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Power BI

Transform your data analysis skills with our Power BI Mastery course, unlocking the power of data visualization, igniting business growth, and seizing new opportunities.

Duration: 3 Months

Learning Curriculum

What is Data Analysis?: Definition, Importance, and Real-World Applications
Data Types and Formats: Structured, Semi-structured, Unstructured Data
The Data Analysis Process: Collection, Cleaning, Transformation, and Interpretation
Roles in Data Analysis: Data Analyst, Business Analyst, Data Scientist

Primary and Secondary Data Sources: Understanding the differences
Methods of Data Collection: Surveys, Databases, Web Scraping, APIs
Data Reliability and Validity: Ensuring the quality of collected data
Case Study: Collecting data for a business scenario

Importance of Data Cleaning: Why clean data is crucial for analysis
Handling Missing Data: Techniques such as Mean/Median Imputation, Dropping Missing Values
Identifying and Handling Outliers: Detecting anomalies and their impact on analysis
Data Transformation: Standardization, Normalization, and Encoding Categorical Data
Practical Exercise: Cleaning a messy dataset using Excel/Python (Pandas)

Measures of Central Tendency: Mean, Median, Mode
Measures of Dispersion: Range, Variance, Standard Deviation, Quartiles
Understanding Data Distribution: Skewness, Kurtosis, and its implications
Hands-on Exercise: Descriptive statistics with real datasets using Python (Pandas)

Why Visualize Data?: Understanding the importance of visualization in analysis
Introduction to Basic Charts: Line, Bar, Pie, Histogram, and Scatter Plots
Choosing the Right Chart for Your Data
Hands-on Practice: Creating basic visualizations using Excel and Python (Matplotlib/Seaborn)

Advanced Visualizations: Box Plots, Heatmaps, Violin Plots, Bubble Charts
Introduction to Data Dashboards: What are they and how are they used?
Tools for Advanced Visualizations: Tableau, Power BI, Python (Seaborn, Plotly)
Practical Exercise: Creating dashboards with interactive visualizations using Tableau or Power BI

Understanding Databases and Relational Models: What is SQL and how is it used in data analysis?
Basic SQL Queries: SELECT, WHERE, GROUP BY, HAVING, JOIN
Data Aggregation Functions: COUNT, SUM, AVG, MIN, MAX
Hands-on SQL: Writing queries to extract and analyze data from a sample database

Joins in SQL: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
Subqueries and Nested Queries: Writing complex queries for analysis
Window Functions: ROW_NUMBER, RANK, PARTITION BY
Case Study: Using SQL to analyze data from a real-world business database

What is EDA?: Overview of the process and importance of exploring data before analysis
EDA Techniques: Univariate, Bivariate, and Multivariate analysis
Identifying Trends, Patterns, and Anomalies in Data
Correlation Analysis: Understanding relationships between variables
Practical Case Study: Performing EDA on a dataset using Python (Pandas, NumPy)

Introduction to Correlation: Pearson, Spearman correlation coefficients
Linear Regression: Simple and Multiple Linear Regression
Interpreting Regression Outputs: Coefficients, R-squared, p-values
Hands-on Example: Running a simple linear regression using Excel and Python (Statsmodels)

Introduction to Time Series Data: Understanding time-dependent data
Components of Time Series: Trend, Seasonality, Cyclicity, and Randomness
Moving Averages and Smoothing Techniques: Simple Moving Averages, Exponential Smoothing
Practical Application: Visualizing time series data and calculating moving averages using Python

Time Series Forecasting: Introduction to basic forecasting techniques (ARIMA, Holt’s Winter Method)
Evaluating Forecast Accuracy: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE)
Final Project: Perform EDA, visualize data, conduct statistical analysis, and present findings
Presentation and Feedback: Final project submission and presentations