Advanced Data Analysis with Python

AI INTEGRATED COURSE

Advanced Data Analysis with Python

Advanced Data Analysis with Python

Course Overview
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Broadway Infosys offers an Advanced Data Analysis with Python course that can upgrade your career and is aimed at helping students and working professionals develop their analytical skills through using Python, SQL, statistics, and business intelligence tools. The participants will demonstrate their ability to analyze complex datasets and develop data-driven solutions by creating data visualizations and extracting business insights through their work with structured workflows that meet industry standards.

Participants begin by learning basic data analysis skills, mastering analytical reasoning, problem definition, KPI formulation, and the comprehensive analysis workflow. The curriculum teaches fundamental Python programming skills together with NumPy and Pandas, which students use for data cleansing and transformation, aggregation, and time-series analysis, while they learn Exploratory Data Analysis (EDA) to discover patterns and trends.

The course enables students to use SQL databases, perform statistical analysis for business decision-making, and execute advanced analysis methods, including segmentation and cohort analysis, A/B testing, and anomaly detection. Participants in the course develop their data visualization skills by creating interactive dashboards with Power BI and Tableau while learning to present insights that drive impactful business results.

The training concludes with real-world projects that require participants to demonstrate their skills through actual work challenges, enabling them to develop the skills employers need for their jobs.

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Lesson 1: Foundations of Data Analysis

  • Understanding Data Analysis in Industry
    • Responsibilities of Data Analysts
    • Data Analysis vs Data Science vs Business Intelligence
    • Data-driven decision-making

  • Critical thinking approaches
  • Problem-solving frameworks

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics (Analytical Perspective)
  • Prescriptive Analytics

  • Business problem understanding
  • Data acquisition
  • Data cleaning and preparation
  • Exploratory analysis
  • Statistical analysis
  • Visualization and reporting
  • Communicating insights

  • Translating business questions into analytical problems
  • Hypothesis formulation
  • KPI and metric definition

  • Environment setup (Jupyter Notebook, virtual environments)
  • Variables and data types
  • Control structures
  • Functions and modular programming
  • File handling (CSV, JSON, text)

  • Array Operations
    • Array creation
    • Indexing and slicing
    • Broadcasting
  • Numerical Operations
    • Vectorized computations
    • Statistical calculations

  • DataFrame Fundamentals
    • Series vs DataFrame
    • Indexing (.loc, .iloc)
    • Data types and memory usage
  • Data Input and Output
    • Reading CSV, Excel, JSON
    • SQL connections
  • Data Cleaning
    • Missing value handling
    • Removing duplicates
    • Data type conversion
    • String operations
  • Data Transformation
    • Filtering and sorting
    • GroupBy and aggregation
    • Pivot tables
    • Applying custom functions
  • Time Series Basics
    • Datetime conversion
    • Resampling
    • Rolling statistics

  • Data profiling
  • Summary statistics
  • Detecting anomalies

  • Distribution analysis
  • Skewness and kurtosis
  • Outlier detection

  • Correlation analysis
  • Cross-tabulation
  • Feature relationships

  • Generating analytical questions
  • Identifying patterns and trends

  • Relational databases
  • Schema design
  • Primary and foreign keys

  • SELECT statements
  • Filtering (WHERE, BETWEEN, IN, LIKE)
  • Sorting and limiting

  • COUNT, SUM, AVG, MIN, MAX
  • GROUP BY and HAVING

  • Joins (Inner, Left, Right, Full)
  • Self joins
  • Subqueries
  • Common Table Expressions (CTEs)

  • Window functions
  • Ranking and cumulative calculations
  • Time-based analysis

  • Indexing concepts
  • Performance best practices

  • Central tendency
  • Variability measures
  • Distribution properties

  • Conditional probability
  • Independence
  • Bayes theorem

  • Random sampling
  • Bias and variance
  • Sample size considerations

  • Confidence intervals
  • Hypothesis testing
  • p-value interpretation
  • Statistical Tests
    • t-test
    • z-test
    • Chi-square test
    • ANOVA

  • Interpretation challenges
  • Practical implications

  • Imputation strategies
  • Practical implications

  • IQR method
  • Z-score method
  • Business context evaluation

  • One-hot encoding
  • Label encoding
  • Feature scaling

  • Calculated metrics
  • Ratio features
  • Time-based features

  • Customer segmentation
  • RFM analysis
  • Behavioral grouping

  • Retention analysis
  • Lifecycle tracking

  • Trend detection
  • Seasonality analysis
  • Moving averages

  • Experiment design basics
  • Statistical evaluation
  • Business interpretation

  • Statistical threshold methods
  • Monitoring use cases

  • Chart selection
  • Visual clarity
  • Avoiding misleading visuals

  • Matplotlib fundamentals
  • Seaborn statistical plots
  • Plotly interactive visualization

  • Data modeling
  • Power Query transformations
  • Measures and calculated columns
  • DAX basics
  • Dashboard design
  • Time intelligence
  • Geo visualization

  • Metric design
  • Leading vs lagging indicators

  • Executive summaries
  • Insight-focused reporting

  • Narrative structure
  • Visual storytelling techniques

  • End-to-end analytical project
  • Real business dataset analysis
  • Insight presentation
  • Documentation standards
  • Optional deployment (Streamlit dashboard)
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