Business Analytics using Data Science

Explore Python basics with variables and data types, including integers, floats, strings, and booleans, and learn how dynamic typing and the print function reveal values.

Explore lists, tuples, and dictionaries in Python, learning how to access, modify, and maintain order, along with basic operations, conditionals, loops, and functions.

Explore Python libraries and models, import math, compute the square root of 16 and pi, and perform basic file operations; use numpy and pandas for data analysis.

Learn to create line plots in Python using matplotlib.pyplot as plt, including x and y axis labels, a title, and displaying the plot.

Explore business analytics with Python, using pandas, numpy, matplotlib, seaborn, scikit-learn, statsmodels, and SQL alchemy to collect, clean, visualize, and model data for insights.

Explore sales analytics with interactive Plotly visualizations and dash dashboards, clean data, perform eda, and build a random forest model evaluated by mean absolute error.

Explore data visualization with Python and Tableau, using Matplotlib, Seaborn, Plotly, Altair, and Bokeh to create static, animated, and interactive plots, with pandas and NumPy integration.

Explore Seaborn, a matplotlib-based Python visualization library that simplifies creating attractive, informative statistical graphics, and compare static Seaborn plots with Plotly's interactive charts for dashboards.

Explore how to create interactive data visualizations and dashboards in Tableau by loading data from Excel or CSV and building sheets for sales over time, by region, and product performance.

Extend Tableau analytics by integrating Python with TabPy to run machine learning and data manipulation inside Tableau, using calculated fields to execute Python code via TabPy.

Integrate Python with Tableau to preprocess data, train a linear regression model, and visualize actual versus forecasted sales using calculated fields and dashboards in Tableau.

Learn how statistical methods underpin analytics, from descriptive statistics and visualization to inferential tests, confidence intervals, hypothesis testing, regression, and predictive tools that drive data-driven business decisions.

Explore hypothesis testing by formulating null and alternative hypotheses, interpreting p-values against alpha 0.05, and applying t-tests and confidence intervals to business data.

Understand regression analysis to model the relationship between variables and predict outcomes with linear regression, using Python with pandas and statsmodels to fit an OLS model and inspect the summary.

Explore logistic regression for binary classification and probability output, with practical Python code using sklearn to train, test, and measure accuracy, plus sampling methods for scalable data analysis.

Apply statistics to business decision making through data collection, analysis, and hypothesis testing. Use regression and time series forecasting to inform pricing, marketing, and operations.

Define the problem of increasing sales by optimizing the marketing strategy and collect sales, expenses, and demographics data to analyze with descriptive and inferential statistics, regression, and segmentation.

The lecture demonstrates descriptive statistics with Python, using pandas to load data and compute mean, median, and standard deviation, and performs a one-sample t-test against a population mean of 500.

Learn to perform regression analysis with python statsmodels, prepare data with an intercept, fit an ols model, and interpret p-values and r-squared to decide on marketing spend.

Plan, monitor, and align business analysis activities with organizational goals by establishing a framework, gathering and analyzing requirements, selecting methods, engaging stakeholders, and tracking progress.

Plan and monitor business analytics activities by outlining tasks, estimating effort, developing a schedule, and assigning responsibilities to ensure stakeholder requirements are met and value is delivered.

Plan how to inform stakeholders and team members through emails, meetings, and reports. Identify communication needs, select methods, and develop a schedule to ensure timely updates.

Analyze the current and future state using swot analysis, pest analysis, and Porter's five forces; assess risks, evaluate options, and develop a change plan aligned with strategic goals and kpis.

Evaluate and select strategies by generating options, scoring feasibility, roi, cost, risk, and alignment with goals, then engage stakeholders before choosing the highest-scoring plan.

Develop a change strategy by building an implementation plan with timeline, resources, and responsibilities, and aligning stakeholders via a clear communication and training plan. Monitor progress and adjust as needed.

Explore strategic planning tools and techniques, including swot and pest analyses, porter's five forces, balanced scorecard, risk assessment, fmea, scenario planning, and collaboration tools for business analytics.

Explore how business intelligence transforms raw data into actionable insights for strategic and tactical decisions through data collection, integration, storage, analysis, and visualization.

Explore how BI tools automate data collection to visualization, improving accuracy and analytics. See Tableau, Power BI, QlikView, and Locker data platform, with AI and NLP driving insights.

Explore how a business process links design, mapping, analysis, and automation to deliver products and services efficiently while boosting quality and customer satisfaction.

Design processes by identifying objectives, inputs and outputs, activities, and roles to create efficient, waste-minimizing workflows. Map, document, and standardize with SOPs and continuous improvement methods.

Identify automation opportunities and deploy bots using tools like UiPath, Blue Prism, and Automation Anywhere to automate data entry, invoice processing, and reporting. Monitor performance, boost efficiency and reduce errors.

Define key performance indicators and monitor performance to keep processes within defined parameters. Compare results to kpis, control variance, and take corrective actions for continuous improvement using feedback and data.

Master Excel basics and advanced features, from formulas, cell references, and charts to vlookup, index-match, and pivot tables, to analyze data and drive decisions.

Explore conditional formatting to highlight cells above thresholds and data points. Automate tasks with macros and Visual Basic for Applications, and leverage Power Query and Power Pivot for analytics.

Explore an example of Power Query to import data from an external database, filter data, and build data models with PowerPivot and Dax for advanced calculations.

Define project objectives, collect and clean data, explore and analyze, visualize results, interpret findings, and implement monitoring to turn raw data into actionable insight.

Learn how to explore data with descriptive statistics, correlation analysis, hypothesis testing, and segmentation to uncover patterns, relationships, and anomalies for better analytics decisions.

Learn to visualize data by selecting appropriate charts, building dashboards, and designing clear visualizations using tools like Tableau, Power BI, matplotlib, and ggplot2.

Interpret results by extracting insights from data visualizations and statistics to draw conclusions aligned with business objectives, highlighting key patterns, diminishing returns, and implications for marketing spend and sales.

Communicate findings to stakeholders through clear, actionable reports and presentations. Tailor communications for executives or technical teams, provide recommendations, and use charts, graphs, and tables to support insights.

Implement recommendations and monitor their impact by tracking results and adjusting strategies as needed. Communicate the action plan to stakeholders and track KPIs and dashboards to measure ROI.

Explore essential data science learning resources for business, including books, online courses, and certifications, and master a practical data analytics process from data collection to decision making.