| 1 |
Introduction to Data Analysis (Data Analysis Basics) |
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| 2 |
Understanding Data Types (Structured, Unstructured, Semi-Structured) |
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| 3 |
Understanding Data Sources (Databases, APIs, Files, Web Scraping) |
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| 4 |
Statistics Basics (Mean, Median, Mode, Variance, Standard Deviation) |
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| 5 |
Probability Basics (Probability Rules, Distributions, Conditional Probability) |
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| 6 |
Descriptive Statistics (Summarizing & Visualizing Data) |
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| 7 |
Inferential Statistics (Hypothesis Testing, Confidence Intervals, p-value) |
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| 8 |
Python for Data Analysis (Python Basics) |
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| 9 |
Python Libraries for Data Analysis (NumPy, Pandas, Matplotlib, Seaborn) |
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| 10 |
Data Cleaning & Preprocessing (Handling Missing Data, Outliers, Duplicates) |
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| 11 |
Data Transformation (Scaling, Encoding, Normalization, Aggregation) |
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| 12 |
Exploratory Data Analysis (EDA) (Descriptive Stats, Visualization, Correlation Analysis) |
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| 13 |
Data Visualization (Matplotlib, Seaborn, Plotly, Tableau, Power BI) |
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| 14 |
Charts & Graphs (Line, Bar, Pie, Histogram, Scatter, Box Plot) |
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| 15 |
Dashboarding & Reporting (Tableau, Power BI, Google Data Studio) |
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| 16 |
SQL for Data Analysis (Basics of SQL) |
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| 17 |
Advanced SQL Queries (Joins, Subqueries, Aggregations, Window Functions) |
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| 18 |
Data Extraction & Manipulation (Filtering, Sorting, Grouping) |
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| 19 |
Time Series Analysis (Trends, Seasonality, Forecasting) |
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| 20 |
Correlation & Regression Analysis (Linear, Multiple Regression) |
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| 21 |
Hypothesis Testing (t-test, chi-square test, ANOVA) |
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| 22 |
Python Advanced Libraries (SciPy, Statsmodels, Scikit-learn Basics) |
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| 23 |
Feature Engineering & Selection (Creating & Selecting Features for Analysis) |
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| 24 |
Data Modeling Basics (Introduction to Predictive Analytics) |
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| 25 |
Clustering & Segmentation (K-Means, Hierarchical Clustering) |
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| 26 |
Classification Basics (Decision Trees, Logistic Regression) |
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| 27 |
Working with APIs & External Data Sources |
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| 28 |
Web Scraping (BeautifulSoup, Scrapy) |
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| 29 |
Big Data Basics (Hadoop, Spark Overview) |
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| 30 |
Data Cleaning & Preprocessing in Big Data (PySpark Basics) |
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| 31 |
Data Ethics & Privacy (GDPR, CCPA, HIPAA Basics) |
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| 32 |
Best Practices in Data Analysis (Documentation, Reproducibility, Version Control) |
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| 33 |
Continuous Learning (Advanced Visualization, Predictive Modeling, AI/ML Basics for Analysts) |
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