visualize data within the business intelligence workflow by creating standard graphs with Python and Matplotlib, transforming data for clear charts that support decision making.

Develop interactive charts in Python for business intelligence, including Venn diagrams, interactive bar charts comparing banks, and pie charts of assets and equity.

Apply set theory in Python to analyze customers for marketing, using union, intersection, and difference and visualize results with a three-set Venn diagram for targeted campaigns.

Visualize data in a business intelligence project by modeling data into graphs and charts, applying standard and interactive visualizations to support decision making.

Visualize financial data with Python by creating Venn diagrams, interactive bar charts, and pie charts to compare assets across CAIB and MediaBank, including balance sheet components.

Define the objective and collect data from sources and APIs with Python. Clean and analyze data with statistical methods, translate results to business language, visualize insights, and repeat the cycle.

Learn to compute mean, median, mode, standard deviation, variance, and percentile in Python using standard libraries and built-in functions for data analysis.

Explore the basics of statistics with Python, calculating mean, median, mode, standard deviation, variance, and percentile to transform data into actionable business insights.

Apply linear regression to historical balance sheets to predict future deposits from securities; load data from a secure server or Excel and visualize observed versus predicted deposits.

Use linear regression on Bank of Canada balance sheet data with Python to predict next-year deposits from securities, after loading, transforming, and analyzing financial data.

Apply linear regression in Python to financial data from historical balance sheets, predicting deposits and securities figures, and visualize results with Matplotlib to guide future decisions.

Learn to formulate and solve linear programming problems in business by building constraints and inequalities, defining an objective function, and solving with Python using interior point or simplex methods.

Connect to the stock market API using pandas data reader and Yahoo Finance, transform data, visualize stock direction, and fetch prices for multiple companies.

Learn to connect to Yahoo Finance with pandas-datareader, fetch and transform stock data, and visualize open and close prices across multiple companies using Python and matplotlib.

Launch the notebook server from the command line, access it via localhost or the computer name, and configure IP address and port to share analysis reports across the local network.

Configure a jupyter notebook server on the local area network by setting an IP address and port, start it with Anaconda, and test accessibility from other LAN machines.

Access the notebook server from another computer on a local area network by typing the server IP in a browser and using remote desktop to share the data analysis report.

Secure the notebook server by setting a password to prevent unauthorized access over the local area network, then test remote access from another machine using the lan ip.

Embed html in python by building and rendering an html string as an email document, using basic html structure and tags, and rendering in a browser or email output.

Embed external web sources in a notebook using iframes to display sites like Wikipedia, test Google, include video, images, and web forms to enrich data analysis reports.

Explore the business intelligence context and learn Python techniques to handle the three layers: data sources, transformation, and visualization, and generate web reports.

Explore data sources and the extract, transform, load workflow in Python for business intelligence. Connect to files, databases, and cloud sources, perform in-memory transformations, and visualize results with basic charts.

Identify structured, unstructured, and semi-structured data, and recognize metadata as essential for big data analysis, storage on file servers and relational databases, enabling data visualization and decision support.

Extend Power BI data transformations with Python scripting in Power BI Desktop, using pandas to multiply salary values by 10 across data sources and output the results in a table.

Load data from an Excel or ASV file into Power BI Desktop, set language encoding and delimiter, then create a data model and visualize with bar and scatter charts.

Connect to a secure SQL Server from Power BI to bind balance sheet data with two other sources, choosing import or direct query and configuring server, database, and credentials.

Learn to connect to web data sources in the BI tool, load tables from web pages such as Wikipedia, and create data relationships for analysis.

Learn Python basics through a beginner-friendly review of structures, control flow, functions, and modules. Explore dictionaries, sets, and lists, plus using random and requests modules to build practical projects.