Course Description
This comprehensive course is designed to equip business analysts with the necessary skills and tools to analyze data effectively. The course covers foundational topics in statistics, advanced Excel techniques, programming in Python, data manipulation with NumPy and pandas, and data visualization using Power BI and Tableau. Additionally, the course introduces machine learning concepts and their application in business analysis. The hands-on capstone project will allow participants to apply their learning to real-world business problems.
What you’ll learn
- Understand and apply basic statistical concepts and measures.
- Utilize Excel for data analysis, visualization, and reporting.
- Develop Python programming skills for data manipulation and analysis.
- Leverage NumPy and pandas for efficient data handling.
- Create interactive dashboards and visualizations using Power BI and Tableau.
- Implement machine learning models for business analytics and decision-making.
Basic Statistical Concepts - Introduction to statistics and its importance in business analysis. Understanding types of data (qualitative and quantitative). Measures of central tendency (mean, median, mode).
Measures of Dispersion - Variance, standard deviation, range, interquartile range. Understanding the spread of data.
Probability and Distributions - Basic concepts of probability, including probability rules and conditional probability. Understanding and working with different probability distributions (normal, binomial, Poisson).
Data Visualization and Plots - Creating and interpreting various plots: histograms, bar charts, box plots, scatter plots, and line graphs. Using plots to summarize and explore data.
Inferential Statistics - Concepts of sampling and sampling methods. Hypothesis testing (null and alternative hypotheses). Confidence intervals and their interpretation. P-values and significance levels. Conducting t-tests, chi-square tests, and ANOVA.
Excel Basics - Introduction to Excel and its interface. Basic functions and formulas. Data entry, formatting, and basic data manipulation.
Data Analysis with Excel - Using Excel for data analysis. Working with tables and ranges. Using functions such as VLOOKUP, HLOOKUP, INDEX, MATCH. Data sorting and filtering.
Advanced Excel Techniques - Advanced functions and formulas. Pivot tables and pivot charts. Data visualization using Excel charts and graphs. Using Excel for statistical analysis and data modeling.
Python Basics - Introduction to Python programming. Setting up the Python environment. Basic syntax and programming constructs. Variables, data types, and operators. Control structures (if statements, loops). Functions and modules.
Working with Data Structures in Python - Lists, tuples, dictionaries, and sets. List comprehensions and dictionary comprehensions. File handling and working with text data.
Introduction to Python Libraries - Overview of key Python libraries for data analysis. Installing and importing libraries. Introduction to Jupyter notebooks.
Introduction to NumPy - Introduction to the NumPy library. Creating and manipulating NumPy arrays. Performing mathematical and statistical operations using NumPy. Understanding broadcasting and vectorization.
Advanced NumPy Techniques - Advanced array manipulations, including reshaping, slicing, and indexing. Working with large datasets efficiently. Applying NumPy functions for linear algebra and random number generation.
Introduction to pandas - Introduction to the pandas library. Creating and manipulating DataFrames and Series. Reading data from various sources (CSV, Excel, SQL). Understanding the pandas data structure and basic operations.
Data Manipulation with pandas - Selecting and filtering data in DataFrames. Grouping, merging, and joining datasets. Data cleaning and preprocessing techniques (handling missing values, duplicates, and data transformations).
Advanced Data Manipulation - Creating pivot tables and cross-tabulations for data summarization. Handling time series data. Performing advanced data analysis tasks, such as rolling windows and resampling.
Introduction to Power BI - Overview of Power BI and its components. Connecting to various data sources. Basic data transformations using Power Query. Creating simple visualizations (charts, tables, and maps).
Advanced Power BI Techniques - Creating interactive dashboards. Using DAX (Data Analysis Expressions) for complex calculations. Sharing and collaborating on reports using Power BI Service. Best practices for designing effective Power BI reports.
Introduction to Tableau - Overview of Tableau and its components. Connecting to various data sources. Basic data transformations. Creating simple visualizations (charts, tables, and maps).
Advanced Tableau Techniques - Creating interactive dashboards. Using advanced calculations and parameters. Sharing and collaborating on reports using Tableau Server. Best practices for designing effective Tableau reports.
Introduction to Predictive Analytics - Overview of predictive analytics and its importance in business. Key concepts and terminology in predictive analytics. Applications of predictive analytics in various business domains.
Data Preparation for Predictive Analytics - Importance of data preparation in predictive analytics. Techniques for data cleaning (handling missing values, outliers). Feature selection and engineering. Splitting data into training and test sets.
Building Predictive Models - Introduction to common predictive modeling techniques (linear regression, logistic regression). Building and training predictive models using Python. Evaluating model performance (confusion matrix, ROC curve, AUC).
Advanced Predictive Modeling - Exploring advanced predictive modeling techniques (decision trees, random forests, gradient boosting). Hyperparameter tuning using Grid Search and Random Search. Implementing and interpreting advanced models.
Time Series Forecasting - Introduction to time series data and forecasting. Techniques for time series forecasting (ARIMA, exponential smoothing). Applications of time series forecasting in business (demand forecasting, sales prediction).
Implementing Predictive Analytics Solutions - Developing and deploying predictive analytics models in business environments. Case studies of predictive analytics applications in marketing, finance, and operations. Best practices for integrating predictive analytics solutions into business processes.
Capstone Project Kickoff - Introduction to the capstone project. Defining project goals, selecting appropriate datasets, and outlining the project plan. Discussion on potential project topics and expectations.
Capstone Project Work - Hands-on project work with guidance from instructors. Applying learned skills in data analysis, visualization, and machine learning to solve a real-world business problem. Regular check-ins and feedback sessions.
Project Presentation and Course Review - Presentation of capstone projects by students. Course summary, final Q&A session, and feedback. Providing additional resources and discussing next steps for continued learning and professional development.
Our Student Reviews
4.5
(Based on todays review)
Louis Ferguson
1 days ago
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Frequently Asked Questions
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